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Spatiotemporal variability of winter wheat condition based on TM data and geostatistics

机译:基于TM数据和地统计学的冬小麦状况时空变异

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According to the ground resolution characteristic of Thematic Mapper (TM) image, we correspondingly measured the relative chlorophyll contents in four key developmental stages of winter wheat in the Lower plain of the Hai River Basin, North China, and explored their correlation with the reflected spectral values that can be obtained from TM image. Considering not only NDVI but also the relative content of the chlorophyll, 31 RS variables were selected and the relationship between the variables and the relative content of chlorophyll was established. Regression models were built for quantitatively predicting winter wheat growing condition from TM images. Also the spatiotemporal variability of the winter wheat growth status at heading and booting stages were analyzed by geostatictics approach. The correlated spatial variability of the relative content of chlorophyll existed in the case study area, and the range of correlative distance was from 145.4 to 320.0m. The spatially structured variances were between 75% and 21% of the total variances, and the empirical semivariograms in the four stages could be simulated in spherical models. The result showed that it is feasible to use TM data for real-time and highly accurate monitoring of crop growth status and nutrient management of farmland ecosystems.
机译:根据专题测绘图像的地面分辨率特征,我们分别测量了华北海河流域下游平原冬小麦四个关键发育阶段的相对叶绿素含量,并探讨了它们与反射光谱的相关性。可以从TM图像获得的值。不仅考虑NDVI,还考虑叶绿素的相对含量,选择了31个RS变量,建立了变量与叶绿素相对含量的关系。建立了回归模型,用于根据TM图像定量预测冬小麦的生长状况。还通过静力学方法分析了抽穗期和孕穗期冬小麦生长状况的时空变化。案例研究区存在叶绿素相对含量的相关空间变异性,相关距离范围为145.4〜320.0m。空间结构的方差在总方差的75%到21%之间,可以在球形模型中模拟四个阶段的经验半方差图。结果表明,利用TM数据实时,高精度监测作物生长状况和农田生态系统养分管理是可行的。

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