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

作者：Wei Wei, Jiping Wang, Libang Ma, Xufeng Wang, Bin‐Bin Xie, Junju Zhou, Haoyan Zhang · 发表于：Land · 年份：2024 · DOI：10.3390/land13010095 · 被引用次数：16 · 研究领域：Hydrology and Drought Analysis、Hydrology and Watershed Management Studies、Climate variability and models

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, th...