In-season early mapping of rice area and flooding dynamics from optical and SAR satellite data

Abstract

Rice mapping products were derived from Sentinel-1A and Landsat-8 OLI multi-temporal imagery over Northern Italy at the early stages of the 2015 growing season. A rule-based algorithm was applied to synthetic statistical metrics (TSDs-Temporal Spectra Descriptors) computed from temporal datasets of optical spectral indices and SAR backscattering coefficient. Temporal series are available up to the tillering/full canopy cover stage which is identified as the optimum timing for delivering in-season information on rice area (i.e. mid July). The approach relies on a-priori knowledge on crop dynamics to adapt time horizons for TSD computation and thresholds to local conditions. Output products consist of maps of rice cultivated areas, rice seeding techniques (dry and flooded rice) and flooding practices. Validation showed rice mapping overall accuracy to be 87.8% with commission and omission errors of 3.5% and 24.7%, respectively. Mapping of rice seeding technique showed good agreement with farmer declarations aggregated at the municipality scale (dry rice r2 = 0.71 and flooded rice r2 = 0.91). Finally, flood maps have an overall accuracy above 70%. Geo-products on rice areas and flooding occurrence are relevant information for water management at regional scale especially during summer in presence of multiple crops and water shortage.

Keywords

• paddy rice
• dry seeding rice
• rule-based algorithm
• landsat oli
• sentinel 1