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1
Kuester, Theres, und Daniel Spengler. „Structural and Spectral Analysis of Cereal Canopy Reflectance and Reflectance Anisotropy“. Remote Sensing 10, Nr. 11 (08.11.2018): 1767. http://dx.doi.org/10.3390/rs10111767.
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The monitoring of agricultural areas is one of the most important topics for remote sensing data analysis, especially to assist food security in the future. To improve the quality and quantify uncertainties, it is of high relevance to understand the spectral reflectivity regarding the structural and spectral properties of the canopy. The importance of understanding the influence of plant and canopy structure is well established, but, due to the difficulty of acquiring reflectance data from numerous differently structured canopies, there is still a need to study the structural and spectral dependencies affecting top-of-canopy reflectance and reflectance anisotropy. This paper presents a detailed study dealing with two fundamental issues: (1) the influence of plant and canopy architecture changes due to crop phenology on nadir acquired cereal top-of-canopy reflectance, and (2) the anisotropic reflectance of cereal top-of-canopy reflectance and its inter-annual variations as affected by varying contents of biochemical constituents and changes on canopy structure across green phenological stages between tillering and inflorescence emergence. All of the investigations are based on HySimCaR, a computer-based approach using 3D canopy models and Monte Carlo ray tracing (drat). The achieved results show that the canopy architecture significantly influences top-of-canopy reflectance and the bidirectional reflectance function (BRDF) in the VNIR (visible and near infrared), and SWIR (shortwave infrared) wavelength ranges. In summary, it can be said that the larger the fraction of the radiation reflected by the plants, the stronger is the influence of the canopy structure on the reflectance signal. A significant finding for the anisotropic reflectance is that the relative row orientation of the cereal canopies is mapped in the 3D-shape of the BRDF. Summarised, this study provides fundamental knowledge for improving the retrieval of biophysical vegetation parameters of agricultural areas for current and upcoming sensors with large FOV (field of view) with respect to the quantification of uncertainties.
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Moriya, Érika Akemi Saito;, Nilton Nobuhiro Imai und Antonio Maria Garcia Tommaselli. „A STUDY ON THE EFFECTS OF VIEWING ANGLE VARIATION IN SUGARCANE RADIOMETRIC MEASURES“. Boletim de Ciências Geodésicas 24, Nr. 1 (März 2018): 85–97. http://dx.doi.org/10.1590/s1982-21702018000100007.
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Abstract: Remote Sensing techniques, such as field spectroscopy provide information with a large level of detail about spectral characteristics of plants enabling the monitoring of crops. The aim of this study is to analyze the influence of viewing angle in estimating the Bidirectional Reflectance Distribution Function (BRDF) for the case of sugarcane. The study on the variation of the spectral reflectance profile can help the improvement of algorithms for correction of BRDF in remote sensing images. Therefore, spectral measurements acquired on nadir and different off-nadir view angle directions were considered in the experiments. Change both anisotropy factor and anisotropy index was determined in order to evaluate the BRDF variability in the spectral data of sugarcane. BRDF correction was applied using the Walthall model, thus reducing the BRDF effects. From the results obtained in the experiments, the spectral signatures showed a similar spectral pattern varying mainly in intensity. The anisotropy factor which showed a similar pattern in all wavelengths. The visual analysis of the spectral reflectance profile of sugarcane showed variation mainly in intensity at different angles. The use of Walthall model reduced the BRDF effects and brought the spectral reflectance profiles acquired on different viewing geometry close to nadir viewing. Therefore, BRDF effects on remote sensing data of vegetation cover can be minimized by applying this model. This conclusion contributes to developing suitable algorithms to produce radiometrically calibrated mosaics with remote sensing images taken by aerial platforms.
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Shi, Shuo, Wei Gong, Lin Du, Jia Sun und Jian Yang. „POTENTIAL APPLICATION OF NOVEL HYPERSPECTRAL LIDAR FOR MONITORING CROPS NITROGEN STRESS“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (24.06.2016): 1043–47. http://dx.doi.org/10.5194/isprsarchives-xli-b8-1043-2016.
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Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by over-fertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.
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Shi, Shuo, Wei Gong, Lin Du, Jia Sun und Jian Yang. „POTENTIAL APPLICATION OF NOVEL HYPERSPECTRAL LIDAR FOR MONITORING CROPS NITROGEN STRESS“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (24.06.2016): 1043–47. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1043-2016.
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Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by over-fertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.
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Alordzinu, Kelvin Edom, Jiuhao Li, Yubin Lan, Sadick Amoakohene Appiah, Alaa AL Aasmi, Hao Wang, Juan Liao, Livingstone Kobina Sam-Amoah und Songyang Qiao. „Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils“. Sensors 21, Nr. 17 (24.08.2021): 5705. http://dx.doi.org/10.3390/s21175705.
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Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC) was deployed to schedule irrigation and management of crops’ water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R2 above 0.95 p < 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R2 = 0.9758 and 0.9816 p < 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices; (2) water overtone vegetation indices; (3) Photochemical Reflectance Index centered at 570 nm (PRI570), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI1640, PRI570, and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R2 above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types.
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Moharana, S., und S. Dutta. „Hyperspectral remote sensing of paddy crop using insitu measurement and clustering technique“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (28.11.2014): 845–51. http://dx.doi.org/10.5194/isprsarchives-xl-8-845-2014.
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Rice Agriculture, mainly cultivated in South Asia regions, is being monitored for extracting crop parameter, crop area, crop growth profile, crop yield using both optical and microwave remote sensing. Hyperspectral data provide more detailed information of rice agriculture. The present study was carried out at the experimental station of the Regional Rainfed Low land Rice Research Station, Assam, India (26.1400° N, 91.7700° E) and the overall climate of the study area comes under Lower Brahmaputra Valley (LBV) Agro Climatic Zones. The hyperspectral measurements were made in the year 2009 from 72 plots that include eight rice varieties along with three different level of nitrogen treatments (50, 100, 150 kg/ha) covering rice transplanting to the crop harvesting period. With an emphasis to varieties, hyperspectral measurements were taken in the year 2014 from 24 plots having 24 rice genotypes with different crop developmental ages. All the measurements were performed using a spectroradiometer with a spectral range of 350–1050 nm under direct sunlight of a cloud free sky and stable condition of the atmosphere covering more than 95 % canopy. In this study, reflectance collected from canopy of rice were expressed in terms of waveforms. Furthermore, generated waveforms were analysed for all combinations of nitrogen applications and varieties. A hierarchical clustering technique was employed to classify these waveforms into different groups. By help of agglomerative clustering algorithm a few number of clusters were finalized for different rice varieties along with nitrogen treatments. By this clustering approach, observational error in spectroradiometer reflectance was also nullified. From this hierarchical clustering, appropriate spectral signature for rice canopy were identified and will help to create rice crop classification accurately and therefore have a prospect to make improved information on rice agriculture at both local and regional scales. From this hierarchical clustering, spectral signature library for rice canopy were identified which will help to create rice crop classification maps and critical wave bands like green (519,559 nm), red (649 nm), red edge (729 nm) and NIR region (779,819 nm) were marked sensitive to nitrogen which will further help in nitrogen mapping of paddy agriculture over therefore have the prospect to make improved informed decisions.
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Singh, S. K., Sujay Dutta und Nishith Dharaiya. „A study on geospatial technology for detecting and mapping of Solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton crop“. Journal of Applied and Natural Science 8, Nr. 4 (01.12.2016): 2175–81. http://dx.doi.org/10.31018/jans.v8i4.1108.
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Detection of crop stress is one of the major applications of remote sensing in agriculture. Many researchers have confirmed the ability of remote sensing techniques for detection of pest/disease on cotton. The objective of the present study was to evaluate the relation between the mealybug severity and remote sensing indices and development of a model for mapping of mealybug damage using remote sensing indices. The mealybug-infested cotton crop had a significantly lower reflectance (33%) in the near infrared region and higher (14%) in the visible range of the spectrum when compared with the non-infested cotton crop having near infrared and visible region reflectance of 48 % and 9% respectively. Multiple Linear regression analysis showed that there were varying relationships between mealybug severity and spectral vegetation indices, with coefficients of determination (r2) ranging from 0.63 to0.31. Model developed in this study for the mealybug damage assessment in cotton crop yielded significant relationship (r2=0.863) and was applied on satellite data of 21st September 2009 which revealed high severity of mealybug and it was low on 24th September 2010 which confirmed the significance of the model and can be used in the identification of mealybug infested cotton zones. These results indicate that remote sensing data have the potential to distinguish damage by mealybug and quantify its abundance in cotton.
