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Journal articles on the topic 'Leaf reflectance modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Hu, Hao, Guangzhi Zhang, and Kefeng Zheng. "Modeling Leaf Image, Chlorophyll Fluorescence, Reflectance From SPAD Readings." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 11 (2014): 4368–73. http://dx.doi.org/10.1109/jstars.2014.2325812.

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HOUBORG, R., and E. BOEGH. "Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data." Remote Sensing of Environment 112, no. 1 (2008): 186–202. http://dx.doi.org/10.1016/j.rse.2007.04.012.

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3

Bhadra, Sourav, Vasit Sagan, Maitiniyazi Maimaitijiang, et al. "Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning." Remote Sensing 12, no. 13 (2020): 2082. http://dx.doi.org/10.3390/rs12132082.

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Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make com

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Dawson, Terence P., Paul J. Curran, and Stephen E. Plummer. "LIBERTY—Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra." Remote Sensing of Environment 65, no. 1 (1998): 50–60. http://dx.doi.org/10.1016/s0034-4257(98)00007-8.

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Ebengo, Dav M., Florian de Boissieu, Grégoire Vincent, Christiane Weber, and Jean-Baptiste Féret. "Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling." Remote Sensing 13, no. 11 (2021): 2120. http://dx.doi.org/10.3390/rs13112120.

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Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART (Discrete Anisotropic Radiative Transfer) to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in a complex tropical forest, and to reproduce spectral dissimilarity within and among species,

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Temizel, Kadir, Mehmet Odabas, Nurettin Senyer, et al. "Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions." Open Life Sciences 9, no. 12 (2014): 1226–34. http://dx.doi.org/10.2478/s11535-014-0356-4.

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AbstractLack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that th

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Lu, Bu, and Lu. "Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance." Sensors 19, no. 19 (2019): 4059. http://dx.doi.org/10.3390/s19194059.

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As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (Brassica chinensis L. var. Shanghai Qing), Chinese white cabbage (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee), and Romaine lettuce (Lactuca sativa var longifoliaf. Lam) were measured

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Yin, Tiangang, Jianbo Qi, Bruce D. Cook, Douglas C. Morton, Shanshan Wei, and Jean-Philippe Gastellu-Etchegorry. "Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index." Remote Sensing 12, no. 1 (2019): 4. http://dx.doi.org/10.3390/rs12010004.

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Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with f

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Wang, Ce, Xiuhua Li, Lijia Wang, et al. "Prediction of N, P, and K Contents in Sugarcane Leaves by VIS-NIR Spectroscopy and Modeling of NPK Interaction Effects." Transactions of the ASABE 62, no. 6 (2019): 1427–33. http://dx.doi.org/10.13031/trans.13086.

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Abstract. Methods were studied to predict the N, P, and K contents in sugarcane leaves quickly and accurately at the seedling, tillering, and elongation stages from leaf spectral reflectance. A total of 117 valid leaf samples were used to obtain leaf spectral reflectance with an indoor VIS-NIR spectrophotometer. Using the spectral data processed by CARS-PCA as an independent variable, a six-fold cross-validated PLS model for N, P, and K contents was established. The R2 values of the CARS-PCA-PLS models for N, P, and K prediction were 0.859, 0.677, and 0.932, respectively. Correlation analysis

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10

Hikosaka, Kouki, and Hibiki M. Noda. "Modeling leaf CO2 assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index." Plant, Cell & Environment 42, no. 2 (2018): 730–39. http://dx.doi.org/10.1111/pce.13461.

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11

Zeng, Yelu, Baodong Xu, Gaofei Yin, et al. "Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations." Remote Sensing 10, no. 10 (2018): 1508. http://dx.doi.org/10.3390/rs10101508.

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This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean

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Lin, Wenpeng, Xumiao Yu, Di Xu, Tengteng Sun, and Yue Sun. "Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes." Remote Sensing 13, no. 18 (2021): 3570. http://dx.doi.org/10.3390/rs13183570.

