Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution
With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-07-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/8/7/613 |
| _version_ | 1818974307028566016 |
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| author | Andreas Steinberg Sabine Chabrillat Antoine Stevens Karl Segl Saskia Foerster |
| author_facet | Andreas Steinberg Sabine Chabrillat Antoine Stevens Karl Segl Saskia Foerster |
| author_sort | Andreas Steinberg |
| collection | DOAJ |
| description | With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fed), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale. |
| first_indexed | 2024-12-20T15:37:58Z |
| format | Article |
| id | doaj.art-87e1dac32e424d7ea294ac400d03212e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T15:37:58Z |
| publishDate | 2016-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-87e1dac32e424d7ea294ac400d03212e2022-12-21T19:35:20ZengMDPI AGRemote Sensing2072-42922016-07-018761310.3390/rs8070613rs8070613Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial ResolutionAndreas Steinberg0Sabine Chabrillat1Antoine Stevens2Karl Segl3Saskia Foerster4Helmholtz Center Potsdam GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing, Telegrafenberg, Potsdam 14473, GermanyHelmholtz Center Potsdam GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing, Telegrafenberg, Potsdam 14473, GermanyGeorges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université Catholique de Louvain, Place Louis Pasteur 3, bte L4.03.08, Louvain-la-Neuve 1348, BelgiumHelmholtz Center Potsdam GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing, Telegrafenberg, Potsdam 14473, GermanyHelmholtz Center Potsdam GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing, Telegrafenberg, Potsdam 14473, GermanyWith the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fed), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale.http://www.mdpi.com/2072-4292/8/7/613imaging spectroscopyairbornesatellitesimulated EnMAPsoil propertiesPartial-Least-Square regressionvariogramautoPLSR |
| spellingShingle | Andreas Steinberg Sabine Chabrillat Antoine Stevens Karl Segl Saskia Foerster Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution Remote Sensing imaging spectroscopy airborne satellite simulated EnMAP soil properties Partial-Least-Square regression variogram autoPLSR |
| title | Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution |
| title_full | Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution |
| title_fullStr | Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution |
| title_full_unstemmed | Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution |
| title_short | Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution |
| title_sort | prediction of common surface soil properties based on vis nir airborne and simulated enmap imaging spectroscopy data prediction accuracy and influence of spatial resolution |
| topic | imaging spectroscopy airborne satellite simulated EnMAP soil properties Partial-Least-Square regression variogram autoPLSR |
| url | http://www.mdpi.com/2072-4292/8/7/613 |
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