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Pascucci, Simone, Stefano Pignatti, Raffaele Casa, Roshanak Darvishzadeh und Wenjiang Huang. „Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”“. Remote Sensing 12, Nr. 21 (09.11.2020): 3665. http://dx.doi.org/10.3390/rs12213665.
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The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.
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Svotwa, Ezekia, Anxious J. Masuka, Barbara Maasdorp, Amon Murwira und Munyaradzi Shamudzarira. „Remote Sensing Applications in Tobacco Yield Estimation and the Recommended Research in Zimbabwe“. ISRN Agronomy 2013 (15.12.2013): 1–7. http://dx.doi.org/10.1155/2013/941873.
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Tobacco crop area and yield forecasts are important in stabilizing tobacco prices at the auction floors. Tobacco yield estimation in Zimbabwe is currently based on statistical surveys and ground-based field reports. These methods are costly, time consuming, and are prone to large errors. Remote sensing can provide timely information on crop spectral characteristics which can be used to estimate crop yields. Remote sensing application on agriculture in Zimbabwe is still very limited. Research should focus on identifying suitable reflectance indices that are related to tobacco growth and yield. Varietal yield response to fertiliser and planting dates as well as suitable temporal windows for spectral data collection should be identified. The challenges of the different tobacco land sizes have to be overcome by identifying suitable satellite platform, with sufficient spectral resolution to separate the tobacco crop from the adjacent competing crops and noncrop vegetative surfaces. The identified suitable index should be strongly correlated with tobacco in season dry mass and yield. The suitable vegetative indices can be employed in establishing tobacco cropped area and then apply the long-term area yield relationship from government and nongovernmental statistical departments to estimate yield from remote sensing derived cropped area.
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Zhao, Huijie, Bolun Cui und Guorui Jia. „A Flight Direction Design Method for Airborne Spectral Imaging Considering the Anisotropy Reflectance of the Target in Rugged Terrain“. Sensors 19, Nr. 12 (17.06.2019): 2715. http://dx.doi.org/10.3390/s19122715.
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An excellent mission plan is the prerequisite for the acquisition of high quality airborne hyperspectral images which are useful for environmental research, mining etc. In order to minimize the radiance non-uniformity caused by the anisotropic reflectance of targets, the flight direction is mostly designed on the solar azimuth or 180° from it for whiskbroom and pushbroom imagers. However, the radiance to the observer is determined not only by the reflectance of the target, but also by the terrain, the illumination direction and the observation direction. So, the flight direction which is defined to minimize radiance non-uniformity might change with the terrain. In order to find the best flight direction for rugged terrain, we firstly analyze the causes of the effect of terrain on radiation non-uniformity based on the radiative transfer process. Then, the flight direction design method is proposed for composite sloping terrain. Tested by digital and physical simulation experiments, the radiance non-uniformity is minimized when the aircraft flies in the designated direction. Finally, a workflow for flight direction planning and optimizing is summarized, considering the flight mission planning techniques and the workflow of remote sensing missions.
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Akiyama, Tsuyoshi, Y. Inoue, M. Shibayama, Y. Awaya und N. Tanaka. „Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing“. Agricultural and Food Science 5, Nr. 3 (01.05.1996): 367–76. http://dx.doi.org/10.23986/afsci.72741.
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LANDSAT/TM data, which are characterized by high spectral/spatial resolutions, are able to contribute to practical agricultural management. In the first part of the paper, the authors review some recent applications of satellite remote sensing in agriculture. Techniques for crop discrimination and mapping have made such rapid progress that we can classify crop types with more than 80% accuracy. The estimation of crop biomass using satellite data, including leaf area, dry and fresh weights, and the prediction of grain yield, has been attempted using various spectral vegetation indices. Plant stresses caused by nutrient deficiency and water deficit have also been analysed successfully. Such information may be useful for farm management. In the latter half of the paper, we introduce the Arctic Science Project, which was carried out under the Science and Technology Agency of Japan collaborating with Finnish scientists. In this project, monitoring of the boreal forest was carried out using LANDSAT data. Changes in the phenology of subarctic ground vegetation, based on spectral properties, were measured by a boom-mounted, four-band spectroradiometer. The turning point dates of the seasonal near-infrared (NIR) and red (R) reflectance factors might indicate the end of growth and the beginning of autumnal tints, respectively.
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Kovar, Marek, Marian Brestic, Oksana Sytar, Viliam Barek, Pavol Hauptvogel und Marek Zivcak. „Evaluation of Hyperspectral Reflectance Parameters to Assess the Leaf Water Content in Soybean“. Water 11, Nr. 3 (01.03.2019): 443. http://dx.doi.org/10.3390/w11030443.
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Nondestructive assessment of water content and water stress in plants is an important component in the rational use of crop irrigation management in precision agriculture. Spectral measurements of light reflectance in the UV/VIS/NIR region (350–1075 nm) from individual leaves were acquired under a rapid dehydration protocol for validation of the remote sensing water content assessment in soybean plants. Four gravimetrical approaches of leaf water content assessment were used: relative water content (RWC), foliar water content as percent of total fresh mass (FWCt), foliar water content as percent of dry mass (FWCd), and equivalent water thickness (EWT). Leaf desiccation resulted in changes in optical properties with increasing relative reflectance at wavelengths between 580 and 700 nm. The highest positive correlations were observed for the relations between the photochemical reflectance index (PRI) and EWT (rP = 0.860). Data analysis revealed that the specific water absorption band at 970 nm showed relatively weaker sensitivity to water content parameters. The prediction of leaf water content parameters from PRI measurements was better with RMSEs of 12.4% (rP = 0.786), 9.1% (rP = 0.736), and 0.002 (rP = 0.860) for RWC, FWCt, and EWT (p < 0.001), respectively. The results may contribute to more efficient crop water management and confirmed that EWT has a statistically closer relationship with reflectance indices than other monitored water parameters.
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Xavier, Alexandre Cândido, Bernardo Friedrich Theodor Rudorff, Mauricio Alves Moreira, Brummer Seda Alvarenga, José Guilherme de Freitas und Marcus Vinicius Salomon. „Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height“. Scientia Agricola 63, Nr. 2 (April 2006): 130–38. http://dx.doi.org/10.1590/s0103-90162006000200004.
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Hyperspectral crop reflectance data are useful for several remote sensing applications in agriculture, but there is still a need for studies to define optimal wavebands to estimate crop biophysical parameters. The objective of this work is to analyze the use of narrow and broad band vegetation indices (VI) derived from hyperspectral field reflectance measurements to estimate wheat (Triticum aestivum L.) grain yield and plant height. A field study was conducted during the winter growing season of 2003 in Campinas, São Paulo State, Brazil. Field canopy reflectance measurements were acquired at six wheat growth stages over 80 plots with four wheat cultivars (IAC-362, IAC-364, IAC-370, and IAC-373), five levels of nitrogen fertilizer (0, 30, 60, 90, and 120 kg of N ha-1) and four replicates. The following VI were analyzed: a) hyperspectral or narrow-band VI (1. optimum multiple narrow-band reflectance, OMNBR; 2. narrow-band normalized difference vegetation index, NB_NDVI; 3. first- and second-order derivative of reflectance; and 4. four derivative green vegetation index); and b) broad band VI (simple ratio, SR; normalized difference vegetation index, NDVI; and soil-adjusted vegetation index, SAVI). Hyperspectral indices provided an overall better estimate of biophysical variables when compared to broad band VI. The OMNBR with four bands presented the highest R² values to estimate both grain yield (R² = 0.74; Booting and Heading stages) and plant height (R² = 0.68; Heading stage). Best results to estimate biophysical variables were observed for spectral measurements acquired between Tillering II and Heading stages.
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Gu, Lingxiao, Yanmin Shuai, Congying Shao, Donghui Xie, Qingling Zhang, Yaoming Li und Jian Yang. „Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring“. Remote Sensing 13, Nr. 9 (27.04.2021): 1699. http://dx.doi.org/10.3390/rs13091699.
