The　main　objective　of　this　project　is　to　develop　and　test　methodology　using　ENVISAT　ASAR　data　for　agricultural　applications,　with　an　emphasis　on　land　use,　land　cover　classification　and　rice　mapping.　An　optimal　pre-processing　chain　for　ASAR　data　is　first　constructed　to　provide　input　data　to　the　classification　steps.　Experiments　at　the　Zhangzhou　test　site,　Fujian　province,　southern　China,　indicate　that　rice　mapping　based　on　Principal　Components　Analysis　is　effective　at　packing　multi-temporal　information　on　rice　fields　into　a　dominant　component　and　gives　results　similar　to　a　rule-based　approach.　A　radial　basis　function　neural　network　approach　provides　reasonable　accuracies　for　broad　land　cover　classification,　with　clear　gains　in　statistical　accuracy　as　more　data　are　added,　but　without　major　impacts　on　the　visual　quality　of　the　classified　images.