NamSoil v1.0: Predicted Extractable Sodium (mg kg-1) for Namibia at 90 m resolution (0–30, 30–60 and 60–100 cm)

Dataset Overview This dataset provides spatial predictions of Extractable Sodium (mg kg-1) content across Namibia at 90 m spatial resolution for three standard soil depth intervals: 0–30, 30–60, 60–100 cm. For each depth interval, the following outputs are provided: predicted mean; 5th percentile; 95th percentile; 90% prediction interval (PI90). The maps are intended for national- and regional-scale applications and support environmental modelling, land evaluation, and resource management. Input Soil Data Model training was based on analytical data from the Namibian Soil Profile Database (NSPD2025) (https://zenodo.org/records/17618737). Profile locations have spatial accuracy better than 0.0001° and were reprojected to WGS84. Soil observations were depth-harmonised to the three standard depth intervals prior to modelling. Summary statistics of observed Extractable Sodium (mg kg-1): 0–30 cm 30–60 cm 60–100 cm n 721 390 300 Min 0.00 0.00 0.00 Max 880.23 1684.00 833.44 Mean 43.60 52.39 44.29 Median 17.10 21.75 18.17 SD 110.08 146.29 93.48 Skewness 5.29 7.64 5.07 Selected environmental covariates Environmental covariates included in the final model for each depth interval are: 0–30 cm: dem, savi_w, msavi_w, ndvi_s, savi_s, msavi_s, evi_s, kndvi_s, flow_lend_d, pet, arid_ind, aspp, prec_wc2, tavg_wc2, rock_out 30–60 cm: dem, ndvi_s, kndvi_s, aspp, tavg_wc2, geology_a 60–100 cm: dem, evi_w, ndvi_s, savi_s, msavi_s, evi_s, kndvi_s, arid_ind, VerticalCurvature, prec_wc2, tavg_wc2, geology_a Full stack of environmental covariates Covariate Description dem Digital elevation model (altitude in metres) Slope Terrain gradient in degrees Aspect Slope facing direction (0–360°) Eastness East-west slope orientation (sin of aspect) Northness North-south slope orientation (cos of aspect) HorizontalCurvature Plan curvature; lateral flow convergence/divergence VerticalCurvature Profile curvature; flow acceleration along slope chili Continuous heat-insolation load index tpi Multi-scale topographic position index (ridges vs valleys) topo_diver Topographic diversity (habitat temperature/moisture variety) landforms_alos Hillslope position classes (15 landform types) flow_dir Local drainage flow direction hand Height above nearest drainage flow_accumul Upstream drainage area (km²) river_dist Distance to nearest drainage line flow_lend_d Flow length downstream to pour point flow_len_up Flow length upstream to farthest source landcover Land cover classes (11 classes, Sentinel-based) Prec_wc2 Mean annual precipitation 1970–2000 (mm) tavg_wc2 Mean annual temperature 1970–2000 (°C) arid_ind Aridity index (precipitation / potential evapotranspiration) pet Potential evapotranspiration 1970–2000 blue_s Landsat blue band (summer) blue_w Landsat blue band (winter) green_s Landsat green band (summer) green_w Landsat green band (winter) red_s Landsat red band (summer) red_w Landsat red band (winter) nir_s Landsat near-infrared band (summer) nir_w Landsat near-infrared band (winter) swir1_s Landsat shortwave infrared 1 (summer) swir1_w Landsat shortwave infrared 1 (winter) swir2_s Landsat shortwave infrared 2 (summer) swir2_w Landsat shortwave infrared 2 (winter) ndvi_s Normalized Difference Vegetation Index (summer) ndvi_w Normalized Difference Vegetation Index (winter) savi_s Soil Adjusted Vegetation Index (summer) savi_w Soil Adjusted Vegetation Index (winter) msavi_s Modified Soil Adjusted Vegetation Index (summer) msavi_w Modified Soil Adjusted Vegetation Index (winter) evi_s Enhanced Vegetation Index (summer) evi_w Enhanced Vegetation Index (winter) kndvi_s Kernel NDVI (summer) kndvi_w Kernel NDVI (winter) carb_diff Carbonate normalization ratio (Landsat) clay_diff Clay normalization ratio (Landsat) ferr_diff Ferrous minerals normalization ratio (Landsat) iron Iron normalization ratio (Landsat) rock_out Rock outcrop normalization ratio (Landsat) kaolinite index ASTER kaolinite mineral index calcite index ASTER calcite mineral index quartz index ASTER quartz mineral index carbonate index ASTER carbonate mineral index mafic index ASTER mafic mineral index Aez Agro-ecological zones of Namibia (1996, categorical) aez_n Updated agro-ecological zones of Namibia (2021) cc Potential carrying capacity of Namibia (2021) namsoil_13 National soil map (13 WRB reference soil groups) aspp Average seasonal plant productivity (1999–2019) veg_types Vegetation types geology_a Major rock groups by type and age geology Lithology units (geological map) Landform_iwa Iwahashi-Pike landform classification (slope, texture, convexity) convex Terrain convexity (ratio of positive curvature cells) curv_max Terrain curvature (rate of change in slope) The complete description and source details can be found in S5 – Environmental covariates assembled in the predictor stack.pdf file. Modelling Framework Spatial prediction was performed using the Random Forest algorithm. A bootstrap resampling strategy (20 iterations) was implemented, using an 80:20 calibration–validation split with replacement and a fixed random seed. Soil data preprocessing, hyperparameter tuning, feature selection, post-modelling metrics and external validation were executed in R, while covariate preparation, model implementation, and uncertainty quantification were conducted in Google Earth Engine. The Random Forest hyperparameters were: Depth interval ntree mtry nodesize sampsize 0–30 cm 150 1 25 0.54 30–60 cm 150 6 10 0.54 60–100 cm 150 6 18 0.88 where:ntree: number of decision trees in the forestmtry: the number of predictors randomly sampled at each RF splitnodesize: the minimum number of samples required at a leaf node to prevent overfittingsampsize: the in-bag (internal RF bootstrap) sample size drawn to train each tree Model Performance Model performance was evaluated for each bootstrap iteration using Root Mean Square Error (RMSE) to quantify prediction errors and Coefficient of Determination (R²) to measure explained variance. The performance metrics, averaged across the 20 bootstrap runs, are: Depth interval R² calibration RMSE calibration R² validation RMSE validation 0–30 cm 0.475 90.270 0.107 101.822 30–60 cm 0.252 130.696 0.153 114.165 60–100 cm 0.284 80.741 0.224 76.925 Uncertainty Quantification Uncertainty estimates were derived from the bootstrap prediction distributions. The 5th and 95th percentile maps represent lower and upper prediction limits. The 90% Prediction Interval Coverage Probability (PICP90) of Extractable Sodium for the three depth classes were: Depth interval PICP90 0–30 cm 96.67 30–60 cm 96.92 60–100 cm 96.33 Data Outputs Map outputs are provided as Cloud-Optimised GeoTIFFs (WGS84) for GIS and modelling applications, and PNG format for visualisation and reporting. Data Access The input soil data used for model training is available in the Namibian Soil Profile Database (NSPD2025) at https://doi.org/10.5281/zenodo.17618737.Predicted soil maps can be retrieved directly from Zenodo using the quick-start scripts for