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Optical remote sensing indices play an important role in vegetation information extraction and have been widely serving ecology, agriculture and forestry, urban monitoring, and other communities. Remote sensing indices are constructed from individual bands depending on special characteristics to enhance the typical spectral features for the identification or distinction of surface land covers. With the development of quantitative remote sensing, there is a rapid increasing requirement for accurate data processing and modeling. It is well known that the geometry-induced variation observed in surface reflectance is not ignorable, but the situation of uncertainty thereby introduced into these indices still needs further detailed understanding. We adopted the ground multi-angle hyperspectrum, spectral response function (SRF) of Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), Operational Land Imager (OLI), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Multi-Spectral Instrument (MSI) optical sensors and simulated their sensor-like spectral reflectance; then, we investigated the potential angle effect uncertainty on optical indices that have been frequently involved in vegetation monitoring and examined the forward/backward effect over both the ground-based level and the actual Landsat TM/ETM+ overlapped region. Our results on the discussed indices and sensors show as following: (1) Identifiable angle effects exist with a more elevated influence than that introduced by band difference among sensors; (2) The absolute difference between forward and backward direction can reach up to −0.03 to 0.1 within bands of the TM/ETM+ overlapped region; (3) The investigation at ground level indicates that there are different variations of angle effect transmitted to each remote sensing index. Regarding cases of crop canopy at various growth phases, most of the discussed indices have more than a 20% relative difference to nadir value except Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) with the magnitude lower than 10%, and less than 16% of Normalized Burn Ratio (NBR). For the case of wax maturity stage, the relative difference to nadir value of Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI), Char Soil Index (CSI), NBR, Normalized Difference Moisture Index (NDMI), and SWIR2/NIR exceeded 50%, while the values for NBR and NDMI can reach up to 115.8% and 206.7%, respectively; (4) Various schemes of index construction imply different propagation of angle effect uncertainty. The “difference” indices can partially suppress the directional influence, while the “ratio” indices show high potential to amplify the angle effect. This study reveals that the angle-induced uncertainty of these indices is greater than that induced by the spectrum mismatch among sensors, especially under the case of senescence. In addition, based on this work, indices with a suppressed potential of angle effect are recommended for vegetation monitoring or information retrieval to avoid unexpected effects.
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Marang, Ian J., Patrick Filippi, Tim B. Weaver, Bradley J. Evans, Brett M. Whelan, Thomas F. A. Bishop, Mohammed O. F. Murad, Dhahi Al-Shammari und Guy Roth. „Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status“. Remote Sensing 13, Nr. 8 (07.04.2021): 1428. http://dx.doi.org/10.3390/rs13081428.
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Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R2 = 0.8) and novel combinations of spectra (R2 = 0.85). The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm). Satellite multispectral was compared against the UAV hyperspectral remote sensing’s performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B3; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704.9 nm) with the lower 10 m resolution Green band reporting an R2 = 0.85, compared with the R2 = 0.78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R2 = 0.48). Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R2 = 0.81) being the best index. The machine learning approach narrowed the range of bands required to investigate plant condition over this trial site, greatly improved processing time and reduced processing complexity. While Sentinel performed well in this comparison and would be useful in a broadacre crop production context, the impact of pixel boundaries relative to a region of interest and coarse spatial and temporal resolution impacts its utility in a research capacity.
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Patel, P., H. Bhatt und A. K. Shukla. „Absolute Vicarious Calibration of recently launched Indian Meteorological Satellite: INSAT-3D imager“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (28.11.2014): 291–98. http://dx.doi.org/10.5194/isprsarchives-xl-8-291-2014.
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Looking towards the advancements and popularity of remote sensing and an ever increasing need for the development of a variety of new and complex satellite sensors, it has become even more essential to continually upgrade the ability to provide absolute calibration of sensors. This article describes a simple procedure to implement post-launch calibration for VIS and SWIR channels of INSAT-3D imager over land site (Little Rann of Kutch (ROK), Gujarat) on three different days to account for characterization errors or undetermined post-launch changes in spectral response of the sensor. The measurements of field reflectance of study site (of extent ~6 km x 6 km) in the wavelength range 325–2500 nm, along with atmospheric parameters (Aerosol Optical Depth, Total Columnar Ozone, Water Vapor) and sensor spectral response functions, were input to the 6S radiative transfer model to simulate radiance at top of the atmosphere (TOA) for VIS and SWIR bands. The uncertainty in vicarious calibration coefficients due to measured spatial variability of field reflectance along with due to aerosol types were also computed for the INSAT-3D imager. The effect of surface anisotropy on TOA radiance was studied using a MODIS Bidirectional Reflectance Distribution Function (BRDF) product covering the experimental site. The results show that there is no indication of change in calibration coefficients in INSAT- 3D imager, for VIS and SWIR band over Little ROK. Comparison made between the INSAT-3D imager measured radiance and 6S simulated radiance. Analysis shows that for clear sky days, the INSAT-3D imager overestimates TOA radiance in the VIS band by 5.1 % and in the SWIR band by 11.7 % with respect to 6S simulated radiance. For these bands, in the inverse mode, the 6S corrected surface reflectance was closer to field surface reflectance. It was found that site spatial variability was a critical factor in estimating change in sensor calibration coefficients and influencing uncertainty in TOA radiance for Little ROK.
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Zhang, Hua, Steven M. Gorelick und Paul V. Zimba. „Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability“. Remote Sensing 12, Nr. 3 (04.02.2020): 506. http://dx.doi.org/10.3390/rs12030506.
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The quantification of impervious surface through remote sensing provides critical information for urban planning and environmental management. The acquisition of quality reference data and the selection of effective predictor variables are two factors that contribute to the low accuracies of impervious surface in urban remote sensing. A hybrid method was developed to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates ancillary datasets from OpenStreetMap, National Wetland Inventory, and National Cropland Data to generate training and validation samples in a semi-automatic manner, significantly reducing the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes. This method was applied to 1-m National Agriculture Imagery Program (NAIP) imagery of three sites with different levels of land development and data availability. Results indicate improved extractions of impervious surface with user’s accuracies ranging from 69% to 90% and producer’s accuracies from 88% to 95%. The results were compared to the 30-m percent impervious surface data of the National Land Cover Database, demonstrating the potential of this method to validate and complement satellite-derived medium-resolution datasets of urban land cover and land use.
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Lopes, J. W. B., F. B. Lopes, E. M. de Andrade, L. C. G. Chaves und M. G. R. Carneiro. „Spectral Response of Water Under Different Concentrations of Suspended Sediment: Measurement and Simplified Modeling“. Journal of Agricultural Science 11, Nr. 3 (15.02.2019): 327. http://dx.doi.org/10.5539/jas.v11n3p327.
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Understanding the spectral behaviour of water is of the greatest importance to the quality management of water resources. Continuous monitoring by remote sensing is therefore essential for administrators seeking the efficient management of its many uses. The aim of this research was to characterise the spectral response of water submitted to different concentrations of sediments of varying textural properties, organic matter and salts, and to identify the implications of these characteristics using simplified modelling. The experiment was conducted at the Radiometry Laboratory of the Department of Agricultural Engineering of the Federal University of Ceará, Brazil. The soils used in the research came from two areas of irrigated agriculture in Ceará, one in Morada Nova and the other in Pentecoste. Both soils were classified as Fluvic Neosols; the first saline and the second saline-sodic, and presented significant differences in granulometric composition and organic matter content. From the results, it can be concluded that: (i) sediments added at different concentrations cause an increase and deformation of the reflectance curves, and that maximum spectral partitioning occurs at two reflectance peaks; (ii) derivative analysis favours the identification of wavelengths that best differentiate sediment concentration, allowing more-efficient modelling of the process; (iii) the characteristics of texture, organic matter and salt content have little effect on estimating suspended-sediment concentration in the water, making multiple linear regression modelling a viable option for this purpose.
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Khadr, Mosaad, Mohamed Gad, Salah El-Hendawy, Nasser Al-Suhaibani, Yaser Hassan Dewir, Muhammad Usman Tahir, Muhammad Mubushar und Salah Elsayed. „The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation“. Water 13, Nr. 1 (27.12.2020): 35. http://dx.doi.org/10.3390/w13010035.
