School of Computer Science and Technology, Remote Sensing and Climate Changing, Qingdao University, Shandong, 266071, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Environ Sci Pollut Res Int. 2019 Nov;26(32):33568-33581. doi: 10.1007/s11356-019-06500-4. Epub 2019 Oct 4.
South Asia is susceptible to drought due to high variation in monthly precipitation. The drought indices deriving from remote sensing data have been used to monitor drought events. To secure agricultural land in South Asia, timely and effective drought monitoring is very important. In this study, TRMM data was utilized along with remote sensing techniques for reliable drought monitoring. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), NDVI, and Normalized Vegetation Supply Water Index (NVSWI) are more helpful in describing the drought events in South Asia due to the dryness and low vegetation. To categorize drought-affected areas, the spatial maps of TRMM were used to confirm MODIS-derived TVDI, DSI, and NVSWI. The DSI, TVDI, NVSWI, and Normalized Monthly Precipitation Anomaly Percentage (NAP) indices with an integrated use of MODIS-derived ET/PET and NDVI were selected as a tool for monitoring drought in South Asia. The seasonal DSI, TVDI, NVSWI, NAP, and NDVI values confirmed that South Asia suffered an extreme drought in 2001, which continued up to 2003. The correlation was generated among DSI, NAP, NVWSI, NDVI, TVDI, and TCI on a seasonal basis. The significantly positive correlation values of DSI, TVDI, and NVSWI were in DJF, MAM, and SON seasons, which were described as good drought monitoring indices during these seasons. During summer, the distribution values of drought indicated that more droughts occurred in the southwest regions as compared to the northeast region of South Asia. From 2001 to 2017, the change trend of drought was characterized; the difference of drought trend was obviously indicated among different regions. In South Asia, generally, the frequency of drought showed declining trends from 2001 to 2017. It was verified that these drought indices are a comprehensive drought monitoring indicator and would reduce drought risk in South Asia.
南亚由于月降水量变化大，容易发生干旱。基于遥感数据的干旱指数已被用于监测干旱事件。为了保护南亚的农业用地，及时有效地监测干旱非常重要。本研究利用 TRMM 数据结合遥感技术进行可靠的干旱监测。由于干燥和低植被，干旱严重指数（DSI）、温度植被干旱指数（TVDI）、归一化植被指数（NDVI）和归一化供水植被指数（NVSWI）在描述南亚干旱事件方面更有帮助。为了对受干旱影响的地区进行分类，使用 TRMM 的空间图来确认 MODIS 衍生的 TVDI、DSI 和 NVSWI。DSI、TVDI、NVSWI 和归一化月度降水异常百分比（NAP）指数与 MODIS 衍生的 ET/PET 和 NDVI 综合使用，被选为监测南亚干旱的工具。基于季节性 DSI、TVDI、NVSWI、NAP 和 NDVI 值，确认南亚在 2001 年遭受了极端干旱，一直持续到 2003 年。在季节性基础上生成了 DSI、NAP、NVWSI、NDVI、TVDI 和 TCI 之间的相关性。在 DJF、MAM 和 SON 季节，DSI、TVDI 和 NVSWI 的相关性呈显著正相关，这些指数在这些季节被描述为良好的干旱监测指标。在夏季，干旱的分布值表明，与南亚东北部相比，西南部地区发生的干旱更多。2001 年至 2017 年，干旱趋势发生了变化；不同地区的干旱趋势差异明显。在南亚，一般来说，2001 年至 2017 年，干旱的频率呈下降趋势。验证了这些干旱指数是一种综合的干旱监测指标，可以降低南亚的干旱风险。
