Satellite-based drought monitoring using optimal indices for diverse climates and land types |
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| Institution: | 1. Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran;2. Department of Geography, Faculty of Geography, University of Tehran, Tehran, Iran;1. Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;2. Department of Agricultural Engineering Research, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran;3. Ph.D Graduated of Mechanic of Biosystems Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran;1. Department of Geography, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran;2. Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran;3. Research group on \"Fuzzy Set Theory and Optimal Decision-making Model in Economics and Management\", Vietnam National University, Hanoi, 144 Xuan Thuy str., Hanoi 100000, Viet Nam;1. School of Geomatics, Liaoning Technical University, Fuxin 123000, China;2. Chinese Academy of Surveying & Mapping, Beijing 100036, China;3. School of Management, China University of Mining & Technology (Beijing) Beijing, 100083, China;4. School of Economics, Digital Economy and Data Governance Key Laboratory, Guangdong University of Technology, Guangzhou 510520, China;5. School of Research Center of Industrial and Regional Development, Sun Yat-Sen University, Guangzhou 510275, China;6. Hubei Provincial Geography and National Condition Monitoring Center, Wuhan 430071, China;7. Shanxi Provincial Institute of Surveying and Mapping Geographic Information, Taiyuan 030001, China |
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| Abstract: | Drought is considered one of the most destructive natural disasters, and many areas are experiencing water scarcity. Expanding knowledge of this phenomenon is a prerequisite for developing drought monitoring and forecasting tools. To this end, various indices are available for studying drought in different environments using field and remote sensing data. This study applies satellite-based indices for monitoring drought in different land cover, landforms, and climate classes. The in-situ standardized precipitation index (SPI) with a three-month time scale was applied to evaluate the performance of 13 remote sensing indices and parameters. The results indicated that the indices based on actual evapotranspiration, precipitation, and soil moisture, respectively, performed best in different parts of the basin. After additional analysis, the evapotranspiration condition index (ETCI), derived from actual evapotranspiration data, was deemed the optimal metric. The accuracy assessment results indicated that the correlation between the ETCI and the three-month SPI was 0.655, which was slightly higher than the actual evapotranspiration (0.637), and that the root-mean-squared error (RMSE) decreased from 0.71 to 0.65, indicating the best performance among the indices evaluated in the study area. Moreover, the drought map of the region was developed using the optimal indices, including the ETCI, the precipitation condition index (PCI), and the random forest (RF) algorithm. According to the results of the accuracy evaluation, the correlation between the estimated model and the observed three-month SPI values in 2017 was 0.72, with an RMSE of 0.60. |
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