Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy
Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO<sub>2</sub>) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/5/897 |
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| author | Nicolas Francos Paolo Nasta Carolina Allocca Benedetto Sica Caterina Mazzitelli Ugo Lazzaro Guido D’Urso Oscar Rosario Belfiore Mariano Crimaldi Fabrizio Sarghini Eyal Ben-Dor Nunzio Romano |
| author_facet | Nicolas Francos Paolo Nasta Carolina Allocca Benedetto Sica Caterina Mazzitelli Ugo Lazzaro Guido D’Urso Oscar Rosario Belfiore Mariano Crimaldi Fabrizio Sarghini Eyal Ben-Dor Nunzio Romano |
| author_sort | Nicolas Francos |
| collection | DOAJ |
| description | Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO<sub>2</sub>) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R<sup>2</sup> = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R<sup>2</sup> = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R<sup>2</sup> = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R<sup>2</sup> = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. |
| first_indexed | 2024-04-25T00:20:18Z |
| format | Article |
| id | doaj.art-9c1c896243af477d9dee6725d03810d3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-25T00:20:18Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9c1c896243af477d9dee6725d03810d32024-03-12T16:54:22ZengMDPI AGRemote Sensing2072-42922024-03-0116589710.3390/rs16050897Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern ItalyNicolas Francos0Paolo Nasta1Carolina Allocca2Benedetto Sica3Caterina Mazzitelli4Ugo Lazzaro5Guido D’Urso6Oscar Rosario Belfiore7Mariano Crimaldi8Fabrizio Sarghini9Eyal Ben-Dor10Nunzio Romano11The Remote Sensing Laboratory, Tel Aviv University, Tel Aviv 699780, IsraelDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyThe Remote Sensing Laboratory, Tel Aviv University, Tel Aviv 699780, IsraelDepartment of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, ItalyMapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO<sub>2</sub>) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R<sup>2</sup> = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R<sup>2</sup> = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R<sup>2</sup> = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R<sup>2</sup> = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks.https://www.mdpi.com/2072-4292/16/5/897AVIRIS–NGsoil spectroscopydata analysisrandom forestorganic carbon stock |
| spellingShingle | Nicolas Francos Paolo Nasta Carolina Allocca Benedetto Sica Caterina Mazzitelli Ugo Lazzaro Guido D’Urso Oscar Rosario Belfiore Mariano Crimaldi Fabrizio Sarghini Eyal Ben-Dor Nunzio Romano Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy Remote Sensing AVIRIS–NG soil spectroscopy data analysis random forest organic carbon stock |
| title | Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy |
| title_full | Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy |
| title_fullStr | Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy |
| title_full_unstemmed | Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy |
| title_short | Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy |
| title_sort | mapping soil organic carbon stock using hyperspectral remote sensing a case study in the sele river plain in southern italy |
| topic | AVIRIS–NG soil spectroscopy data analysis random forest organic carbon stock |
| url | https://www.mdpi.com/2072-4292/16/5/897 |
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