Harvesting spatially dense legacy soil datasets for digital soil mapping of available water capacity in Southern France

Abstract

Although considerable work has been conducted in recent decades to build soil databases, the legacy data from a lot of former soil survey campaigns still remain unused. The objective of this study was to determine the interest in harvesting such legacy data for mapping the soil available water capacities (SAWCs) at different rooting depths (30 cm, 60 cm, 100 cm) and to the maximal observation depth, over the commune of Bouillargues (16 km2, Occitanie region, southern France).An increasing number of available auger hole observations with SAWC estimations – from 0 to 2781 observations – were added to the existing soil profiles to calibrate quantile regression forests (QRFs) using the Euclidean buffer distances from the sites as soil covariates. The SAWC was first mapped separately for different soil layers, and the mapping outputs were pooled to estimate the required SAWC. The uncertainty of the SAWC prediction was estimated from the estimated mapping uncertainties of the individual soil layers by an error propagation model using a first-order Taylor analysis.The performances of the SAWC predictions and their uncertainties were evaluated with a 10-fold cross validation that was iterated 20 times. The results showed that the use of a quantile regression forest that was fed with auger hole observations and that used the Euclidean buffer distances as soil covariates considerably augmented the performances of the SAWC predictions (percentages of explained variance from 0.39 to 0.70) compared to the performance of a classical DSM approach, i.e., a QRF that solely used soil profiles and only environmental covariates (percentages of explained variance from 0.04 to 0.51). The analysis of the results revealed that the performances were also dependent on the spatial patterns of the different examined SAWCs and was limited by the observational uncertainties of the SAWCs determined from auger holes. The best performance tended to also provide the best view of the uncertainty patterns with an overestimation of uncertainty.Despite these gains in performance, the cost-efficiency analysis showed that the augmentation of soil observations was not cost efficient because of the highly time-consuming manual data harvesting protocol. However, this result did not account for the observed gain in map details. Furthermore, the cost efficiency could be further improved by automation.