RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS
RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS
复制标题
使用 Sentinel-1A 物候指标绘制水稻作物图
DOI：
10.5194/isprs-archives-xli-b8-863-2016
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
S. Chiang
中科院分区：
文献类型：
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作者：
C. F. Chen;N. Son;Cheng;L. Chang;S. Chiang
抽象的。水稻是越南最重要的粮食作物，为超过 9000 万人提供食物，并被视为大多数农村人口的重要收入来源。因此，监测水稻种植区对于制定成功的国家粮食安全战略非常重要。本文旨在开发一种根据多时相 Sentinel-1A 数据估算农作物面积的方法。我们通过三个主要步骤处理了越南湄公河三角洲（MRD）两个主要种植季节（例如冬春、夏秋）的数据：（1）数据预处理，（3）基于作物物候指标的水稻分类，以及（4）制图结果的准确性评估。分类结果与地面参考数据相比，总体准确率为86.2%，Kappa系数为0.72。政府对此类作物的水稻面积统计数据之间的密切相关性（R2 > 0.95）再次证实了这些结果。冬春季和夏秋季的面积相对误差值分别为-3.6%和6.7%。本研究证明了多时相 Sentinel-1A 数据在利用研究区域作物物候信息进行水稻作物制图方面的潜在应用。
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.