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Physics-Aware Neural Framework for Multidepth Soil Carbon Mapping
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DOI：10.1109/LGRS.2025.3632815.png)
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Abstract
En 中文
Depth-resolved estimation of soil organic carbon (SOC) remains challenging because optical measurements originate at the surface while carbon dynamics vary vertically. We propose a physics-aware uncrewed aerial vehicle (UAV) framework that integrates multispectral imagery (MSI) and hyperspectral imagery (HSI) to estimate SOC concentration (%) across five depths. The experiment was conducted at Plantheaven Farms, Missouri, with ten sorghum genotypes across three replicates. Feature construction combined spectral derivatives from HSI with texture features from MSI, compressed via principal component analysis (PCA). Physics-based regularization was implemented through: 1) a second-difference penalty to enforce vertical smoothness and 2) a profile-integral consistency constraint to preserve whole-profile balance. Four model configurations evaluated on local data showed progressive improvements: MSI-only, MSI + HSI, MSI + HSI with smoothness, and MSI + HSI with full physics constraints. In addition, transfer learning from the open soil spectral library (OSSL) was tested to address data limitations. Model fitting on the available data achieved ${R} ^{2} = 0.72$ at 0–30 cm, with physics-aware constraints notably improving vertical coherence. The physics-aware model reduced variance and improved plausibility. In-sample, transfer learning achieved ${R} ^{2}=0.60$ at 0–30 cm, with conservative interpretation below 90 cm due to reduced optical sensitivity. Exploratory genotype patterns suggested higher surface SOC percent for PI 656 029 and PI 656 057, and lower values for PI 276 837 and PI 656 044.
Keywords:
Depth-resolved estimation
hyperspectral imaging
multispectral uncrewed aerial vehicle (UAV)
physics-aware neural networks
soil organic carbon (SOC)
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