BIS-4D: Maps of soil properties and their uncertainties at 25 m resolution in the Netherlands

This dataset is an asset of the scientific manuscript "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). It contains maps of soil properties and their uncertainties at 25m resolution in the Netherlands obtained using the BIS-4D soil modelling and mapping platform. BIS-4D is based on well-established digital soil mapping practices. This dataset includes maps of predictions of the mean, 0.05, 0.50 (median) and 0.95 quantiles and the 90th prediction interval width (PI90) of clay content [%], silt content [%], sand content [%], bulk density (BD) [g/cm3], soil organic matter (SOM) [%], pH [KCl], total N (Ntot) [mg/kg], oxalate-extractable P (Pox) [mmol/kg] and cation exchange capacity (CEC) [mmol(c)/kg]. Prediction maps are available for the standard depth layers specified by the GlobalSoilMap initiative (0-5, 5-15, 15-30, 30-60, 60-100 and 100-200cm). For SOM, these prediction maps are available for the years 1953, 1960, 1970, 1980, 1990, 2000, 2010, 2020 and 2023 based on changing land use, peat classes and peat occurrence over time. BIS-4D uses georeferenced soil point data (field estimates and laboratory measurements), spatially explicit environmental variables (covariates), and machine learning to predict in 3D space, and for SOM, in 3D space and time. The coordinate reference system is EPSG:28992 (Amersfoort/RD New).<br>More information about how these maps were created, the BIS-4D soil modelling and mapping platform, accuracy assessment, strengths, limitations, map assessment scale and specific user recommendations can be found in the scientific paper "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). The BIS-4D model code is available on GitLab.<br>Please note that an earlier version of soil pH prediction maps were published. In comparison, this version contains several important updates. Firstly, covariates of peat classes, groundwater classes in agricultural areas and Sentinel 2 RGB and NIR bands and spectral indices were added, all of which were selected and thus used for model calibration and prediction of the updated BIS-4D prediction maps. We also included de-correlation and recursive feature elimination to increase the signal to noise ratio, make models more parsimonious and increase reproducibility.<br>Please consider the following file naming structure to make it easier to find the prediction maps you need:File naming structure: "[soil property]_d_[upper depth layer boundary]_[lower depth layer boundary]_QRF_[PI90/pred type]_[processed].tif"Example: "clay_per_d_0_5_QRF_pred_mean_processed.tif"Soil property denotes the target soil property (listed above), depth upper and lower boundaries indicate the prediction target depth, QRF = quantile regression forest, which is the algorithm used for model calibration and prediction, PI90 is a measure of prediction uncertainy and is the 95th - 5th quantile, "pred_mean" indicates mean predictions, "pred50" indicates median predictions, "pred5" indicates 5th quantile prediction and "pred95" indicates 95th quantile prediction. For clay, silt and sand content, predictions were post-processed so that they add up to 100% and therefore for those GeoTIFF files the names contain "_processed". For SOM, the target prediction year is also indicated directly after "SOM_per", e.g. "SOM_per_2023_d_0_5_QRF_pred_mean.tif".

本数据集隶属于学术论文《BIS-4D：荷兰25米分辨率土壤属性及其不确定性制图》（Helfenstein等，2024年，待刊）。本数据集包含基于BIS-4D土壤建模与制图平台生成的荷兰地区25米分辨率土壤属性及其不确定性空间分布图。
BIS-4D依托成熟的数字土壤制图技术体系构建。
本数据集包含以下土壤属性的平均预测值、0.05分位数、0.50分位数（中位数）、0.95分位数以及90%预测区间宽度（PI90）的空间分布图：黏粒含量（%）、粉粒含量（%）、砂粒含量（%）、容重（BD）[g/cm³]、土壤有机质（SOM）（%）、氯化钾浸提pH（pH[KCl]）、全氮（Ntot）[mg/kg]、草酸浸提态磷（Pox）[mmol/kg]以及阳离子交换量（CEC）[mmol(c)/kg]。
预测图层覆盖GlobalSoilMap倡议规定的标准土壤深度分层：0-5cm、5-15cm、15-30cm、30-60cm、60-100cm及100-200cm。
针对土壤有机质（SOM），本数据集还提供了1953年、1960年、1970年、1980年、1990年、2000年、2010年、2020年及2023年的时空预测图层，其生成依据为随时间动态变化的土地利用格局、泥炭地类型及泥炭分布状况。
BIS-4D平台通过地理配准的土壤点位数据（野外估算值与实验室测定值）、空间显式环境变量（协变量）以及机器学习算法，实现三维空间维度的土壤属性预测；针对土壤有机质（SOM）则额外实现了三维空间-时间联合预测。本数据集采用的坐标参考系统为EPSG:28992（Amersfoort/RD New）。

有关本数据集的制图流程、BIS-4D土壤建模与制图平台详情、精度评估方法、模型优势与局限、图层评估尺度以及具体用户使用建议的详细信息，请参阅前述学术论文《BIS-4D：荷兰25米分辨率土壤属性及其不确定性制图》（Helfenstein等，2024年，待刊）。BIS-4D模型代码已公开至GitLab平台。

请注意，本数据集的土壤pH预测图层此前已发布过版本。与旧版相比，本次更新包含多项重要改进：其一，新增泥炭地类型、农业区地下水类型以及Sentinel-2的RGB、近红外波段与光谱指数作为协变量，所有新增协变量均经过筛选后用于新版BIS-4D模型的校准与预测；其二，引入去相关分析与递归特征消除算法，以提升信噪比、精简模型结构并增强研究可重复性。

为便于快速检索所需的预测图层，请遵循以下文件命名规则：
文件命名格式："[土壤属性]_d_[上层深度边界]_[下层深度边界]_QRF_[PI90/预测类型]_[已后处理].tif"
示例："clay_per_d_0_5_QRF_pred_mean_processed.tif"
其中，`土壤属性`指代目标土壤属性（详见前文列表），`上层深度边界`与`下层深度边界`表示预测目标的土层深度范围；`QRF`为分位数回归森林（quantile regression forest），即本数据集采用的模型校准与预测算法；`PI90`代表90%预测区间宽度，其值为95分位数与5分位数的差值；`pred_mean`、`pred50`、`pred5`及`pred95`分别指代平均预测、中位数预测、5分位数预测与95分位数预测。针对黏粒、粉粒及砂粒含量的预测结果已进行后处理以确保三者总和为100%，因此对应GeoTIFF文件的命名中包含`_processed`字段。针对土壤有机质（SOM）的预测图层，其目标预测年份会直接标注在`SOM_per`之后，例如"SOM_per_2023_d_0_5_QRF_pred_mean.tif"。