Relevant bibliographies by topics / Remote sensing and vegetation condition index (VCI)

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Academic literature on the topic 'Remote sensing and vegetation condition index (VCI)'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Remote sensing and vegetation condition index (VCI)"

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Andujar, Erika, Nir Y. Krakauer, Chuixiang Yi, and Felix Kogan. "Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing." Advances in Meteorology 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8434020.

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Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived from polar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome. The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts.
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Mutowo, Godfrey, and David Chikodzi. "Remote sensing based drought monitoring in Zimbabwe." Disaster Prevention and Management 23, no. 5 (October 28, 2014): 649–59. http://dx.doi.org/10.1108/dpm-10-2013-0181.

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Purpose – Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years. Design/methodology/approach – The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal. Findings – The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone. Originality/value – Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe.
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Dubey, S. K., A. Gavli, and S. S. Ray. "VEGETATION CONDITION INDEX: A POTENTIAL YIELD ESTIMATOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 211–15. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-211-2019.

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<p><strong>Abstract.</strong> Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. Current study explored a remote sensing-based approach of predicting the yield of Wheat, Kharif Rice and Rabi Rice at district level, using Vegetation Condition Index (VCI), under the FASAL programme. In order to make the estimates 14-years’ historical database (2003&amp;ndash;2016) of NDVI was used to derive the VCI. The yield estimation was carried out for 335 districts (136 districts of Wheat, 23 districts of Rabi Rice and 159 districts of Kharif Rice) for the period of 2016&amp;ndash;17. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of peak growing period of crop were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of crops. Estimated yields are good in agreement with the actual district level yield with the R<sup>2</sup> of, 0.78 for Wheat, 0.52 for Rabi Rice and 0.69 for Kharif Rice. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level crop yield. Though there was variation in model performance in different states and crops, overall, the study showed the usefulness of VCI, which can be used as an input for operational crop yield forecasting, at district level.</p>
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Filgueiras, Roberto, Donizeti Aparecido Pastori Nicolete, Antonio Ribeiro Cunha, and Célia Regina Lopes Zimback. "VARIAÇÃO ESPAÇO-TEMPORAL DA CONDIÇÃO DA VEGETAÇÃO NO INTERIOR PAULISTA." Nativa 7, no. 5 (September 12, 2019): 582. http://dx.doi.org/10.31413/nativa.v7i5.7121.

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A compreensão da dinâmica das condições da vegetação ao longo do tempo tem sido ampliada devido ao avanço das técnicas de sensoriamento remoto. O presente trabalho teve como objetivo analisar a dinâmica espaço-temporal da vegetação estratificada por diferentes usos e cobertura da terra, em área localizada no município de Botucatu-SP. Para isso, foram utilizadas 21 imagens da plataforma Landsat-5/TM, totalizando uma série temporal de 25 anos (1985 a 2010). As imagens foram submetidas aos processos de conversão dos números digitais para valores físicos, correção atmosférica e correção topográfica. As imagens corrigidas foram utilizadas para estimar os valores do VCI (Vegetation Condition Index). Os resultados da estimativa do VCI foram promissores para subsidiar a análise espaço-temporal da condição da vegetação em nível local, sendo sensível às variações locais de precipitação pluviométrica, amplificando a variabilidade intra-classe de uso da terra para o vigor da vegetação. O comportamento característico da floresta semidecidual (classe de uso da terra = Floresta) presente na propriedade foi perceptível nessa análise, fato que faz com se recomende essa metodologia em pesquisas futuras relacionadas a análise da condição da vegetação.Palavras-chave: monitoramento ambiental; sensoriamento remoto; séries temporais. SPATIAL AND TEMPORAL CHANGES IN VEGETATION CONDITIONS IN PAULIST INTERIOR ABSTRACT: There is an increasing demand to better understand the dynamics of the vegetation conditions over time as a result of the improvement of remote sensing techniques. Yhis study aimed to analyse the spatio-temporal behavior of vegetation, stratified by land use in area located in Botucatu-SP. We sused21 Landsat-5 TM images in 25 years (1985 – 2010) of analysis. We applied conversion of the digital numbers to physical values, atmospheric and topographic corrections, which allowed to analyze the vegetation changes by using the VCI (Vegetation Condition Index) calculation. The VCI showed a good performance in analyzing the spatiotemporal vegetation condition at a local level, it is sensitive to local variations of rainfall, it enhances the variability of the intra-class land use for the vigor of vegetation. By applying the VCI we were able to observe the vegetation pattern of the semideciduous forest (land use class = Forest) present in the area, fact that makes this methodology recommended in future researches related to the analysis of the vegetation condition.Keywords: environmental monitoring; remote sensing; time series.
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Sur, Chanyang, Dongkyun Kim, Joo-Heon Lee, Muhammad Mazhar Iqbal, and Minha Choi. "Hydrological Drought Assessment of Energy-Based Water Deficit Index (EWDI) at Different Geographical Regions." Advances in Meteorology 2019 (May 29, 2019): 1–11. http://dx.doi.org/10.1155/2019/8512727.

