{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T05:17:15Z","timestamp":1776057435538,"version":"3.50.1"},"reference-count":102,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CAS Strategic Priority Research Program","award":["XDA19030402"],"award-info":[{"award-number":["XDA19030402"]}]},{"name":"Natural Science Foundation of China","award":["31671585 & 41871253"],"award-info":[{"award-number":["31671585 & 41871253"]}]},{"name":"Key Basic Research Project of Shandong Natural Science Foundation of China","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001\u20132016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.<\/jats:p>","DOI":"10.3390\/rs13091715","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T22:29:07Z","timestamp":1619648947000},"page":"1715","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3109-0558","authenticated-orcid":false,"given":"Foyez Ahmed","family":"Prodhan","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Department of Agricultural Extension and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Fengmei","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Lamei","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9211-8238","authenticated-orcid":false,"given":"Til Prasad","family":"Pangali Sharma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Da","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Dan","family":"Cao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Minxuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5373-2377","authenticated-orcid":false,"given":"Naveed","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mountain Surface Process and Ecological Regulations, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}]},{"given":"Hasiba Pervin","family":"Mohana","sequence":"additional","affiliation":[{"name":"College of Economics and Management, China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1175\/1520-0477-83.8.1149","article-title":"A Review of Twentieth-Century Drought Indices Used in the United States","volume":"83","author":"Heim","year":"2002","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1002\/wcc.81","article-title":"Drought under global warming: A review","volume":"2","author":"Dai","year":"2011","journal-title":"Wiley Interdiscip. Rev. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"140001","DOI":"10.1038\/sdata.2014.1","article-title":"Global integrated drought monitoring and prediction system","volume":"1","author":"Hao","year":"2014","journal-title":"Sci. Data"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"170145","DOI":"10.1038\/sdata.2017.145","article-title":"Data Descriptor: High-resolution near real-time drought monitoring in South Asia","volume":"4","author":"Aadhar","year":"2017","journal-title":"Sci. Data"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wilhite, D.A., Hayes, M.J., and Knutson, C.L. (2005). Drought and Water Crises: Science, Technology, and Management Issues, CRC Press.","DOI":"10.1201\/9781420028386.pt4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jenvman.2016.10.050","article-title":"Development and evaluation of a comprehensive drought index","volume":"185","author":"Esfahanian","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1109\/LGRS.2014.2349957","article-title":"Validating the modified perpendicular drought index in the North China region using in situ soil moisture measurement","volume":"12","author":"Zhang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1080\/15481603.2017.1286728","article-title":"Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: First results","volume":"54","author":"Tadesse","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1256\/qj.05.13","article-title":"The Indian drought of 2002\u2014A sub-seasonal phenomenon?","volume":"132","author":"Bhat","year":"2006","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12102","DOI":"10.1002\/2016GL071407","article-title":"On the frequency of the 2015 monsoon season drought in the Indo-Gangetic Plain","volume":"43","author":"Mishra","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11470","DOI":"10.1007\/s11356-019-04512-8","article-title":"Analysis of vegetation dynamics, drought in relation with climate over South Asia from 1990 to 2011","volume":"26","author":"Ali","year":"2019","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_12","unstructured":"Ahmad, S., Hussain, Z., Qureshi, A.S., Majeed, R., and Saleem, M. (2004). Drought Mitigation in Pakistan: Current Status and Options for Future Strategies, International Water Management Institute. [3nd ed.]."},{"key":"ref_13","first-page":"89","article-title":"Assessing Environmental and Health Impact of Drought in the Northwest Bangladesh","volume":"4","author":"Dey","year":"2011","journal-title":"J. Environ. Sci. Nat. Resour."