Cropland intensity extraction combined using optical and SAR time-series in cloudy and rainy areas of southern China
Timely and accurate acquisition of spatiotemporal distribution information regarding cropping intensity holds signifi-cant reference value for adjusting agricultural production layout and making grain production decisions.Current research on cropping intensity extraction primarily relies on optical data and phenological knowledge.However,critical phenological parameters for multi-season cropping cropland are often missing in cloudy and rainy regions of the South China,and confusable vegetation with phenological characteristics similar to crops is difficult to cull,and the salt-and-pepper noise is obvious in the pixel-level results.This paper introduces a novel method for extracting cropping intensity by integrating optical phenological parameters,SAR temporal features,and superpixel optimization based on time-series optical and SAR data.Initially,optical NDVI and LSWI temporal curves are utilized to acquire the number and duration of growth periods.Subsequently,SAR temporal features are employed to identify early-season signals of transplanting and irrigation.Finally,spatial contextual infor-mation is utilized for superpixel optimization of the cropping intensity extraction results.The effectiveness of the proposed methodis validated using time-series Sentinel-1/2 data from Honghu city in 2020-2021,yielding an overall accuracy of 92.02％and a Kappa coefficient of 0.84.Results indicate that incorporating growth period duration effectively mitigates the influence of mixed vegetation,SAR temporal features accurately classify double-season rice,and superpixel optimization enhances the accuracy and completeness of planting intensity results.This method proves capable of accurately capturing crop-ping intensity distribution in regions with cloudy and rainy complex cropping pattern.
万方数据
维普
