Parcel-level mapping of crops in a smallholder agricultural area: A case of central China using single-temporal VHSR imagery

摘要

Timely and accurate monitoring of crop patterns in smallholder agricultural areas is essential for guiding local crop yield estimates, agricultural subsidy allocations, and food security policy formulation. The household-operated land management model leads to a highly fragmented and heterogeneous agricultural landscape; therefore, fine crop mapping in smallholder agricultural areas remains challenging. By using very high spatial resolution (VHSR) images, this study aimed to explore parcel-level crop mapping methods using the case of a typical smallholder agricultural area in Wuhan, China. Object-based image analysis techniques as well as random forest (RF) and support vector machine (SVM) classifiers were used to classify WorldView-2 (WV2) images into eight crop-level agricultural land use categories. Several classification models were built using the combination of two classifiers and different image features, including spectral, geometrical, and textural features. The results showed that the classification model using the RF classifier and all 27 selected features had the highest accuracy, with an overall accuracy of 80.04% and a kappa value of 0.78; specifically, the user's and producer's accuracies of rice, cotton, lotus, bare paddy field and bare upland field exceeded 80%. We found that the performance of the RF and SVM classifiers was generally comparable, although as the input features increased, the accuracy of the RF was slightly higher than that of the SVM. The use of spatial features, such as the gray level cooccurrence matrix (GLCM) standard deviation, GLCM correlation, and area of image objects, could help improve the accuracy of parcel-level crop mapping. Our research confirmed the practical value of single-temporal VHRS images and RF classifiers in mapping parcel-level crops in complex agricultural areas. This framework provides a methodological reference for accurately monitoring crop distribution in smallholder agriculture areas to support the development of local precision agriculture.