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Sustainable agriculture in arid regions necessitates that the quality of groundwater be carefully monitored; otherwise, low-quality irrigation water may cause soil degradation and negatively impact crop productivity. This study aimed to evaluate the quality of groundwater samples collected from the wells in the quaternary aquifer, which are located in the Western Desert (WD) and the Central Nile Delta (CND), by integrating a multivariate analysis, proximal remote sensing data, and data-driven modeling (adaptive neuro-fuzzy inference system (ANFIS) and support vector machine regression (SVMR)). Data on the physiochemical parameters were subjected to multivariate analysis to ease the interpretation of groundwater quality. Then, six irrigation water quality indices (IWQIs) were calculated, and the original spectral reflectance (OSR) of groundwater samples were collected in the 302–1148 nm range, with the optimal spectral wavelength intervals corresponding to each of the six IWQIs determined through correlation coefficients (r). Finally, the performance of both the ANFIS and SVMR models for evaluating the IWQIs was investigated based on effective spectral reflectance bands. From the multivariate analysis, it was concluded that the combination of factor analysis and principal component analysis was found to be advantageous to examining and interpreting the behavior of groundwater quality in both regions, as well as predicting the variables that may impact groundwater quality by illuminating the relationship between physiochemical parameters and the factors or components of both analyses. The analysis of the six IWQIs revealed that the majority of groundwater samples from the CND were highly suitable for irrigation purposes, whereas most of the groundwater from the WD can be used with some limitations to avoid salinity and alkalinity issues in the long term. The high r values between the six IWQIs and OSR were located at wavelength intervals of 302–318, 358–900, and 1074–1148 nm, and the peak value of r for these was relatively flat. Finally, the ANFIS and SVMR both obtained satisfactory degrees of model accuracy for evaluating the IWQIs, but the ANFIS model (R2 = 0.74–1.0) was superior to the SVMR (R2 = 0.01–0.88) in both the training and testing series. Finally, the multivariate analysis was able to easily interpret groundwater quality and ground-based remote sensing on the basis of spectral reflectance bands via the ANFIS model, which could be used as a fast and low-cost onsite tool to estimate the IWQIs of groundwater.
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Mahlangu, Precious, Renaud Mathieu, Konrad Wessels, Laven Naidoo, Michel Verstraete, Gregory Asner und Russell Main. „Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data“. Remote Sensing 10, Nr. 10 (25.09.2018): 1537. http://dx.doi.org/10.3390/rs10101537.
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Forest structural data are essential for assessing biophysical processes and changes, and promoting sustainable forest management. For 18+ years, the Multi-Angle Imaging SpectroRadiometer (MISR) instrument has been observing the land surface reflectance anisotropy, which is known to be related to vegetation structure. This study sought to determine the performance of a new MISR-High Resolution (HR) dataset, recently produced at a full 275 m spatial resolution, and consisting of 36 Bidirectional Reflectance Factors (BRF) and 12 Rahman–Pinty–Verstraete (RPV) parameters, to estimate the mean tree height (Hmean) and canopy cover (CC) across structurally diverse, heterogeneous, and fragmented forest types in South Africa. Airborne LiDAR data were used to train and validate Random Forest models which were tested across various MISR-HR scenarios. The combination of MISR multi-angular and multispectral data was consistently effective in improving the estimation of structural parameters, and produced the lowest relative root mean square error (rRMSE) (33.14% and 38.58%), for Hmean and CC respectively. The combined RPV parameters for all four bands yielded the best results in comparison to the models of the RPV parameters separately: Hmean (R2 = 0.71, rRMSE = 34.84%) and CC (R2 = 0.60, rRMSE = 40.96%). However, the combined RPV parameters for all four bands in comparison to the MISR-HR BRF 36 band model it performed poorer (rRMSE of 5.1% and 6.2% higher for Hmean and CC, respectively). When considered separately, savanna forest type had greater improvement when adding multi-angular data, with the highest accuracies obtained for the Hmean parameter (R2 of 0.67, rRMSE of 31.28%). The findings demonstrate the potential of the optical multi-spectral and multi-directional newly processed data (MISR-HR) for estimating forest structure across Southern African forest types.
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Bangelesa, Freddy, Elhadi Adam, Jasper Knight, Inos Dhau, Marubini Ramudzuli und Thabiso M. Mokotjomela. „Predicting Soil Organic Carbon Content Using Hyperspectral Remote Sensing in a Degraded Mountain Landscape in Lesotho“. Applied and Environmental Soil Science 2020 (13.04.2020): 1–11. http://dx.doi.org/10.1155/2020/2158573.
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Soil organic carbon constitutes an important indicator of soil fertility. The purpose of this study was to predict soil organic carbon content in the mountainous terrain of eastern Lesotho, southern Africa, which is an area of high endemic biodiversity as well as an area extensively used for small-scale agriculture. An integrated field and laboratory approach was undertaken, through measurements of reflectance spectra of soil using an Analytical Spectral Device (ASD) FieldSpec® 4 optical sensor. Soil spectra were collected on the land surface under field conditions and then on soil in the laboratory, in order to assess the accuracy of field spectroscopy-based models. The predictive performance of two different statistical models (random forest and partial least square regression) was compared. Results show that random forest regression can most accurately predict the soil organic carbon contents on an independent dataset using the field spectroscopy data. In contrast, the partial least square regression model overfits the calibration dataset. Important wavelengths to predict soil organic contents were localised around the visible range (400–700 nm). This study shows that soil organic carbon can be most accurately estimated using derivative field spectroscopy measurements and random forest regression.
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Lu, Han, Tianxing Fan, Prakash Ghimire und Lei Deng. „Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors“. Remote Sensing 12, Nr. 16 (07.08.2020): 2542. http://dx.doi.org/10.3390/rs12162542.
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In recent years, the use of unmanned aerial vehicles (UAVs) has received increasing attention in remote sensing, vegetation monitoring, vegetation index (VI) mapping, precision agriculture, etc. It has many advantages, such as high spatial resolution, instant information acquisition, convenient operation, high maneuverability, freedom from cloud interference, and low cost. Nowadays, different types of UAV-based multispectral minisensors are used to obtain either surface reflectance or digital number (DN) values. Both the reflectance and DN values can be used to calculate VIs. The consistency and accuracy of spectral data and VIs obtained from these sensors have important application value. In this research, we analyzed the earth observation capabilities of the Parrot Sequoia (Sequoia) and DJI Phantom 4 Multispectral (P4M) sensors using different combinations of correlation coefficients and accuracy assessments. The research method was mainly focused on three aspects: (1) consistency of spectral values, (2) consistency of VI products, and (3) accuracy of normalized difference vegetation index (NDVI). UAV images in different resolutions were collected using these sensors, and ground points with reflectance values were recorded using an Analytical Spectral Devices handheld spectroradiometer (ASD). The average spectral values and VIs of those sensors were compared using different regions of interest (ROIs). Similarly, the NDVI products of those sensors were compared with ground point NDVI (ASD-NDVI). The results show that Sequoia and P4M are highly correlated in the green, red, red edge, and near-infrared bands (correlation coefficient (R2) > 0.90). The results also show that Sequoia and P4M are highly correlated in different VIs; among them, NDVI has the highest correlation (R2 > 0.98). In comparison with ground point NDVI (ASD-NDVI), the NDVI products obtained by both of these sensors have good accuracy (Sequoia: root-mean-square error (RMSE) < 0.07; P4M: RMSE < 0.09). This shows that the performance of different sensors can be evaluated from the consistency of spectral values, consistency of VI products, and accuracy of VIs. It is also shown that different UAV multispectral minisensors can have similar performances even though they have different spectral response functions. The findings of this study could be a good framework for analyzing the interoperability of different sensors for vegetation change analysis.
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Fan, W., X. Xu, X. Liu, B. Yan und Y. Cui. „Accurate LAI retrieval method based on PROBA/CHRIS data“. Hydrology and Earth System Sciences Discussions 6, Nr. 6 (12.11.2009): 7001–24. http://dx.doi.org/10.5194/hessd-6-7001-2009.
