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

Andreas Steinberg,Karl Segl,Antoine Stevens,Sabine Chabrillat,Saskia Foerster

doi:10.3390/rs8070613

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.

Highlights

There is a renewed awareness of the finite nature of the world’s soil resources, growing concern about soil security, and significant uncertainties about the carrying capacity of the planet [1,2]
Soil spectroscopy based on laboratory, field, and airborne data was shown to be an adequate method for the mapping of the spatial distribution of soil surface properties such as iron oxide, clay, and soil organic carbon (SOC) content, moving into the quantitative domain based on multivariate statistics methodologies, as long as soil chromophores are present, the soils are well exposed and homogeneously distributed, and local ground data are available
PLS approaches applied to soil spectroscopy have been recognized for its potential to deliver fast and low-cost high quality geo-referenced soil maps for the assessment of soil properties and for soil degradation indicators, and are used here

Summary

Introduction

There is a renewed awareness of the finite nature of the world’s soil resources, growing concern about soil security, and significant uncertainties about the carrying capacity of the planet [1,2]. It has been answered with a growing number of soil policies and regulations around the world concerned with, e.g., increasing soil degradation and loss of organic carbon in top soils, and aiming at more soil management and soil protection. A particular issue is the clear demand for a new regional to global coverage with accurate, up-to-date, and spatially referenced soil information as expressed by the scientific community, farmers and land users, and policy and decision makers [5].

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Methods

Results

Conclusion