{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:45:56Z","timestamp":1774554356265,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Polish National Centre for Research and Development","award":["BIO-STRATEG3\/347837\/11\/NCBR\/2017"],"award-info":[{"award-number":["BIO-STRATEG3\/347837\/11\/NCBR\/2017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Surface soil moisture (SSM) is one of the factors affecting plant growth. Methods involving direct soil moisture measurement in the field or requiring laboratory tests are commonly used. These methods, however, are laborious and time-consuming and often give only point-by-point results. In contrast, SSM can vary across a field due to uneven precipitation, soil variability, etc. An alternative is using satellite data, for example, optical data from Sentinel-2 (S2). The main objective of this study was to assess the accuracy of SSM determination based on S2 data versus standard measurement techniques in three different agricultural areas (with irrigation and drainage systems). In the field, we measured SSM manually using non-destructive techniques. Based on S2 data, we estimated SSM using the optical trapezoid model (OPTRAM) and calculated eighteen vegetation indices. Using the OPTRAM model gave a high SSM estimating accuracy (R2 = 0.67, RMSE = 0.06). The use of soil porosity in the OPTRAM model significantly improved the results. Among the vegetation indices, at the NDVI \u2264 0.2, the highest value of R2 was obtained for the STR to OPTRAM index, while at the NDVI &gt; 0.2, the shadow index had the highest R2 comparable with OPTRAM.<\/jats:p>","DOI":"10.3390\/rs15235576","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T07:44:42Z","timestamp":1701330282000},"page":"5576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Surface Soil Moisture Determination of Irrigated and Drained Agricultural Lands with the OPTRAM Method and Sentinel-2 Observations"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0065-9668","authenticated-orcid":false,"given":"Tomasz","family":"Sta\u0144czyk","sequence":"first","affiliation":[{"name":"Department of Hydrology, Meteorology and Water Management, Institute of Environmental Engineering, Warsaw University of Life Sciences\u2014SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8265-8786","authenticated-orcid":false,"given":"Wies\u0142awa","family":"Kasperska-Wo\u0142owicz","sequence":"additional","affiliation":[{"name":"Institute of Technology and Life Sciences\u2014National Research Institute, Falenty, Hrabska 3, 05-090 Raszyn, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1044-6682","authenticated-orcid":false,"given":"Jan","family":"Szaty\u0142owicz","sequence":"additional","affiliation":[{"name":"Department of Environmental Development, Institute of Environmental Engineering, Warsaw University of Life Sciences\u2014SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7502-7553","authenticated-orcid":false,"given":"Tomasz","family":"Gnatowski","sequence":"additional","affiliation":[{"name":"Department of Environmental Development, Institute of Environmental Engineering, Warsaw University of Life Sciences\u2014SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2556-0162","authenticated-orcid":false,"given":"Ewa","family":"Papierowska","sequence":"additional","affiliation":[{"name":"Water Centre, Warsaw University of Life Sciences\u2014SGGW, Jana Ciszewskiego 6, 02-766 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2019.03.002","article-title":"Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 Using Machine Learning Methods Trained with Radiative Transfer Simulations","volume":"225","author":"Wolanin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"665","DOI":"10.5194\/isprs-archives-XLII-5-665-2018","article-title":"A Comparative Study of Advanced Land Use\/Land Cover Classification Algorithms Using Sentinel-2 Data","volume":"XLII\u20135","author":"Priyadarshini","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2019.1650447","article-title":"Land Cover and Land Use Classification Performance of Machine Learning Algorithms in a Boreal Landscape Using Sentinel-2 Data","volume":"57","author":"Abdi","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.5194\/isprs-archives-XLI-B8-1055-2016","article-title":"Assessment of Classification Accuracies of Sentinel-2 and Landsat-8 Data for Land Cover\/Use Mapping","volume":"XLI-B8","author":"Sertel","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_7","first-page":"32","article-title":"Use of Sentinel-2 for Forest Classification in Mediterranean Environments","volume":"42","author":"Puletti","year":"2018","journal-title":"Ann. Silvic. