Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR
Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/20/3451 |
| _version_ | 1797550386947555328 |
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| author | Kathrin J. Ward Sabine Chabrillat Maximilian Brell Fabio Castaldi Daniel Spengler Saskia Foerster |
| author_facet | Kathrin J. Ward Sabine Chabrillat Maximilian Brell Fabio Castaldi Daniel Spengler Saskia Foerster |
| author_sort | Kathrin J. Ward |
| collection | DOAJ |
| description | Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R<sup>2</sup> = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R<sup>2</sup> = 0.78, RPD = 2.19; two-step: R<sup>2</sup> = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R<sup>2</sup> = 0.77, RPD = 2.15; two-step: R<sup>2</sup> = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration. |
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| format | Article |
| id | doaj.art-616b601a30194e90a60693fd3b6afc76 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:28:33Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-616b601a30194e90a60693fd3b6afc762023-11-20T17:51:09ZengMDPI AGRemote Sensing2072-42922020-10-011220345110.3390/rs12203451Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSRKathrin J. Ward0Sabine Chabrillat1Maximilian Brell2Fabio Castaldi3Daniel Spengler4Saskia Foerster5Helmholtz Center Potsdam, GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg A17, 14473 Potsdam, GermanyHelmholtz Center Potsdam, GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg A17, 14473 Potsdam, GermanyHelmholtz Center Potsdam, GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg A17, 14473 Potsdam, GermanyResearch Institute for Agriculture, Fisheries and Food—ILVO, Burgemeester Van Gansberghelaan 92 box 1, 9820 Merelbeke, BelgiumHelmholtz Center Potsdam, GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg A17, 14473 Potsdam, GermanyHelmholtz Center Potsdam, GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg A17, 14473 Potsdam, GermanySoil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R<sup>2</sup> = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R<sup>2</sup> = 0.78, RPD = 2.19; two-step: R<sup>2</sup> = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R<sup>2</sup> = 0.77, RPD = 2.15; two-step: R<sup>2</sup> = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.https://www.mdpi.com/2072-4292/12/20/3451soil organic carbonmappinghyperspectralEnMAPLUCASlocalPLSR |
| spellingShingle | Kathrin J. Ward Sabine Chabrillat Maximilian Brell Fabio Castaldi Daniel Spengler Saskia Foerster Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR Remote Sensing soil organic carbon mapping hyperspectral EnMAP LUCAS localPLSR |
| title | Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR |
| title_full | Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR |
| title_fullStr | Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR |
| title_full_unstemmed | Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR |
| title_short | Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR |
| title_sort | mapping soil organic carbon for airborne and simulated enmap imagery using the lucas soil database and a local plsr |
| topic | soil organic carbon mapping hyperspectral EnMAP LUCAS localPLSR |
| url | https://www.mdpi.com/2072-4292/12/20/3451 |
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