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This study applied the remote sensing-based drought index, namely, the Energy-Based Water Deficit Index (EWDI), across Mongolia, Australia, and Korean Peninsula for the period between 2000 and 2010. The EWDI is estimated based on the hydrometeorological variables such as evapotranspiration, soil moisture, solar radiation, and vegetation activity which are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) imageries. The estimated EWDI was compared with the Evaporative Stress Index (ESI), the Vegetation Condition Index (VCI), and the Standardized Precipitation Index (SPI). The correlation coefficients between the drought indices are as follows: 0.73–0.76 (EWDI vs ESI), 0.64–0.71 (EWDI vs VCI), 0.54–0.64 (EWDI vs SPI-3), 0.69–0.71 (ESI vs VCI), 0.55–0.62 (ESI vs SPI-3), and 0.53–0.57 (VCI vs SPI-3). The drought prediction accuracy of each index according to error matrix analysis is as follows: 83.33–94.17% (EWDI), 70.00–91.67% (ESI), 47.50–85.00% (VCI), and 61.67–88.33% (SPI-3). Based on the results, the EWDI and ESI were found to be more accurate in capturing moderate drought conditions than the SPI at different geographical regions.
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Han, Yang, Ziying Li, Chang Huang, Yuyu Zhou, Shengwei Zong, Tianyi Hao, Haofang Niu, and Haiyan Yao. "Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices." Remote Sensing 12, no. 3 (February 6, 2020): 530. http://dx.doi.org/10.3390/rs12030530.

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Various drought indices have been developed to monitor drought conditions. Each index has typical characteristics that make it applicable to a specific environment. In this study, six popular drought indices, namely, precipitation condition index (PCI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), scaled drought condition index (SDCI), and temperature–vegetation dryness index (TVDI), have been used to monitor droughts in the Greater Changbai Mountains(GCM) in recent years. The spatial pattern and temporal trend of droughts in this area in the period 2001–2018 were explored by calculating these indices from multi-source remote sensing data. Significant spatial–temporal variations were identified. The results of a slope analysis along with the F-statistic test showed that up to 20% of the study area showed a significant increasing or decreasing trend in drought. It was found that some drought indices cannot be explained by meteorological observations because of the time lag between meteorological drought and vegetation response. The drought condition and its changing pattern differ from various land cover types and indices, but the relative drought situation of different landforms is consistent among all indices. This work provides a basic reference for reasonably choosing drought indices for monitoring drought in the GCM to gain a better understanding of the ecosystem conditions and environment.
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Macarof, Paul, Florian Statescu, Cristian Iulian Birlica, and Paul Gherasim. "IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY." Present Environment and Sustainable Development 13, no. 2 (October 15, 2019): 179–86. http://dx.doi.org/10.15551/pesd2019132013.

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In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.
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Faridatul, Mst Ilme, and Bayes Ahmed. "Assessing Agricultural Vulnerability to Drought in a Heterogeneous Environment: A Remote Sensing-Based Approach." Remote Sensing 12, no. 20 (October 15, 2020): 3363. http://dx.doi.org/10.3390/rs12203363.

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Agriculture is one of the fundamental economic activities in most countries; however, this sector suffers from various natural hazards including flood and drought. The determination of drought-prone areas is essential to select drought-tolerant crops in climate sensitive vulnerable areas. This study aims to enhance the detection of agricultural areas with vulnerability to drought conditions in a heterogeneous environment, taking Bangladesh as a case study. The normalized difference vegetation index (NDVI) and land cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images have been incorporated to compute the vegetation index. In this study, a modified vegetation condition index (mVCI) is proposed to enhance the estimation of agricultural drought. The NDVI values ranging between 0.44 to 0.66 for croplands are utilized for the mVCI. The outcomes of the mVCI are compared with the traditional vegetation condition index (VCI). Precipitation and crop yield data are used for the evaluation. The mVCI maps from multiple years (2006–2018) have been produced to compute the drought hazard index (DHI) using a weighted sum overlay method. The results show that the proposed mVCI enhances the detection of agricultural drought compared to the traditional VCI in a heterogeneous environment. The “Aus” rice-growing season (sown in mid-March to mid-April and harvested in mid-July to early August) receives the highest average precipitation (>400 mm), and thereby this season is less vulnerable to drought. A comparison of crop yields reveals the lowest productivity in the drought year (2006) compared to the non-drought year (2018), and the DHI map presents that the north-west region of Bangladesh is highly vulnerable to agricultural drought. This study has undertaken a large-scale analysis that is important to prioritize agricultural zones and initiate development projects based on the associated level of vulnerability.
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SHAHABFAR, A., and J. EITZINGER. "Agricultural drought monitoring in semi-arid and arid areas using MODIS data." Journal of Agricultural Science 149, no. 4 (January 18, 2011): 403–14. http://dx.doi.org/10.1017/s0021859610001309.

