{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:43:47Z","timestamp":1778604227050,"version":"3.51.4"},"reference-count":106,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China-ASEAN Big Earth Data Platform and Applications","award":["CADA, guikeAA20302022"],"award-info":[{"award-number":["CADA, guikeAA20302022"]}]},{"name":"China-ASEAN Big Earth Data Platform and Applications","award":["42171078"],"award-info":[{"award-number":["42171078"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CADA, guikeAA20302022"],"award-info":[{"award-number":["CADA, guikeAA20302022"]}],"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":["42171078"],"award-info":[{"award-number":["42171078"]}],"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>Drought poses a serious threat to agricultural production and food security in the context of global climate change. Few studies have explored the response mechanism and lag time of agricultural drought to meteorological drought from the perspective of cultivated land types. This paper analyzes the spatiotemporal evolution patterns and hysteresis relationship of meteorological and agricultural droughts in the middle and lower reaches of the Yangtze River in China. Here, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products and surface temperature products were selected to calculate the Temperature Vegetation Dryness Index (TVDI) from 2010 to 2015. Furthermore, we obtained the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) for the same period. Based on these indices, we analyzed the correlation and the hysteresis relationship between agricultural and meteorological drought in rainfed and irrigated arable land. The results showed that, (1) compared with SPEI, the high spatial resolution PDSI data were deemed more suitable for the subsequent accurate and scientific analysis of the relationship between meteorological and agricultural droughts. (2) When meteorological drought occurs, irrigated arable land is the first to experience agricultural drought, and then alleviates when the drought is most severe in rainfed arable land, indicating that irrigated arable land is more sensitive to drought events when exposed to the same degree of drought risk. However, rainfed arable land is actually more susceptible to agricultural drought due to the intervention of irrigation measures. (3) According to the cross-wavelet transform analysis, agricultural droughts significantly lag behind meteorological droughts by about 33 days during the development process of drought events. (4) The spatial distribution of the correlation coefficient between the PDSI and TVDI shows that the area with negative correlations of rainfed croplands and the area with positive correlations of irrigated croplands account for 77.55% and 68.04% of cropland areas, respectively. This study clarifies and distinguishes the details of the meteorological-to-agricultural drought relationship in rainfed and irrigated arable land, noting that an accurate lag time can provide useful guidance for drought monitoring management and irrigation project planning in the middle and lower reaches of the Yangtze River.<\/jats:p>","DOI":"10.3390\/rs15061689","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T06:56:48Z","timestamp":1679381808000},"page":"1689","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Spatiotemporal Evolution and Hysteresis Analysis of Drought Based on Rainfed-Irrigated Arable Land"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3319-6495","authenticated-orcid":false,"given":"Enyu","family":"Du","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0312-7376","authenticated-orcid":false,"given":"Huicong","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7163-3644","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2650-9521","authenticated-orcid":false,"given":"Aqiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.1007\/s11069-020-04421-x","article-title":"Spatial assessment of drought disasters, vulnerability, severity and water shortages: A potential drought disaster mitigation strategy","volume":"105","author":"Orimoloye","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0022-1694(03)00233-6","article-title":"Estimation of regional meteorological and hydrological drought characteristics: A case study for Denmark","volume":"281","author":"Hisdal","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4637","DOI":"10.1002\/joc.7091","article-title":"Copula based analysis of meteorological, hydrological and agricultural drought characteristics across Indian river basins","volume":"41","author":"Poonia","year":"2021","journal-title":"Int. J. Climatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105108","DOI":"10.1016\/j.atmosres.2020.105108","article-title":"Conditional distribution selection for SPEI-daily and its revealed meteorological drought characteristics in China from 1961 to 2017","volume":"246","author":"Ma","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102504","DOI":"10.1016\/j.ijdrr.2021.102504","article-title":"Study