On current limits of soil moisture retrieval from ERS-SAR data

The objective of this paper is to assess the feasibility of retrieving soil moisture content over smooth bare-soil fields using current and near- future C-band ERS-SAR datasystems. The roughness conditions considered in this study correspond to those observed in agricultural fields at the time of sowing. Within this context, the retrieval possibilities of a single-parameter ERS-SAR configuration, is assessed using appropriately suitably trained neural networks. Three sources of error affecting soil moisture retrieval estimation (inversion-, measurement- and model errors) are identified and their relative influence on retrieval performance is assessed using synthetic datasets as as well as a large pan-European database of ground and ERS-1/2 measurements. The results from this study indicate that no more than two soil moisture classes can reliably be distinguished using the ERS-configuration, even for the restricted roughness range considered.