Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method

Using remote sensing data to estimate crop area is efficient to a wide range of end-users, including government agencies, farmers and researchers. Moderate spatial resolution (MSR) image data are widely used to estimate crop area. But its accuracy can’t meet the demands of precision. Spatial sampling techniques integrated the strengths of remote sensing and sampling survey are being widely used. This method need large sample size which is cannot be guaranteed by remote sensing due to weather. The Unmanned Aerial Vehicle (UAV) can be used as an effective way to guarantee enough sample size. This paper proposed a spatial sampling method using MSR image classification results and UAV transects, a stratified random sampling method was proposed, area-scale (from MSR image classification) was used as auxiliary variable to guide the distribution of UAV transects, which had proved that 2% sampling ratio can make the crop area estimation accuracy more than 95% with a 95% confidence interval.