on spatiotemporal distribution characteristics of flood and drought disaster impacts on agriculture in China","volume":"64","author":"Guan","year":"2021","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103210","DOI":"10.1016\/j.ijdrr.2022.103210","article-title":"Climate change impacts of drought on the livelihood of dryland smallholders: Implications of adaptation challenges","volume":"80","author":"Ahmad","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111291","DOI":"10.1016\/j.rse.2019.111291","article-title":"Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities","volume":"232","author":"West","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"158474","DOI":"10.1016\/j.scitotenv.2022.158474","article-title":"High emissions could increase the future risk of maize drought in China by 60\u201370%","volume":"852","author":"Jia","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/02508068508686328","article-title":"Understanding: The drought phenomenon: The role of definitions","volume":"10","author":"Wilhite","year":"1985","journal-title":"Water Int."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, Y., Chen, F., Jia, H., Wang, L., and Bondur, V.G. (2020). Different drought legacies of rain-fed and irrigated croplands in a typical Russian agricultural region. Remote Sens., 12.","DOI":"10.3390\/rs12111700"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1029\/WR016i002p00297","article-title":"On the definition of drought","volume":"16","author":"Darcup","year":"1980","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.gloplacha.2015.01.003","article-title":"Climate change impacts on meteorological, agricultural and hydrological droughts in China","volume":"126","author":"Leng","year":"2015","journal-title":"Glob. Planet. Chang."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"W09527","DOI":"10.1029\/2010WR009845","article-title":"Climate change impact on meteorological, agricultural, and hydrological drought in central Illinois","volume":"47","author":"Wang","year":"2011","journal-title":"Water Resour. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6547209","DOI":"10.1155\/2016\/6547209","article-title":"Propagation of Drought: From Meteorological Drought to Agricultural and Hydrological Drought","volume":"2016","author":"Wang","year":"2016","journal-title":"Adv. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1007\/s00477-015-1080-y","article-title":"Exploring spatiotemporal relationships among meteorological, agricultural, and hydrological droughts in Southwest China","volume":"30","author":"Wu","year":"2016","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106996","DOI":"10.1016\/j.agwat.2021.106996","article-title":"Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China","volume":"255","author":"Ding","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s13201-020-01345-6","article-title":"Meteorological and hydrological drought monitoring using several drought indices","volume":"11","author":"Salimi","year":"2021","journal-title":"Appl. Water Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"148090","DOI":"10.1016\/j.scitotenv.2021.148090","article-title":"Benchmarking of drought and climate indices for agricultural drought monitoring in Argentina","volume":"790","author":"Puertas","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/JSTARS.2022.3146430","article-title":"Res2-Unet, a new deep architecture for building detection from high spatial resolution images","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"143530","DOI":"10.1016\/j.scitotenv.2020.143530","article-title":"Performance and relationship of four different agricultural drought indices for drought monitoring in China\u2019s mainland using remote sensing data","volume":"759","author":"Javed","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112733","DOI":"10.1016\/j.jenvman.2021.112733","article-title":"Monitoring drought dynamics in China using Optimized Meteorological Drought Index (OMDI) based on remote sensing data sets","volume":"292","author":"Wei","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s40068-022-00251-x","article-title":"Meteorological drought monitoring across the main river basins of Ethiopia using satellite rainfall product","volume":"11","author":"Lemma","year":"2022","journal-title":"Environ. Syst. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"126638","DOI":"10.1016\/j.jhydrol.2021.126638","article-title":"Integrated meteorological drought monitoring framework using multi-sensor and multi-temporal earth observation datasets and machine learning algorithms: A case study of central India","volume":"601","author":"Neeti","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shahzaman, M., Zhu, W., Ullah, I., Mustafa, F., Bilal, M., Ishfaq, S., Nisar, S., Arshad, M., Iqbal, R., and Aslam, R.W. (2021). Comparison of multi-year reanalysis, models, and satellite remote sensing products for agricultural drought monitoring over south asian countries. Remote Sens., 13.","DOI":"10.3390\/rs13163294"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"20408","DOI":"10.1007\/s11356-020-12120-0","article-title":"Comparative evaluation of drought indices for monitoring drought based on remote sensing data","volume":"28","author":"Wei","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_26","first-page":"102853","article-title":"HADeenNet: A hierarchical-attention multi-scale deconvolution network for landslide detection","volume":"111","author":"Yu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","first-page":"17","article-title":"The standardized precipitation index\u2013an overview","volume":"12","author":"Cheval","year":"2015","journal-title":"Rom. J. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"523","DOI":"10.17521\/cjpe.2004.0071","article-title":"Comparison between standardized precipitation index and Z-index in China","volume":"28","author":"Yuan","year":"2004","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_29","unstructured":"(2017). Grades of Meteorological Drought. Standard No. GB\/T 20481-2017."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1175\/2009JCLI2909.1","article-title":"A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index","volume":"23","year":"2010","journal-title":"J. Clim."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104951","DOI":"10.1016\/j.envint.2019.104951","article-title":"Modified palmer drought severity index: Model improvement and application","volume":"130","author":"Yu","year":"2019","journal-title":"Environ. Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113163","DOI":"10.1016\/j.envres.2022.113163","article-title":"Drought identification based on Palmer drought severity index and return period analysis of drought characteristics in Huaibei Plain China","volume":"212","author":"Zhou","year":"2022","journal-title":"Environ. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"D12115","DOI":"10.1029\/2010JD015541","article-title":"Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900\u20132008","volume":"116","author":"Dai","year":"2011","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wardlow, B., Anderson, M., and Verdin, J. (2012). Remote Sensing of Drought: Innovative Monitoring Approaches, CRC Press.","DOI":"10.1201\/b11863"},{"key":"ref_35","unstructured":"Hayes, M.J., Svoboda, M.D., Wardlow, B.D., Anderson, M.C., and Kogan, F. (2012). Remote Sensing of Drought: Innovative Monitoring Approaches, CRC Press."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1080\/19475705.2022.2044394","article-title":"A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale","volume":"13","author":"Alahacoon","year":"2022","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1175\/2009JCLI2968.1","article-title":"Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China","volume":"23","author":"Zhai","year":"2010","journal-title":"J. Clim."},{"key":"ref_39","first-page":"709","article-title":"Advances in adaptability of meteorological drought indices in China","volume":"35","author":"Yiping","year":"2017","journal-title":"J. Arid Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.pce.2015.10.022","article-title":"Timescale differences between SC-PDSI and SPEI for drought monitoring in China","volume":"102","author":"Zhao","year":"2017","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1002\/2015JD024285","article-title":"Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982\u20132011 by using PDSI indices and agriculture drought survey data","volume":"121","author":"Yan","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9821275","DOI":"10.34133\/2022\/9821275","article-title":"Glacial Lake Area Changes in High Mountain Asia during 1990\u20132020 Using Satellite Remote Sensing","volume":"2022","author":"Zhang","year":"2022","journal-title":"Research"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"106119","DOI":"10.1016\/j.landusepol.2022.106119","article-title":"Spatio-temporal variation and coupling coordination relationship between urbanisation and habitat quality in the Grand Canal, China","volume":"117","author":"Tang","year":"2022","journal-title":"Land Use Policy"},{"key":"ref_44","first-page":"102930","article-title":"SNNFD, spiking neural segmentation network in frequency domain using high spatial resolution images for building extraction","volume":"112","author":"Yu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"741","DOI":"10.5194\/essd-13-741-2021","article-title":"Annual 30 m dataset for glacial lakes in High Mountain Asia from 2008 to 2017","volume":"13","author":"Chen","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_46","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_47","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_48","doi-asserted-by":"crossref","first-page":"103198","DOI":"10.1016\/j.gloplacha.2020.103198","article-title":"The roles of NDVI and Land Surface Temperature when using the Vegetation Health Index over dry regions","volume":"190","author":"Bento","year":"2020","journal-title":"Glob. Planet. Chang."