reading, cropping, and exporting NamSoil layers — without downloading the full files — available at: https://github.com/Gelsleichter/acquire_NamSoil/.These scripts enable reproducible data retrieval workflows, allowing users to fetch and process specific layers programmatically. Code Availability The complete source code for data preprocessing, feature selection, hyperparameter tuning, model implementation, and post-processing is available at:https://doi.org/10.5281/zenodo.18776302, also published on https://github.com/Gelsleichter/NamSoil.The Google Earth Engine scripts for covariate preparation, regression matrix export, and Random Forest modelling with 20-iteration bootstrap are available at: https://code.earthengine.google.com/?accept_repo=users/Namibia_map/Soil_properties.Note that the GEE repository runs at a coarser spatial resolution than the published maps to reduce computational cost, memory usage, and export time within the Earth Engine environment. Users can adjust the output resolution to 90 m (or other) by modifying the scale parameter in the export functions, although this will require longer processing times and larger storage allocation.All scripts, fixed random seeds, and parameter configurations are provided to ensure full reproducibility of the modelling pipeline — from covariate preparation through spatial prediction and uncertainty quantification. Users can replicate the entire workflow or adapt individual components to other study areas or soil properties. Related Publication A full methodological description, model evaluation framework, and interpretation of results are provided in:[Publication DOI to be added]数据集概览 本数据集针对纳米比亚全域，以90米空间分辨率，针对三个标准土壤深度层（0–30 cm、30–60 cm、60–100 cm）提供可交换钠（Extractable Sodium，单位为mg kg⁻¹）含量的空间预测结果。针对每个深度层，输出内容包含：预测均值、5%分位数、95%分位数以及90%预测区间（PI90）。 该空间预测地图适用于国家及区域尺度应用，可支撑环境建模、土地评价与资源管理工作。 输入土壤数据 模型训练基于纳米比亚土壤剖面数据库（Namibian Soil Profile Database, NSPD2025，https://zenodo.org/records/17618737）的分析数据。剖面点位的空间精度优于0.0001°，并被重投影至WGS84坐标系。建模前，土壤观测数据已被统一匹配至上述三个标准土壤深度层。 观测可交换钠（mg kg⁻¹）的统计摘要如下： | 深度层 | 样本量(n) | 最小值 | 最大值 | 平均值 | 中位数 | 标准差(SD) | 偏度(Skewness) | |--------------|-----------|--------|----------|--------|--------|------------|----------------| | 0–30 cm | 721 | 0.00 | 880.23 | 43.60 | 17.10 | 110.08 | 5.29 | | 30–60 cm | 390 | 0.00 | 1684.00 | 52.39 | 21.75 | 146.29 | 7.64 | | 60–100 cm | 300 | 0.00 | 833.44 | 44.29 | 18.17 | 93.48 | 5.07 | 遴选环境协变量 针对每个土壤深度层，最终模型纳入的环境协变量如下： - 0–30 cm：dem、savi_w、msavi_w、ndvi_s、savi_s、msavi_s、evi_s、kndvi_s、flow_lend_d、pet、arid_ind、aspp、prec_wc2、tavg_wc2、rock_out - 30–60 cm：dem、ndvi_s、kndvi_s、aspp、tavg_wc2、geology_a - 60–100 cm：dem、evi_w、ndvi_s、savi_s、msavi_s、evi_s、kndvi_s、arid_ind、VerticalCurvature、prec_wc2、tavg_wc2、geology_a 完整环境协变量集 | 协变量名称 | 描述 | |------------------|----------------------------------------------------------------------| | dem | 数字高程模型（Digital Elevation Model, DEM），单位为米的海拔高度 | | Slope | 坡度，单位为度的地形梯度 | | Aspect | 坡向，坡面朝向（0–360°） | | Eastness | 东西向坡向，即坡向的正弦值 | | Northness | 南北向坡向，即坡向的余弦值 | | HorizontalCurvature | 平面曲率，即侧向水流的汇聚/发散程度 | | VerticalCurvature | 剖面曲率，即沿坡面的水流加速度 | | chili | 连续热辐射负荷指数 | | tpi | 多尺度地形位置指数，用于区分山脊与山谷 | | topo_diver | 地形多样性，即生境温度/湿度的变异程度 | | landforms_alos | 坡位分类，共15种地貌类型 | | flow_dir | 局地排水流向 | | hand | 距最近排水点的海拔高度 | | flow_accumul | 上游集水面积，单位为平方千米 | | river_dist | 距最近排水线的距离 | | flow_lend_d | 至汇点的下游水流长度 | | flow_len_up | 至最远源点的上游水流长度 | | landcover | 土地覆盖类型，共11类，基于Sentinel影像构建 | | Prec_wc2 | 1970–2000年平均年降水量，单位为毫米 | | tavg_wc2 | 1970–2000年平均年气温，单位为摄氏度 | | arid_ind | 