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SUMMARYThe performances of two remote sensing drought indices were evaluated at selected agricultural sites in different agro-climatic zones in Iran to detect the severity of drought phenomena related to temporal variation and different climatic conditions. The indices used were the perpendicular drought index (PDI) and the modified perpendicular drought index (MPDI), which are derived from moderate resolution imaging spectroradiometer (MODIS) satellite images (MOD13A3 V005). The correlations between these perpendicular indices and two other remote sensing indices in ten different agro-climatic zones of Iran from February 2000 to December 2005 were analysed. The additional indices evaluated were the enhanced vegetation index (EVI) and the vegetation condition index (VCI) along with five water balance parameters, including climatic water balance (CL), crop water balance (CR), monthly reference crop evapotranspiration (ET0), crop evapotranspiration (ETc) and required irrigation water (I). Winter wheat was selected as the reference crop because it is grown in the majority of climatic conditions in Iran.The results show that in several climatic regions, there is a statistically significant correlation between PDI and MPDI and the water balance parameters, indicating an acceptable performance in detecting crop drought stress conditions. In all zones except at the sites located in northwest and northeast of Iran, VCI and EVI are less correlated with the applied water balance indicators compared to PDI and MPDI. In a temporal analysis, PDI and MPDI showed a greater ability to detect CR conditions than VCI and EVI in the most drought-sensitive winter wheat-growing stages. Since Iran is characterized by arid or semi-arid climatic conditions and winter wheat is a major agricultural crop, a combination of both PDI and MPDI could be used as simple remote sensing-based tool to map drought conditions for crops in Iran and in other developing countries with similar climatic conditions.
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Sur, Koyel, and M. M. Lunagaria. "Association between drought and agricultural productivity using remote sensing data: a case study of Gujarat state of India." Journal of Water and Climate Change 11, S1 (April 29, 2020): 189–202. http://dx.doi.org/10.2166/wcc.2020.157.

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Abstract Drought is a complex hazard which directly affects the water balance of any region. It impacts agricultural, ecological and socioeconomical spheres. It is a global concern. The occurrence of drought is triggered by climatic phenomena which cannot be eliminated. However, its effect can be well managed if actual spatio-temporal information related to crop status influenced by drought is available to decision-makers. This study attempted to assess the efficiency of remote sensing products from space sensors for monitoring the spatio-temporal status of meteorological drought in conjunction with impact on vegetation condition and crop yield. Time series (2000–2019) datasets of the Tropical Rainfall Measuring Mission (TRMM) were used to compute Standardized Precipitation Index (SPI) and MODIS (MODerate resolution Imaging Spectroradiometer) was used to compute Vegetation Condition Index (VCI). Association between SPI and VCI was explored. YAI was calculated from the statistical data records. Final observations are that the agricultural crop yield changed as per the climate variability specific to location. The study indicates drought indices derived from remote sensing give a synoptic view because of the course resolution of the satellite images. It does not reveal the precise relationship to the small-scale crop yield. Remote sensing can be an effective way to monitor and understand the dynamics of the drought and agriculture pattern over any region.
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Dissertations / Theses on the topic "Remote sensing and vegetation condition index (VCI)"

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Makaudze, Ephias M. "Do seasonal climate forecasts and crop insurance really matter for smallholder farmers in Zimbabwe? Using contingent valuation method and remote sensing applications." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1110389049.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xiii, 155 p.; also includes map, graphics (some col.) Includes bibliographical references (p. 149-155). Available online via OhioLINK's ETD Center
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Mühlbauer, Stefan Ludwig. "Recent droughts and the impact of North Atlantic Oscillation in Iberia; a spatiotemporal analysis based on vegetation temperature condition Index (VTCI)." Master's thesis, 2013. http://hdl.handle.net/10362/9233.

