农作物种植面积遥感抽样调查的误差影响因素分析

空间抽样技术在农作物种植面积调查中具有不可替代的作用,各抽样要素(抽样率、抽样调查单元尺寸及布局)对于抽样精度的影响至关重要。该文以湖南省晚稻为研究对象,设计了9种抽样调查单元和31种抽样率水平,以晚稻面积百分比为分层标志进行空间分层抽样,分析抽样格网大小、抽样率及样本空间分布格局对面积估算精度的敏感性及控制途径,并建立3种影响因素对面积估算的综合评估模型。结果表明:1)作物面积估计的平均抽样误差随抽样格网尺寸的增加而增加(R2=0.92),当抽样格网控制在5 km以内时,平均误差基本限制在5%以下,标准差变幅稳定在0.12以内;2)作物面积估计的平均抽样误差随抽样率的增加而逐渐降低(R2=0.82),当抽样率达到0.4%时,平均误差基本限制在5%以内,标准差变幅稳定在0.12以内;3)在抽样率确定的情况下,样本的空间分布是影响抽样精度的重要因素,随着样本空间分布由近似均匀分布向随机分布再向集群分布变化,作物面积估计量的平均抽样误差逐渐增大,当样本空间分布的方差均值比指标0.7时,平均误差控制在5%以内,标准差变幅稳定在0.1以内;4)得到3种影响因素对面积估算精度的定量评估模型。该成果揭示了农作物种植面积抽样过程中样方尺寸、抽样率和样本空间分布对精度影响的敏感性,为农作物种植面积监测空间抽样方案的选取以及确定特定的抽样方案可以达到的面积估算水平提供了理论基础。

Analysis on estimation accuracy of crop area caused by spatial sampling factors based on remote sensing data

Abstract: Crop acreage estimation is important for the assurance of food security and establishment of national socio-economic development planning. During the current development period, rapid, accurate and reliable estimation for crop acreage is particularly significant in China since the estimation can be affected by many factors such as ecological degradation and farmland reduction. Spatial sampling technology plays an important and irreplaceable role in crop acreage investigation and estimation. However, the effects of sampling factors on estimation are unclear. This study analyzed data on late-season rice in paddy field of Hunan province of China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns, in order to quantitatively evaluate the influence and sensibility of various monitoring factors (sampling ratio, sampling grid, and sample distribution) on sampling efficiency of the existing space sampling techniques for estimating crop planting acreage. Nine kinds of sampling units and 31 kinds of sampling ratio levels were designed. Spatial stratified sampling was used, and the late rice planting proportion was considered as the stratification symbol. 1000 times repeated trials were conducted based on every kind of sampling plan. Spatial distribution (Variance to Mean Ratio, VMR) of every sampling units and sampling ratio levels were determined. Spatial statistics methods and manifold accuracy evaluation indices (relative estimation error and standard deviation) were used to analyze the acreage estimation results obtained based on the different sampling plans. Then a comprehensive model based on sampling grid, sampling ratio, and sample distribution was developed to assess the sampling monitoring error rate of crop acreage estimation. The result demonstrated that: 1) With the increasing of the sampling grid, the average estimation error increased (R2=0.92), and when the sampling grid was less than 5 km, the estimation error rate was controlled within 5%, the standard deviation was not more than 0.12; 2) With the increasing of the sampling ratio, the average estimation error decreased (R2=0.82), and when the sampling ratio was greater than 0.4%, the estimation error rate was controlled within 5%, and the standard deviation was less than 0.12; 3) Under the condition in which the sampling ratio had been determined, the sample spatial distribution of the sample was an important factor affecting the accuracy of sampling. With the sample distribution tending to cluster distribution, the average estimation error rate increased, and when the variance to mean ratio (VMR) was less than 0.7 the estimation error rate and the standard deviation was controlled within 5% and 0.1, respectively; 4) The quantitative model reflecting the influence of the three factors on crop acreage estimation accuracy was developed. In summary, this study revealed the influence rules and sensibility of sampling factors (sampling ratio, sampling grid, and sample distribution) on crop acreage estimation. In addition, a good method was developed for optimizing spatial sampling and improving the accuracy of crop acreage estimation based on the particular sampling program.