Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data

Wei Wei, Jiping Wang (), Libang Ma, Xufeng Wang, Binbin Xie, Junju Zhou and Haoyan Zhang
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Wei Wei: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Jiping Wang: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Libang Ma: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Xufeng Wang: Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environmental and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Binbin Xie: School of Urban Economics and Tourism Culture, Lanzhou City University, Lanzhou 730070, China
Junju Zhou: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Haoyan Zhang: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China

Land, 2024, vol. 13, issue 1, 1-19

Abstract: Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture (SM), and precipitation (P), using the spatial distance model (SDM). The results showed that the consistent area of SDDI with the 1-month and 3-month standardized precipitation-evapotranspiration index (SPEI1 and SPEI3), and the self-calibrating Palmer drought severity index (scPSDI) accounted for 85.5%, 87.3%, and 85.1% of the global land surface area, respectively, indicating that the index can be used to monitor global drought-wetness conditions. Over the past two decades (2001–2020), a discernible spatial distribution pattern has emerged in global drought-wetness conditions. This pattern was characterized by the extreme drought mainly distributed deep within the continent, surrounded by expanding moderate drought, mild drought, and no drought areas. On the annual scale, the global drought-wetness conditions exhibited an upward trend, while on the seasonal and monthly scales, it fluctuated steadily within a certain cycle. Through this research, we found that the sensitive areas of drought-wetness conditions were mainly found on the east coast of Australia, the Indus Basin of the Indian Peninsula, the Victoria and Katanga Plateau areas of Africa, the Mississippi River Basin of North America, the eastern part of the Brazilian Plateau and the Pampas Plateau of South America.

Keywords: drought; spatial distance model; sensitive areas; remote sensing; global scale (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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