东北农牧交错带耕地土壤有机质遥感反演研究

农牧交错带是农耕区与草原牧区的过渡带，土壤有机质(SOM)的精确估算与变化监测对碳库估算与农业生产具有重要研究意义。以东北典型农牧交错带为研究区，Landsat 8 OLI影像和ALOS 12.5m DEM为数据源，基于波段反射率、反射率对数、亮度指数与相关地形因子，分别利用多元线性逐步回归(MLSR)模型、随机森林(RF)模型和BP神经网络(BPNN)模型，构建农牧交错带SOM多光谱反演模型。结果表明：(1)根据重要性排序，选择Landsat8OLI第4波段的对数、第5波段、第6波段和亮度指数作为输入量，RF和BPNN模型的精度优于MLSR模型。(2)引入高程(E)与坡向变率(SOA)后，3种模型的预测精度提高，BPNN模型精度提高最多，R2提高了0.22,RMSE降低了0.40 g/kg。3种模型最优反演精度由高到低为：BPNN模型(R2=0.82,RMSE=1.4 g/kg)>RF模型(R2=0.71,RMSE=1.9 g/kg)>MLSR模型(R2=0.66,RMSE=8.8 g/kg)。研究结果可为农牧交错带SOM时空变化研究提供方法支撑。

Soil Organic Matter Inversion in Agro-pastoral Ecotone of Northeast China

The Agro-pastoral Ecotone is a transitional zone between farming areas and grassland pastoral areas. Accurate estimation and monitoring of soil organic matter (SOM) has important significance for carbon pool estimation and agricultural production. Taking the typical Agro-pastoral Ecotone as the study area, Landsat8 OLI image and ALOS 12.5m DEM as the data sources, based on band reflectivity, reflectivity logarithm, brightness index and related terrain factors, the multi-spectral inversion model of SOM in the the Agro-pastoral Ecotone was constructed by using multiple linear stepwise regression (MLSR) model, random forest (RF) model and BP neural network (BPNN) model, respectively. The results show that: 1) According to the order of importance, the logarithm of band 4, band 5, band 6 and brightness index of Landsat8 OLI are selected as input quantities, and the accuracy of RF and BPNN model is better than that of MLSR model. 2) After introducing elevation (E) and Slope of Aspect (SOA), the prediction accuracy of the three models all improved, and the accuracy of BPNN model improved most, with R2 increased by 0.32 and RMSE decreased by 0.02. The optimal inversion accuracy of the three models from high to low is: BPNN model (R²=0.82, RMSE=0.14)>RF model (R²=0.71, RMSE=0.19)>MLSR model (R²=0.66, RMSE=0.88). The research can provide methodological support for the study of SOM spatial and temporal changes in agro-pastoral ecotone.