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Remote Sens. 2014, 6(11), 10773-10812; doi:10.3390/rs61110773

Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

1
International Rice Research Institute (IRRI), Los Baños 4031, Philippines
2
Sarmap, Purasca 6989, Switzerland
3
Philippine Rice Research Institute (PhilRice), Muñoz 3119, Philippines
4
Indonesian Center for Agricultural Land Resources Research and Development (ICALRD), Bogor 16123, Indonesia
5
Directorate of Crop Management, Tamil Nadu Agricultural University (TNAU), Coimbatore 641003, India
6
Geo-Informatics and Space Technology Development Agency (GISTDA), Bangkok 10210, Thailand
7
Thailand Rice Department (TRD), Bangkok 10400, Thailand
8
Department of Land Resources, Can Tho University (CTU), Can Tho 92000, Vietnam
9
Institute of Meteorology, Hydrology and Environment (IMHEN), Hanoi 100000, Vietnam
10
Cambodian Agricultural Research and Development Institute (CARDI), Phnom Penh 12413, Cambodia
11
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, New Delhi 110029, India
12
Swiss Agency for Development and Cooperation (SDC)-Cambodia, Phnom Penh 12413, Cambodia
13
Swiss Agency for Development and Cooperation (SDC)-Vietnam, Hanoi 100000, Vietnam
*
Authors to whom correspondence should be addressed.
Received: 30 June 2014 / Revised: 3 October 2014 / Accepted: 28 October 2014 / Published: 6 November 2014
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
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Abstract

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. View Full-Text
Keywords: rice; food security; SAR; Asia; COSMO Skymed; TerraSAR-X rice; food security; SAR; Asia; COSMO Skymed; TerraSAR-X
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Nelson, A.; Setiyono, T.; Rala, A.B.; Quicho, E.D.; Raviz, J.V.; Abonete, P.J.; Maunahan, A.A.; Garcia, C.A.; Bhatti, H.Z.M.; Villano, L.S.; Thongbai, P.; Holecz, F.; Barbieri, M.; Collivignarelli, F.; Gatti, L.; Quilang, E.J.P.; Mabalay, M.R.O.; Mabalot, P.E.; Barroga, M.I.; Bacong, A.P.; Detoito, N.T.; Berja, G.B.; Varquez, F.; Wahyunto; Kuntjoro, D.; Murdiyati, S.R.; Pazhanivelan, S.; Kannan, P.; Mary, P.C.N.; Subramanian, E.; Rakwatin, P.; Intrman, A.; Setapayak, T.; Lertna, S.; Minh, V.Q.; Tuan, V.Q.; Duong, T.H.; Quyen, N.H.; Van Kham, D.; Hin, S.; Veasna, T.; Yadav, M.; Chin, C.; Ninh, N.H. Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project. Remote Sens. 2014, 6, 10773-10812.

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