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More than 50% of the world’s population consumes rice. Accurate and up-to-date information on rice field extent is important to help manage food and water security. Currently, field surveys or MODIS satellite data are used to estimate rice growing areas. This study presents a cost-effective methodology for near-real-time mapping and monitoring of rice growth extent and cropping patterns over a large area. This novel method produces high-resolution monthly maps (10 m resolution) of rice growing areas, as well as rice growth stages. The method integrates temporal Sentinel-1 data and rice phenological parameters with the Google Earth Engine (GEE) cloud-based platform. It uses monthly median time series of Sentinel-1 at VH polarization from September 2016 to October 2018. The two study areas are the northern region of West Java, Indonesia (0.75 million ha), and the Kedah and Perlis states in Malaysia (over 1 million ha). K-means clustering, hierarchical cluster analysis (HCA), and a visual interpretation of VH polarization time series profiles are used to generate rice extent, cropping patterns, and spatiotemporal distribution of growth stages. To automate the process, four supervised classification methods (support vector machine (SVM), artificial neural networks (ANN), random forests, and C5.0 classification models) were independently trialled to identify cluster labels. The results from each classification method were compared. The method can also forecast rice extent for up to two months. The VH polarization data can identify four growth stages of rice—T&P: tillage and planting (30 days); V: vegetative-1 and 2 (60 days); R: reproductive (30 days); M: maturity (30 days). Compared to field survey data, this method measures overall rice extent with an accuracy of 96.5% and a kappa coefficient of 0.92. SVM and ANN show better performance than random forest and C5.0 models. This simple and robust method could be rolled out across Southeast Asia, and could be used as an alternative to time-consuming, expensive field surveys.

A soil hydraulic model that considers capillary hysteretic and adsorptive water retention as well as capillary and film conductivity covering the complete soil moisture range is presented. The model was obtained by incorporating the capillary hysteresis model of Parker and Lenhard into the hydraulic model of Peters‐Durner‐Iden (PDI) as formulated for the van Genuchten (VG) retention equation. The formulation includes the following processes: capillary hysteresis accounting for air entrapment, closed scanning curves, nonhysteretic sorption of water retention onto mineral surfaces, a hysteretic function for the capillary conductivity, a nonhysteretic function for the film conductivity, and a nearly nonhysteretic function of the conductivity as a function of water content (θ) for the entire range of water contents. The proposed model only requires two additional parameters to describe hysteresis. The model was found to accurately describe observed hysteretic water retention and conductivity data for a dune sand. Using a range of published data sets, relationships could be established between the capillary water retention and film conductivity parameters. Including vapor conductivity improved conductivity descriptions in the very dry range. The resulting model allows predictions of the hydraulic conductivity from saturation until complete dryness using water retention parameters.

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