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.agrformet.2018.05.014","article-title":"A climatological assessment of drought impact on vegetation health index","volume":"259","author":"Bento","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s12524-013-0264-5","article-title":"The second modified perpendicular drought index (MPDI1): A combined drought monitoring method with soil moisture and vegetation index","volume":"41","author":"Li","year":"2013","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1080\/15481603.2017.1287397","article-title":"Mapping drought-impacted vegetation stress in California using remote sensing","volume":"54","author":"Rao","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s00254-006-0544-2","article-title":"Designing of the perpendicular drought index","volume":"52","author":"Ghulam","year":"2007","journal-title":"Environ. Geol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, H., Chen, H., Shen, S., and Zou, C. (2008, January 10). The application of Modified Perpendicular Drought Index (MPDI) method in drought remote sensing monitoring. Proceedings of the Remote Sensing and Modeling of Ecosystems for Sustainability, San Diego, CA, USA.","DOI":"10.1117\/12.795613"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0273-1177(95)00079-T","article-title":"Application of vegetation index and brightness temperature for drought detection","volume":"15","author":"Kogan","year":"1995","journal-title":"Adv. Space Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2006.06.003","article-title":"A comparative study of NOAA\u2013AVHRR derived drought indices using change vector analysis","volume":"105","author":"Bayarjargal","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1029\/WR013i003p00651","article-title":"Wheat canopy temperature: A practical tool for evaluating water requirements","volume":"13","author":"Jackson","year":"1977","journal-title":"Water Resour. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/0034-4257(94)90020-5","article-title":"Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index","volume":"49","author":"Moran","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_60","first-page":"631","article-title":"Use of surface water supply index to assessing of water resources management in Colorado and Oregon, US","volume":"3","author":"Valipour","year":"2013","journal-title":"Adv. Agric. Sci. Eng. Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1061\/(ASCE)0733-9496(1993)119:4(437)","article-title":"Revised surface-water supply index for western United States","volume":"119","author":"Garen","year":"1993","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_62","unstructured":"Doesken, N.J., McKee, T.B., and Kleist, J.D. (1991). Development of a Surface Water Supply Index for the Western United States, Colorado State University. Climatology Report 91-3."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1002\/wat2.1154","article-title":"Drought indicators revisited: The need for a wider consideration of environment and society","volume":"3","author":"Bachmair","year":"2016","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"108821","DOI":"10.1016\/j.agrformet.2022.108821","article-title":"Spatiotemporal patterns of maize drought stress and their effects on biomass in the Northeast and North China Plain from 2000 to 2019","volume":"315","author":"Wan","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Du, L., Song, N., Liu, K., Hou, J., Hu, Y., Zhu, Y., Wang, X., Wang, L., and Guo, Y. (2017). Comparison of two simulation methods of the temperature vegetation dryness index (TVDI) for drought monitoring in semi-arid regions of China. Remote Sens., 9.","DOI":"10.3390\/rs9020177"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1016\/S2095-3119(14)60813-3","article-title":"Drought change trend using MODIS TVDI and its relationship with climate factors in China from 2001 to 2010","volume":"13","author":"Liang","year":"2014","journal-title":"J. Integr. Agric."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Shashikant, V., Mohamed Shariff, A.R., Wayayok, A., Kamal, M.R., Lee, Y.P., and Takeuchi, W. (2021). Utilizing TVDI and NDWI to classify severity of agricultural drought in Chuping, Malaysia. Agronomy, 11.","DOI":"10.3390\/agronomy11061243"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Weidan, W., Li, S., Zhiyuan, P., Yuanyuan, C., and Mo, D. (2021, January 26\u201329). Comparison of TVDI and soil moisture response based on various vegetation indices. Proceedings of the 2021 9th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Shenzhen, China.","DOI":"10.1109\/Agro-Geoinformatics50104.2021.9530348"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"126814","DOI":"10.1016\/j.jhydrol.2021.126814","article-title":"Climate change impacts and uncertainty on spatiotemporal variations of drought indices for an irrigated