干旱区生态环境知识资源中心

In the dry tropical forest and semi-arid savanna information about the soil moisture dynamics and distribution are vital to better understand and mitigate damages caused by drought. To estimate the soil moisture over large areas from 50-m resolution ALOS-2 ScanSAR data, we adopt dual-polarization L-band algorithms originally developed for high resolution ALOS data. Based on the partial-polarimetric eigenvector decomposition, the scattering entropy and alpha angles are employed to correct for the above ground vegetation effects. The large incidence angle variation along the 350-km ScanSAR swath requires the use of an incidence angle correction factor. However, due to the relative poor data quality of the ScanSAR products suffering from scalloping and near-range/far-range striping along the subswathes, we observe lower accuracies than Stripmap mode. While in the dry season soil moisture retrieval in the open forest is well possible, in the rainy season the entropy exceeds the validity range due to the larger amounts of canopy biomass resulting in unreliable soil moisture estimates. Savanna vegetation allows a biomass-corrected soil moisture estimation throughout the whole year. Due to the lack of distributed in-situ data, we use coarse scale SMOS and SMAP soil moisture data to validate the accuracy of dual-polarization ScanSAR soil moisture estimates. Comparisons between the kilometer-scale radiometer derived soil moisture estimates and 50-m ALOS-2 results show a good agreement in the dry season with RMSEs smaller 5 Vol.-%, while in the rainy season the RMSEs can be as high as 15 Vol.-%.