Predicting Soil Organic Carbon Stock in Plateau Swamp Wetlands Using Multisource Remote Sensing and Spectral Measurements: A Case Study of the Dianchi Basin

作者：Fangliang Cai, Bo‐Hui Tang, Xinran Ji, Junyi Chen, Zhitao Fu, Zhongxi Ge · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2025 · DOI：10.1109/tgrs.2025.3541122 · 被引用次数：5 · 研究领域：Soil Geostatistics and Mapping、Remote Sensing in Agriculture、Peatlands and Wetlands Ecology

Soil organic carbon density (SOCD) in swamp wetlands is a critical indicator for assessing global carbon stocks. In plateau wetlands, challenges such as dense vegetation cover and fragmented land distribution complicate SOCD research. The availability of high-resolution optical and radar satellite data introduces new possibilities for precise carbon stock predictions. This study proposes a framework that combines multisource remote sensing data with the sparrow search algorithm random forest (SSA-RF) algorithm to predict SOCD in plateau swamp wetlands. It also compares the effectiveness of laboratory spectroscopy and multisource remote sensing in monitoring SOCD. We integrated 24 features from Sentinel-1 (S1), Sentinel-2 (S2), topographic, and climatic data, along with spectral data ranging from 550 to 1400 nm, to construct the SSA-RF model and map the SOCD distribution of swamp wetlands in Dianchi Basin. Additionally, we estimated the total soil organic carbon (SOC) stock in these wetlands. The results indicate that the multisource remote sensing SSA-RF model (S1+ S2+ topographic + climatic SSA-RF) achieved an$R ^{2}$of 0.76, a root-mean-square error (RMSE) of 1.14, a mean absolute error (MAE) of 0.65, and a residual predictive deviation (RPD) of 1.98. Compared to the spectral model, this model improved the$R ^{2}$by 0.24 and the RPD by 0.5. Relative to the S1+ S2 SSA-RF model, the$R ^{2}$is increased by 0.15, and the RMSE is decreased by 0.46. The total SOC stock of the swa...