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.rse.2016.01.017","article-title":"Discrimination of Tropical Forest Types, Dominant Species, and Mapping of Functional Guilds by Hyperspectral and Simulated Multispectral Sentinel-2 Data","volume":"176","author":"Puletti","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2019.03.016","article-title":"Estimation of the Forest Stand Mean Height and Aboveground Biomass in Northeast China Using SAR Sentinel-1B, Multispectral Sentinel-2A, and DEM Imagery","volume":"151","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"271","DOI":"10.5194\/isprs-annals-IV-4-W4-271-2017","article-title":"Mapping and Monitoring Wetlands Using Sentinel-2 Satellite Imagery","volume":"IV-4\/W4","author":"Kaplan","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.isprsjprs.2018.04.001","article-title":"Monitoring Andean High Altitude Wetlands in Central Chile with Seasonal Optical Data: A Comparison between Worldview-2 and Sentinel-2 Imagery","volume":"145","author":"Lopatin","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","first-page":"175","article-title":"Spatial Mapping of the Leaf Area Index Using Remote Sensing and Ground Measurements\u2014The Biebrza National Park Case Study","volume":"32","author":"Ignar","year":"2023","journal-title":"Sci. Rev. Eng. Env. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the Temporal Behavior of Crops Using Sentinel-1 and Sentinel-2-like Data for Agricultural Applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/978-1-4612-5046-3_8","article-title":"Relationship between Soil Physical Properties and Crop Production","volume":"Volume 1","author":"Stewart","year":"1958","journal-title":"Advances in Soil Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1007\/s11119-020-09711-9","article-title":"Remote Sensing and Machine Learning for Crop Water Stress Determination in Various Crops: A Critical Review","volume":"21","author":"Virnodkar","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.measurement.2014.04.007","article-title":"A Critical Review of Soil Moisture Measurement","volume":"54","author":"Lekshmi","year":"2014","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"358","DOI":"10.2136\/vzj2007.0143","article-title":"Soil Moisture Measurement for Ecological and Hydrological Watershed-Scale Observatories: A Review","volume":"7","author":"Robinson","year":"2008","journal-title":"Vadose Zone J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109","DOI":"10.5194\/gi-5-109-2016","article-title":"Soil Moisture Sensor Calibration for Organic Soil Surface Layers","volume":"5","author":"Bircher","year":"2016","journal-title":"Geosci. Instrum. Method. Data Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gnatowski, T., Szaty\u0142owicz, J., Pawlu\u015bkiewicz, B., Oleszczuk, R., Janicka, M., Papierowska, E., and Szejba, D. (2018). Field Calibration of TDR to Assess the Soil Moisture of Drained Peatland Surface Layers. Water, 10.","DOI":"10.3390\/w10121842"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/0022-1694(86)90097-1","article-title":"A Method of Measuring Soil Moisture by Time-Domain Reflectometry","volume":"88","author":"Ledieu","year":"1986","journal-title":"J. Hydrol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"660","DOI":"10.2136\/sssaj1993.03615995005700030005x","article-title":"Improved Calibration of Time Domain Reflectometry Soil Water Content Measurements","volume":"57","author":"Dirksen","year":"1993","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1111\/j.1365-2389.1996.tb01409.x","article-title":"Improving the Calibration of Dielectric TDR Soil Moisture Determination Taking into Account the Solid Soil","volume":"47","author":"Malicki","year":"1996","journal-title":"Eur. J. Soil Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115288","DOI":"10.1016\/j.geoderma.2021.115288","article-title":"Dielectric Models for Moisture Determination of Soils with Variable Organic Matter Content","volume":"401","author":"Lewandowski","year":"2021","journal-title":"Geoderma"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1029\/WR016i003p00574","article-title":"Electromagnetic Determination of Soil Water Content: Measurements in Coaxial Transmission Lines","volume":"16","author":"Topp","year":"1980","journal-title":"Water Resour. Res."},{"key":"ref_25","first-page":"1","article-title":"Influence of Temperature on Soil Dielectric Spectra in the 20 MHz\u20133 GHz Frequency Range","volume":"61","author":"Lewandowski","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1365-2389.1992.tb00115.x","article-title":"Empirical Evaluation of the Relationship between Soil Dielectric Constant and Volumetric Water Content as the Basis for Calibrating Soil Moisture Measurements by TDR","volume":"43","author":"Roth","year":"1992","journal-title":"J. Soil Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.1029\/97WR01699","article-title":"Two- and Three-Parameter Calibrations of Time Domain Reflectometry for Soil Moisture Measurement","volume":"33","author":"Yu","year":"1997","journal-title":"Water Resour. