Abstract
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 R2 = 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: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 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.
Highlights
Soil degradation is a serious concern worldwide with implications for climate change and food security [1]
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 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
Summary
Soil degradation is a serious concern worldwide with implications for climate change and food security [1]. An alternative to wet chemistry is soil spectroscopy which uses spectral information to derive key soil properties. It is a well-established method under laboratory conditions using point spectrometers [8] which measure the radiation reflected by an object at a single point. SOC shows strong interactions with the electromagnetic radiation in the spectral region visible for human eyes and soils rich in SOC appear darker as a general decrease in reflectance is observed with an increasing SOC. A general flattening of the reflectance spectral curve in the visible (VIS) is typical of SOC in agricultural soils, related to the decomposition of chlorophyll pigments which originally causes a wide spectral absorption feature around 664 nm [10,11]. Weak and superimposing absorption features of soils, most studies use partial least squares regression (PLSR) or similar multivariate modelling techniques as they cope well with a high collinearity and a large number of predictor variables [13,14]
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