干旱指数，即降水量与潜在蒸散量的比值 | | pet | 1970–2000年潜在蒸散量 | | blue_s | Landsat蓝色波段（夏季） | | blue_w | Landsat蓝色波段（冬季） | | green_s | Landsat绿色波段（夏季） | | green_w | Landsat绿色波段（冬季） | | red_s | Landsat红色波段（夏季） | | red_w | Landsat红色波段（冬季） | | nir_s | Landsat近红外波段（夏季） | | nir_w | Landsat近红外波段（冬季） | | swir1_s | Landsat短波红外1波段（夏季） | | swir1_w | Landsat短波红外1波段（冬季） | | swir2_s | Landsat短波红外2波段（夏季） | | swir2_w | Landsat短波红外2波段（冬季） | | ndvi_s | 归一化植被指数（Normalized Difference Vegetation Index, NDVI，夏季） | | ndvi_w | 归一化植被指数（NDVI，冬季） | | savi_s | 土壤调节植被指数（Soil Adjusted Vegetation Index, SAVI，夏季） | | savi_w | 土壤调节植被指数（SAVI，冬季） | | msavi_s | 修正型土壤调节植被指数（Modified Soil Adjusted Vegetation Index, MSAVI，夏季） | | msavi_w | 修正型土壤调节植被指数（MSAVI，冬季） | | evi_s | 增强型植被指数（Enhanced Vegetation Index, EVI，夏季） | | evi_w | 增强型植被指数（EVI，冬季） | | kndvi_s | 核归一化植被指数（Kernel NDVI, KNDVI，夏季） | | kndvi_w | 核归一化植被指数（KNDVI，冬季） | | carb_diff | 碳酸盐归一化比值（基于Landsat影像） | | clay_diff | 黏土归一化比值（基于Landsat影像） | | ferr_diff | 亚铁矿物归一化比值（基于Landsat影像） | | iron | 铁元素归一化比值（基于Landsat影像） | | rock_out | 裸岩归一化比值（基于Landsat影像） | | kaolinite index | ASTER高岭石矿物指数 | | calcite index | ASTER方解石矿物指数 | | quartz index | ASTER石英矿物指数 | | carbonate index | ASTER碳酸盐矿物指数 | | mafic index | ASTER铁镁矿物指数 | | Aez | 纳米比亚农业生态区（1996年，分类变量） | | aez_n | 2021年更新版纳米比亚农业生态区 | | cc | 2021年纳米比亚潜在载畜量 | | namsoil_13 | 国家土壤图，包含13种世界土壤资源参比基础（WRB）参考土壤组 | | aspp | 1999–2019年平均季节植物生产力 | | veg_types | 植被类型 | | geology_a | 按类型与年龄划分的主要岩石群 | | geology | 岩性单元（基于地质图） | | Landform_iwa | Iwahashi-Pike地貌分类，基于坡度、纹理与凸度 | | convex | 地形凸度，即正曲率单元格的占比 | | curv_max | 地形曲率，即坡度的变化速率 | 完整的描述与源细节可参见S5——《预测变量集中组装的环境协变量.pdf》文件。 建模框架 本研究采用随机森林（Random Forest）算法开展空间预测。采用bootstrap重采样策略（共20次迭代），以80:20的比例划分为校准集与验证集，采用有放回抽样，并设置固定随机种子。土壤数据预处理、超参数调优、特征选择、建模后指标计算与外部验证均在R语言环境中完成；协变量制备、模型实现与不确定性量化则在谷歌地球引擎（Google Earth Engine, GEE）中完成。 随机森林的超参数设置如下： | 深度层 | ntree | mtry | nodesize | sampsize | |--------------|-------|------|----------|----------| | 0–30 cm | 150 | 1 | 25 | 0.54 | | 30–60 cm | 150 | 6 | 10 | 0.54 | | 60–100 cm | 150 | 6 | 18 | 0.88 | 其中：ntree为森林中决策树的数量；mtry为每次随机森林分裂时随机采样的预测变量数量；nodesize为防止过拟合所需的叶节点最小样本数；sampsize为训练每棵树时抽取的袋内（随机森林内部bootstrap）样本量。 模型性能 针对每次bootstrap迭代，采用均方根误差（Root Mean Square Error, RMSE）量化预测误差，采用决定系数（Coefficient of Determination, R²）衡量解释方差。20次bootstrap迭代的平均性能指标如下： | 深度层 | R²校准集 | RMSE校准集 | R²验证集 | RMSE验证集 | |--------------|---------|------------|---------|------------| | 0–30 cm | 0.475 | 90.270 | 0.107 | 101.822 | | 30–60 cm | 0.252 | 130.696 | 0.153 | 114.165 | | 60–100 cm | 0.284 | 80.741 | 0.224 | 76.925 | 不确定性量化 不确定性估计值源自bootstrap预测分布。5%与95%分位数地图分别代表预测下限与上限。三个土壤深度层的可交换钠90%预测区间覆盖概率（PICP90）如下： | 深度层 | PICP90 | |--------------|-------| | 0–30 cm | 96.67 | | 30–60 cm | 96.92 | | 60–100 cm | 96.33 | 数据输出 地图输出采用云优化型GeoTIFF（Cloud-Optimised GeoTIFF）格式（WGS84坐标系），适用于GIS与建模应用；同时提供PNG格式文件，用于可视化与报告制作。 数据获取 模型训练所用的输入土壤数据可从纳米比亚土壤剖面数据库（NSPD2025）获取，链接为https://doi.org/10.5281/zenodo.17618737。预测土壤地图可通过快速启动脚本直接从Zenodo获取，该脚本支持读取、裁剪与导出NamSoil图层，无需下载完整数据集，其仓库地址为：https://github.com/Gelsleichter/acquire_NamSoil/。该脚本可实现可复现的数据获取工作流，支持用户通过编程方式获取并处理指定图层。 代码可用性 数据预处理、特征选择、超参数调优、模型实现与后处理的完整源代码可从https://doi.org/10.5281/zenodo.18776302获取，同时发布于https://github.com/Gelsleichter/NamSoil。用于协变量制备、回归矩阵导出与20次迭代bootstrap随机森林建模的谷歌地球引擎脚本地址为：https://code.earthengine.google.com/?accept_repo=users/Namibia_map/Soil_properties。请注意，该GEE仓库采用的空间分辨率低于已发布的地图，以降低计算成本、内存占用与导出时间。用户可通过修改导出函数中的scale参数，将输出分辨率调整为90米（或其他数值），但这将需要更长的处理时间与更大的存储空间。所有脚本、固定随机种子与参数配置均已提供，以确保建模流程从协变量制备、空间预测到不确定性量化的完全可复现。用户可复现完整工作流，或针对其他研究区域与土壤属性调整单个组件。 相关出版物 完整的方法学描述、模型评价框架与结果阐释可参见：[待补充的出版物DOI]