catchment","volume":"601","author":"Seidenfaden","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ku\u015bmierek-Tomaszewska, R., and \u017barski, J. (2021). Assessment of Meteorological and Agricultural Drought Occurrence in Central Poland in 1961\u20132020 as an Element of the Climatic Risk to Crop Production. Agriculture, 11.","DOI":"10.3390\/agriculture11090855"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1007\/s00704-019-02878-w","article-title":"Linkage of agricultural drought with meteorological drought in different climates of Iran","volume":"138","author":"Khosravi","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11069-021-04940-1","article-title":"Spatial\u2013temporal changes in meteorological and agricultural droughts in Northeast China: Change patterns, response relationships and causes","volume":"110","author":"Du","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"107809","DOI":"10.1016\/j.agrformet.2019.107809","article-title":"Quantitative analysis of agricultural drought propagation process in the Yangtze River Basin by using cross wavelet analysis and spatial autocorrelation","volume":"280","author":"Li","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"105804","DOI":"10.1016\/j.catena.2021.105804","article-title":"A novel comprehensive agricultural drought index reflecting time lag of soil moisture to meteorology: A case study in the Yangtze River basin, China","volume":"209","author":"Tian","year":"2022","journal-title":"Catena"},{"key":"ref_76","first-page":"1","article-title":"Analysis of the relationship between the meteorological, agriculture and hydrological drought","volume":"39","author":"Hu","year":"2016","journal-title":"Meteorol. Environ. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"107428","DOI":"10.1016\/j.agwat.2021.107428","article-title":"High-resolution propagation time from meteorological to agricultural drought at multiple levels and spatiotemporal scales","volume":"262","author":"Li","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Alahacoon, N., Edirisinghe, M., and Ranagalage, M. (2021). Satellite-based meteorological and agricultural drought monitoring for agricultural sustainability in Sri Lanka. Sustainability, 13.","DOI":"10.3390\/su13063427"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"153270","DOI":"10.1016\/j.scitotenv.2022.153270","article-title":"Effects and contributions of meteorological drought on agricultural drought under different climatic zones and vegetation types in Northwest China","volume":"821","author":"Cao","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_80","first-page":"374","article-title":"Research on meteorological drought in the middle and lower reaches of the Yangtze River","volume":"34","author":"Li","year":"2019","journal-title":"Nat. Resour."},{"key":"ref_81","first-page":"1969","article-title":"Methods for diagnosis and assessment of meteorological drought and application in the middle and lower Yangtze Basin","volume":"24","author":"Qin","year":"2015","journal-title":"Resour. Environ. Yangtze Basin"},{"key":"ref_82","first-page":"1245","article-title":"Remote Sensing Monitoring of Agricultural Drought and Vegetation Sensitivity Analysis in the Middle and Lower Reaches of the Yangtze River from 2001 to 2019","volume":"47","author":"Yin","year":"2022","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_83","first-page":"209","article-title":"Monitoring and comparison of drought in five provinces of the middle and lower reaches of the Yangtze River based on the multiple drought indices","volume":"37","author":"Siqi","year":"2019","journal-title":"J. Arid Meteorol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"102724","DOI":"10.1016\/j.ijdrr.2021.102724","article-title":"Flood risk management in the Yangtze River basin\u2014Comparison of 1998 and 2020 events","volume":"68","author":"Jia","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Jia, H., Chen, F., Zhang, J., and Du, E. (2020). Vulnerability analysis to drought based on remote sensing indexes. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17207660"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Giri, C., Milesi, C., Ozdogan, M., Congalton, R., Tilton, J., and Sankey, T.T. (2015). Land Resources Monitoring, Modeling, and Mapping with Remote Sensing (Remote Sensing Handbook), CAB Direct.","DOI":"10.1201\/b19322"},{"key":"ref_87","unstructured":"Gumma, M., Thenkabail, P., Teluguntla, P., Oliphant, A., Xiong, J., Congalton, R., Yadav, K., and Smith, C. (2017). NASA EOSDIS Land Processes DAAC, USGS."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Giri, C., Milesi, C., Ozdogan, M., Congalton, R.G., and Tilton, J. (2015). Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, CRC Press.","DOI":"10.1201\/b19322"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Yadav, K., and Congalton, R.G. (2018). Accuracy assessment of global food security-support analysis data (GFSAD) cropland extent maps produced at three different spatial resolutions. Remote Sens., 10.","DOI":"10.3390\/rs10111800"},{"key":"ref_90","unstructured":"Angulo Mart\u00ednez, M., Beguer\u00eda, S., El-Kenawy, A., L\u00f3pez-Moreno, J., and Vicente Serrano, S. (2010, January 10\u201314). The SPEIbase: A new gridded product for the analysis of drought variability and drought impacts. Proceedings of the European Conference on Applied Climatology (ECAC) & European Meterological Society (EMS), Lodz, Poland."