Agricultural drought monitoring with Sentinel-1 cross‑ratio in heterogeneous tropical agriculture: a Mozambique case study

Agricultural drought monitoring is essential for food security in tropical regions where rain-fed agriculture predominates. Operational early warning systems rely primarily on optical vegetation indices, but cloud cover limits their usability during critical growing periods. SAR satellites can observe through clouds, yet their adoption in agricultural drought monitoring remains limited. This study evaluates Sentinel-1 CR, a SAR-based vegetation indicator sensitive to crop water content and canopy structure, for drought monitoring across six districts in central and southern Mozambique. Using data from 2019 to 2024, we track drought propagation from precipitation (CHIRPS) through soil moisture (ASCAT) to vegetation response (CR and NDVI). Results show that CR correlates positively with NDVI over cropland, and that vegetation response corresponds more closely with soil moisture than with precipitation across most land cover types, though shrubland shows stronger precipitation dependence, indicating land-cover-specific pathways. CR captured the 2019/2020 El Niño drought and subsequent recovery across all districts. However, CR interpretation depends on land cover composition: cropland-dominated areas show clear seasonal patterns enabling anomaly-based drought detection, while heterogeneous cropland-forest/shrub mosaics typical of rain-fed systems require land cover stratified analysis at high resolution to isolate cropland-specific responses. These findings demonstrate that SAR-based vegetation monitoring can complement optical approaches for drought early warning in cloud-prone tropical regions.