{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:17:48Z","timestamp":1770747468488,"version":"3.49.0"},"reference-count":73,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021YFB3901201"],"award-info":[{"award-number":["2021YFB3901201"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42077436"],"award-info":[{"award-number":["42077436"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFB3901201"],"award-info":[{"award-number":["2021YFB3901201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42077436"],"award-info":[{"award-number":["42077436"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study simulated the canopy reflectance of spring wheat at five distinct growth stages (jointing, booting, heading, flowering, and pustulation) and under four drought scenarios (no drought, mild drought, moderate drought, and severe drought) using the PROSAIL radiative transfer model, and it identified the wavelength range most sensitive to drought. Additionally, the efficacy of 5 mainstream satellites (Sentinel-2, Landsat 8, Worldview-2, MODIS, and GF-2) and 20 commonly utilized remote sensing vegetation indicators (NDVI, SAVI, EVI, ARVI, GVMI, LSWI, VSDI, NDGI, SWIRR, NDWI, PRI, NDII, MSI, WI, SRWI, DSWI, NDREI1, NDREI2, ZMI, and MTCI) in drought monitoring was evaluated. The results indicated that the spectral response characteristics of spring wheat canopy reflectance vary significantly across the growth stages. Notably, the wavelength ranges of 1405\u20131505 nm and 2140\u20132190 nm were identified as optimal for drought monitoring throughout the growth period. Considering only the spectral bands, MODIS band 7 was determined to be the most suitable satellite band for monitoring drought in spring wheat at different growth stages. Among the 20 indices examined, WI, MSI, and SRWI, followed by LSWI and GVMI calculated using MODIS bands 2 and 6 as well as bands 8 and 11 of Sentinel-2, demonstrated superior capabilities in differentiating drought scenarios. These conclusions have important implications because they provide valuable guidance for selecting remote sensing drought monitoring data and vegetation indices, and they present insights for future research on the design of new remote sensing indices for assisting drought monitoring and the configuration of remote sensing satellite sensors.<\/jats:p>","DOI":"10.3390\/rs15194838","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T07:49:29Z","timestamp":1696578569000},"page":"4838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Evaluation of the Monitoring Capability of 20 Vegetation Indices and 5 Mainstream Satellite Band Settings for Drought in Spring Wheat Using a Simulation Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Chang","family":"Xiao","sequence":"first","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Yinan","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6660-2034","authenticated-orcid":false,"given":"Xiufang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1007\/s11442-016-1297-9","article-title":"Agricultural Drought Monitoring: Progress, Challenges, and Prospects","volume":"26","author":"Liu","year":"2016","journal-title":"J. Geogr. Sci."},{"key":"ref_2","first-page":"35","article-title":"Propagation of Drought: From Meteorological Drought to Agricultural and Hydrological Drought","volume":"33","author":"Wang","year":"2016","journal-title":"Adv. Meteorol."},{"key":"ref_3","unstructured":"IPCC (2018). Global Warming of 1.5 \u00b0C-IPCC Special Report-Summary for Policymakers, Cambridge University Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1038\/20859","article-title":"Climate and Atmospheric History of the Past 420,000 Years from the Vostok Ice Core, Antarctica","volume":"399","author":"Petit","year":"1999","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1002\/fes3.15","article-title":"Prospects of Doubling Global Wheat Yields","volume":"2","author":"Hawkesford","year":"2013","journal-title":"Food Energy Secur."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0378-4290(02)00175-2","article-title":"Effect of Water Deficits on within-Plot Variability in Growth and Grain Yield of Spring Wheat in Northwest China","volume":"80","author":"Pan","year":"2003","journal-title":"Field Crops Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"486","DOI":"10.2134\/agronj1971.00021962006300030042x","article-title":"Factors Affecting the Spectral Properties of Leaves with Special Emphasis on Leaf Water Status","volume":"63","author":"Carlson","year":"1971","journal-title":"Agron. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bayat, B., van der Tol, C., and Verhoef, W. (2016). Remote Sensing of Grass Response to Drought Stress Using Spectroscopic Techniques and Canopy Reflectance Model Inversion. Remote Sens., 8.","DOI":"10.3390\/rs8070557"},{"key":"ref_9","first-page":"1669","article-title":"Spectral Reflectance Relationships to Leaf Water-Stress","volume":"52","author":"Ripple","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT+ SAIL Models: A Review of Use for Vegetation Characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1051\/agro:2000105","article-title":"Investigation of a Model Inversion Technique to Estimate Canopy Biophysical Variables from Spectral and Directional Reflectance Data","volume":"20","author":"Weiss","year":"2000","journal-title":"Agronomie"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1117\/1.JRS.10.026035","article-title":"Estimation of Carotenoid Content at the Canopy Scale Using the Carotenoid Triangle Ratio Index from in Situ and Simulated Hyperspectral Data","volume":"10","author":"Kong","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.scitotenv.2014.09.099","article-title":"Integration of Remote Sensing Datasets for Local Scale Assessment and Prediction of Drought","volume":"505","author":"Nichol","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4998","DOI":"10.3390\/rs6064998","article-title":"Characterization of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China","volume":"6","author":"Abbas","year":"2014","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Reinermann, S., Gessner, U., Asam, S., Kuenzer, C., and Dech, S. (2019). The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics. Remote Sens., 11.","DOI":"10.3390\/rs11151783"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dabrowska-Zielinska, K., Malinska, A., Bochenek, Z., Bartold, M., Gurdak, R., Paradowski, K., and Lagiewska, M. (2020). Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions. Remote Sens., 12.","DOI":"10.3390\/rs12182944"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.catena.2019.104394","article-title":"A Remote Sensing and Artificial Neural Network-Based Integrated Agricultural Drought Index: Index Development and Applications","volume":"186","author":"Liu","year":"2020","journal-title":"Catena"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhong, W., Pan, S., Xie, Q., and Kim, T.-W. (2020). Comprehensive Drought Assessment Using a Modified Composite Drought Index: A Case Study in Hubei Province, China. Water, 12.","DOI":"10.3390\/w12020462"},{"key":"ref_20","first-page":"270","article-title":"Combination of Multi-Sensor Remote Sensing Data for Drought Monitoring over Southwest China","volume":"35","author":"Hao","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, H., Feng, M., Xiao, L., Yang, W., Wang, C., Jia, X., Zhao, Y., Zhao, C., Muhammad, S.K., and Li, D. (2019). Assessment of Plant Water Status in Winter Wheat (Triticum Aestivum L.) Based on Canopy Spectral Indices. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0216890"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8449","DOI":"10.1080\/01431161.2013.843806","article-title":"Hyperspectral Characteristics and Growth Monitoring of Rice (Oryza Sativa) under Asymmetric Warming","volume":"34","author":"Xie","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/10106049209354353","article-title":"Using Spectral Vegetation Indices to Estimate Rangeland Productivity","volume":"7","author":"Richardson","year":"1992","journal-title":"Geocarto Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"640","DOI":"10.2134\/agronj1968.00021962006000060016x","article-title":"Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1","volume":"60","author":"Birth","year":"1968","journal-title":"Agron. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.tree.2005.05.011","article-title":"Using the Satellite-Derived NDVI to Assess Ecological Responses to Environmental Change","volume":"20","author":"Pettorelli","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1007\/s004420050337","article-title":"The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency across Species, Functional Types, and Nutrient Levels","volume":"112","author":"Gamon","year":"1997","journal-title":"Oecologia"},{"key":"ref_28","first-page":"926","article-title":"Applicability of PROSAIL Model to Spring Wheat in Semi-Arid Region of the Loess Plateau under Different Drought Stress","volume":"35","author":"Ge","year":"2017","journal-title":"J. Arid. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4943","DOI":"10.1080\/01431160802036268","article-title":"Plant Growth Monitoring and Potential Drought Risk Assessment by Means of Earth Observation Data","volume":"29","author":"Richter","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., D\u2019Urso, G., Mauser, W., Vuolo, F., and Hank, T. (2018). Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sens., 10.","DOI":"10.3390\/rs10010085"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Yu, L., Du, Z., Peng, D., Hao, P., Zhang, Y., and Gong, P. (2022). An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends. Remote Sens., 14.","DOI":"10.3390\/rs14081863"},{"key":"ref_32","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 the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/S0034-4257(02)00036-6","article-title":"Designing a Spectral Index to Estimate Vegetation Water Content from Remote Sensing Data","volume":"82","author":"Pc","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2003.11.008","article-title":"Satellite-Based Modeling of Gross Primary Production in an Evergreen Needleleaf Forest","volume":"89","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1080\/01431161.2013.779046","article-title":"Vsdi: A Visible and Shortwave Infrared Drought Index for Monitoring Soil and Vegetation Moisture Based on Optical Remote Sensing","volume":"34","author":"Zhang","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compag.2018.02.013","article-title":"Vineyard Water Status Estimation Using Multispectral Imagery from an UAV Platform and Machine Learning Algorithms for Irrigation Scheduling Management","volume":"147","author":"Romero","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1142\/S0218488517400062","article-title":"Identification of Agricultural Management Zones through Clustering Algorithms with Thermal and Multispectral Satellite Imagery","volume":"25","author":"Arango","year":"2017","journal-title":"Int. J. Uncertain. Fuzziness Knowl. Based Syst."},{"key":"ref_39","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."},{"key":"ref_40","first-page":"77","article-title":"The Influence of Soil Salinity, Growth Form, and Leaf Moisture on the Spectral Radiance of Spartina Alterniflora Canopies","volume":"49","author":"Hardisky","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"439","DOI":"10.2307\/1310339","article-title":"Remote Detection of Forest Damage","volume":"36","author":"Rock","year":"1986","journal-title":"BioScience"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1080\/014311697217396","article-title":"Estimation of Plant Water Concentration by the Reflectance Water Index Wi (R900\/R970)","volume":"18","author":"Penuelas","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","unstructured":"Zarco-Tejada, P.J., and Ustin, S.L. (2001, January 9\u201313). Modeling Canopy Water Content for Carbon Estimates from MODIS Data at Land EOS Validation Sites. