Large-scale, high-resolution mapping of soil aggregate stability in croplands using APEX hyperspectral imagery
(2020) Remote Sensing — Vol. 12, n° 4, p. 15 p (2020)
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- Investigations into the spatial dynamics of soil aggregate stability (AS) are urgently neededto better target areas that have undergone soil degradation. However, due to the lack of efficientalternatives to the conventional labor-intensive methods to quantify AS, detailed information on itsspatial structure across scales are scarce. The objective of this study was to explore the possibility ofusing hyperspectral remote sensing imagery to rapidly produce a high-resolution AS map at regionalscale. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 230 km2in theBelgian loam belt were used together with a local topsoil dataset. Partial least squares regression (PLSR)models were developed for three AS indexes (i.e., mean weight diameter (MWD), microaggregateand macroaggregate fractions) and soil organic carbon (SOC), and evaluated against an independentvalidation dataset. The prediction models were then applied to more than 700 bare soil fields for theproduction of high resolution (2×2 m) MWD and SOC maps. The PLSR models had a satisfactorylevel of accuracy for all four variables (R2>0.5, RPD>1.4), and the predicted maps were capable ofcapturing the fine-scale as well as the between-field variabilities of soil properties. Variogram analysison the spatial structure of MWD showed a clear spatial organization at the catchment scale (range:1.3 km) that is possibly driven by erosion-induced soil redistribution processes. Further analysis inrestricted areas displayed contrasting spatial structures where spatial auto-correlation of AS was onlyfound at field scale, thus highlighting the potential of hyperspectral remote sensing as a promisingtechnique to investigate the spatial variability of AS across multiple scales
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Shi, P., Castaldi, F., van Wesemael, B., & Van Oost, K. (2020). Large-scale, high-resolution mapping of soil aggregate stability in croplands using APEX hyperspectral imagery. Remote Sensing, 12(4), 15 p. https://doi.org/10.3390/rs12040666 (Original work published 2020)