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Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers a chance to derive LAI in regional scales accurately. Variations of background, atmospheric conditions and the anisotropy of canopy reflectance are three factors that can strongly restrain the accuracy of retrieved LAI. Based on the hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. So the inversion precision and the dynamic range can be improved remarkably, which has been proved by numerical simulations. As the derivative method is very sensitive to the random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove the random noise effectively; therefore, the method can be performed to the remotely sensed hyperspectral image. The study region is situated in Zhangye, Gansu Province, China; the hyperspectral and multi-angular image of the study region has been acquired from Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 and 14 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieve LAI was validated by the ground truth of 11 sites. It shows that by applying innovative filtering method, the new LAI inversion method is accurate and effective.
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Dominiak-Świgoń, Martyna, Paweł Olejniczak, Maciej Nowak und Marlena Lembicz. „Hyperspectral imaging in assessing the condition of plants: strengths and weaknesses“. Biodiversity Research and Conservation 55, Nr. 1 (01.09.2019): 25–30. http://dx.doi.org/10.2478/biorc-2019-0011.
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Abstract Hyperspectral remote sensing of plants is widely used in agriculture and forestry. Fast, large-area monitoring is applied, among others, in detecting and diagnosing diseases, stress conditions or predicting the yields. Using available tools to increase the yields of most important crop plants (wheat, rice, corn) without posing threat to food security is essential in the situation of current climate changes. Spectral plant indices are associated with biochemical and biophysical plant characteristics. Using the plant spectral properties (mainly chlorophyll red light absorption and near-infrared range light reflectance in leaf intercellular spaces), it is possible to estimate plant condition, water and carotenoid contents or detect disease. More and more often, based on commonly used hyperspectral vegetation indices, new, more sensitive indices are introduced. Furthermore, to facilitate data processing, artificial intelligence is employed, i.e., neural networks and deep convolutional neural networks. It is important in ecological research to carry out long-term observations and measurements of organisms throughout their lifespan. A non-invasive, quick method ensures that it may be used many times and at each stage of plant development.
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Hamada, Yuki, David Cook und Donald Bales. „EcoSpec: Highly Equipped Tower-Based Hyperspectral and Thermal Infrared Automatic Remote Sensing System for Investigating Plant Responses to Environmental Changes“. Sensors 20, Nr. 19 (23.09.2020): 5463. http://dx.doi.org/10.3390/s20195463.
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Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350–2500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR; the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.
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Fan, W. J., X. R. Xu, X. C. Liu, B. Y. Yan und Y. K. Cui. „Accurate LAI retrieval method based on PROBA/CHRIS data“. Hydrology and Earth System Sciences 14, Nr. 8 (10.08.2010): 1499–507. http://dx.doi.org/10.5194/hess-14-1499-2010.
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Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers an opportunity to accurately derive LAI at regional scales. The anisotropy of canopy reflectance, variations in background characteristics, and variability in atmospheric conditions constitute three factors that can strongly constrain the accuracy of retrieved LAI. Based on a hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. With the aid of a widely-accepted atmospheric model, the influence of atmospheric conditions can be minimized as well. Thus the inversion precision and the dynamic range can be markedly improved, which has been proved by numerical simulations. As the derivative method is very sensitive to random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove random noise effectively; therefore, the method can be applied to hyperspectral images. The study region was situated in Zhangye, Gansu Province, China; hyperspectral and multi-angular images of the study region were acquired via the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieved LAI was validated by ground reference data at 11 sites. Results show that the new LAI inversion method is accurate and effective with the aid of the innovative filtering method.
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Meng, Ran, Zhengang Lv, Jianbing Yan, Gengshen Chen, Feng Zhao, Linglin Zeng und Binyuan Xu. „Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification“. Remote Sensing 12, Nr. 19 (04.10.2020): 3233. http://dx.doi.org/10.3390/rs12193233.
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Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.
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Sun, Haoran, Lei Wang, Rencai Lin, Zhen Zhang und Baozhong Zhang. „Mapping Plastic Greenhouses with Two-Temporal Sentinel-2 Images and 1D-CNN Deep Learning“. Remote Sensing 13, Nr. 14 (18.07.2021): 2820. http://dx.doi.org/10.3390/rs13142820.
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Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of PGs, of which many map PGs based on spectral responses and geometric shapes. In this study, we proposed a new fine- and coarse-scale mapping approach using two-temporal Sentinel-2 images with various seasonal characteristics and a one-dimensional convolutional neural network (1D-CNN). Having applied this approach in a pilot area study, the results were summarized as follows: (1) A time-series analysis of Sentinel-2 images showed that the reflectance of greenhouses changes during crop growth and development. In particular, the red-edge and near-infrared bands undergo a significant increase and then decrease during the whole crop growth period. Thus, two critical period images, containing a substantial difference in greenhouse reflectance, were sufficient to carry out an accurate and efficient mapping result. (2) The 1D-CNN classifier was used to map greenhouses by capturing subtle details and the overall trend of the spectrum curve. Overall, our approach showed higher classification accuracy than other approaches using support vector machines (SVM) or random forests (RF). In addition, the greenhouse area identified was highly consistent with the existing surfaces observed in very high-resolution images, with a kappa co-efficient of 0.81. (3) The narrow band feature differences (red-edge and near infrared narrow bands) in two-temporal Sentinel-2 images played a significant role in high-precision greenhouse mapping. The classification accuracy with narrow band features was much better than the maps produced without narrow band features. This scheme provided a method to digitize greenhouse precisely and publish its statistics for free, which enable advanced decision support for agriculture management.
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Tripathy, R., K. N. Chaudhary, R. Nigam, K. R. Manjunath, P. Chauhan, S. S. Ray und J. S. Parihar. „Operational semi-physical spectral-spatial wheat yield model development“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (28.11.2014): 977–82. http://dx.doi.org/10.5194/isprsarchives-xl-8-977-2014.
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Spectral yield models based on Vegetation Index (VI) and the mechanistic crop simulation models are being widely used for crop yield prediction. However, past experience has shown that the empirical nature of the VI based models and the intensive data requirement of the complex mechanistic models has limited their use for regional and spatial crop yield prediction especially for operational use. The present study was aimed at development of an intermediate method based on the use of remote sensing and the physiological concepts such as the photo-synthetically active solar radiation (PAR) and the fraction of PAR absorbed by the crop (fAPAR) in Monteith’s radiation use efficiency based equation (Monteith, 1977) for operational wheat yield forecasting by the Department of Agriculture (DoA). Net Primary Product (NPP) has been computed using the Monteith model and stress has been applied to convert the potential NPP to actual NPP. Wheat grain yield has been computed using the actual NPP and Harvest index. Kalpana-VHRR insolation has been used for deriving the PAR. Maximum radiation use efficiency has been collected from literature and wheat crop mask was derived at MNCFC, New Delhi using RS2-AWiFS data. Water stress has been derived from the Land Surface Water Index (LSWI) which has been derived periodically from the MODIS surface reflectance data (NIR and SWIR1). Temperature stress has been derived from the interpolated daily mean temperature. Results indicated that this model underestimated the yield by 3.45 % as compared to the reported yield at state level and hence can be used to predict wheat yield at state level. This study will be able to provide the spatial wheat yield map, as well as the district-wise and state level aggregated wheat yield forecast. It is possible to operationalize this remote sensing based modified Monteith’s efficiency model for future yield forecasting with around 0.15 t ha-1 RMSE at state level.
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Ma, Xuanlong, Alfredo Huete, Ngoc Tran, Jian Bi, Sicong Gao und Yelu Zeng. „Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8“. Remote Sensing 12, Nr. 8 (23.04.2020): 1339. http://dx.doi.org/10.3390/rs12081339.
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Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.
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Denis, Antoine, Baudouin Desclee, Silke Migdall, Herbert Hansen, Heike Bach, Pierre Ott, Amani Louis Kouadio und Bernard Tychon. „Multispectral Remote Sensing as a Tool to Support Organic Crop Certification: Assessment of the Discrimination Level between Organic and Conventional Maize“. Remote Sensing 13, Nr. 1 (31.12.2020): 117. http://dx.doi.org/10.3390/rs13010117.
