Multi-Sensor Satellite Analysis of Drought Impact on Vegetation in Central Germany: Insights from MODIS and Sentinel-2 Time Series Analysis

Abstract

Severe droughts have had unprecedented impacts on vegetation in German ecosystems in recent years. Accurate assessment of the temporal and spatial dynamics of vegetation affected by drought stress requires high to medium spatial resolution images (10-m) and frequent in-situ observations. However, the lack of dense long-term 10-m image time series hinders remote sensing-based high spatial resolution drought assessments. The objective of this study was to evaluate drought conditions in Central Germany using a multi-sensor satellite time series with varying spatial and temporal resolutions. We calculated several drought indices, including the Vegetation Condition Index (VCI), anomalies of the Normalized Vegetation Index (NDVI) and Normalized Water Index (NDWI), and anomalies of land surface temperature (LST), from pre-processed 250m-1km MODIS (Moderate Resolution Imaging Spectroradiometer)-time series and a regular synthetic Sentinel-2 time series. Overall, the spatial patterns of drought were similar between the same drought index time series from both sensors, while variations were observed in the identified severity levels of drought and the level of spatial detail in the mapped drought patterns. Our findings indicated that the study area was predominantly affected by drought during the 2018 growing season, with less extensive drought-affected areas also observed in 2017 and 2020. In-situ drought index time series consistently recorded the presence of drought conditions throughout the summer seasons of 2018–2020, confirming the results of our satellite-based analysis. Future research should explore the feasibility of employing fusion techniques to downscale moderate-resolution drought analysis to a spatial resolution of 10m while maintaining a long-term image time series. The integration of such datasets holds significant implications for environmental monitoring and assessment, enabling more accurate and timely interventions in the face of severe climatic events.