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.

尽管在最近几十年中为建立土壤数据库进行了大量工作，但许多以前的土壤调查活动留下的遗留数据仍未使用。这项研究的目的是确定是否有兴趣收集这些遗留数据，以绘制不同生根深度（30 cm，60 cm，100 cm）和最大观测深度的土壤有效水容量（SAWC），以了解Bouillargues（16 km ²，Occitanie地区，法国南部）。

已有越来越多的带有SAWC估计值的可用螺旋钻洞观测值（从0到2781个观测值）被添加到现有土壤剖面中，以距站点的欧几里德缓冲距离作为土壤协变量来校准分位数回归森林（QRF）。首先为不同的土壤层分别绘制SAWC，然后汇总映射输出以估计所需的SAWC。SAWC预测的不确定性是通过使用一阶泰勒分析的误差传播模型，根据各个土壤层的测绘不确定性来估计的。

SAWC预测的性能及其不确定性通过重复20次的10倍交叉验证进行评估。结果表明，使用分位数回归森林进行木钻洞观测，并使用欧几里得缓冲区距离作为土壤协变量，大大提高了SAWC预测的性能（解释方差百分比从0.39到0.70），经典DSM方法（即仅使用土壤剖面且仅使用环境协变量的QRF）（解释方差的百分比从0.04到0.51）。对结果的分析表明，性能还取决于不同检查的SAWC的空间模式，并且受从螺旋钻孔确定的SAWC的观测不确定性的限制。

尽管在性能方面取得了这些进步，但成本效益分析表明，由于耗时的手动数据收集协议非常耗时，因此增加土壤观测数据的成本效益不高。但是，此结果并未说明在地图详细信息中观察到的增益。此外，可以通过自动化进一步提高成本效率。