Measurement of sown area of winter wheat based on per-field classification and remote sensing imagery
以地块分类为核心的冬小麦种植面积遥感估算

With the significantly improved data availability in remote sensing technology, mid-resolution images have become the primary data source for crop sown area estimation in large scale. However, it is still difficult to solve the problems of spectrum heterogeneity in one field and spectra similarity between fields, especially in transitional region by using mid-resolution images. In order to maximally avoid above motioned problems and accurately measure the sown area of winter wheat, this paper developed per-field classification method and tested the method in an urban agriculture region with complex planting structure through several steps: first, digitalizing field boundary from QuickBird image; second, extracting characteristic index including spectrum and texture information as well as vegetation index for each field from the multi-temporal TM images; third, operating support vector machine (SVM) and maximum likelihood classification (MLC) with different field characteristic index; finally, estimating the accuracy of our method. Results show that the per-field classification method has a higher accuracy than per-pixel classification both in amount (estimated sown area of winter wheat divide by reference sown area of winter wheat, Kr) and position (equal to product accuracy, Kp). Although both SVM and MLC could get very high amount and position accuracy (97% and 90% respectively), the estimations of SVM are more stable. The errors of per-field classification mainly happened at the fragmentized parcels. Additionally, characteristic information could enhance the performance of per-field classification. Our method also has an outstanding advantage that no optimum period requires on satellite imagery which could enhance practicability and operationality of our method.