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, Sydney, Australia."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.rse.2004.11.012","article-title":"Discrimination of Sugarcane Varieties in Southeastern Brazil with EO-1 Hyperion Data","volume":"94","author":"Formaggio","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus Hippocastanum L. And Acer Platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_46","first-page":"226","article-title":"Remote Sensing Techniques to Assess Chlorophyll Fluorescence in Support of Crop Monitoring in Poland","volume":"25","author":"Gurdak","year":"2021","journal-title":"Misc. Geogr."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/36.934080","article-title":"Scaling-up and Model Inversion Methods with Narrowband Optical Indices for Chlorophyll Content Estimation in Closed Forest Canopies with Hyperspectral Data","volume":"39","author":"Miller","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The Meris Terrestrial Chlorophyll Index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_50","unstructured":"Kaufman, L., and Rousseeuw, P.J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis, Wiley Online Library."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11069-008-9274-y","article-title":"Spatial and Temporal Pattern of Precipitation and Drought in Gansu Province, Northwest China","volume":"49","author":"Zhai","year":"2009","journal-title":"Nat. Hazards"},{"key":"ref_52","first-page":"136","article-title":"Drought Events and Its Influence in 2011 in China","volume":"30","author":"Duan","year":"2012","journal-title":"J. Arid. Meteorol."},{"key":"ref_53","unstructured":"Baoji Bureau of Statistics (2017). Baoji Statistical Yearbook (2017), China Statistics Press."},{"key":"ref_54","first-page":"342","article-title":"Drought Events and Its Influence in 2016 in China","volume":"35","author":"Wang","year":"2017","journal-title":"J. Arid. Meteorol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"395","DOI":"10.5194\/essd-15-395-2023","article-title":"ChinaCropSM1 Km: A Fine 1km Daily Soil Moisture Dataset for Dryland Wheat and Maize across China During 1993\u20132018","volume":"15","author":"Cheng","year":"2023","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"074003","DOI":"10.1088\/1748-9326\/ab80f0","article-title":"Identifying the Spatiotemporal Changes of Annual Harvesting Areas for Three Staple Crops in China by Integrating Multi-Data Sources","volume":"15","author":"Luo","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1364\/AO.4.000011","article-title":"Spectral Properties of Plants","volume":"4","author":"Gates","year":"1965","journal-title":"Appl. Opt."},{"key":"ref_59","unstructured":"Alexander, H. (1954). Radiation Biology. Volume III. Visible and Near-Visible Light, McGraw-Hill."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1101\/SQB.1935.003.01.023","article-title":"The Absorption of Radiation by Leaves and Algae","volume":"3","author":"Mestre","year":"1935","journal-title":"Cold Spring Harb. Symp. Quant. Biol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.1109\/TGRS.2013.2278838","article-title":"Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales","volume":"52","author":"Xiao","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/0034-4257(80)90096-6","article-title":"Remote Sensing of Leaf Water Content in the near Infrared","volume":"10","author":"Tucker","year":"1980","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1364\/AO.9.000545","article-title":"Relation of Light Reflectance to Histological and Physical Evaluations of Cotton Leaf Maturity","volume":"9","author":"Gausman","year":"1970","journal-title":"Appl. Opt."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.rse.2003.07.002","article-title":"Derivation of a Shortwave Infrared Water Stress Index from MODIS near-and Shortwave Infrared Data in a Semiarid Environment","volume":"87","author":"Fensholt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2511","DOI":"10.1007\/s11069-021-05146-1","article-title":"Investigating Remote Sensing Indices to Monitor Drought Impacts on a Local Scale (Case Study: Fars Province, Iran)","volume":"111","author":"Mikaili","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.isprsjprs.2007.03.002","article-title":"Modified Perpendicular Drought Index (MPDI): A Real-Time Drought Monitoring Method","volume":"62","author":"Ghulam","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Bartold, M., and Kluczek, M. (2023). A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands. Remote Sens., 15.","DOI":"10.3390\/rs15092392"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Jing, X., Zou, Q., Yan, J., Dong, Y., and Li, B. (2022). Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on Mrmr-Xgboost Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14030756"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Danner, M., Berger, K., Wocher, M., Mauser, W., and Hank, T. (2019). Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies. Remote Sens., 11.","DOI":"10.3390\/rs11101150"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/JSTARS.2010.2091492","article-title":"Evaluation of Sentinel-2 Spectral Sampling for Radiative Transfer Model Based Lai Estimation of Wheat, Sugar Beet, and Maize","volume":"4","author":"Richter","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and Product Vision for Terrestrial Global Change Research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/36.20292","article-title":"MODIS: Advanced Facility Instrument for Studies of the Earth as a System","volume":"27","author":"Salomonson","year":"1989","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:01:52Z","timestamp":1760130112000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":73,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194838"],"URL":"https:\/\/doi.org\/10.3390\/rs15194838","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,6]]}}}