Soil Organic Carbon Estimation and Transfer Framework in Agricultural Areas Based on Spatiotemporal Constraint Strategy Combined with Active and Passive Remote Sensing
Mapping soil organic carbon (SOC) plays a crucial role in agricultural productivity and water management. This study discusses the potential of active and passive remote sensing for SOC estimation modeling in agricultural areas, incorporating synthetic aperture radar (SAR) data (L-band quad-polariza...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/2/333 |
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| Summary: | Mapping soil organic carbon (SOC) plays a crucial role in agricultural productivity and water management. This study discusses the potential of active and passive remote sensing for SOC estimation modeling in agricultural areas, incorporating synthetic aperture radar (SAR) data (L-band quad-polarization and C-band dual-polarization), multi-spectrum (MS) data, and brightness temperature (TB) data. The performance of five advanced machine learning regression (MLR) models for SOC modeling was assessed, focusing on spatial interpolation accuracy and cross-spatial transfer accuracy, using two field observation datasets for modeling and validation. Results indicate that the SOC estimation accuracy when using MS data alone is comparable to that of using TB data alone, and both perform slightly better than SAR data. Radar cross-polarization ratio index, microwave polarization difference index, shortwave infrared reflectance, and soil parameters (elevation and soil moisture) demonstrate high correlation with the measured SOC. Incorporating temporal features, as opposed to single-phase features, allows each regression model to reach its upper limit of SOC estimation accuracy. The spatial interpolation accuracy of each MLR algorithm is satisfactory, with the Gaussian process regression (GPR) model demonstrating optimal modeling performance. When SAR, MS, or TB data are used individually in modeling, the estimation errors (RMSE) for SOC are 0.637 g/kg, 0.492 g/kg, and 0.229 g/kg for the SMAPVEX12 sampling campaign, and 0.706 g/kg, 0.454 g/kg, and 0.474 g/kg for the SMAPVEX16-MB sampling campaign, respectively. After incorporating soil moisture and topographic factors, the above RMSEs for SOC are further reduced by 57.8%, 35.6%, and 3.5% for the SMAPVEX12, and by 18.4%, 8.8%, and 3.4% for the SMAPVEX16-MB, respectively. However, cross-spatial transfer accuracy of the regression models remains limited (RMSE = 0.866–1.043 g/kg and 0.995–1.679 g/kg for different data sources). To address this, this study reduces uncertainties in SOC cross-spatial transfer by introducing terrain factors sensitive to SOC (RMSE = 0.457–0.516 g/kg and 0.799–1.198 g/kg for different data sources). The proposed SOC estimation and transfer framework, based on active and passive remote sensing data, provides guidance for high-resolution regional-scale SOC mapping and applications. |
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| ISSN: | 2072-4292 |