Improved field-scale drought monitoring using MODIS and Sentinel-2 data for vegetation temperature condition index generation through a fusion framework

Drought has a wide range of damaging impacts. Continuous and precise time series drought monitoring is crucial for agriculture. Most existing drought monitoring studies lack sufficient spatiotemporal resolution, making them inadequate for field-scale drought monitoring. In the past decades, Vegetation Temperature Condition Index (VTCI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) has proven effective for drought monitoring. However, only using MODIS data to derive VTCI for drought monitoring presents a limitation in spatial resolution. To address these limitations, this study combined spatiotemporal fusion techniques and machine learning to develop a novel framework for drought monitoring at both a fine resolution (20 m) and a 10-day interval. The framework includes using biophysical parameters calculated by Sentinel-2 data and Digital Elevation Model (DEM) data as downscaling parameters to perform land Surface Temperature (LST) spatial downscaling. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was applied to fuse Sentinel-2 and MODIS data. Two fusion strategies were applied for calculating field-scale VTCI: Blend-then-Index (BI) and Index-then-Blend (IB). Results showed that the two fusion strategies effectively enhanced the spatial resolution of VTCI compared to MODIS VTCI. However, the BI fusion strategy represents drought conditions effectively in cropland, and shows higher consistency (R > 0.83) and lower RMSE (RMSE < 0.05) with MODIS VTCI. In addition, the downscaled LST has consistency with MODIS LST (Correlation Coefficient (R) > 0.77, Root Mean Squared Error (RMSE) < 1.42 K) and retained more spatial details. Overall, we achieved continuous time series drought monitoring at the field scale and 10-day intervals.