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"33568","DOI":"10.1007\/s11356-019-06500-4","article-title":"Characterization of drought monitoring events through MODIS- and TRMM-based DSI and TVDI over South Asia during 2001\u20132017","volume":"26","author":"Ali","year":"2019","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.agsy.2019.03.015","article-title":"Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia","volume":"173","author":"Feng","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.agrformet.2015.10.011","article-title":"Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions","volume":"216","author":"Park","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.agrformet.2009.11.015","article-title":"Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas","volume":"150","author":"Quiring","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"134585","DOI":"10.1016\/j.scitotenv.2019.134585","article-title":"Monitoring drought using composite drought indices based on remote sensing","volume":"711","author":"Liu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6772","DOI":"10.1029\/2017WR021959","article-title":"A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index","volume":"54","author":"Yin","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"915053","DOI":"10.1155\/2012\/915053","article-title":"Statistical uncertainty estimation using random forests and its application to drought forecast","volume":"2012","author":"Chen","year":"2012","journal-title":"Math. Probl. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s11269-015-1184-7","article-title":"Hydrological drought class transition using SPI and SRI time series by loglinear regression","volume":"30","author":"Li","year":"2016","journal-title":"Water Resour. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.asej.2015.11.005","article-title":"Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia)","volume":"7","author":"Khadr","year":"2016","journal-title":"Ain Shams Eng. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1002\/joc.1498","article-title":"Drought forecasting using artificial neural networks and time series of drought indices","volume":"27","author":"Morid","year":"2007","journal-title":"Int. J. Climatol."},{"key":"ref_25","first-page":"48","article-title":"Construction of a drought monitoring model using deep learning based on multi-source remote sensing data","volume":"79","author":"Shen","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agrformet.2005.07.012","article-title":"Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring","volume":"133","author":"Narasimhan","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, S., Meng, D., Gong, H., Li, X., and Wu, X. (2018). Soil Drought and Vegetation Response during 2001\u20132015 in North China Based on GLDAS and MODIS Data. Adv. Meteorol., 2018.","DOI":"10.1155\/2018\/1818727"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s12517-016-2750-x","article-title":"Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN)","volume":"9","author":"Borji","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"44025","DOI":"10.1088\/1748-9326\/ab005e","article-title":"Improving meteorological drought monitoring capability over tropical and subtropical water-limited ecosystems: Evaluation and ensemble of the Microwave Integrated Drought Index","volume":"14","author":"Zhang","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"455","DOI":"10.4028\/www.scientific.net\/AMM.145.455","article-title":"Reservoir drought prediction using support vector machines","volume":"145","author":"Chiang","year":"2012","journal-title":"Appl. Mech. Mater."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/15481603.2018.1489943","article-title":"Estimation of soil moisture using deep learning based on satellite data: A case study of South Korea","volume":"56","author":"Lee","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, W., Huang, W., Hong, Z., and Meng, L. (2017). Upscaling of surface soil moisture using a deep learning model with VIIRS RDR. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050130"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Agana, N.A., and Homaifar, A. (April, January 30). A deep learning based approach for long-term drought prediction. Proceedings of the SoutheastCon 2017, Concord, NC, USA.","DOI":"10.1109\/SECON.2017.7925314"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105111","DOI":"10.1016\/j.atmosres.2020.105111","article-title":"Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia","volume":"246","author":"Zhai","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_36","unstructured":"(2020, November 03). World Bank Population total: South Asia. Available online: https:\/\/data.worldbank.org\/indicator\/SP.POP.TOTL?locations=8S."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.wace.2014.06.003","article-title":"Droughts in asian least developed countries: Vulnerability and sustainability","volume":"7","author":"Miyan","year":"2015","journal-title":"Weather Clim. Extrem."