Soil organic carbon (SOC) is important in the global carbon cycle. Accurate estimation of SOC content in cultivated land is a prerequisite for evaluating the carbon sequestration potential and quality of soils. However, existing SOC prediction studies based on hyperspectral remote sensing neglect the spectral response of the physical properties of surface soil, leading to inadequate model generalization. With the exponential growth of remote sensing data, the development of pixel-level soil spectral correction methods based on multi-source remote sensing data has become an interesting and challenging topic. This method aims to minimize the effect of soil physical properties on spectra, thus addressing the poor spatiotemporal transferability of SOC prediction models due to uncertain variations in surface soil physical properties. In this study, a soil spectral correction strategy is constructed using satellite hyperspectral image (HSI) and synthetic aperture radar (SAR) images through multi-order polynomial regression and convolutional neural networks. This strategy considers soil physical variables such as soil moisture (SM) content and root mean square height (RMSH) of soil surface roughness. The soil spectral correction model and SOC content prediction model were established using 80 soil samples collected from Site 1. Afterward, the performance and transferability of both models were verified using the remaining 25 samples from Site 1 and 50 samples from Site 2. The results showed that: 1) The effect of SM and RMSH on the soil pixel spectrum can be significantly reduced after correcting HSI using soil spectral correction strategy. The correlation coefficients between the corrected pixel spectrum and the ground-based spectrum increase by over 60 % compared with those between the original spectrum and the ground-based spectrum. 2) Soil spectral correction improves the prediction accuracy and mapping capability of HSI for SOC content, with the highest of 0.743 and of 3.455 g/kg at Site 1. 3) Compared with the original HSI-based SOC prediction model, the soil spectral correction strategy based on multi-order polynomial and convolutional neural network reduced the of SOC prediction results at Site 2 by 5.082 g/kg and 5.454 g/kg, and the increased by 0.390 and 0.409, respectively. 4) When predicting SOC content from raw HIS, SM and RMSH contribute to more than 60 % of the bias, with SM having a larger bias than RMSH. The findings of this study emphasize the influence of soil physical properties on SOC prediction and contribute to the existing research on SOC mapping using HSI and SAR data.

中文翻译：

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高光谱与雷达遥感相结合估算土壤有机碳，减少土壤表面水分与粗糙度的耦合影响

土壤有机碳（SOC）在全球碳循环中很重要。准确估算耕地SOC含量是评价土壤固碳潜力和质量的前提。然而，现有基于高光谱遥感的SOC预测研究忽略了表层土壤物理性质的光谱响应，导致模型泛化能力不足。随着遥感数据的指数级增长，基于多源遥感数据的像素级土壤光谱校正方法的发展成为一个有趣且具有挑战性的课题。该方法旨在最小化土壤物理性质对光谱的影响，从而解决由于表层土壤物理性质的不确定变化而导致的SOC预测模型时空可迁移性差的问题。在本研究中，利用卫星高光谱图像（HSI）和合成孔径雷达（SAR）图像，通过多阶多项式回归和卷积神经网络构建土壤光谱校正策略。该策略考虑土壤物理变量，例如土壤湿度（SM）含量和土壤表面粗糙度的均方根高度（RMSH）。利用站点1采集的80个土壤样品建立了土壤光谱校正模型和SOC含量预测模型。随后，利用站点1的剩余25个样品和站点2的50个样品验证了两个模型的性能和可移植性。结果表明表明： 1）采用土壤光谱校正策略对HSI进行校正后，可以显着降低SM和RMSH对土壤像元光谱的影响。校正后的像元光谱与地基光谱的相关系数较原始光谱与地基光谱的相关系数提高了60%以上。 2）土壤光谱校正提高了HSI对SOC含量的预测精度和制图能力，在站点1处最高达到0.743和3.455 g/kg。 3）与原始基于HSI的SOC预测模型相比，土壤光谱校正基于多阶多项式和卷积神经网络的策略使2号站点SOC预测结果分别降低了5.082 g/kg和5.454 g/kg，分别提高了0.390和0.409。 4) 当从原始 HIS 预测 SOC 含量时，SM 和 RMSH 贡献了超过 60% 的偏差，其中 SM 的偏差比 RMSH 更大。本研究的结果强调了土壤物理性质对 SOC 预测的影响，并有助于利用 HSI 和 SAR 数据进行 SOC 绘图的现有研究。