MAPPING IRRIGATED AREAS USING RANDOM FOREST BASED ON GF-1 MULTI-SPECTRAL DATA
The irrigation districts need high-resolution spatial distribution information of irrigated fields to manage irrigation water effectively and achieve sustainable water resources management, especially for fragmented croplands such as China. However, most irrigated area mapping methods by remote sensing are based on MODIS time series with a relatively low resolution of 250–1000 m. To fill this gap, this study attempted to use pixel-based random forest to map irrigated areas based on two multi-spectral images from GF-1 satellite with a resolution of 16 m in an irrigated district of China, during the winter-spring irrigation period of 2018. Accuracy of the retrieved 16-m map was assessed by accuracy error matrix using 210 ground-truth samples. The result had an overall accuracy of 93.33% with a Kappa Coefficient of 0.9164. The 16-m resulting map shows that the area of irrigated wheat, rain-fed wheat, irrigated fruit tree, and fallow croplands in the study area were 52066.48 ha, 12932.33 ha, 18104.32 ha, and 4641.25 ha respectively, accounting for 52.57%, 13.06%, 18.28% and 4.69% of the total study area, which are basically consistent with those obtained from field investigations. Compared with SVM, the random forest results are more accurate with fewer misclassifications. The pixel-based random forest for irrigated area mapping at high resolution can obtain more refined spatial distribution of irrigated areas than low-resolution images, which is suitable for fragmented croplands. Besides, this method can effectively distinguish irrigated crops from rain-fed crops, proving the classification ability of random forest in high-resolution irrigation area mapping only by two images.