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Using reflectance spectroscopy to monitor vegetation pigments is a crucial method to know the nutritional status, environmental stress, and phenological phase of vegetation. Defining cities as targeted areas and common greening plants as research objects, the pigment concentrations and dust deposition amounts of the urban plants were classified to explore the spectral difference, respectively. Furthermore, according to different dust deposition levels, this study compared and discussed the prediction models of chlorophyll concentration by correlation analysis and linear regression analysis. Th

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Kallel, Abdelaziz. "FluLCVRT: Reflectance and fluorescence of leaf and canopy modeling based on Monte Carlo vector radiative transfer simulation." Journal of Quantitative Spectroscopy and Radiative Transfer 253 (September 2020): 107183. http://dx.doi.org/10.1016/j.jqsrt.2020.107183.

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Miraglio, Thomas, Karine Adeline, Margarita Huesca, Susan Ustin, and Xavier Briottet. "Joint Use of PROSAIL and DART for Fast LUT Building: Application to Gap Fraction and Leaf Biochemistry Estimations over Sparse Oak Stands." Remote Sensing 12, no. 18 (2020): 2925. http://dx.doi.org/10.3390/rs12182925.

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Gap Fraction, leaf pigment contents (content of chlorophylls a and b (Cab) and carotenoids content (Car)), Leaf Mass per Area (LMA), and Equivalent Water Thickness (EWT) are considered relevant indicators of forests’ health status, influencing many biological and physical processes. Various methods exist to estimate these variables, often relying on the extensive use of Radiation Transfer Models (RTMs). While 3D RTMs are more realistic to model open canopies, their complexity leads to important computation times that limit the number of simulations that can be considered; 1D RTMs, although les

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Blinn, Christine, Matthew House, Randolph Wynne, Valerie Thomas, Thomas Fox, and Matthew Sumnall. "Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations." Forests 10, no. 3 (2019): 222. http://dx.doi.org/10.3390/f10030222.

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Leaf area index (LAI) is an important biophysical parameter used to monitor, model, and manage loblolly pine plantations across the southeastern United States. Landsat provides forest scientists and managers the ability to obtain accurate and timely LAI estimates. The objective of this study was to investigate the relationship between loblolly pine LAI measured in situ (at both leaf area minimum and maximum through two growing seasons at two geographically disparate study areas) and vegetation indices calculated using data from Landsat 7 (ETM+) and Landsat 8 (OLI). Sub-objectives included exam

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16

Dutta, Debsunder, David S. Schimel, Ying Sun, Christiaan van der Tol, and Christian Frankenberg. "Optimal inverse estimation of ecosystem parameters from observations of carbon and energy fluxes." Biogeosciences 16, no. 1 (2019): 77–103. http://dx.doi.org/10.5194/bg-16-77-2019.

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Abstract. Canopy structural and leaf photosynthesis parameterizations such as maximum carboxylation capacity (Vcmax), slope of the Ball–Berry stomatal conductance model (BBslope) and leaf area index (LAI) are crucial for modeling plant physiological processes and canopy radiative transfer. These parameters are large sources of uncertainty in predictions of carbon and water fluxes. In this study, we develop an optimal moving window nonlinear Bayesian inversion framework to use the Soil Canopy Observation Photochemistry and Energy fluxes (SCOPE) model for constraining Vcmax, BBslope and LAI with

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Jay, S., R. Bendoula, X. Hadoux, and N. Gorretta. "MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 467–72. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-467-2015.

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Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral meas

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Gara, Tawanda W., Parinaz Rahimzadeh-Bajgiran, and Roshanak Darvishzadeh. "Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook." Remote Sensing 13, no. 17 (2021): 3352. http://dx.doi.org/10.3390/rs13173352.

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Quantitative remote sensing of leaf traits offers an opportunity to track biodiversity changes from space. Augmenting field measurement of leaf traits with remote sensing provides a pathway for monitoring essential biodiversity variables (EBVs) over space and time. Detailed information on key leaf traits such as leaf mass per area (LMA) is critical for understanding ecosystem structure and functioning, and subsequently the provision of ecosystem services. Although studies on remote sensing of LMA and related constituents have been conducted for over three decades, a comprehensive review of rem

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19

Wang, Jie, Wei Xue, Xiaojun Shi, Yangchun Xu, and Caixia Dong. "Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy." Sensors 21, no. 18 (2021): 6260. http://dx.doi.org/10.3390/s21186260.

