The Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Drought Severity Index (DSI) can be produced at a 1-km spatial resolution and can be used for a wide range of water-resource and ecological applications. This study aims to understand the robustness and sensitivity of the DSI in East Asia, and we investigate the performance of the annual DSI using different Normalized Difference Vegetation Index (NDVI) datasets. Additionally, the MODIS-based DSI is compared to other drought indices, including the DSI with Advanced Very High Resolution Radiometer (AVHRR) NDVI (DSIAVHRR) and the Standardized Precipitation Evapotranspiration Index (SPEI) with the Climate Research Unit (CRU) dataset. Three different drought indices are estimated in East Asia from 2000 to 2013 and compared via a correlation analysis based on a 5° × 5° grid. Specifically, the correlation between the DSI and DSIAVHRR is relatively high (0.796), which suggests the potential use of the DSI based on combined products that include parameters such as the NDVI, although the DSI originally used only MODIS-based products. Characteristics such as the frequency and spatial extent of droughts based on the DSI are compared to those based on the SPEI using the drought classification schemes that were originally proposed for the SPEI and DSI, including mild, moderate, severe and extremely dry classes. Based on the results, we suggest a revised classification according to a comparison of the DSI and SPEI. The frequency and spatial extent results of the SPEI and DSI exhibit good agreement when using this classification. Moreover, the DSI from the revised classification is used to evaluate drought events in East Asia in 2003, 2006, 2008 and 2009. Overall, this study shows the potential of using the DSI with datasets that differ from the originally suggested datasets; however, caution must be taken when classifying and identifying drought events.
|Number of pages||11|
|Journal||Remote Sensing of Environment|
|Publication status||Published - 2018 May|
Bibliographical noteFunding Information:
This study was supported by the Korea Meteorological Administration R&D Program under Grant KMIPA 2015-6180 and the Basic Science Research Program through the National Research Foundation of Korea, which was funded by the Ministry of Science, ICT & Future Planning ( 2015R1C1A2A01054800 ).
© 2018 Elsevier Inc.
All Science Journal Classification (ASJC) codes
- Soil Science
- Computers in Earth Sciences