基于改进U-Net的多源遥感分类与区域碳储量估测研究OACHSSCD

Under the"dual carbon"goals,efficient and accurate estimation of terrestrial ecosystem carbon storage is of great significance.To effectively use multi-source remote sensing data to obtain the spatial distribution of carbon storage,this study proposes a carbon storage estimation method based on an improved U-Net model,integrating GF-1 and Sentinel-1 remote sensing features for land use classification and subsequently estimating carbon storage by combining classification results with carbon density survey data.The architecture employs dual encoders to individually extract multi-scale features from GF-1 and Sentinel-1 imagery,adopts a segment-wise feature fusion strategy,and leverages attention mechanisms with skip connections to focus on high-level semantic features.Finally,a classifier outputs land use catego-ries,and carbon storage distribution is obtained by multiplying the classification results with carbon density values derived from field surveys.The results show that the improved U-Net model fully extracts land cover feature details and semantic features,achieving an overall classification accuracy of 87.34%;the model-estimated carbon storage is 31.28 Tg,with an overall error of 0.84%compared to measured carbon storage.In the classification results,the area proportions of forest,cultivated land,garden land,residential land,water bodies,roads,and other unused land are 83.30%,4.42%,6.47%,2.72%,1.02%,1.10%,and 0.97%,respectively.Natural forests are the main carbon storage reservoir,ac-counting for 91.51%of the total carbon storage;the contribution rates of cultivated land,garden land,residential land,water bodies,and other unused land are 2.76%,5.55%,0.03%,0.06%,and 0.09%,respectively.Compared with the random forest(RF)and linear regression(LR)models,the proposed model reduces the carbon storage estimation error by 1.91 and 2.94 percentage points,respectively.These results demonstrate that optical images and synthetic aperture ra-dar(SAR)images possess good complementarity,and that the U-Net model combined with the channel-spatial attention module(CSAM)realizes the fusion of shallow and deep features,thereby improving land cover classification accuracy.The model uses widely accessible,low-cost data and has good reliability,providing a reference for sustained and efficient dynamic monitoring of carbon storage.