{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:20:57Z","timestamp":1775593257973,"version":"3.50.1"},"reference-count":98,"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":[{"name":"Key Research and Development (R&amp;D) Project of the Department of Science and Technology of Yunnan Province","award":["202203AC100005"],"award-info":[{"award-number":["202203AC100005"]}]},{"name":"Key Research and Development (R&amp;D) Project of the Department of Science and Technology of Yunnan Province","award":["202203AC10000"],"award-info":[{"award-number":["202203AC10000"]}]},{"name":"Key Research and Development (R&amp;D) Project of the Department of Science and Technology of Yunnan Province","award":["IAM202201"],"award-info":[{"award-number":["IAM202201"]}]},{"name":"Drought Meteorological Science Research Fund of the China Meteorological Administration","award":["202203AC100005"],"award-info":[{"award-number":["202203AC100005"]}]},{"name":"Drought Meteorological Science Research Fund of the China Meteorological Administration","award":["202203AC10000"],"award-info":[{"award-number":["202203AC10000"]}]},{"name":"Drought Meteorological Science Research Fund of the China Meteorological Administration","award":["IAM202201"],"award-info":[{"award-number":["IAM202201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A drought results from the combined action of several factors. The continuous progress of remote sensing technology and the rapid development of artificial intelligence technology have enabled the use of multisource remote sensing data and data-driven machine learning (ML) methods to mine drought features from different perspectives. This method improves the generalization ability and accuracy of drought monitoring and prediction models. The present study focused on drought monitoring in southwest China, where drought disasters occur frequently and with a high intensity, especially in areas with limited meteorological station coverage. Several drought indices were calculated based on multisource satellite remote sensing data and weather station observation data. Remote sensing data from multiple sources were combined to build a reconstructed land surface temperature (LST) and drought monitoring method using the two different ML methods of random forest (RF) and eXtreme Gradient Boosting (XGBoost 1.5.1), respectively. A 5-fold cross-validation (CV) method was used for the model\u2019s hyperparameter optimization and accuracy evaluation. The performance of the model was also assessed and validated using several accuracy assessment indicators. The model monitored the results of the spatial and temporal distributions of the drought, drought grades, and influence scope of the drought. These results from the model were compared against historical drought situations and those based on the standardized precipitation evapotranspiration index (SPEI) and the meteorological drought composite index (MCI) values estimated using weather station observation data in southwest China. The results show that the average score of the 5-fold CV for the RF and XGBoost was 0.955 and 0.931, respectively. The root-mean-square error (RMSE) of the LST values reconstructed using the RF model on the training and test sets was 1.172 and 2.236, the mean absolute error (MAE) was 0.847 and 1.719, and the explained variance score (EVS) was 0.901 and 0.858, respectively. Furthermore, the correlation coefficients (CCs) were all greater than 0.9. The RMSE of the monitoring values using the XGBoost model on the training and test sets was 0.135 and 0.435, the MAE was 0.095 and 0.328, the EVS was 0.976 and 0.782, and the CC was 0.982 and 0.868, respectively. The consistency rate between the drought grades identified using SPEI1 (the SPEI values of the 1-month scale) based on the observed data from the 144 meteorological stations and the monitoring values from the XGBoost model was more than 85%. The overall consistency rate between the drought grades identified using the monitoring and MCI values was 67.88%. The aforementioned two different ML methods achieved a high comprehensive performance, accuracy, and applicability. The constructed model can improve the level of dynamic drought monitoring and prediction for regions with complex terrain and topography and formative factors of climate as well as where weather stations are sparsely distributed.