High-Resolution Vegetation Optical Depth retrieved from Sentinel-1 C-band SAR Data

Vegetation Optical Depth (VOD), a microwave-based vegetation index for assessing crop canopy water content and biomass, is becoming increasingly crucial for effective crop and irrigation management as well as yield optimization. Currently, VOD is primarily retrieved from passive microwave data with low resolution, which does not meet the needs for monitoring crop growth dynamics at finer scales. A high resolution Sentinel-1 VOD time series, was developed based on the Water Cloud Model (WCM) coupled with the TU-Wien (Vienna University of Technology, TU-Wien) change detection algorithm over Guangxi, China during 2018–2020. The main challenge in retrieving a high-resolution VOD dataset from Sentinel-1 is the lack of high-resolution soil moisture observations, which impedes the precise correction of soil scattering component interference in the Water Cloud Model (WCM). To address this issue, the corrected soil scattering components from the TU-Wien algorithm are used as input parameters for the WCM to construct the VOD time series. Consequently, the TU-Wien algorithm provides a 10-meter resolution surface soil moisture (SSM)observation dataset, serving as a bridge to retrieve high-resolution VOD. The results show that the S1 SSM product shows strong agreement with the CLDAS-V data in southern Guangxi, indicating satisfactory performance in this region during 2019, with most pixels exhibiting R values greater than 0.50. Across most areas of the study region, the correlation between VOD and NDVI is positive, with R values exceeding 0.6. The high-resolution VOD we retrieved can be effectively used to monitor crop water stress, growth stages, and yield conditions with greater spatial detail, supporting precision agriculture and contributing to regional food security assessments.