{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T02:21:37Z","timestamp":1773195697031,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,6]],"date-time":"2020-04-06T00:00:00Z","timestamp":1586131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.<\/jats:p>","DOI":"10.3390\/rs12071176","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T03:58:39Z","timestamp":1586231919000},"page":"1176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery"],"prefix":"10.3390","volume":"12","author":[{"given":"Yukun","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Department, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8283-6407","authenticated-orcid":false,"given":"Zhe","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA"},{"name":"Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4639-1791","authenticated-orcid":false,"given":"Wenxuan","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA"},{"name":"Department of Soil and Crop Sciences, Texas A&amp;M AgriLife Research, Lubbock, TX 79403, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazhou","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"The Climate Corporation, San Francisco, CA 94103, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valeriy","family":"Kovalskyy","sequence":"additional","affiliation":[{"name":"The Climate Corporation, Saint Louis, MO 63141, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,6]]},"reference":[{"key":"ref_1","unstructured":"Lisar, S.Y.S., Motafakkerazad, R., Hossain, M.M., and Rahman, I.M.M. (2002). Introductory Chapter Water Stress in Plants: Causes, Effects and Responses. Water Stress, 300."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2014.00086","article-title":"Response of plants to water stress","volume":"5","author":"Osakabe","year":"2014","journal-title":"Front. Plant Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1038\/nature11575","article-title":"Little change in global drought over the past 60 years","volume":"491","author":"Sheffield","year":"2012","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.envexpbot.2018.03.009","article-title":"Effect of water stress \u201cmemory\u201d on plant behavior during subsequent drought stress","volume":"150","author":"Tombesi","year":"2018","journal-title":"Environ. Exp. Bot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7468","DOI":"10.1038\/501S1a","article-title":"Agriculture and Drought","volume":"501","author":"Grayson","year":"2013","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mancosu, N., Snyder, R.L., Kyriakakis, G., and Spano, D. (2015). Water Scarcity and Future Challenges for Food Production. Water, 975\u2013992.","DOI":"10.3390\/w7030975"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Roth, G., Harris, G., Gillies, M., Montgomery, J., and Wigginton, D. (2013). Water-use efficiency and productivity trends in Australian irrigated cotton: A review. Crop Pasture Sci., 1033\u20131048.","DOI":"10.1071\/CP13315"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.indcrop.2018.12.070","article-title":"Progress and perspective on drought and salt stress tolerance in cotton","volume":"130","author":"Abdelraheem","year":"2019","journal-title":"Ind. Crops Prod."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Adeyemi, O., Grove, I., Peets, S., and Norton, T. (2017). Advanced monitoring and management systems for improving sustainability in precision irrigation. Sustainability, 9.","DOI":"10.3390\/su9030353"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1093\/jxb\/eri174","article-title":"Estimation of leaf water potential by thermal imagery and spatial analysis","volume":"56","author":"Cohen","year":"2005","journal-title":"J. Exp. Bot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.compag.2015.09.006","article-title":"Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold","volume":"118","author":"Osroosh","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"357","DOI":"10.5344\/ajev.2005.56.4.357","article-title":"Relationships among vine- and soil-based measures of water status in a Thompson Seedless vineyard in response to high-frequency drip irrigation","volume":"56","author":"Williams","year":"2005","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_13","first-page":"189","article-title":"Analysis of crop water stress index (CWSI) for estimating stem water potential in grapevines: Comparison between natural reference and baseline approaches","volume":"1150","author":"Espinace","year":"2017","journal-title":"Acta Hortic."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.agwat.2018.06.002","article-title":"Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies","volume":"208","author":"Rubio","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1007\/s11119-013-9322-9","article-title":"Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard","volume":"14","author":"Nortes","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1007\/s11119-018-9607-0","article-title":"Prediction of plant water status in almond and walnut trees using a continuous leaf monitoring system","volume":"20","author":"Dhillon","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.agwat.2019.02.003","article-title":"A comprehensive stress indicator for evaluating plant water status in almond trees","volume":"216","author":"Drechsler","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.3390\/rs5041588","article-title":"Estimating crop coefficients using remote sensing-based vegetation index","volume":"5","author":"Kamble","year":"2013","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.agwat.2013.03.024","article-title":"Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. II. Application on basin scale","volume":"125","author":"Escuin","year":"2013","journal-title":"Agric. Water Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agwat.2005.02.013","article-title":"Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices","volume":"79","author":"Duchemin","year":"2006","journal-title":"Agric. Water Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2008.07.007","article-title":"Geographical