Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.

大多数研究已经通过实验室光谱法快速准确地测定了土壤有机碳（SOC）。然而，仍然难以绘制SOC的空间分布图。为了在区域尺度上预测和绘制SOC，我们从高分5号（GF-5）卫星获得了14张高光谱图像，并使用离散小波变换（DWT）分解并重建了原始反射率（OR）和一阶导数反射率（FDR）。 ）的规模。在这些不同的尺度上，我们使用主成分分析选择权系数最高的3个最佳波段，并选择与指数相关性最强的归一化差异指数（NDI），比率指数（RI）和差异指数（DI）。 SOC含量采用等高线图法。这些输入然后用于使用随机森林（RF），支持向量机（SVM）和反向传播神经网络（BPNN）算法来构建区域级SOC预测模型。结果表明：1）DWT在低分解尺度下可以有效消除卫星高光谱数据中的噪声，FDR与DWT相结合可以显着提高SOC预测精度；2）使用主成分分析和等高线图选择输入的方法可以消除高光谱数据的冗余，同时保留输入的物理含义。对于预测精度最高的模型，所有输入均来自SOC变化的波长范围。3）不同预测模型之间的预测精度差异很小；和4）与以前使用多光谱卫星数据进行的SOC预测研究相比，使用高光谱卫星数据进行的SOC预测准确性大大提高。这项研究为预测​​和绘制区域SOC含量提供了一种高度可靠且准确的方法。