Improving the Digital Mapping of Soil Organic Carbon using Environmental Covariates and Machine Learning Algorithms in Nepal

The primary data were collected from the field soil survey in the Palung Catchment, Makwanpur District of Nepal and analysed in the laboratory of the Soil Science Division (now renamed as the National Soil Science Research Centre) of the Nepal Agricultural Research Council, Khumaltar, Lalitpur, Nepal. Two sets of soil samples were collected through two different sampling designs. The calibration dataset was collected using the conditioned Latin Hypercube Sampling technique. The validation dataset was collected following a simple random sampling technique. A GPS device was used to locate and record the actual soil sample location. Soil samples were analysed to obtain the percentage of organic carbon, sand, silt, clay, and bulk density. Soil organic carbon was analyzed using Walkley-Black wet oxidation method. Soil particle sizes (sand silt and clay) were determined using the hydrometer method. The bulk density of the soil cores was determined using oven-dry mass of the soil core and the volume of the inner space of the core. These data, in conjunction with other covariates obtained from different secondary sources such as remote sensing, digital elevation model, climatological datasets, soil maps and geological maps were used in the framework of digital soil mapping to predict and validate the prediction of soil organic carbon. As the research encompassed a large geographic extent, covering the entire country, it was essential to use a large volume of secondary data as well. The details of the primary data and the sources of the secondary data are included in the attached data and metadata files.

本研究的原始数据采集自尼泊尔马克万普尔县帕隆流域的实地土壤调查，并于尼泊尔拉利特普尔市库马尔塔尔的尼泊尔农业研究理事会土壤科学部（现更名为国家土壤科学研究中心，National Soil Science Research Centre）实验室完成分析。本研究采用两种不同采样设计采集两组土壤样品：校正数据集采用条件拉丁超立方体采样（conditioned Latin Hypercube Sampling）技术采集，验证数据集则采用简单随机采样技术获取。研究人员使用GPS设备定位并记录土壤样品的实际采集点位。对土壤样品开展检测分析，以获取有机碳、砂粒、粉粒、黏粒的百分含量及容重：其中土壤有机碳含量采用沃克莱-布莱克湿氧化法（Walkley-Black wet oxidation method）测定；土壤颗粒组成（砂粒、粉粒、黏粒）采用比重计法（hydrometer method）测定；土壤容重则通过测定土芯烘干质量与土芯内部容积计算得到。本研究将上述实测数据，与从遥感、数字高程模型（digital elevation model）、气候数据集、土壤图及地质图等不同二手数据源获取的协变量数据相结合，基于数字土壤制图（digital soil mapping）框架开展土壤有机碳含量的预测与验证工作。由于本研究覆盖全国范围的广阔地理区域，因此也需要使用大量二手数据。本次研究的原始数据细节及二手数据来源均已纳入附带的数据与元数据文件中。