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Zribi, M., and Bazzi, H. (2017). Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sens., 9.","DOI":"10.3390\/rs9121292"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., El Hajj, M., Baghdadi, N., Lili-Chabaane, Z., Gao, Q., and Fanise, P. (2018). Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10121953"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2017.05.041","article-title":"The Optical Trapezoid Model: A Novel Approach to Remote Sensing of Soil Moisture Applied to Sentinel-2 and Landsat-8 Observations","volume":"198","author":"Sadeghi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2015.04.007","article-title":"A Linear Physically-Based Model for Remote Sensing of Soil Moisture Using Short Wave Infrared Bands","volume":"164","author":"Sadeghi","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"102113","article-title":"Retrieving Soil Moisture in Rainfed and Irrigated Fields Using Sentinel-2 Observations and a Modified OPTRAM Approach","volume":"89","author":"Ambrosone","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","first-page":"256","article-title":"Concept and Implementation of the Polish Innovative Agro-Hydro-Meteorological Monitoring (AgHMM) in INOMEL Project","volume":"51","author":"Bolewski","year":"2021","journal-title":"J. Water Land Dev."},{"key":"ref_34","unstructured":"Kaca, E. (2020). Operacyjne Sterowanie Procesem Nawodnie\u0144 Podsi\u0105kowych i Odwodnie\u0144\u2014Komputerowy System Wspomagania Decyzji Wraz z Przyk\u0142adami Zastosowania, Bogucki Wydawnictwo Naukowe."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0378-3774(00)00080-9","article-title":"Remote Sensing for Irrigated Agriculture: Examples from Research and Possible Applications","volume":"46","author":"Bastiaanssen","year":"2000","journal-title":"Agric. Water Manag."},{"key":"ref_36","first-page":"65","article-title":"Soil Moisture and Seed Germination","volume":"Volume 3","author":"Kozlowski","year":"1972","journal-title":"Water Deficits and Plant Growth"},{"key":"ref_37","unstructured":"(2019, July 07). Institute of Meteorology and Water Management (IMGW\u2014PIB). Available online: https:\/\/danepubliczne.imgw.pl."},{"key":"ref_38","first-page":"93","article-title":"Climatic and Agricultural Water Balance for Grasslands in Poland Using the Penman-Monteith Method","volume":"37","year":"2006","journal-title":"Ann. Wars. Agric. Univ. Land Reclam."},{"key":"ref_39","first-page":"3","article-title":"Irrigation in Poland\u2013Current Status after Reforms in Agriculture and Future Development","volume":"11","year":"2007","journal-title":"J. Water Land Dev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"13545","DOI":"10.3390\/s121013545","article-title":"A TDR-Based Soil Moisture Monitoring System with Simultaneous Measurement of Soil Temperature and Electrical Conductivity","volume":"12","author":"Skierucha","year":"2012","journal-title":"Sensors"},{"key":"ref_41","unstructured":"Delta-T Devices (1999). ThetaProbe Soil Moisture Sensor, Type ML2x, User Manual, ML2x-UM-1.21, Delta-T Devices."},{"key":"ref_42","first-page":"229","article-title":"Particle Density","volume":"Volume 5","author":"Flint","year":"2002","journal-title":"Methods of Soil Analysis: Part 4 Physical Methods"},{"key":"ref_43","unstructured":"Dane, J.H., and Clarke Topp, G. (2018). SSSA Book Series, Soil Science Society of America."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.rse.2018.04.029","article-title":"Mapping Soil Moisture with the OPtical TRApezoid Model (OPTRAM) Based on Long-Term MODIS Observations","volume":"211","author":"Babaeian","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e2020EA001108","DOI":"10.1029\/2020EA001108","article-title":"Evaluation of the OPTRAM Model to Retrieve Soil Moisture in the Sanjiang Plain of Northeast China","volume":"7","author":"Chen","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11","DOI":"10.52939\/ijg.v17i1.1699","article-title":"Crop Water Condition Mapping by Optical Remote Sensing","volume":"17","author":"Wojtaszek","year":"2021","journal-title":"Int. J. Geoinform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"034519","DOI":"10.1117\/1.JRS.14.034519","article-title":"Modification on Optical Trapezoid Model for Accurate Estimation of Soil Moisture Content in a Maize Growing Field","volume":"14","author":"Hassanpour","year":"2020","journal-title":"J. Appl. Rem. Sens."},{"key":"ref_48","unstructured":"Sinshaw, B.G., Moges, M.A., Tilahun, S.A., Dokou, Z., Moges, S., Anagnostou, E., Eshete, D.G., Kindie, A.T., Bekele, E., and Asese, M. (2020). Advances of Science and Technology, Proceedings of the 7th EAI International Conference, ICAST 2019, Bahir Dar, Ethiopia, 2\u20134 August 2019, Springer."