Spatiotemporal Analysis of Drought Characteristics Based on MODIS and GEE

Abstract

Drought is a major agricultural disaster that has long posed significant challenges to China's agriculture and ecological environment. Its latent, widespread, and vulnerability characteristics make it crucial to obtain effective large-scale, long-term remote sensing data for spatiotemporal analysis and drought forecasting. With the advancement of remote sensing technology, the spatiotemporal resolution of remote sensing data has improved, providing robust support for analyzing drought spatiotemporal variations. However, traditional remote sensing data analysis methods require extensive preprocessing, involve high computational costs, and face difficulties in data acquisition. These challenges highlight the need for remote sensing big data platforms. Google Earth Engine (GEE), a cloud-based platform, offers powerful data storage and analytical capabilities that effectively support large-scale remote sensing data processing. The Vegetation Supply Water Index (VSWI), which integrates vegetation conditions and surface temperature information, is a simple yet effective tool for drought assessment and demonstrates good correlation with the widely used Palmer Drought Severity Index (PDSI). In this study, we used the GEE platform to construct VSWI based on MODIS datasets, utilizing Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) data to conduct a temporal analysis of drought conditions in the three northeastern provinces of China from 2010 to 2020. A comparative analysis with PDSI revealed that VSWI effectively captures the trends in drought variation in the region, demonstrating good spatial and temporal consistency. The findings indicate that the VSWI drought index constructed on the GEE platform provides a simple and efficient method for drought assessment in the region, laying the foundation for developing more complex drought assessment models in the future.