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Different cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars. This study proposes a new technique based on in-field visible-near infrared (VIS-NIR) spectroscopy and the Adaboost algorithm initiated with machine learning methods. The performance was evaluated by estimating leaf N concentration for a total of 1285 samples from different cultivars, growth regions

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20

Verhoef, Wout, and Heike Bach. "Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data." Remote Sensing of Environment 109, no. 2 (2007): 166–82. http://dx.doi.org/10.1016/j.rse.2006.12.013.

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Adeline, Karine R. M., Xavier Briottet, Sidonie Lefebvre, Nicolas Rivière, Jean-Philippe Gastellu-Etchegorry, and Fabrice Vinatier. "Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios." Remote Sensing 13, no. 5 (2021): 931. http://dx.doi.org/10.3390/rs13050931.

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With the advancement of high spatial resolution imaging spectroscopy, an accurate surface reflectance retrieval is needed to derive relevant physical variables for land cover mapping, soil, and vegetation monitoring. One challenge is to deal with tree shadows using atmospheric correction models if the tree crown transmittance Tc is not properly taken into account. This requires knowledge of the complex radiation mechanisms that occur in tree crowns, which can be provided by coupling the physical modeling of canopy radiative transfer codes (here DART) and the 3D representations of trees. First

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22

Anderson, Martha C. "Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at regional scales." Journal of Applied Remote Sensing 3, no. 1 (2009): 033529. http://dx.doi.org/10.1117/1.3141522.

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23

Siebers, Matthew H., Nuria Gomez-Casanovas, Peng Fu, Katherine Meacham-Hensold, Caitlin E. Moore, and Carl J. Bernacchi. "Emerging approaches to measure photosynthesis from the leaf to the ecosystem." Emerging Topics in Life Sciences 5, no. 2 (2021): 261–74. http://dx.doi.org/10.1042/etls20200292.

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Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorologi

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Houborg, Rasmus, Martha Anderson, and Craig Daughtry. "Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale." Remote Sensing of Environment 113, no. 1 (2009): 259–74. http://dx.doi.org/10.1016/j.rse.2008.09.014.

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Lobos, Gustavo, Alejandro Escobar-Opazo, Félix Estrada, et al. "Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions." Remote Sensing 11, no. 3 (2019): 329. http://dx.doi.org/10.3390/rs11030329.

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To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear R

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Majasalmi, Titta, and Ryan M. Bright. "Evaluation of leaf-level optical properties employed in land surface models." Geoscientific Model Development 12, no. 9 (2019): 3923–38. http://dx.doi.org/10.5194/gmd-12-3923-2019.

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Abstract. Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although plant functional type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the “time-invariant optical properties table” of the Simple Biosphere (SiB) model (later referred to as the “SiB table”) presented 30 years ago by Dorman and Sellers (1989), which has since been adopted by many LSMs. This revisit was needed as many of the data underlyi

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Jiang, Fugen, Andrew R. Smith, Mykola Kutia, Guangxing Wang, Hua Liu, and Hua Sun. "A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China." Remote Sensing 12, no. 11 (2020): 1884. http://dx.doi.org/10.3390/rs12111884.

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As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of

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He, Liming, Rong Wang, Georgy Mostovoy, et al. "Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data." Remote Sensing 13, no. 4 (2021): 806. http://dx.doi.org/10.3390/rs13040806.

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We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop

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Jang, Youjung, Yangdam Eo, Meongdo Jang, et al. "Impact of Land Cover and Leaf Area Index on BVOC Emissions over the Korean Peninsula." Atmosphere 11, no. 8 (2020): 806. http://dx.doi.org/10.3390/atmos11080806.

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Biogenic volatile organic compound (BVOCs) emissions are the largest VOC emission source globally, and are precursors to ozone and secondary organic aerosols, both of which are strong, short-lived climate pollutants. BVOC emissions are usually estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN), which requires Plant Functional Types (PFTs) and Leaf Area Indexes (LAIs) as inputs. Herein, the effects of refined input data on regional BVOC emission estimates are analyzed. For LAIs, lower resolution MODerate-resolution Imaging Spectroradiometer (MODIS), and higher spat

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Miraglio, Thomas, Margarita Huesca, Jean-Philippe Gastellu-Etchegorry, et al. "Impact of Modeling Abstractions When Estimating Leaf Mass per Area and Equivalent Water Thickness over Sparse Forests Using a Hybrid Method." Remote Sensing 13, no. 16 (2021): 3235. http://dx.doi.org/10.3390/rs13163235.

