Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios

The use of digital soil mapping, with the help of spectroscopic data, provides a non-destructive and cost-efficient alternative to soil property laboratory measurements. Visible, near-infrared and short wave infrared (VNIR/ SWIR, 400-2500 nm) hyperspectral imaging is one of the most promising tools for topsoil property mapping. The aim of this study was to test the sensitivity of soil property prediction results to coarsening image spectral resolution. This may offer an analysis of the potential of forthcoming hyperspectral satellite sensors, e.g., HYPerspectral X IMagery (HYPXIM) or Environmental Mapping and Analysis Program (EnMAP), and existing multispectral sensors, e.g., SENTINEL-2 Multispectral Sensor Instrument (MSI) or LANDSAT-8 Operational Land Imager (OLI), for soil properties mapping. This study used VNIR/SWIR hyperspectral airborne data acquired by the AISA-DUAL sensor (initial spectral and spatial resolutions of approximately 5 nm and 5 m, respectively) over a 300 km(2) Mediterranean rural region. Ten spectral configurations were built and divided in the following two groups: i) six spectral configurations corresponding to simulated sensors with regular spectral resolution from 5 nm to 200 nm (i.e., the Full Width at Half Maximum (FWHM) remains constant throughout the considered spectral domain; this includes the simulation of the forthcoming HYPXIM and EnMAP hyperspectral satellites) and ii) four spectral configurations corresponding to existing multispectral sensors with irregular spectral resolution (i.e., the FWHM differs from spectral sampling interval; Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SENTINEL-2 MSI, LANDSAT-7 Enhanced Thematic Mapper (ETM +) and LANDSAT-8 OLI). The soil property studied in this paper is the clay content, defined as the percentage of granulometric fraction finer than 2 mu m by weight of the soil, which will be estimated using the partial least squares regression method. Our results showed that i) spectral configurations with regular spectral resolutions from 5 to 100 nm provided similar and good clay content prediction performances (R-val(2) > 0.7 and RPIQ > 3) and allowed clay mapping with correct short-scale variations, ii) the spectral configuration with a regular spectral resolution of 200 nm provided unsatisfactory clay content prediction performance (R-val(2) similar or equal to 0.01 and RPIQ similar or equal to 1.65) and iii) the ASTER sensor was the only existing multispectral sensor that provided both correct performance of clay content estimation (R-val(2) similar or equal to 0.8 and RPIQ similar or equal to 3.72) and correct clay mapping. Therefore, clay mapping by the ASTER multispectral data should be pursued while awaiting the launch of forthcoming hyperspectral satellite sensors (e.g., HYPXIM and EnMAP), which will be good candidates for future large clay mapping campaigns over bare soils.