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The annual certification of organic agriculture products includes an in situ inspection of the fields declared organic. This inspection is more difficult, time-consuming, and costly for large farms or in production regions located in remote areas. The global objective of this research is to assess how spatial remote sensing may support the organic crop certification process by developing a method that would enable certification bodies to target for priority in situ control crop fields declared as organic but that would show on satellite imagery an appearance closer to conventional fields. For this purpose, the ability of multispectral satellite images to discriminate between organic and conventional maize fields was assessed through the use of a set of four satellite images of different spatial and spectral resolutions acquired at different crop growth stages over a large number of maize fields (32) that are part of an operational farm in Germany. In support of this main objective, a set of in situ measurements (leaf hyperspectral reflectance, chlorophyll, and nitrogen content and dry matter percentage, crop canopy cover, height, wet biomass and dry matter percentage, soil chemical composition) was conducted to characterize the nature of the biochemical and biophysical differences between organic and conventional maize fields. The results of this research showed that highly significant biochemical and biophysical differences between a large number of organic and conventional maize fields may exist at identified crop growth stages and that these differences may be sufficiently pronounced to enable the complete discrimination between crop management modes using satellite images issued from quite common multispectral satellite sensors through the use of spectral or spatial heterogeneity indices. These results are very encouraging and suggest, for the first time, that satellite images could effectively support the organic maize certification process.
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Montgomery, Kellyn, Josh Henry, Matthew Vann, Brian E. Whipker, Anders Huseth und Helena Mitasova. „Measures of Canopy Structure from Low-Cost UAS for Monitoring Crop Nutrient Status“. Drones 4, Nr. 3 (22.07.2020): 36. http://dx.doi.org/10.3390/drones4030036.
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Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.
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Kreuter, Axel, Mario Blumthaler, Martin Tiefengraber, Richard Kift und Ann R. Webb. „Sky radiance at a coastline and effects of land and ocean reflectivities“. Atmospheric Chemistry and Physics 17, Nr. 23 (04.12.2017): 14353–64. http://dx.doi.org/10.5194/acp-17-14353-2017.
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Abstract. We present a unique case study of the spectral sky radiance distribution above a coastline. Results are shown from a measurement campaign in Italy involving three diode array spectroradiometers which are compared to 3-D model simulations from the Monte Carlo model MYSTIC. On the coast, the surrounding is split into two regions, a diffusely reflecting land surface and a water surface which features a highly anisotropic reflectance function. The reflectivities and hence the resulting radiances are a nontrivial function of solar zenith and azimuth angle and wavelength. We show that for low solar zenith angles (SZAs) around noon, the higher land albedo causes the sky radiance at 20° above the horizon to increase by 50 % in the near infrared at 850 nm for viewing directions towards the land with respect to the ocean. Comparing morning and afternoon radiances highlights the effect of the ocean's sun glint at high SZA, which contributes around 10 % to the measured radiance ratios. The model simulations generally agree with the measurements to better than 10 %. We investigate the individual effects of model input parameters representing land and ocean albedo and aerosols. Different land and ocean bi-directional reflectance functions (BRDFs) do not generally improve the model agreement. However, consideration of the uncertainties in the diurnal variation of aerosol optical depth can explain the remaining discrepancies between measurements and model. We further investigate the anisotropy effect of the ocean BRDF which is featured in the zenith radiances. Again, the uncertainty of the aerosol loading is dominant and obscures the modelled sun glint effect of 7 % at 650 nm. Finally, we show that the effect on the zenith radiance is restricted to a few kilometres from the coastline by model simulations along a perpendicular transect and by comparing the radiances at the coast to those measured at a site 15 km inland. Our findings are relevant to, for example, ground-based remote sensing of aerosol characteristics, since a common technique is based on sky radiance measurements along the solar almucantar.
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Fu, Zhaopeng, Jie Jiang, Yang Gao, Brian Krienke, Meng Wang, Kaitai Zhong, Qiang Cao et al. „Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle“. Remote Sensing 12, Nr. 3 (05.02.2020): 508. http://dx.doi.org/10.3390/rs12030508.
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Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.
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Kuska, Matheus Thomas, Jan Behmann und Anne-Katrin Mahlein. „Potential of hyperspectral imaging to detect and identify the impact of chemical warfare compounds on plant tissue“. Pure and Applied Chemistry 90, Nr. 10 (25.10.2018): 1615–24. http://dx.doi.org/10.1515/pac-2018-0102.
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AbstractThe OPCW Member states cover 98% of the global population and landmass. Regrettably, unanticipated chemical warfare agent assaults are reported during the last decades. In addition to the frequent threat situation, the sampling of bio-medical samples from these areas is critical and mainly depends on investigation opportunities of victims. Non-contact sensor technologies are desirable to enable a fast and secure estimation of a situation. Plants react on pollution because of their direct interaction with gases and it is assumed that chemical warfare agents influence plants, respectively. This impact can be analyzed for the detection and characterization of chemical warfare assaults. Nowadays technological progress in digital technologies provides new innovations in detectors, data analysis approaches and software availability which could improve the screening, monitoring and analysis of chemical warfare. Within this context hyperspectral imaging (HSI) is a promising method. Different applications from remote to close range sensing in medicine, food production, military, geography and agriculture do exist already. During the last years HSI showed high potential to determine and assess different plant parameters, e.g. abiotic and biotic stresses by recording the spectral reflectance of plants. Within the present manuscript, the basics principle of HSI as an innovative technique, aspects of recording and analyzing HSI data is presented using wild growing apple leaves which are treated with sulfuric acid, fire or heat. Resulting spectral signatures showed significant changes among the treatments. Especially the shortwave infrared was sensitive to changes due to the different treatments. Furthermore, the calculation of common spectral indices revealed differences due to the treatments which are not visible to the human eye. The results support HSI applications for the detection of chemical warfare agents and elucidate the impact of chemical warfare on plants.
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Wehrhan, Marc, und Michael Sommer. „A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape“. Remote Sensing 13, Nr. 18 (07.09.2021): 3557. http://dx.doi.org/10.3390/rs13183557.
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Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils’ SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R²) = 0.91; root mean square error (RMSE) = 0.11% and R² = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R² = 0.88, RMSE = 0.07%; R² = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.
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Santos-Rufo, Antonio, Francisco-Javier Mesas-Carrascosa, Alfonso García-Ferrer und Jose Emilio Meroño-Larriva. „Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards“. Remote Sensing 12, Nr. 20 (19.10.2020): 3426. http://dx.doi.org/10.3390/rs12203426.
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Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification.
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Ngandam Mfondoum, A. H., P. G. Gbetkom, R. Cooper, S. Hakdaoui und M. B. Mansour Badamassi. „IMPROVING THE LAND SURFACE GENERAL DROUGHT INDEX MODEL“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (14.02.2020): 101–8. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-101-2020.
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Abstract. Drought affects all human activities and ecosystems. Nearly 40 percent of the world’s population inhabit Drylands, and they depend on agriculture for their food, security and livelihoods. Among the remote sensing indices developed, the Land Surface General Drought Index (LSGDI) was recently proposed. This paper proposes an improved model of LSGDI to face the issue of drought in semi-arid and arid regions. The experiment was conducted for the Maga’s floodplain, in North-Cameroon. The method uses satellite images of Landsat in 1987, 2003 and 2018, for January and March or April, corresponding to the middle and the end of the dry season. A Vegetation Moisture Index (VMI) and a Normalized Difference Soil Drought Index (NDSoDI) are both developed. On an orthogonal plan, their projections give a drought line that expresses the improved LSGDI (LSGDI2) as the root sum square of the NDSoDI and the VMI. The LSGDI2 results are ranged in [0.09 – 0.14] interval, which is used to define the threshold and ease the qualifiers for drought classes. The visual patterns easily match the sandy areas of the original Landsat images with the highest values, while the vegetation and water areas match the lowest values. Compared with the LSGDI and Second Modified Perpendicular drought Index (MPDI1), the new index reflectance values are higher. Finally, although LSGDI2 curve’s evolution follows the NDSoDI one at 94%, the new spectral index values depends on the both components, helping to map highest values of drought and moisture in Maga’s floodplain, for a sustainable rice culture expansion.
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Gold, Kaitlin M., Philip A. Townsend, Adam Chlus, Ittai Herrmann, John J. Couture, Eric R. Larson und Amanda J. Gevens. „Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato“. Remote Sensing 12, Nr. 2 (15.01.2020): 286. http://dx.doi.org/10.3390/rs12020286.
