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Spatial Variability of Crops in Sampling Units and its Effect on Sampling Extrapolation Efficiency

机译:抽样单位农作物的空间变异性及其对抽样外推效率的影响

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Timely and accurate acquisition of crop area is of great significance for monitoring crop growth and estimation of crop yields. And it's important for strengthening agricultural management, ensuring food security and supply of agricultural products. The combination of remote sensing data with traditional sampling methods is an effective way to monitor and estimate crop area on a large scale. The traditional sampling method, on the basis of traditional statistics theory which studies the laws of random variables, requires that the sampling units should satisfy the principle of mutual independence. However, the regional crops, which are affected by natural conditions and socioeconomic factors, are often spatial variable. There is no report on whether and how spatial variability will influence the sampling efficiency of crop acreage. Therefor the further improvement of sampling efficiency is limited. To solve this problem, a variation function model was constructed with the area ratio between maize or rice and sampling units as object variable on 10 sampling unit scales combining remote sensing data, spatial analysis and traditional sampling methods with Dehui, Jilin as study area. Selecting base value as index for spatial variability within the sampling unit, the effect of sampling unit scales on spatial variability of maize and rice were quantitatively analyzed. Three commonly used sampling schemes (random sampling, systematic sampling and stratified sampling) were selected for sample selection, overall extrapolation and error estimation. The sampling efficiency was quantitatively evaluated using overall relative error (r) of the sampling extrapolation, the coefficient of variation (CV) of the total estimate and the sample size (n) on different sampling unit scales. The results show that the spatial variability of maize and rice decreases with the increase of the sampling unit scale. The range of base value is [0.15, 0.20] and [0.05, 0.14] for maize and wheat respectively. Using the three sampling methods to extrapolate the maize and rice area separately: 1) Under the same sampling ratio, the relative sampling errors of the two crops' area estimations gradually increase with the decrease of spatial variability (the sampling unit size increases), the coefficient of variation also shows an increasing trend, and the sampling accuracy and extrapolation stability is affected by spatial variability; 2) The relative error in estimation of the two crop areas varies with the sampling method, when using simple random sampling and systematic sampling, the relative error of the two crops was within the range of (1 %, 50%) and (0.5%, 40%), and the coefficient of variation was limited to (1 %, 75%) and In the range of (1 %, 88 %), the relative errors at all scales were limited to 10% or less with stratified sampling, and the fluctuations of the coefficient of variation were small (both within 0.3 % and 20
机译:及时准确地获取作物面积对于监测作物生长和估计作物产量具有重要意义。这对于加强农业管理,确保粮食安全和农产品供应非常重要。遥感数据与传统采样方法的结合是大规模监测和估计作物面积的有效方法。传统的抽样方法是在研究随机变量定律的传统统计理论的基础上,要求抽样单位应满足相互独立的原则。但是,受自然条件和社会经济因素影响的区域性作物通常是空间变量。没有关于空间变异是否以及如何影响作物种植面积采样效率的报道。因此,采样效率的进一步提高受到限制。为解决这一问题,以吉林省德惠县为研究区域,结合遥感数据,空间分析方法和传统采样方法,建立了以10个采样单位尺度上玉米或水稻面积比与采样单位为对象变量的变异函数模型。选择基值作为采样单位内空间变异性的指标,定量分析了采样单位尺度对玉米和水稻空间变异性的影响。选择了三种常用的采样方案(随机采样,系统采样和分层采样)进行样本选择,整体外推和误差估计。使用抽样外推法的总相对误差(r),总估计值的变异系数(CV)和样本大小(n)在不同的抽样单位尺度上定量评估抽样效率。结果表明,玉米和水稻的空间变异性随采样单位尺度的增加而减小。玉米和小麦的基准值范围分别为[0.15,0.20]和[0.05,0.14]。使用三种采样方法分别推断玉米和水稻面积:1)在相同的采样率下,两种作物面积估计的相对采样误差随着空间变异性的减小而逐渐增加(采样单位大小增加),变异系数也显示出增加的趋势,并且采样精度和外推稳定性受空间变异性的影响; 2)两种作物面积的估计相对误差随采样方法的不同而不同,在使用简单随机抽样和系统抽样时,两种作物的相对误差在(1%,50%)和( 0.5%,40%),并且变异系数限制为(1%,75%),并且在(1%,88%)范围内,所有尺度的相对误差均限于分层抽样时,不超过10%,且变异系数的波动很小(均在0.3%和20之间

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