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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator
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Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator

机译：利用部分间接采样和自适应回归估计量估算华北平原县级作物面积

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摘要

Image classifications, including sub-pixel analysis, are often used to estimate crop acreage directly. However, this type of assessment often leads to a biased estimation, because commission and omission errors generally do not compensate for each other. Regression estimators combine remote sensing information with more accurate ground data on a field sample, and can result in more accurate and cost-effective assessments of crop acreage. In this pilot study, which aims to produce crop statistics in Guoyang County, the area frame sampling approach is adapted to a strip-like cropping pattern on the North China Plain. Remote sensing information is also used to perform a stratification in which non-agricultural areas are excluded from the ground survey. In order to compute crop statistics, 202 ground points in the agriculture stratum were surveyed. Image classification was included as an auxiliary variable in the subsequent analysis to obtain a regression estimator. The results of this pilot study showed that the integration of remote sensing information as an auxiliary variable can improve the accuracy of estimation by reducing the variance of the estimates, as well as the cost effectiveness of an operational application at the county level in the region.
机译：图像分类（包括亚像素分析）通常用于直接估算农作物种植面积。但是，由于佣金和遗漏错误通常无法相互补偿，因此这种评估通常会导致估算结果有偏差。回归估计器将遥感信息与田间样本上的更准确的地面数据结合起来，可以对作物种植面积进行更准确和更具成本效益的评估。在这项旨在产生国阳县农作物统计数据的初步研究中，区域框架抽样方法适用于华北平原的条带状种植模式。遥感信息还用于进行分层，在这种分层中，将非农业地区排除在地面调查之外。为了计算作物统计数据，对农业阶层中的202个地面点进行了调查。在随后的分析中将图像分类作为辅助变量包括在内，以获得回归估计量。这项初步研究的结果表明，将遥感信息作为辅助变量进行集成可以通过减少估算值的方差来提高估算的准确性，以及该地区县级应用程序的成本效益。

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