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Han, H., Bai, J., Yan, J., Yang, H., and Ma, G. (2019). A combined drought monitoring index based on multi-sensor remote sensing data and machine learning. Geocarto Int.","DOI":"10.1080\/10106049.2019.1633423"},{"key":"ref_39","unstructured":"Didan, K., Munoz, A.B., Solano, R., and Huete, A. (2015). MODIS Vegetation Index User\u2019s Guide (Collection 6), The University of Arizona."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Park, S., Park, S., Im, J., Rhee, J., Shin, J., and Park, J.D. (2017). Downscaling GLDAS Soil moisture data in East Asia through fusion of Multi-Sensors by optimizing modified regression trees. Water, 9.","DOI":"10.3390\/w9050332"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2006.06.026","article-title":"New refinements and validation of the MODIS Land-Surface Temperature\/Emissivity products","volume":"112","author":"Wan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1002\/2015JD024131","article-title":"Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau","volume":"121","author":"Bi","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_43","first-page":"1013","article-title":"The common land model","volume":"84","author":"Dai","year":"2003","journal-title":"Glob. Chang. Newsl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3653","DOI":"10.1029\/2000WR900130","article-title":"Estimating soil water-holding capacities by linking the Food and Agriculture Organization soil map of the world with global pedon databases and continuous pedotransfer functions","volume":"36","author":"Reynolds","year":"2000","journal-title":"Water Resour. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.ejrh.2017.08.004","article-title":"Evaluating the accuracy of Climate Hazard Group (CHG) satellite rainfall estimates for precipitation based drought monitoring in Koshi basin, Nepal","volume":"13","author":"Shrestha","year":"2017","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1080\/19475705.2019.1683082","article-title":"Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan Province, China","volume":"10","author":"Wu","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/978-3-030-35798-6_9","article-title":"Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling","volume":"69","author":"Beck","year":"2020","journal-title":"Adv. Glob. Chang. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1002\/2017RG000574","article-title":"A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons","volume":"56","author":"Sun","year":"2018","journal-title":"Rev. Geophys."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-13-00068.1","article-title":"PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies","volume":"96","author":"Ashouri","year":"2015","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1061\/(ASCE)0733-9437(1985)111:3(265)","article-title":"Irrigation water requirements for senegal river basin","volume":"111","author":"Hargreaves","year":"1985","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1175\/JHM-D-18-0129.1","article-title":"Drought characteristics and propagation in the Semiarid Heihe River Basin in Northwestern China","volume":"20","author":"Ma","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_52","unstructured":"Van Loon, A.F. (2013). On the Propagation of Drought: How Climate and Catchment Characteristics Influence Hydrological Drought Development and Recovery. [Ph.D. Thesis, Wageningen University]."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jhydrol.2010.07.012","article-title":"A review of drought concepts","volume":"391","author":"Mishra","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_54","first-page":"245","article-title":"A comprehensive drought monitoring method integrating MODIS and TRMM data","volume":"23","author":"Du","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1175\/1520-0477-83.8.1181","article-title":"The Drought Monitor","volume":"83","author":"Svoboda","year":"2002","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_56","unstructured":"McKee, T.B., Doesken, N.J., and Kleist, J. (1993, January 17\u201322). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"523","DOI":"10.5194\/hess-9-523-2005","article-title":"Hydrological response to different time scales of climatological drought: An evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin","volume":"9","year":"2005","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1111\/jawr.12275","article-title":"Identifying and Evaluating a Suitable Index for Agricultural Drought Monitoring in the Texas High Plains","volume":"51","author":"Moorhead","year":"2015","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_60","first-page":"447","article-title":"Monitoring global land surface drought based on a hybrid evapotranspiration model","volume":"13","author":"Yao","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","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. Sp. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/S0034-4257(00)00137-1","article-title":"Satellite-observed sensitivity of world land ecosystems to El Ni\u00f1o\/La Ni\u00f1a","volume":"74","author":"Kogan","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1175\/1520-0477(1997)078<0621:GDWFS>2.0.CO;2","article-title":"Global Drought Watch from Space","volume":"78","author":"Kogan","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1175\/1520-0477-83.8.1167","article-title":"The Quantification of Drought: An Evaluation of Drought Indices","volume":"83","author":"Keyantash","year":"2002","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"93264","DOI":"10.1109\/ACCESS.2020.2993025","article-title":"Monitoring of drought condition and risk in bangladesh combined data from satellite and ground meteorological observations","volume":"8","author":"Prodhan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_66","unstructured":"Hayes, M.J., Svoboda, M.D., and Wilhite, D.A. (2020, September 11). Chapter 12 Monitoring Drought Using the Standardized Precipitation Index. Available online: http:\/\/digitalcommons.unl.edu\/droughtfacpub\/70."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/j.1752-1688.1998.tb05964.x","article-title":"Comparing the palmer drought index and the standardized precipitation index","volume":"34","author":"Guttman","year":"1998","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4393","DOI":"10.1080\/0143116031000084323","article-title":"Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India","volume":"24","author":"Singh","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1126\/science.1192666","article-title":"Drought-induced reduction in global terrestrial net primary production from 2000 through 2009","volume":"329","author":"Zhao","year":"2010","journal-title":"Science"},{"key":"ref_70","first-page":"74","article-title":"An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data","volume":"48","author":"Russo","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_71","first-page":"85","article-title":"Geoinformation Dynamics of net primary productivity on the Mongolian Plateau: Joint regulations of phenology and drought","volume":"81","author":"Bao","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_72","unstructured":"Forkel, M., and Wutzler, T. (2021, January 01). Greenbrown-Land Surface Phenology and Trend Analysis. A Package for the R Software, version 2.2; Wien, Austria. Available online: http:\/\/greenbrown.r-forge.r-project.org\/."},{"key":"ref_73","unstructured":"Nisbet, R., Elder, J., and Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications, Academic Press. [2nd ed.]."},{"key":"ref_74","unstructured":"Candel, A., Parmar, V., LeDell, E., and Arora, A. (2016). Deep Learning with H2O, H2O. ai Inc.. [5th ed.]."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Chang, N.B., and Bai, K. (2017). Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, CRC Press.","DOI":"10.1201\/9781315154602"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2307\/1907187","article-title":"Non-Parametric Test Against Trend","volume":"13","author":"Mann","year":"1945","journal-title":"Econometrica"},{"key":"ref_77","unstructured":"Kendall, M.G. (1984). Rank Correlation Methods, Charles Griffin & Company Limited. [4th ed.]."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3390\/cli2020028","article-title":"Spatial and temporal variability of rainfall over the south-west coast of Bangladesh","volume":"2","author":"Hossain","year":"2014","journal-title":"Climate"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"21","DOI":"10.4025\/actasciagron.v37i1.18199","article-title":"The influence of nonlinear trends on the power of the trend-free pre-whitening approach","volume":"37","author":"Blain","year":"2015","journal-title":"Acta Sci. Agron."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1007\/s00704-020-03339-5","article-title":"A monitoring of the spatial and temporal evolutions of aridity in northern Algeria","volume":"142","author":"Derdous","year":"2020","journal-title":"Theor. Appl. Climatol."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Gavrilov, M.B., Radakovi\u0107, M.G., Sipos, G., Mez\u0151si, G., Gavrilov, G., Luki\u0107, T., Basarin, B., Benyhe, B., Fiala, K., and Koz\u00e1k, P. (2020). Aridity in the central and southern Pannonian basin. Atmosphere, 11.","DOI":"10.3390\/atmos11121269"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1111\/j.1467-9671.2011.01280.x","article-title":"A Contextual Mann-Kendall Approach for the Assessment of Trend Significance in Image Time Series","volume":"15","author":"Neeti","year":"2011","journal-title":"Trans. GIS"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1080\/01621459.1968.10480934","article-title":"Estimates of the Regression Coefficient Based on Kendall\u2019s Tau","volume":"63","author":"Sen","year":"1968","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_84","unstructured":"Evans, M.J.S., Jeffrey, A., Evans, S., Murphy, M.A., and Ram, K. (2021, January 01). R Package \u2018spatialEco\u2019, version 1.3-5. Available online: https:\/\/cran.r-project.org\/web\/packages\/spatialEco\/spatialEco.pdf."