<\/jats:p>","DOI":"10.3390\/rs15194840","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T07:49:29Z","timestamp":1696578569000},"page":"4840","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Remote Sensing Monitoring of Drought in Southwest China Using Random Forest and eXtreme Gradient Boosting Methods"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiehui","family":"Li","sequence":"first","affiliation":[{"name":"School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Yunnan R&D Institute of Natural Disaster, Chengdu University of Information Technology, Kunming 650034, China"},{"name":"Key Open Laboratory of Arid Climate Change and Disaster Reduction, China Meteorological Administration\/Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China"}]},{"given":"Hejia","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Xianning Meteorological Service, Xianning 437000, China"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26.1","DOI":"10.1175\/AMSMONOGRAPHS-D-18-0011.1","article-title":"Social Sciences, Weather, and Climate Change","volume":"59","author":"Lemos","year":"2019","journal-title":"Meteorol. Monogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102953","DOI":"10.1016\/j.earscirev.2019.102953","article-title":"A Review of Environmental Droughts: Increased Risk under Global Warming?","volume":"201","author":"Quiring","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_3","unstructured":"Masson-Delmotte, V., Zhai, P.M., Pirani, A., Connors, S.L., P\u00e9an, C., Berger, S., Huang, M.T., Yelek\u00e7i, O., Yu, R., and Zhou, B.Q. (2021). Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1007\/s11069-014-1514-8","article-title":"China\u2019s Regional Drought Risk under Climate Change: A Two-stage Process Assessment Approach","volume":"76","author":"Yuan","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_6","first-page":"398","article-title":"Research Progress on the Key Technologies of Drought Risk Assessment and Control","volume":"47","author":"Jin","year":"2016","journal-title":"Shuili Xuebao"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1007\/s11430-019-9483-5","article-title":"Review of Chinese Atmospheric Science Research over the Past 70 Years: Climate and climate change","volume":"49","author":"Huang","year":"2019","journal-title":"Sci. China Earth Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10441","DOI":"10.1007\/s12652-022-03701-7","article-title":"A Novel Intelligent Deep Learning Predictive Model for Meteorological Drought Forecasting","volume":"14","author":"Danandeh","year":"2022","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_9","unstructured":"Rahimi, B.S. Monitoring of Hydrological Drought in Khazar Basin. Watershed Eng. Manag., 2023."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1007\/s40195-019-00995-z","article-title":"Progress and Prospect on the Study of Causes and Variation Regularity of Droughts in China","volume":"78","author":"Zhang","year":"2020","journal-title":"Acta Meteorol. Sin."},{"key":"ref_11","first-page":"760","article-title":"Characteristics of Long-Cycle Abrupt Drought-Flood Alternations in Southwest China and Atmospheric Circulation in Summer from 1961, to 2019","volume":"40","author":"Wang","year":"2021","journal-title":"Plateau Meteorol."},{"key":"ref_12","first-page":"167","article-title":"Strengthened Relationship between Summer Barents Sea Ice and Autumn Southwest China Drought after the Mid-and Late-1990s","volume":"45","author":"Huan","year":"2022","journal-title":"Trans. Atmos. Sci."},{"key":"ref_13","first-page":"1409","article-title":"The Response of Drought to Climate Warming in Southwest in China","volume":"23","author":"Yao","year":"2014","journal-title":"Ecol. Environ. Sci."},{"key":"ref_14","first-page":"1774","article-title":"Temporal-spatial Abnormity of Drought for Climate Warming in Southwest China","volume":"37","author":"Yao","year":"2015","journal-title":"Resour. Sci."},{"key":"ref_15","first-page":"764","article-title":"Characteristics of the Extreme High Temperature and Drought and Their Main Impacts in Southwestern China of 2022","volume":"40","author":"Sun","year":"2022","journal-title":"J. Arid Meteorol."},{"key":"ref_16","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_17","unstructured":"Mi, Q.C. (2022). Construction and Prediction of the Ensemble Drought Index. [Master\u2019s Thesis, Shenyang Agricultural University]."},{"key":"ref_18","first-page":"10","article-title":"Review of Drought Monitoring based on Remote Sensing Technology","volume":"10","author":"Guo","year":"2020","journal-title":"Adv. Meteorol. