Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation
Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditio...
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2025-07-01
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| author | Asmaa Abdelbaki Robert Milewski Mohammadmehdi Saberioon Katja Berger José A. M. Demattê Sabine Chabrillat |
| author_facet | Asmaa Abdelbaki Robert Milewski Mohammadmehdi Saberioon Katja Berger José A. M. Demattê Sabine Chabrillat |
| author_sort | Asmaa Abdelbaki |
| collection | DOAJ |
| description | Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a novel approach that combines radiative transfer models (RTMs) with open-access soil spectral libraries to address this challenge. Focusing on conditions of low soil moisture content (SMC), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV), the coupled Marmit–Leaf–Canopy (MLC) model is used to simulate early crop growth stages. The MLC model, which integrates MARMIT and PRO4SAIL2, enables the generation of mixed soil–vegetation scenarios. A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. The results demonstrated relatively high SOC prediction accuracy compared to previous approaches that rely only on RTMs and/or machine learning approaches. Incorporating soil moisture content significantly improved performance over bare soil alone, yielding an R<sup>2</sup> of 0.86 and RMSE of 4.05 g/kg, compared to R<sup>2</sup> = 0.71 and RMSE = 6.01 g/kg for bare soil. Adding PV slightly reduced accuracy (R<sup>2</sup> = 0.71, RMSE = 6.31 g/kg), while the inclusion of NPV alongside moisture led to modest improvement (R<sup>2</sup> = 0.74, RMSE = 5.84 g/kg). The most comprehensive model, incorporating bare soil, SMC, PV, and NPV, achieved a balanced performance (R<sup>2</sup> = 0.76, RMSE = 5.49 g/kg), highlighting the importance of accounting for all surface components in SOC estimation. While further validation with additional scenarios and SOC prediction methods is needed, these findings demonstrate, for the first time, using radiative-transfer simulations of mixed vegetation-soil-water environments, that an EO-DSSL approach enhances machine learning-based SOC modeling from EO data, improving SOC mapping accuracy. This innovative framework could significantly improve global-scale SOC predictions, supporting the design of next-generation EO products for more accurate carbon monitoring. |
| format | Article |
| id | doaj-art-c8ec02b13ded461995474a620352a415 |
| institution | DOAJ |
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| publishDate | 2025-07-01 |
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| spelling | doaj-art-c8ec02b13ded461995474a620352a4152025-08-20T02:47:09ZengMDPI AGRemote Sensing2072-42922025-07-011714235510.3390/rs17142355Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon EstimationAsmaa Abdelbaki0Robert Milewski1Mohammadmehdi Saberioon2Katja Berger3José A. M. Demattê4Sabine Chabrillat5GFZ Helmholtz Centre for Geosciences, Telegrafenberg, 14473 Potsdam, GermanyGFZ Helmholtz Centre for Geosciences, Telegrafenberg, 14473 Potsdam, GermanyGFZ Helmholtz Centre for Geosciences, Telegrafenberg, 14473 Potsdam, GermanyGFZ Helmholtz Centre for Geosciences, Telegrafenberg, 14473 Potsdam, GermanyDepartment of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Av. Pádua Dias 11, CP9, Piracicaba 13418-900, SP, BrazilGFZ Helmholtz Centre for Geosciences, Telegrafenberg, 14473 Potsdam, GermanySoils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a novel approach that combines radiative transfer models (RTMs) with open-access soil spectral libraries to address this challenge. Focusing on conditions of low soil moisture content (SMC), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV), the coupled Marmit–Leaf–Canopy (MLC) model is used to simulate early crop growth stages. The MLC model, which integrates MARMIT and PRO4SAIL2, enables the generation of mixed soil–vegetation scenarios. A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. The results demonstrated relatively high SOC prediction accuracy compared to previous approaches that rely only on RTMs and/or machine learning approaches. Incorporating soil moisture content significantly improved performance over bare soil alone, yielding an R<sup>2</sup> of 0.86 and RMSE of 4.05 g/kg, compared to R<sup>2</sup> = 0.71 and RMSE = 6.01 g/kg for bare soil. Adding PV slightly reduced accuracy (R<sup>2</sup> = 0.71, RMSE = 6.31 g/kg), while the inclusion of NPV alongside moisture led to modest improvement (R<sup>2</sup> = 0.74, RMSE = 5.84 g/kg). The most comprehensive model, incorporating bare soil, SMC, PV, and NPV, achieved a balanced performance (R<sup>2</sup> = 0.76, RMSE = 5.49 g/kg), highlighting the importance of accounting for all surface components in SOC estimation. While further validation with additional scenarios and SOC prediction methods is needed, these findings demonstrate, for the first time, using radiative-transfer simulations of mixed vegetation-soil-water environments, that an EO-DSSL approach enhances machine learning-based SOC modeling from EO data, improving SOC mapping accuracy. This innovative framework could significantly improve global-scale SOC predictions, supporting the design of next-generation EO products for more accurate carbon monitoring.https://www.mdpi.com/2072-4292/17/14/2355soil spectroscopyMARMITPROSPECT4SAIL2convolutional neural network (CNN)hybrid inversion modelLUCAS-SSL data |
| spellingShingle | Asmaa Abdelbaki Robert Milewski Mohammadmehdi Saberioon Katja Berger José A. M. Demattê Sabine Chabrillat Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation Remote Sensing soil spectroscopy MARMIT PROSPECT4SAIL2 convolutional neural network (CNN) hybrid inversion model LUCAS-SSL data |
| title | Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation |
| title_full | Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation |
| title_fullStr | Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation |
| title_full_unstemmed | Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation |
| title_short | Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation |
| title_sort | radiative transfer model integrated approach for hyperspectral simulation of mixed soil vegetation scenarios and soil organic carbon estimation |
| topic | soil spectroscopy MARMIT PROSPECT4SAIL2 convolutional neural network (CNN) hybrid inversion model LUCAS-SSL data |
| url | https://www.mdpi.com/2072-4292/17/14/2355 |
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