model for precise agriculture monitoring with real-time remote sensing","volume":"64","author":"Beeri","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.agwat.2015.01.020","article-title":"UAVs challenge to assess water stress for sustainable agriculture","volume":"153","author":"Gago","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1109\/JSTARS.2015.2501343","article-title":"Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi-Arid West Africa","volume":"9","author":"Leroux","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.agwat.2018.05.017","article-title":"Estimating cotton water consumption using a time series of Sentinel-2 imagery","volume":"207","author":"Rozenstein","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.isprsjprs.2013.10.002","article-title":"Assessing canopy PRI from airborne imagery to map water stress in maize","volume":"86","author":"Rossini","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Espinoza, C.Z., Khot, L.R., Sankaran, S., and Jacoby, P.W. (2017). High resolution multispectral and thermal remote sensing-based water stress assessment in subsurface irrigated grapevines. Remote Sens., 9.","DOI":"10.3390\/rs9090961"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Helman, D., Bahat, I., Netzer, Y., Ben-Gal, A., Alchanatis, V., Peeters, A., and Cohen, Y. (2018). Using time series of high-resolution planet satellite images to monitor grapevine stem water potential in commercial vineyards. Remote Sens., 10.","DOI":"10.3390\/rs10101615"},{"key":"ref_28","unstructured":"Deng, C., and Zhu, Z. (2018). Continuous subpixel monitoring of urban impervious surface using Landsat time series. Remote Sens. Environ., 1\u201321."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.agwat.2015.12.009","article-title":"Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index","volume":"167","author":"King","year":"2016","journal-title":"Agric. Water Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.compag.2017.07.026","article-title":"Recent advances in crop water stress detection","volume":"141","author":"Ihuoma","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0034-4257(02)00197-9","article-title":"Water content estimation in vegetation with MODIS reflectance data and model inversion methods","volume":"85","author":"Rueda","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Leslie, C.R., Serbina, L.O., and Miller, H.M. (2017). Landsat and Agriculture \u2014 Case Studies on the Uses and Benefits of Landsat Imagery in Agricultural Monitoring and Production: U.S. Geological Survey Open-File Report.","DOI":"10.3133\/ofr20171034"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.agwat.2017.04.016","article-title":"A satellite based crop water stress index for irrigation scheduling in sugarcane A satellite based crop water stress index for irrigation scheduling in sugarcane fields","volume":"189","author":"Veysi","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6647","DOI":"10.3390\/rs5126647","article-title":"Stem Water Potential Monitoring in Pear Orchards through worldview-2 Multispectral Imagery","volume":"5","author":"Tits","year":"2013","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., Sun, F., and Wu, X. (2018). Evaluating the performance of Sentinel-2, Landsat 8 and Pl\u00e9iades-1 in mapping mangrove extent and species. Remote Sens., 10.","DOI":"10.3390\/rs10091468"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.compag.2017.05.001","article-title":"An overview of current and potential applications of thermal remote sensing in precision agriculture","volume":"139","author":"Khanal","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Moriya, \u00c9.A.S., Bavaresco, L.G., de Lima, B.C., Estrabis, N., Pereira, D.R., Creste, J.E., J\u00fanior, J.M., and Gon\u00e7alves, W.N. (2019). Modeling hyperspectral response of water-stress induced lettuce plants using artificial neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11232797"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3557","DOI":"10.3390\/s8053557","article-title":"Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots","volume":"8","author":"Lelong","year":"2008","journal-title":"Sensors"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cogato, A., Pagay, V., Marinello, F., Meggio, F., Grace, P., and De Antoni Migliorati, M. (2019). Assessing the feasibility of using medium-resolution imagery information to quantify the impact of the heatwaves on irrigated vineyards. Remote Sens., 11.","DOI":"10.3390\/rs11232869"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0185481","article-title":"Plant water potential improves prediction of empirical stomatal models","volume":"12","author":"Anderegg","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_42","unstructured":"Meyer, L.A. (2019). Cotton and Wool Outlook World Cotton Trade Projected at 6-Year High, World Agricultural Supply and Demand Estimates Reports."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1006\/anbo.2000.1361","article-title":"Stem water potential is a sensitive indicator of grapevine water status","volume":"87","author":"Dubourdieu","year":"2001","journal-title":"Ann. Bot."},{"key":"ref_44","first-page":"126","article-title":"Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems","volume":"66","author":"Mura","year":"2018","journal-title":"Int J Appl Earth Obs Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., and Escorihuela, M.J. (2017). Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution. Sensors, 17.","DOI":"10.3390\/s17091966"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X. (2016). Water Bodies\u2019 Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.rse.2018.06.035","article-title":"Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy","volume":"215","author":"Vanino","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.35940\/ijitee.K1596.129219","article-title":"Multi-Spectral Image Segmentation Based on the K-means Clustering","volume":"9","author":"Hamada","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_49","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-knorn, M., and Bieniarz, J. (2016, January 9\u201313). SENTINEL-2 SEN2COR: L2A Processing for Users. Proceedings of the ESA Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Meyer, L.H., Heurich, M., Beudert, B., Premier, J., and Pflugmacher, D. (2019). Comparison of Landsat-8 and Sentinel-2 data for estimation of leaf area index in temperate forests. Remote Sens., 11.","DOI":"10.3390\/rs11101160"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s00271-012-0382-9","article-title":"Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV)","volume":"30","author":"Baluja","year":"2012","journal-title":"Irrig. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(94)90090-6","article-title":"Relations between evaporation coefficients and vegetation indices studied by model simulations","volume":"50","author":"Choudhury","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1002\/j.1537-2197.1991.tb14495.x","article-title":"Primary and secondary effects of water content on the spectral reflectance of leaves","volume":"78","author":"Carter","year":"1991","journal-title":"Am. J. Bot."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"266","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_58","first-page":"167","article-title":"Fluorescence, PRI and canopy temperature for water stress detection in cereal crops","volume":"30","author":"Panigada","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_59","first-page":"750","article-title":"High spectral resolution: Determination of spectral shifts between the red and the near infrared","volume":"11","author":"Guyot","year":"1988","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","first-page":"27","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"22","author":"Huete","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0034-4257(90)90085-Z","article-title":"Calculating the vegetation index faster","volume":"34","author":"Crippen","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_65","unstructured":"Deering, D.W. (1975, January 6\u201310). Measuring \u201cforage production\u201d of grazing units from Landsat MSS data. Proceedings of the 10th International Symposium of Remote Sensing of the Environment, Ann Arbor, MI, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote sensing of chlorophyll concentration in higher plant leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Sp. Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/BF00031911","article-title":"GEMI: A Non-Linear Index to Monitor Global Vegetation from Satellites GEMI: A non-linear index to monitor global vegetation from satellites","volume":"101","author":"Pinty","year":"2011","journal-title":"Vegetatio"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_69","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00059-5","article-title":"Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches","volume":"66","author":"Blackburn","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_71","unstructured":"Pearson, R.L., and Miller, L.D. (1972). Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Remote Sens. Environ., 1355."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0034-4257(89)90076-X","article-title":"Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture","volume":"29","author":"Clevers","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_73","unstructured":"Daughtry, C.S.T., Walthall, C.L., Kim, M.S., and Colstoun, E.B. (1993). De Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ., 4257."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_75","unstructured":"Breiman, L. (2001). Random forests. Mach. Learn., 9."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3764","DOI":"10.1109\/JSTARS.2014.2329763","article-title":"Using Boruta-selected spectroscopic wavebands for the asymptomatic detection of fusarium circinatum stress","volume":"7","author":"Poona","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ballester, C., Brinkhoff, J., Quayle, W.C., and Hornbuckle, J. (2019). Monitoring the effects ofwater stress in cotton using the green red vegetation index and red edge ratio. Remote Sens., 11.","DOI":"10.3390\/rs11070873"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12898-019-0233-0","article-title":"Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize","volume":"19","author":"Zhang","year":"2019","journal-title":"BMC Ecol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2011.07.020","article-title":"Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data","volume":"117","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"5768","DOI":"10.3390\/s140405768","article-title":"Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm","volume":"14","author":"Rozenstein","year":"2014","journal-title":"Sensors"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.rse.2015.12.043","article-title":"Remote Sensing of Environment Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin","volume":"185","author":"Senay","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_83","unstructured":"Flood, N. (2017). Surface Reflectance over Australia. Remote Sens., 1\u201314."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.rse.2013.07.024","article-title":"A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index","volume":"138","author":"Williams","year":"2013","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1176\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:15:57Z","timestamp":1760174157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,6]]},"references-count":84,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071176"],"URL":"https:\/\/doi.org\/10.3390\/rs12071176","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,6]]}}}