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1080\/10106049.2019.1633423","article-title":"A Combined Drought Monitoring Index based on Multi-sensor Remote Sensing Data and Machine Learning","volume":"36","author":"Han","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"112313","DOI":"10.1016\/j.rse.2021.112313","article-title":"Multi-sensor Remote Sensing for Drought Characterization: Current Status, Opportunities and a Roadmap for the Future","volume":"256","author":"Jiao","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qin, Q.M., Wu, Z.H., Zhang, T.Y., Sagan, V., Zhang, Z.X., Zhang, Y., Zhang, C.Y., Ren, H.Z., Sun, Y.H., and Xu, W. (2021). Optical and Thermal Remote Sensing for Monitoring Agricultural Drought. Remote Sens., 13.","DOI":"10.3390\/rs13245092"},{"key":"ref_23","first-page":"815","article-title":"Satellite-based Drought Forecasting: Research Trends, Challenges, and Future Directions","volume":"37","author":"Son","year":"2021","journal-title":"Korean J. Remote Sens."},{"key":"ref_24","unstructured":"Li, Z. (2021). Drought Characteristics and Prediction Models in Northeast China. [Ph.D. Thesis, Shenyang Agricultural University]."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-022-00484-6","article-title":"A Review of Agricultural Drought Assessment with Remote Sensing Data: Methods, Issues, Challenges and Opportunities","volume":"15","author":"Mullapudi","year":"2023","journal-title":"Appl. Geomat."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1080\/0143116031000116417","article-title":"Quality Assessment and Validation of the MODIS Global Land Surface Temperature","volume":"25","author":"Wan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1038\/s41598-022-08342-6","article-title":"Coupling Antecedent Rainfall for Improving the Performance of Rainfall Thresholds for Suspended Sediment Simulation of Semiarid Catchments","volume":"12","author":"Yin","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_30","first-page":"36","article-title":"Research Progress of Composite Drought Index","volume":"37","author":"Wu","year":"2021","journal-title":"Water Resour. Prot."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1175\/1520-0450(2000)039<1071:IVOTGP>2.0.CO;2","article-title":"Initial Validation of the Global Precipitation Climatology Project Monthly Rainfall over the United States","volume":"39","author":"Krajewski","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112960","DOI":"10.1016\/j.rse.2022.112960","article-title":"Evaluation of the Tau-omega Model over Bare and Wheat-covered Flat and Periodic Soil Surfaces at P-and L-band","volume":"273","author":"Shen","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101136","DOI":"10.1016\/j.ecoinf.2020.101136","article-title":"A Review of Drought Monitoring with Big Data: Issues, Methods, Challenges and Research Directions","volume":"60","author":"Balti","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1080\/01431161.2017.1416696","article-title":"An Investigation of Drought Prediction Using Various Remote-sensing Vegetation Indices for Different Time Spans","volume":"39","author":"Heydari","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"108275","DOI":"10.1016\/j.agrformet.2020.108275","article-title":"Integrating Multi-source Data for Rice Yield Prediction across China Using Machine Learning and Deep Learning Approaches","volume":"297","author":"Cao","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sardar, V., Chaudhari, S., Anchalia, A., Kakati, A., Paudel, A., and Bhavana, B.N. (2022, January 7\u20139). Ensemble Learning with CNN and BMO for Drought Prediction. Proceedings of the 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), Bangalore, India.","DOI":"10.1109\/GCAT55367.2022.9971991"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"161394","DOI":"10.1016\/j.scitotenv.2023.161394","article-title":"Assessment and Prediction of Index Based Agricultural Drought Vulnerability Using Machine Learning Algorithms","volume":"867","author":"Kafy","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhao, Y.Y., Zhang, J.H., Bai, Y., Zhang, S., Yang, S.S., Henchiri, M., Seka, A.M., and Nanzad, L. (2022). Drought Monitoring and Performance Evaluation based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors. Remote Sens., 14.","DOI":"10.3390\/rs14246398"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ali, S., Khorrami, B., Jehanzaib, M., Tariq, A., Ajmal, M., Arshad, A., Shafeeque, M., Dilawar, A., Basit, I., and Zhang, L. (2023). Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS). Remote Sens., 15.","DOI":"10.3390\/rs15040873"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108080","DOI":"10.1016\/j.asoc.2021.108080","article-title":"Artificial Neural Networks in Drought Prediction in the 21st Century\u2014A Scientometric Analysis","volume":"114","author":"Dikshit","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"149797","DOI":"10.1016\/j.scitotenv.2021.149797","article-title":"Interpretable and Explainable AI (XAI) Model for Spatial Drought Prediction","volume":"801","author":"Dikshit","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1007\/s11069-015-1679-9","article-title":"Increase in Flood and Drought Disasters during 1500\u20132000, in Southwest China","volume":"77","author":"Ji","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Fu, R., Chen, R., Wang, C., Chen, X., Gu, H., Wang, C., Xu, B., Liu, G., and Yin, G. (2022). Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14071662"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mei, P., Liu, J., Liu, C., and Liu, J.N. (2022). A Deep Learning Model and Its Application to Predict the Monthly MCI Drought Index in the Yunnan Province of China. Atmosphere, 13.","DOI":"10.3390\/atmos13121951"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"471","DOI":"10.3724\/SP.J.1006.2008.00471","article-title":"Wind Speed Changes and Its Influencing Factors in Southwestern China","volume":"34","author":"Zhang","year":"2014","journal-title":"Acta Ecol. Sin."},{"key":"ref_48","first-page":"372","article-title":"Climatic Variation of Rainfall and Rain Day in Southwest China for Last 48 Years","volume":"33","author":"Zhang","year":"2014","journal-title":"Plateau Meteorol."},{"key":"ref_49","first-page":"960","article-title":"Temporal and Spatial Distribution Characteristics of Short-time Heavy Rainfall during Southwest Vortex Rainstorm in Sichuan Basin","volume":"39","author":"Li","year":"2020","journal-title":"Plateau Meteorol."},{"key":"ref_50","first-page":"323","article-title":"Correlation Analysis on Normalized Difference Vegetation Index (NDVI) of Different Vegetations and Climatic Factors in Southwest China","volume":"22","author":"Zhang","year":"2011","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_51","first-page":"292","article-title":"Vegetation Changes and Its Response to Climate Change in China Since","volume":"40","author":"Zhao","year":"2021","journal-title":"Plateau Meteorol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"717","DOI":"10.5194\/essd-11-717-2019","article-title":"Evolution of the ESA CCI Soil Moisture Climate Data Records and Their Underlying Merging Methodology","volume":"11","author":"Gruber","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2017.07.001","article-title":"ESA CCI Soil Moisture for Improved Earth System Understanding: State-of-the Art and Future Directions","volume":"203","author":"Dorigo","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_54","first-page":"185","article-title":"Analysis of Spatial and Temporal Variations of Soil Moisture Content and Drought Degree based on MODIS","volume":"26","author":"Guo","year":"2019","journal-title":"Resour. Soil. Water Conserv."},{"key":"ref_55","first-page":"137","article-title":"Analysis of Vegetation Cover Change and Its Driving over the Past Ten Years in Qinghai Province","volume":"25","author":"Ma","year":"2018","journal-title":"Resour. Soil. Water Conserv."},{"key":"ref_56","unstructured":"(2018). National Standard of the People\u2019s Republic of China. Meteorological Drought Level. Standard No. GB\/T 20481-2017."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8399","DOI":"10.1007\/s00500-019-04120-1","article-title":"Drought Prediction based on SPI and SPEI with Varying Timescales using LSTM Recurrent Neural Network","volume":"23","author":"Poornima","year":"2019","journal-title":"Soft Comput."},{"key":"ref_58","first-page":"742","article-title":"TVDI based Soil Moisture Retrieval from Remotely Sensed Data Over Large Arid Areas","volume":"26","author":"Zhao","year":"2011","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_59","first-page":"446","article-title":"Estimating Soil Water in Northern China based on Vegetation Temperature Condition Index","volume":"35","author":"Wang","year":"2012","journal-title":"Arid. Land Geogr."