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1007\/s12665-016-5917-6","article-title":"Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches","volume":"75","author":"Im","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Rojas, R. (1996). Neural Networks, Springer. [1st ed.].","DOI":"10.1007\/978-3-642-61068-4"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.enbuild.2017.04.038","article-title":"Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption","volume":"147","author":"Ahmad","year":"2017","journal-title":"Energy Build."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, S., Mukherjee, A., Pan, I., Dutta, P., and Bhaumik, A.K. (2017). Hybrid Intelligence Techniques for Handwritten Digit Recognition. Hybrid Intelligent Techniques for Pattern Analysis and Understanding, Chapman and Hall\/CRC.","DOI":"10.1201\/9781315154152"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Krishnan, R., Sanjay, J., Gnanaseelan, C., Mujumdar, M., Kulkarni, A., and Chakraborty, S. (2020). Droughts and Floods. Assessment of Climate Change over the Indian Region: A Report of the Ministry of Earth Sciences (MoES), Government of India, Springer.","DOI":"10.1007\/978-981-15-4327-2"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Neena, J.M., Suhas, E., and Goswami, B.N. (2011). Leading role of internal dynamics in the 2009 Indian summer monsoon drought. J. Geophys. Res. Atmos., 116.","DOI":"10.1029\/2010JD015328"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1175\/2008JAS2723.1","article-title":"Internal feedbacks from monsoon-midlatitude interactions during droughts in the Indian summer monsoon","volume":"66","author":"Krishnan","year":"2009","journal-title":"J. Atmos. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.1175\/2010JAS3440.1","article-title":"Desert air incursions, an overlooked aspect, for the dry spells of the Indian summer monsoon","volume":"67","author":"Krishnamurti","year":"2010","journal-title":"J. Atmos. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"5163","DOI":"10.1175\/2010JCLI3257.1","article-title":"Unusual central Indian drought of summer monsoon 2008: Role of southern tropical Indian Ocean warming","volume":"23","author":"Rao","year":"2010","journal-title":"J. Clim."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1038\/s41467-017-00744-9","article-title":"A threefold rise in widespread extreme rain events over central India","volume":"8","author":"Roxy","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2013.02.023","article-title":"Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data","volume":"134","author":"Zhang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s00704-015-1678-5","article-title":"Multivariate drought frequency estimation using copula method in Southwest China","volume":"127","author":"Hao","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"106195","DOI":"10.1016\/j.agwat.2020.106195","article-title":"Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016","volume":"237","author":"Kalisa","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Krishnan, R., Ramesh, K.V., Samala, B.K., Meyers, G., Slingo, J.M., and Fennessy, M.J. (2006). Indian Ocean-monsoon coupled interactions and impending monsoon droughts. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL025811"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Sinha, A., Berkelhammer, M., Stott, L., Mudelsee, M., Cheng, H., and Biswas, J. (2011). The leading mode of Indian Summer Monsoon precipitation variability during the last millennium. Geophys. Res. Lett., 38.","DOI":"10.1029\/2011GL047713"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"4081","DOI":"10.1007\/s00382-016-3321-2","article-title":"Anomalous convective activity over sub-tropical east Pacific during 2015 and associated boreal summer monsoon teleconnections","volume":"48","author":"Mujumdar","year":"2017","journal-title":"Clim. Dyn."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s00382-015-2886-5","article-title":"Deciphering the desiccation trend of the South Asian monsoon hydroclimate in a warming world","volume":"47","author":"Krishnan","year":"2016","journal-title":"Clim. Dyn."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1715\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:54:53Z","timestamp":1760162093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1715"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,28]]},"references-count":102,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091715"],"URL":"https:\/\/doi.org\/10.3390\/rs13091715","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,28]]}}}