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Equivalent water thickness (EWT) and leaf mass per area (LMA) are important indicators of plant processes, such as photosynthetic and potential growth rates and health status, and are also important variables for fire risk assessment. Retrieving these traits through remote sensing is challenging and often requires calibration with in situ measurements to provide acceptable results. However, calibration data cannot be expected to be available at the operational level when estimating EWT and LMA over large regions. In this study, we assessed the ability of a hybrid retrieval method, consisting o

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Sobejano-Paz, Verónica, Teis Nørgaard Mikkelsen, Andreas Baum, et al. "Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought." Remote Sensing 12, no. 19 (2020): 3182. http://dx.doi.org/10.3390/rs12193182.

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During water stress, crops undertake adjustments in functional, structural, and biochemical traits. Hyperspectral data and machine learning techniques (PLS-R) can be used to assess water stress responses in plant physiology. In this study, we investigated the potential of hyperspectral optical (VNIR) measurements supplemented with thermal remote sensing and canopy height (hc) to detect changes in leaf physiology of soybean (C3) and maize (C4) plants under three levels of soil moisture in controlled environmental conditions. We measured canopy evapotranspiration (ET), leaf transpiration (Tr), l

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Liang, Liang, Di Geng, Juan Yan, et al. "Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method." Remote Sensing 12, no. 21 (2020): 3534. http://dx.doi.org/10.3390/rs12213534.

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The leaf area index (LAI) is an essential indicator used in crop growth monitoring. In the study, a hybrid inversion method, which combined a physical model with a statistical method, was proposed to estimate the crop LAI. The simulated compact high-resolution imaging spectrometer (CHRIS) canopy spectral crop reflectance datasets were generated using the PROSAIL model (the coupling of PROSPECT leaf optical properties model and Scattering by Arbitrarily Inclined Leaves model) and the CHRIS band response function. Partial least squares (PLS) was then used to reduce the dimension of the simulated

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Lang, Chen, Liang, Ren, and Qian. "Geographic and Climatic Attributions of Autumn Land Surface Phenology Spatial Patterns in the Temperate Deciduous Broadleaf Forest of China." Remote Sensing 11, no. 13 (2019): 1546. http://dx.doi.org/10.3390/rs11131546.

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Autumn vegetation phenology plays a critical role in identifying the end of the growing season and its response to climate change. Using the six vegetation indices retrieved from moderate resolution imaging spectroradiometer data, we extracted an end date of the growing season (EOS) in the temperate deciduous broadleaf forest (TDBF) area of China. Then, we validated EOS with the ground-observed leaf fall date (LF) of dominant tree species at 27 sites and selected the best vegetation index. Moreover, we analyzed the spatial pattern of EOS based on the best vegetation index and its dependency on

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Yang, Hai Qing, and Gang Lv. "Determination of Pear Leaf Nitrogen Content Based on Multi-Spectral Imaging Technology and Multivariate Calibration." Key Engineering Materials 467-469 (February 2011): 718–24. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.718.

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Fast determination of mineral nutrition contents of fruit trees is essential for orchard precision fertilizing management. A multi-spectral imaging system was developed and tested for the measurement of leaf nitrogen content of fruit trees in the study. Images taken using this system included visible images(R-G-B) and near-infrared image(NIR). These images were further processed into several indices such as RVI, NDVI, GNDVI, -log(R) and –log(G). Total 185 leaf samples were picked from Huang-hua pear trees which were planted in three orchards with different nitrogen fertilizing levels. Among th

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Shiyan, Shaum, Triana Hertiani, Ronny Martien, and Akhmad Kharis Nugroho. "OPTIMIZATION OF A NOVEL KINETIC-ASSISTED INFUNDATION FOR RICH-EGCG AND POLYPHENOLS OF WHITE TEA (CAMELLIA SINENSIS) USING CENTRAL COMPOSITE DESIGN." International Journal of Applied Pharmaceutics 10, no. 6 (2018): 259. http://dx.doi.org/10.22159/ijap.2018v10i6.29654.

