Sergio Ruggieri

On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites

The objective of this paper is to report on the activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains.

Assimilation of COSMO-SkyMed-derived LAI maps into the AQUATER crop growth simulation model. Capitanata (Southern Italy) case study

Abstract AQUATER is a Decision Support System (DSS) developed to drive crop management decisions at district level in a Mediterranean area; it integrates information from soil and climatic databases with a crop growth simulation model and provides estimates of crop yield at regional scale. AQUATER can assimilate LAI maps derived from Earth observation data in order to mitigate the risk of erroneous model predictions over large areas. In this study, time-series of LAI maps derived from COSMO-SkyMed SAR images, acquired over the Capitanata plain (Puglia region) in 2010 and 2011, have been assimilated by a forcing procedure in AQUATER and the improvements of its predictions have been assessed. Results indicate that the LAI assimilation leads to significant improvements in the yield forecast of sugar beet and tomato crops, whereas in the case of wheat the improvements are marginal.

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.

LAI estimation of agricultural crops from optical data at different spatial resolution

In this study, LAI maps derived from SPOT and MERIS data have been compared. The analysis has been conducted over an agricultural site located in Southern Italy, where temporal series of ground and SPOT and MERIS data have been acquired during the 2006–2008 growing seasons. LAI retrieved from SPOT data has been firstly validated by using in situ measurements. Then, LAI derived from SPOT and MERIS over large fields of wheat, sugar beet and tomato has been compared. Results show that LAI retrieved from MERIS data is underestimated as compared with LAI retrieved from SPOT data. However, for wheat and beet crops, the root mean square error for LAI-MERIS tested over a large number of fields is about 1 m2/m2, whereas is higher for tomato crop.