Time series of Cosmo-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites

This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop classification and that the integration of maps of SAR-derived surface parameters into crop growth and/or hydrologic models, in general, leads to significant improvements in the model performances.