What is the Best Inference Trajectory for Mapping Soil Functions: An Example of Mapping Soil Available Water Capacity over Languedoc Roussillon (France)

Quentin Styc,Philippe Lagacherie

doi:10.3390/soilsystems3020034

Quentin Styc, Philippe Lagacherie

Open Access

https://doi.org/10.3390/soilsystems3020034

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Abstract

Extending digital soil mapping to the mapping of soil functions that can support end-user decisions comes to coupling a digital soil mapping procedure and a soil function assessment method. This can be done following various possible inference trajectories following the order with which “combining primary soil properties”, “aggregating soil layers across depths” and “mapping” are executed to provide the targeted output. Eighteen inference trajectories, designed for computing soil available water capacity maps in the Languedoc–Roussillon region (France), were compared with regard to their mapping performances. The best performance (SSMSE = 0.42) was obtained by a trajectory that, before mapping, combined the three first GlobalSoilMap soil layers and computed the available water capacity of each layer. The worst (SSMSE = 0.07) was observed when all the soil layers and soil properties were combined prior to mapping. We explain the observed differences between trajectories by examining the differences in mapping errors and in error propagation between the compared trajectories, which involve both the correlations between the soil properties and between their mapping errors. This paves the way to spatial soil inference systems that could perform an ex ante selection of the best possible inference trajectory for mapping a soil function.

Highlights

IntroductionIt is increasingly recognized that soils and their functions have a part to play in the large existential challenges that have been recognized for the sustainable development of humanity and planet Earth [1]
It is increasingly recognized that soils and their functions have a part to play in the large existential challenges that have been recognized for the sustainable development of humanity and planet Earth [1].Addressing such challenges needs to appropriately informing local and global decision making, which requires a knowledge of soils at fine resolution and global extent [2]
After the pioneering paper of Carré et al [6] that advocated for digital soil assessment approaches, there has been abundant literature providing conceptual advances for the description of soil functions and the related ecosystem services [7] and on the valuations of soil services [8]

Summary

Introduction

It is increasingly recognized that soils and their functions have a part to play in the large existential challenges that have been recognized for the sustainable development of humanity and planet Earth [1]. Addressing such challenges needs to appropriately informing local and global decision making, which requires a knowledge of soils at fine resolution and global extent [2]. Various applications of DSM across the globe [5] demonstrated that DSM can operationally produce sets of high resolution images representing the spatial variations of the most currently required soil properties or “primary soil properties” (e.g., soil textural fractions, soil carbon content, available water capacity, etc.). After the pioneering paper of Carré et al [6] that advocated for digital soil assessment approaches, there has been abundant literature providing conceptual advances for the description of soil functions and the related ecosystem services [7] and on the valuations of soil services [8]

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Discussion

Conclusion