},{"key":"ref_60","first-page":"187","article-title":"Applicability Analysis of Remote Sensing based Drought Indices in Drought Monitoring of Apple in Luochuan","volume":"36","author":"Zhang","year":"2021","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, X.H., Mao, F.Y., Wang, L., and Yang, J.K. (2021, January 6\u20138). Future Drought Projection of Southwestern China based on CMIP5 Model and MCI Index. Proceedings of the 2021, 7th International Conference on Hydraulic and Civil Engineering & Smart Water Conservancy and Intelligent Disaster Reduction Forum (ICHCE & SWIDR), Nanjing, China.","DOI":"10.1109\/ICHCESWIDR54323.2021.9656446"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.agrformet.2017.02.022","article-title":"Drought Monitoring using High Resolution Soil Moisture Through Multi-sensor Satellite Data Fusion over the Korean Peninsula","volume":"237","author":"Park","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"413","DOI":"10.35633\/inmateh-64-41","article-title":"Study on Remote Sensing Monitoring Model of Agricultural Drought based on Random Forest Deviation Correction","volume":"64","author":"Li","year":"2021","journal-title":"INMATEH-Agric. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Gu, Q.Y., Han, Y., Xu, Y.P., Yao, H.Y., Niu, H.F., and Huang, F. (2022). Laboratory Research on Polarized Optical Properties of Saline-alkaline Soil based on Semi-empirical Models and Machine Learning Methods. Remote Sens., 14.","DOI":"10.3390\/rs14010226"},{"key":"ref_66","first-page":"26","article-title":"Improving Classification Accuracy Based on Random Forest Model with Uncorrelated High Performing Trees","volume":"101","author":"Bharathidason","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chen, T.Q., and Guestrin, C. (2016, January 13\u201317). Xgboost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Zhao, W., Ma, M., and He, K. (2021). Gap-free LST Generation for MODIS\/Terra LST Product Using a Random Forest-Based Reconstruction Method. Remote Sens., 13.","DOI":"10.3390\/rs13142828"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Li, Y.X., Wu, H.P., Li, F., Li, Y.Z., and He, L. (October, January 26). Reconstructing Modis LST Products Over Tibetan Plateau based on Random Forest. Proceedings of the IGARSS 2020-2020, IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323582"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Sun, M., Gong, A., Zhao, X., Liu, N., Si, L., and Zhao, S. (2023). Reconstruction of a Monthly 1 km NDVI Time Series Product in China Using Random Forest Methodology. Remote Sens., 15.","DOI":"10.3390\/rs15133353"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"111931","DOI":"10.1016\/j.rse.2020.111931","article-title":"Reconstruction of Daytime Land Surface Temperatures under Cloud-covered Conditions Using Integrated MODIS\/Terra Land Products and MSG Geostationary Satellite Data","volume":"247","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_72","first-page":"1348","article-title":"Fill the Gaps of Eddy Covariance Fluxes Using Machine Learning Algorithms","volume":"39","author":"Wang","year":"2020","journal-title":"Plateau Meteorol."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ma, L.X., Yu, D.S., Feng, K.Y., Wang, X., and Song, J. (2021). Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China. Remote Sens., 14.","DOI":"10.3390\/rs14010148"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.scitotenv.2019.01.431","article-title":"Meteorological Drought Forecasting based on a Statistical Model with Machine Learning Techniques in Shaanxi Province, China","volume":"665","author":"Zhang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_75","first-page":"1633","article-title":"Xgboost Algorithm Optimization Based on Gradient Distribution Harmonized Strategy","volume":"40","author":"Li","year":"2020","journal-title":"J. Comput. Appl."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"9575782","DOI":"10.1155\/2019\/9575782","article-title":"Coupling a Bat Algorithm with Xgboost to Estimate Reference Evapotranspiration in the Arid and Semiarid Regions of China","volume":"2019","author":"Han","year":"2019","journal-title":"Adv. Meteorol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"165509","DOI":"10.1016\/j.scitotenv.2023.165509","article-title":"Explainable Machine Learning for the Prediction and Assessment of Complex Drought Impacts","volume":"898","author":"Zhang","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2015.07.028","article-title":"LiDAR based prediction of forest biomass using hierarchial models with spatially varying coefficients","volume":"169","author":"Babcock","year":"2015","journal-title":"Remote Sens. Env."