Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District
Accurate soil maps are essential for soil protection, management, and digital agriculture. However, traditional soil maps often lack the detail required for local applications, while farm-scale surveys are often not economically viable. This study uses legacy soil data and digital soil mapping (DSM)...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/1/89 |
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| Summary: | Accurate soil maps are essential for soil protection, management, and digital agriculture. However, traditional soil maps often lack the detail required for local applications, while farm-scale surveys are often not economically viable. This study uses legacy soil data and digital soil mapping (DSM) to produce accurate, low-cost maps of key soil properties, namely clay, sand, total lime (CaCO<sub>3</sub>), organic carbon (SOC), total nitrogen (TN), and the cation-exchange capacity (CEC). The DSM procedure involved multivariate stepwise regression kriging that uses the terrain attributes and bare soil reflectance composite (SRC) from Sentinel-2 multitemporal images. The procedure to obtain the SRC was carried out following the Soil Composite Mapping Processor (SCMaP) methodology. The Sentinel-2 bands of the SRC showed strong correlations with soil features, making them very suitable explicative variables for regression kriging. In particular, the SWIR bands (b11 and b12) were important covariates in predicting clay, sand, and CEC maps. The accuracy of the regression models was very good for clay, sand, SOC, and CEC (R<sup>2</sup> > 0.90), while CaCO<sub>3</sub> showed lower accuracy (R<sup>2</sup> = 0.67). Normalization of SOC, TN, and CaCO<sub>3</sub> did not significantly improve the prediction accuracy, except for SOC, which showed a slight improvement. In addition, a supervised classification approach was applied to predict soil typological units (STUs) using the mapped soil attributes. This methodology demonstrates the potential of SRCs and regression kriging to produce detailed soil property maps to support precision agriculture and sustainable land management. |
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| ISSN: | 2072-4292 |