Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 863-865, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-863-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
23 Jun 2016
RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS
C. F. Chen, N. T. Son, C. R. Chen, L. Y. Chang, and S. H. Chiang Center for Space and Remote Sensing Research, National Central University, Jhongli District, Taoyuan City 32001, Taiwan
Keywords: Sentinel-1A, crop phenology, rice crop mapping, Mekong Delta Abstract. Rice is the most important food crop in Vietnam, providing food more than 90 million people and is considered as an essential source of income for majority of rural populations. Monitoring rice-growing areas is thus important to developing successful strategies for food security in the country. This paper aims to develop an approach for crop acreage estimation from multi-temporal Sentinel-1A data. We processed the data for two main cropping seasons (e.g., winter–spring, summer–autumn) in the Mekong River Delta (MRD), Vietnam through three main steps: (1) data pre-processing, (3) rice classification based on crop phenological metrics, and (4) accuracy assessment of the mapping results. The classification results compared with the ground reference data indicated the overall accuracy of 86.2% and Kappa coefficient of 0.72. These results were reaffirmed by close correlation between the government’s rice area statistics for such crops (R2 > 0.95). The values of relative error in area obtained for the winter–spring and summer–autumn were -3.6% and 6.7%, respectively. This study demonstrates the potential application of multi-temporal Sentinel-1A data for rice crop mapping using information of crop phenology in the study region.
Conference paper (PDF, 1038 KB)


Citation: Chen, C. F., Son, N. T., Chen, C. R., Chang, L. Y., and Chiang, S. H.: RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 863-865, https://doi.org/10.5194/isprs-archives-XLI-B8-863-2016, 2016.

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