Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France
Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of...
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| Format: | Article |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2410 |
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| author | Diego Urbina-Salazar Emmanuelle Vaudour Anne C. Richer-de-Forges Songchao Chen Guillaume Martelet Nicolas Baghdadi Dominique Arrouays |
| author_facet | Diego Urbina-Salazar Emmanuelle Vaudour Anne C. Richer-de-Forges Songchao Chen Guillaume Martelet Nicolas Baghdadi Dominique Arrouays |
| author_sort | Diego Urbina-Salazar |
| collection | DOAJ |
| description | Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, “calcl”) from the “late winter–spring” time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view. |
| first_indexed | 2024-03-11T04:08:06Z |
| format | Article |
| id | doaj.art-8ca65c3be741418c914b8778b9752623 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:08:06Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8ca65c3be741418c914b8778b97526232023-11-17T23:39:49ZengMDPI AGRemote Sensing2072-42922023-05-01159241010.3390/rs15092410Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central FranceDiego Urbina-Salazar0Emmanuelle Vaudour1Anne C. Richer-de-Forges2Songchao Chen3Guillaume Martelet4Nicolas Baghdadi5Dominique Arrouays6University of Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 91120 Palaiseau, FranceUniversity of Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 91120 Palaiseau, FranceINRAE, Info&Sols, 45075 Orléans, FranceZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, ChinaBRGM, 3 av. Claude Guillemin, BP36009, CEDEX 2, 45060 Orléans, FranceUMR TETIS, INRAE, University of Montpellier, UMR TETIS, 500 rue François Breton, CEDEX 5, 34093 Montpellier, FranceINRAE, Info&Sols, 45075 Orléans, FranceSatellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, “calcl”) from the “late winter–spring” time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view.https://www.mdpi.com/2072-4292/15/9/2410soil organic carbonSentinel-1/2bare soil mosaicsquantile random forestairborne gamma-raydigital soil mapping |
| spellingShingle | Diego Urbina-Salazar Emmanuelle Vaudour Anne C. Richer-de-Forges Songchao Chen Guillaume Martelet Nicolas Baghdadi Dominique Arrouays Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France Remote Sensing soil organic carbon Sentinel-1/2 bare soil mosaics quantile random forest airborne gamma-ray digital soil mapping |
| title | Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France |
| title_full | Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France |
| title_fullStr | Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France |
| title_full_unstemmed | Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France |
| title_short | Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France |
| title_sort | sentinel 2 and sentinel 1 bare soil temporal mosaics of 6 year periods for soil organic carbon content mapping in central france |
| topic | soil organic carbon Sentinel-1/2 bare soil mosaics quantile random forest airborne gamma-ray digital soil mapping |
| url | https://www.mdpi.com/2072-4292/15/9/2410 |
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