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Remote Sens. 2016, 8(7), 613; doi:10.3390/rs8070613

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

1
Helmholtz Center Potsdam GFZ German Research Center for Geosciences, Section 1.4 Remote Sensing, Telegrafenberg, Potsdam 14473, Germany
2
Institute of Geosciences, University of Kiel, Otto-Hahn-Platz 1, Kiel 24118, Germany
3
Georges 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, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: José A.M. Demattê, Jose Moreno and Prasad S. Thenkabail
Received: 17 March 2016 / Revised: 8 July 2016 / Accepted: 19 July 2016 / Published: 22 July 2016
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
View Full-Text   |   Download PDF [3556 KB, uploaded 22 July 2016]   |  

Abstract

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. View Full-Text
Keywords: imaging spectroscopy; airborne; satellite; simulated EnMAP; soil properties; Partial-Least-Square regression; variogram; autoPLSR imaging spectroscopy; airborne; satellite; simulated EnMAP; soil properties; Partial-Least-Square regression; variogram; autoPLSR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Steinberg, A.; Chabrillat, S.; Stevens, A.; Segl, K.; Foerster, S. 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 Sens. 2016, 8, 613.

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