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Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series

机译:利用MODIS时间序列绘制2002、2005、2010和2014年中国水稻的图谱

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Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China’s Statistical Yearbooks (R 2 of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes.
机译:水稻是重要的粮食作物,也是温室气体相关甲烷的大量生产者。在粮食安全和温室气体排放模型的背景下,准确,及时的稻田地图至关重要。水稻植物在其生命周期中经历了物候学发展,影响了它们与可见光和红外光谱中的波之间的相互作用。水稻生长在遥感数据的时间序列中具有鲜明的特征。我们使用MODIS(中等分辨率成像光谱仪)产品MOD13Q1和MYD13Q1的时间序列以及一类支持向量机来检测这些特征,并对中国大陆的水稻区域进行分类。基于这些分类,我们为中国大陆提供了一种新颖的产品,以250米的分辨率显示了2002、2005、2010和2014年的水稻面积。与我们自己的2014年参考数据集相比,我们的分类的总体准确度为0.90,卡伯系数为0.77,并且与中国统计年鉴中的稻米面积统计数据高度相关(2010年的R 2为0.92,2005年的R 2为0.92,2002年的0.90)。中等分辨率的时间序列分析可通过多种种植方案在大面积地区准确及时地绘制稻田图。

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