TY - JOUR
T1 - Towards an operational SAR-based rice monitoring system in Asia
T2 - Examples from 13 demonstration sites across Asia in the RIICE project
AU - Nelson, Andrew
AU - Setiyono, Tri
AU - Rala, Arnel B.
AU - Quicho, Emma D.
AU - Raviz, Jeny V.
AU - Abonete, Prosperidad J.
AU - Maunahan, Aileen A.
AU - Garcia, Cornelia A.
AU - Bhatti, Hannah Zarah M.
AU - Villano, Lorena S.
AU - Thongbai, Pongmanee
AU - Holecz, Francesco
AU - Barbieri, Massimo
AU - Collivignarelli, Francesco
AU - Gatti, Luca
AU - Quilang, Eduardo Jimmy P.
AU - Mabalay, Mary Rose O.
AU - Mabalot, Pristine E.
AU - Barroga, Mabel I.
AU - Bacong, Alfie P.
AU - Detoito, Norlyn T.
AU - Berja, Glorie Belle
AU - Varquez, Frenciso
AU - Wahyunto, P.
AU - Kuntjoro, Dwi
AU - Murdiyati, Sri Retno
AU - Pazhanivelan, Sellaperumal
AU - Kannan, Pandian
AU - Nirmala Mary, Petchimuthu Christy
AU - Subramanian, Elangovan
AU - Rakwatin, Preesan
AU - Intrman, Amornrat
AU - Setapayak, Thana
AU - Lertna, Sommai
AU - Minh, Vo Quang
AU - Tuan, Vo Quoc
AU - Duong, Trinh Hoang
AU - Quyen, Nguyen Huu
AU - Van Kham, Duong
AU - Hin, Sarith
AU - Veasna, Touch
AU - Yadav, Manoj
AU - Chin, Chharom
AU - Ninh, Nguyen Hong
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Asia
KW - COSMO skymed
KW - Food security
KW - Rice
KW - SAR
KW - TerraSAR-X
U2 - 10.3390/rs61110773
DO - 10.3390/rs61110773
M3 - Article
AN - SCOPUS:84912097595
SN - 2072-4292
VL - 6
SP - 10773
EP - 10812
JO - Remote sensing
JF - Remote sensing
IS - 11
ER -