An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches
The geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO<sub>2</sub> sequestration. Publicly available, large field-scale georeferenced datasets are often limite...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/21/5519 |
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| author | Ahmed Laamrani Paul R. Voroney Daniel D. Saurette Aaron A. Berg Line Blackburn Adam W. Gillespie Ralph C. Martin |
| author_facet | Ahmed Laamrani Paul R. Voroney Daniel D. Saurette Aaron A. Berg Line Blackburn Adam W. Gillespie Ralph C. Martin |
| author_sort | Ahmed Laamrani |
| collection | DOAJ |
| description | The geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO<sub>2</sub> sequestration. Publicly available, large field-scale georeferenced datasets are often limited in number and design to serve these purposes. This study provides the first publicly accessible dataset of georeferenced topsoil SOC measurements (<i>n</i> = 840) over a 26-hectare (ha) agricultural field located in southern Ontario, Canada, with a sampling density of ~32 points per ha. As SOC is usually influenced by site topography (i.e., slope and landscape position), each point of the database is associated with a wide range of remote sensing topographic derivatives; as well as with normalized difference vegetation index (NDVI) based value. The NDVI data were extracted from remote sensing Sentinel-2 imagery from over a five-year period (2017–2021). In this paper, the methodology for topsoil sampling, SOC measurement in the lab, as well as producing the suite of topographic derivatives is described. We discuss the opportunities that the database offers in terms of spatially explicit and continuous soil information to support international efforts in digital soil mapping (i.e., SoilGrids250m) as well as other potential applications detailed in the discussion section. We believe that the database with very dense point location measurements can help in conducting carbon stocks and sequestration studies. Such information can be used to help bridge the gap between ground data and remotely sensed datasets or data-derived products from modeling approaches intended to evaluate field-scale rates of agricultural carbon accumulation. The generated topsoil database in this study is archived and publicly available on the Zenodo open-access repository. |
| first_indexed | 2024-03-09T18:41:20Z |
| format | Article |
| id | doaj.art-b4ddc37a90ef45c8b6a18e852b0d5525 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:20Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b4ddc37a90ef45c8b6a18e852b0d55252023-11-24T06:40:20ZengMDPI AGRemote Sensing2072-42922022-11-011421551910.3390/rs14215519An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling ApproachesAhmed Laamrani0Paul R. Voroney1Daniel D. Saurette2Aaron A. Berg3Line Blackburn4Adam W. Gillespie5Ralph C. Martin6Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir 43150, MoroccoSchool of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, CanadaSchool of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, CanadaDepartment of Geography, Environment & Geomatics, University of Guelph, Guelph, ON 1G 2W1, CanadaSchool of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, CanadaSchool of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, CanadaDepartment of Plant Agriculture, University of Guelph, Guelph, ON 1G 2W1, CanadaThe geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO<sub>2</sub> sequestration. Publicly available, large field-scale georeferenced datasets are often limited in number and design to serve these purposes. This study provides the first publicly accessible dataset of georeferenced topsoil SOC measurements (<i>n</i> = 840) over a 26-hectare (ha) agricultural field located in southern Ontario, Canada, with a sampling density of ~32 points per ha. As SOC is usually influenced by site topography (i.e., slope and landscape position), each point of the database is associated with a wide range of remote sensing topographic derivatives; as well as with normalized difference vegetation index (NDVI) based value. The NDVI data were extracted from remote sensing Sentinel-2 imagery from over a five-year period (2017–2021). In this paper, the methodology for topsoil sampling, SOC measurement in the lab, as well as producing the suite of topographic derivatives is described. We discuss the opportunities that the database offers in terms of spatially explicit and continuous soil information to support international efforts in digital soil mapping (i.e., SoilGrids250m) as well as other potential applications detailed in the discussion section. We believe that the database with very dense point location measurements can help in conducting carbon stocks and sequestration studies. Such information can be used to help bridge the gap between ground data and remotely sensed datasets or data-derived products from modeling approaches intended to evaluate field-scale rates of agricultural carbon accumulation. The generated topsoil database in this study is archived and publicly available on the Zenodo open-access repository.https://www.mdpi.com/2072-4292/14/21/5519agricultural landsoil total carbondatabasetopographic derivativesNDVIdigital soil mapping |
| spellingShingle | Ahmed Laamrani Paul R. Voroney Daniel D. Saurette Aaron A. Berg Line Blackburn Adam W. Gillespie Ralph C. Martin An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches Remote Sensing agricultural land soil total carbon database topographic derivatives NDVI digital soil mapping |
| title | An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches |
| title_full | An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches |
| title_fullStr | An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches |
| title_full_unstemmed | An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches |
| title_short | An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches |
| title_sort | extensive field scale dataset of topsoil organic carbon content aimed to assess remote sensed datasets and data derived products from modeling approaches |
| topic | agricultural land soil total carbon database topographic derivatives NDVI digital soil mapping |
| url | https://www.mdpi.com/2072-4292/14/21/5519 |
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