Implementation of an Automated Vegetation Drought Monitoring System Based on Long-Term Satellite Remote Sensing

Droughts are a severe problem globally, and although satellite remote sensing is an effective tool for monitoring drought, current methods require manual intervention and operation to detect drought. This paper proposes a full-automatic drought monitoring system(FADMS) consisting of four main phases: remote sensing data acquisition and preprocessing, drought index calculation and analysis, time-series image service publishing, and analysis and visualization. This system uses Python-based automation scripts, which can automatically download and preprocess satellite remote sensing data efficiently and reliably. We also employed Web Service technology to serve different vegetation drought index algorithms such as Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Temperature Vegetation Dryness Index (TVDI). Moreover, we designed a time-series image service publishing architecture based on GeoServer and ImageMosaic plug-in, enabling the rapid publication of long-term vegetation drought products. Lastly, we used the GIS spatial statistical analysis module to analyze and visualize drought characteristics such as grade, scope, and duration. To demonstrate the effectiveness of our approach, we conducted experiments using MODIS-derived products from 2000 to 2022 in China's northern region. The results confirmed that FADMS could detect and monitor vegetation droughts accurately and timely, identifying drought events' spatiotemporal distribution and evolution trends. FADMS significantly enhances the efficiency and accuracy of vegetation drought monitoring and is highly reliable and practical.