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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的时空变异

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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:根据主题映射器(TM)图像的地面分辨率特征,我们在海河流域,华北地区冬小麦的四个关键发展阶段相应地测量了相对叶绿素内容,并探讨了与反射光谱的相关性可以从TM图像获得的值。考虑不仅是NDVI,而且选择了叶绿素,31 rs变量的相对含量,并建立了变量与叶绿素的相对含量之间的关系。建立回归模型,用于从TM图像定量预测冬小麦生长条件。通过地稳济方法分析了冬小麦生长状态的冬小麦生长状态的时空变化。在案例研究区域中存在叶绿素相对含量的相关空间可变性,相关距离范围为145.4至320.0m。空间结构化差异在总差异的75%和21%之间,并且可以在球形模型中模拟四个阶段的经验半导体函数。结果表明,使用TM数据进行实时和高度准确地监测农田生态系统的作物生长状态和养分管理是可行的。

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