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Objective: This research aimed to find modeling and optimization of a novel kinetic-assisted infundation for rich-epigallocatechin gallate (EGCG) and polyphenols extraction from white tea leaf (Camellia sinensis L.).Methods: The optimal conditions for the best extraction of kinetic-assisted infundation were determined using central composite design. The content of EGCG, catechin, gallic acid, caffeine, theobromine, and theophylline was quantified using high-performance liquid chromatography with ultraviolet detection (HPLC/UV-detection). The total polyphenolic content (TPC) and total flavonoid

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Yao, Zhifeng, Yu Lei, and Dongjian He. "Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging." Sensors 19, no. 4 (2019): 952. http://dx.doi.org/10.3390/s19040952.

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Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effectiv

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Liu, Yang, Ronggao Liu, Jan Pisek, and Jing M. Chen. "Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data." Biogeosciences 14, no. 5 (2017): 1093–110. http://dx.doi.org/10.5194/bg-14-1093-2017.

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Abstract. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. In this paper, overstory and understory LAI values were estimated separately for global needleleaf and deciduous broadleaf forests by fusing MISR and MODIS observations. Monthly forest understory LAI was retrieve

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Masson, Valéry, Jean-Louis Champeaux, Fabrice Chauvin, Christelle Meriguet, and Roselyne Lacaze. "A Global Database of Land Surface Parameters at 1-km Resolution in Meteorological and Climate Models." Journal of Climate 16, no. 9 (2003): 1261–82. http://dx.doi.org/10.1175/1520-0442-16.9.1261.

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Abstract Ecoclimap, a new complete surface parameter global dataset at a 1-km resolution, is presented. It is intended to be used to initialize the soil–vegetation–atmosphere transfer schemes (SVATs) in meteorological and climate models (at all horizontal scales). The database supports the “tile” approach, which is utilized by an increasing number of SVATs. Two hundred and fifteen ecosystems representing areas of homogeneous vegetation are derived by combining existing land cover maps and climate maps, in addition to using Advanced Very High Resolution Radiometer (AVHRR) satellite data. Then,

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Fu, Zhaopeng, Jie Jiang, Yang Gao, et al. "Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle." Remote Sensing 12, no. 3 (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-spectr

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Wang, Li, Shuisen Chen, Dan Li, et al. "Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery." Remote Sensing 13, no. 15 (2021): 2956. http://dx.doi.org/10.3390/rs13152956.

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Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-ba

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Cui, Lei, Ziti Jiao, Kaiguang Zhao, et al. "Retrieval of Vertical Foliage Profile and Leaf Area Index Using Transmitted Energy Information Derived from ICESat GLAS Data." Remote Sensing 12, no. 15 (2020): 2457. http://dx.doi.org/10.3390/rs12152457.

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The vertical foliage profile (VFP) and leaf area index (LAI) are critical descriptors in terrestrial ecosystem modeling. Although light detection and ranging (lidar) observations have been proven to have potential for deriving the VFP and LAI, existing methods depend only on the received waveform information and are sensitive to additional input parameters, such as the ratio of canopy to ground reflectance. In this study, we proposed a new method for retrieving forest VFP and LAI from Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data over two sites

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Wang, Zhaoqi, Xiang Liu, Hao Wang, et al. "Monitoring Vegetation Greenness in Response to Climate Variation along the Elevation Gradient in the Three-River Source Region of China." ISPRS International Journal of Geo-Information 10, no. 3 (2021): 193. http://dx.doi.org/10.3390/ijgi10030193.

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The Three-River Source Region (TRSR) is vital to the ecological security of China. However, the impact of global warming on the dynamics of vegetation along the elevation gradient in the TRSR remains unclear. Accordingly, we used multi-source remote sensing vegetation indices (VIs) (GIMMS (Global Inventory Modeling and Mapping Studies) LAI (Leaf Area Index), GIMMS NDVI (Normalized Difference Vegetation Index), GLOBMAP (Global Mapping) LAI, MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index), MODIS NDVI, and MODIS NIRv (near-infrared reflectance of vegetation))

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Ghosh, Subhasis, Subhajit Bandopadhyay, and Dany A. Cotrina Sánchez. "Long-Term Sensitivity Analysis of Palmer Drought Severity Index (PDSI) through Uncertainty and Error Estimation from Plant Productivity and Biophysical Parameters." Environmental Sciences Proceedings 3, no. 1 (2020): 57. http://dx.doi.org/10.3390/iecf2020-07956.