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1175\/2010JHM1224.1","article-title":"A new global 0.5 gridded dataset (1901\u20132006) of a multiscalar drought index: Comparison with current drought index datasets based on the Palmer Drought Severity Index","volume":"11","author":"Angulo","year":"2010","journal-title":"J. Hydrometeorol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"170191","DOI":"10.1038\/sdata.2017.191","article-title":"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u20132015","volume":"5","author":"Abatzoglou","year":"2018","journal-title":"Sci. Data"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3511","DOI":"10.15244\/pjoes\/130952","article-title":"Monitoring of Drought in Central Yunnan, China Based on TVDI Model","volume":"30","author":"Deng","year":"2021","journal-title":"Pol. J. Environ. Stud."},{"key":"ref_94","first-page":"130","article-title":"Classificationof drought grades based on temperature vegetation drought index using the MODIS data","volume":"24","author":"Wu","year":"2017","journal-title":"Res. Soil Water Conserv."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"561","DOI":"10.5194\/npg-11-561-2004","article-title":"Application of the cross wavelet transform and wavelet coherence to geophysical time series","volume":"11","author":"Grinsted","year":"2004","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"e4483","DOI":"10.1136\/bmj.e4483","article-title":"Pearson\u2019s correlation coefficient","volume":"345","author":"Sedgwick","year":"2012","journal-title":"BMJ Br. Med. J."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1111\/stan.12111","article-title":"Analytic posteriors for Pearson\u2019s correlation coefficient","volume":"72","author":"Ly","year":"2018","journal-title":"Stat. Neerl."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"104743","DOI":"10.1016\/j.atmosres.2019.104743","article-title":"Investigation to the relation between meteorological drought and hydrological drought in the upper Shaying River Basin using wavelet analysis","volume":"234","author":"Li","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Gao, C., Chen, C., He, Y., Ruan, T., Luo, G., and Sun, Y. (2020). Response of Agricultural Drought to Meteorological Drought: A Case Study of the Winter Wheat above the Bengbu Sluice in the Huaihe River Basin, China. Water, 12.","DOI":"10.3390\/w12102805"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"106479","DOI":"10.1016\/j.agwat.2020.106479","article-title":"The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China","volume":"243","author":"Zhou","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.ecoleng.2018.11.021","article-title":"A SWAT-Copula based approach for monitoring and assessment of drought propagation in an irrigation command","volume":"127","author":"Dash","year":"2019","journal-title":"Ecol. Eng."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"107695","DOI":"10.1016\/j.agwat.2022.107695","article-title":"Changing occurrence of crop water surplus or deficit and the impact of irrigation: An analysis highlighting consequences for rice production in Bangladesh","volume":"269","author":"Mondol","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.2166\/wcc.2019.279","article-title":"Impact of meteorological drought on agriculture in the Tensift watershed of Morocco","volume":"11","author":"Meliho","year":"2020","journal-title":"J. Water Clim. Chang."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"e2020JD033959","DOI":"10.1029\/2020JD033959","article-title":"Characteristics of propagation from meteorological drought to hydrological drought in the Pearl River Basin","volume":"126","author":"Zhou","year":"2021","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"e2020JD033455","DOI":"10.1029\/2020JD033455","article-title":"Propagation of meteorological to hydrological droughts in India","volume":"125","author":"Bhardwaj","year":"2020","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"111980","DOI":"10.1016\/j.jenvman.2021.111980","article-title":"Propagation of meteorological to hydrological drought for different climate regions in China","volume":"283","author":"Ding","year":"2021","journal-title":"J. Environ. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1689\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:59:43Z","timestamp":1760122783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1689"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":106,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061689"],"URL":"https:\/\/doi.org\/10.3390\/rs15061689","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,21]]}}}