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"113443","DOI":"10.1016\/j.rse.2022.113443","article-title":"OPTRAM-ET: A Novel Approach to Remote Sensing of Actual Evapotranspiration Applied to Sentinel-2 and Landsat-8 Observations","volume":"286","author":"Mokhtari","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Burdun, I., Bechtold, M., Sagris, V., Komisarenko, V., De Lannoy, G., and Mander, \u00dc. (2020). A Comparison of Three Trapezoid Models Using Optical and Thermal Satellite Imagery for Water Table Depth Monitoring in Estonian Bogs. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-10544"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"993","DOI":"10.2136\/sssaj2019.01.0018","article-title":"Application of Satellite Remote Sensing for Estimation of Dust Emission Probability in the Urmia Lake Basin in Iran","volume":"83","author":"Effati","year":"2019","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Acharya, U., Daigh, A.L.M., and Oduor, P.G. (2022). Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images. Remote Sens., 14.","DOI":"10.3390\/rs14153801"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sun, H., Liu, H., Ma, Y., and Xia, Q. (2021). Optical Remote Sensing Indexes of Soil Moisture: Evaluation and Improvement Based on Aircraft Experiment Observations. Remote Sens., 13.","DOI":"10.3390\/rs13224638"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"L20405","DOI":"10.1029\/2007GL031021","article-title":"NMDI: A Normalized Multi-Band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing","volume":"34","author":"Wang","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_57","first-page":"39","article-title":"Tropical Forest Cover Density Mapping","volume":"43","author":"Rikimaru","year":"2002","journal-title":"Trop. Ecol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Gir\u00e1ldez, P.J., P\u00e9rez-Palaz\u00f3n, M.J., Polo, M.J., and Gonz\u00e1lez-Dugo, M.P. (2020). Monitoring Grass Phenology and Hydrological Dynamics of an Oak\u2013Grass Savanna Ecosystem Using Sentinel-2 and Terrestrial Photography. Remote Sens., 12.","DOI":"10.3390\/rs12040600"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.3732\/ajb.0800395","article-title":"Nondestructive Estimation of Anthocyanins and Chlorophylls in Anthocyanic Leaves","volume":"96","author":"Gitelson","year":"2009","journal-title":"Am. J. Bot."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"100032","DOI":"10.1016\/j.acags.2020.100032","article-title":"Comparative Analysis of Different Vegetation Indices with Respect to Atmospheric Particulate Pollution Using Sentinel Data","volume":"7","author":"Somvanshi","year":"2020","journal-title":"Appl. Comput. Geosci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"14227","DOI":"10.3390\/rs71014227","article-title":"The Potential of EnMAP and Sentinel-2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities","volume":"7","author":"Dotzler","year":"2015","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.agrformet.2008.03.005","article-title":"Estimating Chlorophyll Content from Hyperspectral Vegetation Indices: Modeling and Validation","volume":"148","author":"Wu","year":"2008","journal-title":"J. Agric. Meteorol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1016\/j.rse.2007.11.014","article-title":"Remote Sensing of Vegetation Water Content from Equivalent Water Thickness Using Satellite Imagery","volume":"112","author":"Yilmaz","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.fcr.2010.01.010","article-title":"Measuring and Predicting Canopy Nitrogen Nutrition in Wheat Using a Spectral Index\u2014The Canopy Chlorophyll Content Index (CCCI)","volume":"116","author":"Fitzgerald","year":"2010","journal-title":"Field Crop. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between Leaf Pigment Content and Spectral Reflectance across a Wide Range of Species, Leaf Structures and Developmental Stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2018.09.016","article-title":"Urban Surface Water Body Detection with Suppressed Built-up Noise Based on Water Indices from Sentinel-2 MSI Imagery","volume":"219","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/01431160701294695","article-title":"Surface Soil Moisture Quantification Models from Reflectance Data under Field Conditions","volume":"29","author":"Haubrock","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","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":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5576\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:34:54Z","timestamp":1760132094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"references-count":68,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235576"],"URL":"https:\/\/doi.org\/10.3390\/rs15235576","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}