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In-vivo foliar spectroscopy, also known as contact hyperspectral reflectance, enables rapid and non-destructive characterization of plant physiological status. This can be used to assess pathogen impact on plant condition both prior to and after visual symptoms appear. Challenging this capacity is the fact that dead tissue yields relatively consistent changes in leaf optical properties, negatively impacting our ability to distinguish causal pathogen identity. Here, we used in-situ spectroscopy to detect and differentiate Phytophthora infestans (late blight) and Alternaria solani (early blight) on potato foliage over the course of disease development and explored non-destructive characterization of contrasting disease physiology. Phytophthora infestans, a hemibiotrophic pathogen, undergoes an obligate latent period of two–seven days before disease symptoms appear. In contrast, A. solani, a necrotrophic pathogen, causes symptoms to appear almost immediately when environmental conditions are conducive. We found that respective patterns of spectral change can be related to these differences in underlying disease physiology and their contrasting pathogen lifestyles. Hyperspectral measurements could distinguish both P. infestans-infected and A. solani-infected plants with greater than 80% accuracy two–four days before visible symptoms appeared. Individual disease development stages for each pathogen could be differentiated from respective controls with 89–95% accuracy. Notably, we could distinguish latent P. infestans infection from both latent and symptomatic A. solani infection with greater than 75% accuracy. Spectral features important for late blight detection shifted over the course of infection, whereas spectral features important for early blight detection remained consistent, reflecting their different respective pathogen biologies. Shortwave infrared wavelengths were important for differentiation between healthy and diseased, and between pathogen infections, both pre- and post-symptomatically. This proof-of-concept work supports the use of spectroscopic systems as precision agriculture tools for rapid and early disease detection and differentiation tools, and highlights the importance of careful consideration of underlying pathogen biology and disease physiology for crop disease remote sensing.
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Praveen, B., S. Mustak und Pritee Sharma. „ASSESSING THE TRANSFERABILITY OF MACHINE LEARNING ALGORITHMS USING CLOUD COMPUTING AND EARTH OBSERVATION DATASETS FOR AGRICULTURAL LAND USE/COVER MAPPING“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (26.07.2019): 585–92. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-585-2019.
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<p><strong>Abstract.</strong> Mapping of agricultural land use/cover was initiated since the past several decades for land use planning, change detection analysis, crop yield monitoring etc. using earth observation datasets and traditional parametric classifiers. Recently, machine learning, cloud computing, Google Earth Engine (GEE) and open source earth observation datasets widely used for fast, cost-efficient and precise agricultural land use/cover mapping and change detection analysis. Main objective of this study was to assess the transferability of the machine learning algorithms for land use/cover mapping using cloud computing and open source earth observation datasets. In this study, the Landsat TM (L5, L8) of 2018, 2009 and 1998 were selected and median reflectance of spectral bands in Kharif and Rabi season were used for the classification. In addition, three important machine learning algorithms such as Support Vector Machine with Radial Basis Function (SVM-RBF), Random forest (RF) and Classification and Regression Tree (CART) were selected to evaluate the performance in transferability for agricultural land use classification using GEE. Seven land use/cover classes such as built-up, cropland, fallow land, vegetation etc. were selected based on literature review and local land use classification scheme. In this classification, several strategies were employed such as feature extraction, feature selection, parameter tuning, sensitivity analysis on size of training samples, transferability analysis to assess the performance of the selected machine learning algorithms for land use/cover classification. The result shows that SVM-RBF outperforms the RF and CART for both spatial and temporal transferability analysis. This result is very helpful for agriculture and remote sensing scientist to suggest promising guideline to land use planner and policy-makers for efficient land use mapping, change detection analysis, land use planning and natural resource management.</p>
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Qian, Xiaojin, und Liangyun Liu. „Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds“. Remote Sensing 12, Nr. 13 (03.07.2020): 2139. http://dx.doi.org/10.3390/rs12132139.
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Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale.
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de Albuquerque, Anesmar Olino, Osmar Abílio de Carvalho Júnior, Osmar Luiz Ferreira de Carvalho, Pablo Pozzobon de Bem, Pedro Henrique Guimarães Ferreira, Rebeca dos Santos de Moura, Cristiano Rosa Silva, Roberto Arnaldo Trancoso Gomes und Renato Fontes Guimarães. „Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data“. Remote Sensing 12, Nr. 13 (06.07.2020): 2159. http://dx.doi.org/10.3390/rs12132159.
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The center pivot irrigation system (CPIS) is a modern irrigation technique widely used in precision agriculture due to its high efficiency in water consumption and low labor compared to traditional irrigation methods. The CPIS is a leader in mechanized irrigation in Brazil, with growth forecast for the coming years. Therefore, the mapping of center pivot areas is a strategic factor for the estimation of agricultural production, ensuring food security, water resources management, and environmental conservation. In this regard, digital processing of satellite images is the primary tool allowing regional and continuous monitoring with low costs and agility. However, the automatic detection of CPIS using remote sensing images remains a challenge, and much research has adopted visual interpretation. Although CPIS presents a consistent circular shape in the landscape, these areas can have a high internal variation with different plantations that vary over time, which is difficult with just the spectral behavior. Deep learning using convolutional neural networks (CNNs) is an emerging approach that provokes a revolution in image segmentation, surpassing traditional methods, and achieving higher accuracy and efficiency. This research aimed to evaluate the use of deep semantic segmentation of CPIS from CNN-based algorithms using Landsat-8 surface reflectance images (seven bands). The developed methodology can be subdivided into the following steps: (a) Definition of three study areas with a high concentration of CPIS in Central Brazil; (b) acquisition of Landsat-8 images considering the seasonal variations of the rain and drought periods; (c) definition of CPIS datasets containing Landsat images and ground truth mask of 256×256 pixels; (d) training using three CNN architectures (U-net, Deep ResUnet, and SharpMask); (e) accuracy analysis; and (f) large image reconstruction using six stride values (8, 16, 32, 64, 128, and 256). The three methods achieved state-of-the-art results with a slight prevalence of U-net over Deep ResUnet and SharpMask (0.96, 0.95, and 0.92 Kappa coefficients, respectively). A novelty in this research was the overlapping pixel analysis in the large image reconstruction. Lower stride values had improvements quantified by the Receiver Operating Characteristic curve (ROC curve) and Kappa, and fewer errors in the frame edges were also perceptible. The overlapping images significantly improved the accuracy and reduced the error present in the edges of the classified frames. Additionally, we obtained greater accuracy results during the beginning of the dry season. The present study enabled the establishment of a database of center pivot images and an adequate methodology for mapping the center pivot in central Brazil.
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& et a, Sharabiani l. „APPLICATION OF SOFT COMPUTING METHODS AND SPECTRAL REFLECTANCE DATA FOR WHEAT GROWTH MONITORING“. IRAQI JOURNAL OF AGRICULTURAL SCIENCES 50, Nr. 4 (30.08.2019). http://dx.doi.org/10.36103/ijas.v50i4.751.
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Technology of precision agriculture has caused to the remote sensors development that compute Normalized Difference Vegetation Index (NDVI) parameters. Vegetation indices obtained from remote sensing data can help to summarize climate conditions. Artificial Neural Networks (ANNs), as a soft computing methods, are one of the most efficient methods for computing as compared to the statistical and analytical techniques for spectral data. This study was employed experimental radial basis function (RBF) of ANN models and adaptive neural-fuzzy inference system (ANFIS) to design the network in order to predict the soil plant analysis development (SPAD), protein content and grain yield of wheat plant based on spectral reflectance value and to compare two models. Results indicated that the obtained results of RBF method with high average correlation coefficient (0.984, 0.981 and 0.9807 in 2015 for SPAD, yield and protein, respectively and 0.979, 0.9805 and 0.984 in 2016) and low RMSE (0.271, 103.315 and 0.111 in 2015 for SPAD, yield and protein, respectively and 0.407, 105.482 and 0.121 in 2016) has the high accuracy and high performance compared to ANFIS models.
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Gogoi, N. K., B. Deka und L. C. Bora. „Remote sensing and its use in detection and monitoring plant diseases: A review“. Agricultural Reviews, of (21.12.2018). http://dx.doi.org/10.18805/ag.r-1835.