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
Drought is a complex phenomenon that afflicts huge areas worldwide with high spatial and temporal variability. Being under influence of the Mediterranean climate the Iberian Peninsula is per se prone to droughts. In this study, we applied the Vegetation Condition Temperature Index for a spatiotemporal drought analysis in Iberia. As a compound remote sensing drought index VTCI incorporates information on Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Suchlike, it overcomes the disadvantage of NDVI, which faces a lagged vegetation response to droughts and makes it less suitable for real-time drought monitoring. We took advantage of the full range of the VTCI scale and thus describe soil moisture conditions from ‘very dry’ to ‘wet’. In order to study the effect of North Atlantic Oscillation on the establishment of droughts during spring and summer, we correlated VTCI with the NAO winter index of one year. The study was accomplished for the years 2001 until 2005; additionally we adjoined the years 2007 and 2010 for their strong positive (negative) NAO winter index. The results as presented in the VTCI time series illustrate a typical ‘Mediterranean’ pattern with humid springs and persistently dry summers inside the Mediterranean fraction of Iberia. At least two months in spring are regarded to be wet, though with a high temporal and spatial variability: One area can be wet in one year and dry in the next one. March often is drier than middle spring months exhibiting accumulated precipitation deficiency of winter months, whereas a weak start into the growing season can be determining for the crop harvest of one year. The summer months become persistently dry in the Mediterranean region, while the North and Northwest as well as high elevation areas generally obtain sufficient precipitation also during the warm season. Spring months are particularly important for vegetation growth in terms of water availability, though the spatial and temporal pattern changes considerably in the intra-annual perspective. We found a dipolar correlation between VTCI and NDVI particularly in April and June. In April the NW of Iberia shows a high negative correlation between NAO and VTCI, the SE a strong positive correlation. In June the pattern flips showing positive correlation in the NW and negative correlation in the SE. The other summer months exhibit a similar pattern as June though with less significant correlation, while the pattern during the spring months alters, which coincides with the climatic variability during this season. The correlation findings accord with observations from the VTCI time series: We gave the example of the Duero Basin in the NW, where in April (negative correlation) indeed NAO+ years were drier (due to decreased VTCI) than NAOyears (increased VTCI) and the SE exhibited the opposite behaviour. Similar findings can be evidenced for the NW and SE of Iberia in June and July and the Balearic Islands in March. In terms of land cover NAO influences geographical areas rather than certain land cover types, which can be explained by the widely human induced landscape pattern of Iberia. From the land use classes agricultural lands and low vegetation are the classes most vulnerable to droughts; forests are the most resilient land classes. The study of correlation between a certain NAO winter index and drought occurrences is particularly interesting for the development of an outlook system of soil moisture for the period of highest vegetation activity in spring. The study detected come correlations between NAO and VTCI that indeed find spatial response on Iberian landmass.
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Conference papers on the topic "Remote sensing and vegetation condition index (VCI)"

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Zambrano, Francisco, Mario Lillo-Saavedra, Koen Verbist, and Octavio Lagos. "Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI)." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2235345.

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Park, Jung-Sool, Kyung-Tak Kim, and Yun-Seok Choi. "Application of Vegetation Condition Index and Standardized Vegetation Index for Assessment of Spring Drought in South Korea." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779463.

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Zhe Li, Debao Tan, Yuanlai Cui, and Sui Zhang. "Remote sensing drought monitoring under dense vegetation cover condition based on Perpendicular Drought Index." In 2009 17th International Conference on Geoinformatics. IEEE, 2009. http://dx.doi.org/10.1109/geoinformatics.2009.5293223.

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Zhang, Hong-wei, Huai-liang Chen, Wei-dong Yu, and Zhong-yang Liu. "The theories of shortwave infrared perpendicular water stress index and its application in soil moisture retrieval under full covered vegetation condition." In Remote Sensing and Modeling of Ecosystems for Sustainability VIII. SPIE, 2011. http://dx.doi.org/10.1117/12.896645.

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M�hlbauer, Stefan. "RECENT DROUGHTS AND THE IMPACT ON LAND USE AND LAND COVER IN THE IBERIAN PENINSULA: A SPATIOTEMPORAL ANALYSIS BASED ON THE VEGETATION TEMPERATURE CONDITION INDEX." In 13th SGEM GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING. Stef92 Technology, 2013. http://dx.doi.org/10.5593/sgem2013/bb2.v2/s10.017.

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