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Assessing the crop acreage at county level on the North China Plain using an adapted regession estimator method

机译:使用适应的裁员估计方法评估北方北方县级的作物种植面积

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Image classifications including sub pixel analysis are often used to estimate directly the crop acreage, while ground data collected during field surveys play a secondary role. This type of crop area assessment using image classifications often leads to a biased estimation due to non-representative selection of training data and subjective a-priori knowledge. Instead regression estimator approach combining remote sensing information with a rigorous ground sampling can result in an accurate assessment of crop acreage. In this study to produce the crop statistics, the area frame sampling approach is adapted to the strip-like cropping pattern on the North China Plain. Remote sensing information is used to perform a cost-efficient stratification from which no-agricultural areas are excluded from ground survey. This information is also included in a later stage as an auxiliary estimator in regression analysis. The results showed that the integration of remote sensing information as an auxiliary estimator can improve the confidence of estimation by reducing the variance of the estimates.
机译:包括子像素分析的图像分类通常用于直接估计作物面积,而在现场调查期间收集的地面数据发挥次要作用。由于非代表性的培训数据和主观a-priorive知识,这种类型的作物区域评估通常导致偏差估计。代替回归估计方法与严格的地面采样相结合的遥感信息,可以准确评估作物面积。在这项研究中,为了产生作物统计,地区帧采样方法适用于华北平原上的条状裁剪模式。遥感信息用于执行经济高效的分层,从该地面调查中排除了非农业区域。该信息还包括在后期作为回归分析中的辅助估计器的阶段。结果表明,通过降低估计的差异,遥感信息作为辅助估计器的集成可以提高估计的置信度。

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