Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project
Authors: Nelson Andrew , Setiyono Tri , Rala Arnel , Quicho Emma , Raviz Jeny , Abonete Prosperidad , Maunahan Aileen , Garcia Cornelia , Bhatti Hannah , Villano Lorena , Thongbai Pongmanee , Holecz Francesco , Barbieri Massimo , Collivignarelli Francesco , Gatti Luca , Quilang Eduardo , Mabalay Mary , Mabalot Pristine , Barroga Mabel , Bacong Alfie , Detoito Norlyn , Berja Glorie , Varquez Frenciso , Wahyunto  , Kuntjoro Dwi , Murdiyati Sri , Pazhanivelan Sellaperumal , Kannan Pandian , Mary Petchimuthu , Subramanian Elangovan , Rakwatin Preesan , Intrman Amornrat , Setapayak Thana , Lertna Sommai , Minh Vo , Tuan Vo , Duong Trinh , Quyen Nguyen , Van Kham Duong , Hin Sarith , Veasna Touch , Yadav Manoj , Chin Chharom , Ninh Nguyen 
Published: November 2014

Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on “temporal feature descriptors” that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.