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Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index

机译:使用作物比例物候指数从MODIS-EVI时间序列数据估算冬小麦面积

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摘要

The global distribution of croplands is of critical interest to a wide group of end-users. Different crops have their own representative phenological stages during their growing seasons, which differ considerably from other natural vegetation types. During the last decade, the Moderate Resolution Imaging Spectroradiometer (MODIS) has become a key tool for vegetation monitoring because of its high temporal resolution, extensive scope, and rapid availability of various products. However, mixed pixels caused by the moderate spatial resolution produce significant errors in crop area estimation. Here we propose a Crop Proportion Phenology Index (CPPI) to express the quantitative relationship between the MODIS vegetation index (VI) time series and winter wheat crop area. The utility of this index was tested in two experimental areas in China: one around Tongzhou and the other around Shuyang, as representative districts around a metropolis and a rural area, respectively. The CPPI performed well in these two regions, with the root mean square error (RMSE) in fractional crop area predictions ranging roughly from 15% in the individual pixels to 5% above 6.25km ~2. The training samples containing mixtures of crop types mitigated the challenges of pure end-member selection in a spectral mixture analysis. A small number of training samples are sufficient to generate the CPPI, which is adaptable to other crop types and larger regions. Estimating the CPPI parameters across larger spatial scales helped improve the stability of the model.
机译:耕地的全球分布对于广泛的最终用户至关重要。不同的农作物在其生长季节都有其自己的代表性物候期,这与其他天然植被类型有很大不同。在过去的十年中,中分辨率成像光谱仪(MODIS)由于其高时间分辨率,广泛的范围和各种产品的快速可用性而成为监测植被的关键工具。但是,由中等空间分辨率引起的混合像素会在作物面积估计中产生重大误差。在这里,我们提出了作物比例物候指数(CPPI)来表达MODIS植被指数(VI)时间序列与冬小麦作物面积之间的定量关系。该指数的效用在中国的两个实验区域进行了测试:一个在通州附近,另一个在Shu阳附近,分别代表大都市和农村地区。 CPPI在这两个区域中表现良好,分数作物面积预测中的均方根误差(RMSE)大致在单个像素的15%到6.25 km〜2以上的5%。包含农作物类型混合物的训练样本减轻了光谱混合物分析中纯末端成员选择的挑战。少量的训练样本足以生成CPPI,该CPPI适用于其他作物类型和较大区域。在更大的空间尺度上估计CPPI参数有助于提高模型的稳定性。

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