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Remote sensing is a rapid, non-invasive and efficient technique which can acquire and analyze spectral properties of earth surfaces from various distances, ranging from satellites to ground-based platforms. This modern technology holds promise in agricultural crop production including crop protection. Variability in the reflectance spectra of plants resulting from occurrence of disease and pests, allows their identification using remote sensing data. Various spectroscopic and imaging techniques like visible, infrared, multiband and fluorescence spectroscopy, fluorescence imaging, multispectral and hyperspectral imaging, thermography, nuclear magnetic resonance spectroscopy etc. have been studied for the detection of plant diseases. Several of these techniques have great potential in phytopathometry. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results and thereby rendering agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.
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Wojtaszek, M. V., und I. Abdurahmanov. „Crop Water Condition Mapping by Optical Remote Sensing“. International Journal of Geoinformatics, 01.02.2021, 11–17. http://dx.doi.org/10.52939/ijg.v17i1.1699.
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Crop water stress monitoring represents a fundamental step in agricultural production. In order to increase water savings and enhance agricultural sustainability, implementation of suitable irrigation scheduling methods is essential, and requires early detection of water stress in crops, before it causes irreversible damage and yield loss. There are different methods to measure water stress, some of them are based on soil moisture measurements while others are based on calculations of vegetation indices, evapotranspiration or soil water balance. Currently, the use of remote sensing technologies for the analysis of plant water status comprises a wide range of available methods such as infrared thermometry for canopy temperature measures, microwave radiation for soil water content assessment, and spectral vegetation indices for the study of the reflectance responses of canopies to different environmental conditions. The aim of the presented work is to investigate the applicability of the optical trapezoid model (OPtical TRApezoid Model) in mapping the moisture content within agricultural field. The model ability to provide vegetation characteristics, and crop water status at the canopy scale can improve the site-specific decision-making process in a precision agriculture.
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Abdullatiff, R. K. „RELATIONSHIP OF SPECTRAL REFLECTANCE AND NDVI TO SOME SOIL PROPERTIES OF BRICKS FACTORIES SOILS IN NAHRAWAN AREA,BAGHDAD IRAQ“. IRAQI JOURNAL OF AGRICULTURAL SCIENCES 50, Nr. 3 (30.06.2019). http://dx.doi.org/10.36103/ijas.v50i3.696.
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A study was conducted to investigate the effect of the brick industry on the environmental system of these project soils of the brick factories in Alnahrawan district. Remote sensing techniques was used to study the relationship between the spectral reflectivity and the vegetative index on the one hand and some surface soil characters of the project and to determine the variation in vegetation cover for the same area and for two different periods.Ten sites were selected to study spectral reflectivity under similar geomorphological conditions near the brickworks project in the Anahrawan district with an area of 10,000 hectares. Soil samples were taken from the surface and at a depth of 0-30 cm. Some chemical and physical characters of research soil were analyzed in the soil department laboratories, college of Agriculture, Baghdad University.Several satellite images taken from the satellite Land sat (ETM) 2013 and another from same satellite in 1990 T.M to determining the change between the two periods. After obtaining remote sensing data (reflectivity and vegetation index).the correlation analysis was carried out between these data. It was observed that the soil salinity values were decreased due to the drainage that the area was confined between the Tigris River and the Diyala tributary which leads to good natural drainage.The attached tables indicate that thedigital numbers of the soil sampling sites in 2013 are highly significant correlated, While some of the characters did not show the use of this region industrially. After calculating the difference between the two images to determine the change. A 100% change was observed and the vegetation cover was sharply reduced between the two images. as well as the extension of the land of empty land, although these lands are still suitable for agriculture.
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Morgan, R. S., M. Abd El-Hady und I. S. Rahim. „Soil salinity mapping utilizing sentinel-2 and neural networks“. Indian Journal Of Agricultural Research, Of (16.08.2018). http://dx.doi.org/10.18805/ijare.a-316.
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Soil salinity is the most important soil property that affects the agriculture productivity. Periodical monitoring of its status is considered a crucial factor in the selection of appropriate agricultural practices to attain a sustainable production. The availability of remote sensing data processed by a somewhat novel method such as artificial neural networks (ANN) offer a potential solution that could easily and affordably replace the in-site monitoring methods. The aim of this work is to use high spectral resolution Sentinel-2 (S2) data for soil salinity prediction utilizing neural networks. The study evaluated three approaches in processing the S2 data for inclusion in the artificial neural network for soil salinity prediction. These approaches included S2 spectral reflectance data, spectral indices and principal component analysis (PCA) of the S2 data. The results revealed that a combination of these approaches including the reflectance data of band 11(shortwave infrared band) of S2, the normalized differential vegetation index (NDVI) and the second PCA (dominated by the near infrared band) gave the best performance when used as input when designing the artificial neural networks to predict the soil salinity. The overall accuracy of this approach has a coefficient of determination (R2) of 0.94 between the actual and predicted soil salinity.
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„Soil Spectral Signature Analysis for Influence of Fertilizers on Two Different Crops in Raver Tahshil“. International Journal of Recent Technology and Engineering 8, Nr. 3 (30.09.2019): 659–63. http://dx.doi.org/10.35940/ijrte.b2640.098319.
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Soils as a significant ingredient of terrestrial ecosystems are extremely important in agriculture field. Soil having different physical, biological, chemical properties. Physicochemical soil properties are the basic indicator of soil efficiency, it is strongly related to agronomic output. Soil parameters are very important in soil fertility that helps for plant growth as well as production. The soil quality is uniformly important as crop invention. The primary goal of this research is to use remote sensing techniques to evaluate soil properties. This goal is satisfied by emerging soil analysis on the basis of spectral data collected by FieldSpec4 spectroradiometer. The spectral analysis technique includes soil samples preparation, acquisition of spectral signatures and selection suitable statistical method. In this regard, soil properties and their consequent soil spectral signature measure. Statistical mean and series of processes are performed using View Spec pro Software. In the collected soil samples, surface soil parameters are more reflected than subsurface soil parameters. The spectral reflectance data can be alternative to the traditional methods for determining soil properties.
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Tamás, J. „New evaluation method to detect physiological stress in fruit trees by airborne hyperspectral image spectroscopy“. International Journal of Horticultural Science 16, Nr. 1 (03.01.2010). http://dx.doi.org/10.31421/ijhs/16/1/860.
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Nowadays airborne remote sensing data are increasingly used in precision agriculture. The fast space-time dependent localization of stresses in orchards, which allows for a more efficient application of horticultural technologies, could lead to improved sustainable precise management. The disadvantage of the near field multi and hyper spectroscopy is the spot sample taking, which can apply independently only for experimental survey in plantations. The traditional satellite images is optionally suitable for precision investigation because of the low spectral and ground resolution on field condition. The presented airborne hyperspectral image spectroscopy reduces above mentioned disadvantages and at the same time provides newer analyzing possibility to the user. In this paper we demonstrate the conditions of data base collection and some informative examination possibility. The estimating of the board band vegetation indices calculated from reflectance is well known in practice of the biomass stress examinations. In this method the N-dimension spectral data cube enables to calculate numerous special narrow band indexes and to evaluate maps. This paper aims at investigating the applied hyperspectral analysis for fruit tree stress detection. In our study, hyperspectral data were collected by an AISADUAL hyperspectral image spectroscopy system, with high (0,5-1,5 m) ground resolution. The research focused on determining of leaves condition in different fruit plantations in the peach orchard near Siófok. Moreover the spectral reflectance analyses could provide more information about plant condition due to changes in the absorption of incident light in the visible and near infrared range of the spectrum.
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Yang, Chenbo, Meichen Feng, Lifang Song, Chao Wang, Wude Yang, Yongkai Xie, Binghan Jing et al. „Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments“. Scientific Reports 11, Nr. 1 (20.09.2021). http://dx.doi.org/10.1038/s41598-021-98143-0.
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AbstractHyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water treatments on soil organic carbon (SOC) content, and the relationship between SOC content and spectral reflectance (350–2500 nm) were studied. 17 kinds of preprocessing algorithm were performed on the original spectral (R), and the five allocation ratios of calibration to verification sets were set. Finally, the model was constructed by partial least squares regression (PLSR). The results showed that the effects of water treatment on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics, and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not good. But the ratio of standard deviation to the standard prediction error (RPD) values of the models were constructed by simple mathematical transformation (T0–T5) and first-order differential transformation (T6–T11) can reach more than 1.4. The simple mathematical transformation (T0–T2, T4–T5) and the first-order differential transformation (T6–T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the process of constructing the hyperspectral monitoring model of SOC.