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Brenning, A. (2012, January 22\u201327). Spatial Cross-validation and Bootstrap for the Assessment of Prediction Rules in Remote Sensing: The R Package Sperrorest. Proceedings of the 2012, IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352393"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.cageo.2013.10.008","article-title":"Geological Mapping Using Remote Sensing Data: A Comparison of Five Machine Learning Algorithms, Their Response to Variations in the Spatial Distribution of Training Data and the Use of Explicit Spatial Information","volume":"63","author":"Cracknell","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"9806479","DOI":"10.1155\/2017\/9806479","article-title":"A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data","volume":"2017","author":"Sharma","year":"2017","journal-title":"Scientifica"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ramezan, A., Warner, C.A., and Maxwell, T.E.A. (2019). Evaluation of Sampling and Cross-validation Tuning Strategies for Regional-scale Machine Learning Classification. Remote Sens., 11.","DOI":"10.3390\/rs11020185"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A Comparison of Pixel-based and Object-based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-validatory Choice and Assessment of Statistical Predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Battineni, G., Sagaro, G.G., Nalini, C., Amenta, F., and Tayebati, S.K. (2019). Comparative Machine-learning Approach: A Follow-up Study on Type 2 Diabetes Predictions by Cross-validation Methods. Machines, 7.","DOI":"10.3390\/machines7040074"},{"key":"ref_87","first-page":"125","article-title":"Construction of a Drought Monitoring Model Using the Random Forest Based on Remote Sensing","volume":"19","author":"Shen","year":"2017","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_88","unstructured":"Deng, J.H. (2021). Deep Learning\u2014Principles, Models and Practice, Posts & Telecom Press."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1109\/36.58983","article-title":"Using Spatial Context in Satellite Data to Infer Regional Scale Evapotranspiration","volume":"28","author":"Price","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_90","first-page":"734","article-title":"Applicability Analysis of Three Meteorological Drought Indices in Sichuan Province","volume":"30","author":"Wang","year":"2021","journal-title":"Resour. Environ. Yangtze Basin"},{"key":"ref_91","unstructured":"Jia, H.J. (2022). Construction and Application of Remote Sensing Drought Monitoring Model based on Machine Learning in Southwestern China. [Master\u2019s Thesis, Chengdu University of Information Technology]."},{"key":"ref_92","first-page":"1136","article-title":"Analysis of the Applicability of Drought Indexes in the Northeast, Southwest and Middle-lower Reaches of Yangtze River of China","volume":"40","author":"Xie","year":"2021","journal-title":"Plateau Meteorol."},{"key":"ref_93","first-page":"126","article-title":"The National Drought Situation and Its Impact and Causes in 2010","volume":"29","author":"Duan","year":"2011","journal-title":"J. Arid Meteorol."},{"key":"ref_94","first-page":"392","article-title":"The National Drought Situation and Its Impact and Causes in the Summer of 2011","volume":"29","author":"Duan","year":"2011","journal-title":"J. Arid Meteorol."},{"key":"ref_95","first-page":"136","article-title":"The National Drought Situation and Its Impact and Causes in 2011","volume":"30","author":"Duan","year":"2012","journal-title":"J. Arid Meteorol."},{"key":"ref_96","unstructured":"Chen, Y. (2020). Remote Sensing Retrieval of Soil Moisture Content Based on Ensemble Learning. [Master\u2019s Thesis, University of Electronic Science and Technology of China]."},{"key":"ref_97","first-page":"474","article-title":"Spatial-temporal Variability Characteristics of Extreme Drought Events based on Daily SPEI in the Southwest China in Recent 55 Years","volume":"38","author":"Jia","year":"2018","journal-title":"Sci. Geogr. Sin."},{"key":"ref_98","first-page":"115","article-title":"Applicability and Revision of MCI in Sichuan Province","volume":"35","author":"Wang","year":"2019","journal-title":"Chin. Agric. Sci. Bull."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4840\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:02:00Z","timestamp":1760130120000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4840"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":98,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194840"],"URL":"https:\/\/doi.org\/10.3390\/rs15194840","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,6]]}}}