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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MDPI AG
2024-12-01
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| author | Monica Zanini Uta Heiden Leonardo Pace Raffaele Casa Simone Priori |
| author_facet | Monica Zanini Uta Heiden Leonardo Pace Raffaele Casa Simone Priori |
| author_sort | Monica Zanini |
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| description | 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. |
| format | Article |
| id | doaj-art-baca855841054a57a2f35da9033ebc85 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Remote Sensing |
| spelling | doaj-art-baca855841054a57a2f35da9033ebc852025-01-10T13:20:11ZengMDPI AGRemote Sensing2072-42922024-12-011718910.3390/rs17010089Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland DistrictMonica Zanini0Uta Heiden1Leonardo Pace2Raffaele Casa3Simone Priori4Department of Agriculture and Forest Sciences, University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, ItalyGerman Aerospace Center (DLR), The Remote Sensing Technology Institute, Muenchener Str. 20, 82234 Wessling, GermanyDepartment of Agriculture and Forest Sciences, University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, ItalyDepartment of Agriculture and Forest Sciences, University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, ItalyDepartment of Agriculture and Forest Sciences, University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, ItalyAccurate 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.https://www.mdpi.com/2072-4292/17/1/89precision agricultureSentinel-2multitemporal imagessoil organic carbonsoil monitoring |
| spellingShingle | Monica Zanini Uta Heiden Leonardo Pace Raffaele Casa Simone Priori Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District Remote Sensing precision agriculture Sentinel-2 multitemporal images soil organic carbon soil monitoring |
| title | Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District |
| title_full | Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District |
| title_fullStr | Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District |
| title_full_unstemmed | Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District |
| title_short | Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District |
| title_sort | soil reflectance composite for digital soil mapping in a mediterranean cropland district |
| topic | precision agriculture Sentinel-2 multitemporal images soil organic carbon soil monitoring |
| url | https://www.mdpi.com/2072-4292/17/1/89 |
| work_keys_str_mv | AT monicazanini soilreflectancecompositefordigitalsoilmappinginamediterraneancroplanddistrict AT utaheiden soilreflectancecompositefordigitalsoilmappinginamediterraneancroplanddistrict AT leonardopace soilreflectancecompositefordigitalsoilmappinginamediterraneancroplanddistrict AT raffaelecasa soilreflectancecompositefordigitalsoilmappinginamediterraneancroplanddistrict AT simonepriori soilreflectancecompositefordigitalsoilmappinginamediterraneancroplanddistrict |