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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Mapping Rice Planted Area Using a New Normalized EVI and SAVI (NVI) Derived From Landsat-8 OLI
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Mapping Rice Planted Area Using a New Normalized EVI and SAVI (NVI) Derived From Landsat-8 OLI

机译：使用新的归一化EVI和Landsat-8 OLI衍生的SAVI（NVI）绘制水稻种植面积图

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摘要

Obtaining annually updated data of actually planted rice paddy is essential for evaluating food security and estimating methane emission. Paddy field in southern China generally displays unique phenological landscape changes from exposed soils, shallow flooding water, to rice plants during the whole growth period in a year. A phenology-based algorithm was developed to map the rice planted paddies in the Poyang Lake Plain (PLP), China, in 2014, a typical region in Central China single- and double-rice cropping belt. This algorithm, or a normalized vegetation index, was based on the normalization of Landsat-8 Operational Land Imager-derived enhanced vegetation index and soil-adjusted vegetation index. It highlighted the temporal differences in vegetation cover and background soil between two critical growth phases of paddy rice, i.e., the flooding to transplanting stage and reproductive to ripening stage. There was estimated to be 7148.31 km2in the PLP, with an overall accuracy of 96.8% and the kappa coefficient of 0.97. Comparison between Landsat-detected results and the statistics of paddy field at the county level showed a high determination coefficient with the$R^{2}$of 0.88. The phenology-based algorithm greatly facilitates rice farming monitoring at the regional scale.
机译：获取每年实际种植的稻田数据对于评估粮食安全和估计甲烷排放至关重要。在一年的整个生长期中，中国南方的稻田通常表现出独特的物候景观变化，从裸露的土壤，浅水淹没到水稻。 2014年，开发了一种基于物候的算法来绘制中国the阳湖平原（PLP）水稻种植水稻的图，该地区是中部单稻和双稻种植带的典型地区。该算法或归一化植被指数基于Landsat-8 Operational Imager衍生的增强植被指数和土壤调整植被指数的归一化。它强调了水稻的两个关键生长阶段，即淹水到移栽阶段和繁殖到成熟阶段，植被覆盖和背景土壤的时间差异。估计为7148.31 km n 2 n在PLP中，总体准确度为96.8％，kappa系数为0.97。 Landsat探测结果与县级稻田统计数据之间的比较显示， n $ R ^ {2} $ < / inline-公式> n为0.88。基于物候的算法极大地方便了区域范围内的水稻种植监测。

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