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Palmer Drought Severity Index (PDSI) is the most effective and well-acknowledged drought severity index that particularly determines the long-term drought conditions over the forest and other terrestrial ecosystems. However, the sensitivity of PDSI has not been explored yet based on productivity (i.e., Gross Primary Productivity (GPP)), biophysical parameters (i.e., biomass—Leaf Area Index (LAI) and Enhanced Vegetation Index (EVI) and greenness content—Normalized Difference Vegetation Index (NDVI)), and absorbed solar radiation by plants (i.e., fraction of Absorbed Solar Radiation (fAPAR)) ove

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Qian, Xiaojin, and 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, no. 13 (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 e

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Kayumba, Patient Mindje, Gonghuan Fang, Yaning Chen, et al. "Modeling the Near-Surface Energies and Water Vapor Fluxes Behavior in Response to Summer Canopy Density across Yanqi Endorheic Basin, Northwestern China." Remote Sensing 13, no. 18 (2021): 3764. http://dx.doi.org/10.3390/rs13183764.

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The Yanqi basin is the main irrigated and active agroecosystem in semi-arid Xinjiang, northwestern China, which further seeks responses to the profound local water-related drawbacks in relation to the unceasing landscape desiccation and scant precipitation. Yet, it comes as an astonishment that a few reported near-surface items and water vapor fluxes as so far required for water resources decision support, particularly in a scarce observation data region. As a contributive effort, here we adjusted the sensible heat flux (H) calibration mechanism of Surface Energy Balance Algorithm for Land (SE

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Kvíčala, Miroslav, Eva Lacková, and Michaela Štamborská. "Internal Reflectance Modelling ofHordeum vulgareLeaves During Drying." Journal of Chemistry 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/210679.

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Spectral reflectance, or indexes that characterize spectral reflectance at concrete wavelength, is commonly used as an indicator of plant stress, or its photosynthetic apparatus status. In this paper, new leaf optical model is presented. Within this paper, experimental determination of surface and internal reflectance of Spring barley leaves and mathematical-physical modelling of internal reflectance were performed. It was proven that a new proposed theoretical model and the experimental spectra of internal reflectance are strongly correlated. It can be concluded that the total reflectance is

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Rouzbeh Kargar, Ali, Richard MacKenzie, Gregory P. Asner, and Jan van Aardt. "A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner." Remote Sensing 11, no. 15 (2019): 1791. http://dx.doi.org/10.3390/rs11151791.

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Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating

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Hu, Qiong, Jingya Yang, Baodong Xu, et al. "Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery." Remote Sensing 12, no. 6 (2020): 912. http://dx.doi.org/10.3390/rs12060912.

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Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consiste

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Cummings, Cadan, Yuxin Miao, Gabriel Dias Paiao, Shujiang Kang, and Fabián G. Fernández. "Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System." Remote Sensing 13, no. 3 (2021): 401. http://dx.doi.org/10.3390/rs13030401.

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Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across d

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Kenjabaev, Shavkat, Christopher Conrad, Odilbek Eshchanov, Aybek Arifjanov, Dilbar Abduraimova, and Umida Voxidova. "Comparison of spectro-biophysical and yield parameters of cotton in irrigated lowlands of Amudaria River." InterCarto. InterGIS 26, no. 3 (2020): 294–308. http://dx.doi.org/10.35595/2414-9179-2020-3-26-294-308.

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This study aims defining the best predictors of biophysical parameters and yield with vegetation indices derived from Landsat 8 OLI surface reflectance data. The study was conducted in 2015 at five crop fields in Kulavat canal irrigation system in Khorezm province, Uzbekistan. The Environment for Visualizing Images (ENVI) ver. 4.5 and R programming software ver. 1.0.143 were used to process, calculate seven vegetation indices (VIs) and predict biophysical parameters and yield of cotton. The trend analysis show that in-situ measured biophysical parameters for the whole growth stage of cotton fo

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