Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging
The acceleration of soil erosion in undulating landscapes due to human activities has led to a larger area of land being affected by intensive soil redistribution. Colluvisols, sedimentary soils formed on concave slope positions, are considered to be important indicators of soil-landscape processes...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Geoderma |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706124003756 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841550712976179200 |
|---|---|
| author | Jessica Reyes-Rojas Julien Guigue Daniel Žížala Vít Penížek Tomáš Hrdlička Petra Vokurková Aleš Vaněk Tereza Zádorová |
| author_facet | Jessica Reyes-Rojas Julien Guigue Daniel Žížala Vít Penížek Tomáš Hrdlička Petra Vokurková Aleš Vaněk Tereza Zádorová |
| author_sort | Jessica Reyes-Rojas |
| collection | DOAJ |
| description | The acceleration of soil erosion in undulating landscapes due to human activities has led to a larger area of land being affected by intensive soil redistribution. Colluvisols, sedimentary soils formed on concave slope positions, are considered to be important indicators of soil-landscape processes and soil organic carbon pools. In this study, we investigated the effectiveness of hyperspectral imaging in visible and near-infrared range to assess the detailed variability (both vertical and within each colluvial layer and in-situ soil horizon) of soil organic carbon (SOC) and CaCO3 concentrations in three deep Colluvisols developed on loess and located at different slope positions in southeast Czechia, and evaluate whether this in-detail mapped microvariability can be used as a proxy to assess the dynamics and history of colluvial sedimentation. A variety of nonlinear machine learning techniques such as cubist regression tree (Cubist), random forest (RF), support vector machine regression (SVMR) and one linear technique partial least square regression (PLSR) were compared to determine the most suitable model for the prediction of SOC and CaCO3 content in each profile. The results showed that RF provided the best performance for both SOC (R2 = 0.75) and CaCO3 (R2 = 0.76) contents. The maps depict significant differences in the vertical variability of the predicted properties in the profiles depending on the different intensity, form and period of sedimentation resulting from the slope position. The within-horizon/layer variability of SOC proves to be a suitable indicator of the character of deposition. High variability has been shown mainly in the medieval layers, where it reflects high-energy material redeposition, while low variability in the oldest and youngest parts of the profiles is probably associated with the type of deposited material and frequent pedoturbation, respectively. The within-horizon/layer variability of CaCO3, on the other hand, is independent of the dynamics of deposition. The study showed that imaging spectroscopy is a suitable tool to capture the detailed pattern of the colluvial matrix and, with appropriate sampling and processing, is applicable even in very deep soil profiles. |
| format | Article |
| id | doaj-art-b9e692af1c58435098f41e97e59052c8 |
| institution | Kabale University |
| issn | 1872-6259 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoderma |
| spelling | doaj-art-b9e692af1c58435098f41e97e59052c82025-01-10T04:36:45ZengElsevierGeoderma1872-62592025-01-01453117146Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imagingJessica Reyes-Rojas0Julien Guigue1Daniel Žížala2Vít Penížek3Tomáš Hrdlička4Petra Vokurková5Aleš Vaněk6Tereza Zádorová7Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Praha 6, Czech Republic; Corresponding author.Soil Science, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, GermanyResearch Institute for Soil and Water Conservation, Prague, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Praha 6, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Praha 6, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Praha 6, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Praha 6, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Praha 6, Czech RepublicThe acceleration of soil erosion in undulating landscapes due to human activities has led to a larger area of land being affected by intensive soil redistribution. Colluvisols, sedimentary soils formed on concave slope positions, are considered to be important indicators of soil-landscape processes and soil organic carbon pools. In this study, we investigated the effectiveness of hyperspectral imaging in visible and near-infrared range to assess the detailed variability (both vertical and within each colluvial layer and in-situ soil horizon) of soil organic carbon (SOC) and CaCO3 concentrations in three deep Colluvisols developed on loess and located at different slope positions in southeast Czechia, and evaluate whether this in-detail mapped microvariability can be used as a proxy to assess the dynamics and history of colluvial sedimentation. A variety of nonlinear machine learning techniques such as cubist regression tree (Cubist), random forest (RF), support vector machine regression (SVMR) and one linear technique partial least square regression (PLSR) were compared to determine the most suitable model for the prediction of SOC and CaCO3 content in each profile. The results showed that RF provided the best performance for both SOC (R2 = 0.75) and CaCO3 (R2 = 0.76) contents. The maps depict significant differences in the vertical variability of the predicted properties in the profiles depending on the different intensity, form and period of sedimentation resulting from the slope position. The within-horizon/layer variability of SOC proves to be a suitable indicator of the character of deposition. High variability has been shown mainly in the medieval layers, where it reflects high-energy material redeposition, while low variability in the oldest and youngest parts of the profiles is probably associated with the type of deposited material and frequent pedoturbation, respectively. The within-horizon/layer variability of CaCO3, on the other hand, is independent of the dynamics of deposition. The study showed that imaging spectroscopy is a suitable tool to capture the detailed pattern of the colluvial matrix and, with appropriate sampling and processing, is applicable even in very deep soil profiles.http://www.sciencedirect.com/science/article/pii/S0016706124003756Visible Near InfraredErosionSedimentation PatternsChernozemLoessCzechia |
| spellingShingle | Jessica Reyes-Rojas Julien Guigue Daniel Žížala Vít Penížek Tomáš Hrdlička Petra Vokurková Aleš Vaněk Tereza Zádorová Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging Geoderma Visible Near Infrared Erosion Sedimentation Patterns Chernozem Loess Czechia |
| title | Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging |
| title_full | Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging |
| title_fullStr | Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging |
| title_full_unstemmed | Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging |
| title_short | Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging |
| title_sort | vertical distribution and variability of soil organic carbon and caco3 in deep colluvisols modeled by hyperspectral imaging |
| topic | Visible Near Infrared Erosion Sedimentation Patterns Chernozem Loess Czechia |
| url | http://www.sciencedirect.com/science/article/pii/S0016706124003756 |
| work_keys_str_mv | AT jessicareyesrojas verticaldistributionandvariabilityofsoilorganiccarbonandcaco3indeepcolluvisolsmodeledbyhyperspectralimaging AT julienguigue verticaldistributionandvariabilityofsoilorganiccarbonandcaco3indeepcolluvisolsmodeledbyhyperspectralimaging AT danielzizala verticaldistributionandvariabilityofsoilorganiccarbonandcaco3indeepcolluvisolsmodeledbyhyperspectralimaging AT vitpenizek verticaldistributionandvariabilityofsoilorganiccarbonandcaco3indeepcolluvisolsmodeledbyhyperspectralimaging AT tomashrdlicka verticaldistributionandvariabilityofsoilorganiccarbonandcaco3indeepcolluvisolsmodeledbyhyperspectralimaging AT petravokurkova verticaldistributionandvariabilityofsoilorganiccarbonandcaco3indeepcolluvisolsmodeledbyhyperspectralimaging AT alesvanek verticaldistributionandvariabilityofsoilorganiccarbonandcaco3indeepcolluvisolsmodeledbyhyperspectralimaging AT terezazadorova verticaldistributionandvariabilityofsoilorganiccarbonandcaco3indeepcolluvisolsmodeledbyhyperspectralimaging |