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Synthetic aperture radar (SAR)-based multi-temporal backscatter analysis is a common approach that has been widely used for rice mapping. Co-polarized C-band microwave backscatter (HH or VV) and the co-polarization ratio (HH/VV) are the most commonly used data sets for rice mapping due to their high data availability while the utilization of cross-polarized backscatter (HV or VH) has received less attention. In this study, Sentinel 1A time series – acquired in the dual-polarized (VV/VH) Interferometric Wide (IW) swath mode during the spring growing season (October 2015 to March 2016) in the Mekong Delta – were used to analyse the relationship between the growing cycle of rice plants and the temporal variation of SAR backscatter at different polarizations. Results show that VH backscatter is more sensitive to rice growth than VV backscatter. Several vegetation phenological parameters including beginning date, heading date and the length of the growing season were extracted from the VH backscatter time series. A decision tree approach was applied to delineate rice-cultivated areas based on seasonal phenological parameters. The classification result was validated against a 2015 land use map. The overall classification accuracy is 87.2% (kappa coefficient– κ = 0.71). In addition, the SAR-derived rice area was compared against ground statistical data at the provincial level (coefficient of determinationR2 = 0.98). [ABSTRACT FROM AUTHOR]

Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM(Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double- and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R² of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions. [ABSTRACT FROM AUTHOR]