Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

Remote Sensing

Duy Ba Nguyen¹  Kersten Clauss²  Senmao Cao¹  Vahid Naeimi¹  Claudia Kuenzer⁴  Wolfgang Wagner¹  James Campbell³  Yoshio Inoue³ 

[1] Department of Geodesy and Geoinformation, Vienna University of Technology, Gusshausstrasse 27-29, A-1040 Vienna, Austria;Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, Oswald-Kuelpe-Weg 86, D-97074 Wuerzburg, Germany;;Department of Geodesy and Geoinformation, Vienna University of Technology, Gusshausstrasse 27-29, A-1040 Vienna, AustriaGerman Remote Sensing Data Center (DFD), Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany;

关键词: Envisat;    ASAR;    WSM;    SAR;    radar;    paddy rice;    rice mapping;    time series;    Mekong Delta;    Vietnam;   

DOI  :  10.3390/rs71215808

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.