Theory of Double Sampling Applied to Main Crops Acreage Monitoring at National Scale Based on 3S in China-CT316

文献类型： 外文期刊

第一作者： Wu, Quan

作者： Wu, Quan;Sun, Li;Wang, Fei;Jia, Shaorong

作者机构：

关键词： Theory of double sampling;Small features;Stratified sampling;estimated accuracy;estimated error;3S

期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE IV, PT 3

ISSN： 1868-4238

年卷期： 2011 年 346 卷

页码：

收录情况： SCI

摘要： Grain production is of great importance to any country, especially to China. It is very important for local and central governments to get accurate main crops acreage information in time. One noticeable problem is that the estimated accuracy for crops acreage in a certain year is not high, so that Remote Sensing Applications Centre (RSAC) has to use investigated data of crops acreage of two consecutive years to estimate the change rate of crops acreage. Aiming at the issue, the theory of double sampling based on operational crops acreage investigation was brought forward by RSAC. The paper has given detailed account of the theory and a typical case. In the double sampling method, the first sampling is to estimate the proportion of small features spatially distributing in crops fields in order to purify the samples acting as basis units for calculation in the second sampling. The second sampling is called a kind of stratified sampling which is used to estimate the crops acreage. The test is by adopting the theory of double sampling with 3S to evaluate the planting acreage of cotton and late-rice, acting as representatives of main crops in China, related to operative task and project research. The experiment result described with statistic methods shows that the theory of double sampling applied to main crops acreage monitoring can efficiently improve the estimated accuracy of crops acreage.

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  • 相关文献

[1]The Quantificational Evaluation of a Sampling Unit Error Derived from Main Crop Area Monitorings at National Scale Based 3S in China. Wu, Quan,Sun, Li,Wang, Fei,Jia, Shaorong.

[2]Semisupervised classification of hyperspectral images based on tri-training algorithm with enhanced diversity. Cui, Ying,Song, Guojiao,Wang, Xueting,Wang, Liguo,Cui, Ying,Lu, Zhongjun. 2017

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