Soil generic group (SGG) DSM data of the Roper catchment NT generated by the Roper River Water Resource Assessment

Soil generic group (SGG) is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Roper River Water Resource Assessment (ROWRA) through the digital soil mapping process (DSM). SGG data has been created to simplify the complex information of individual soils and soil attributes for extension, planning and management. This data simultaneously covers a number of purposes: to be descriptive so as to assist non-expert communication regarding soil and resources; to be relatable to agricultural potential; and to align, where practical, to the classes of the Australian Soil Classification system (ASC) (Isbell and National Committee on Soil and Terrain, 2016). This SGG raster data represents a modelled dataset of 13 classes derived from rules applied to measured site data and modelled with environmental covariates. Descriptions of the 13 SGG classes, their rules and the spatial data value descriptions are supplied with this data. SGG mapping was also used as a minor input into the land suitability framework but primarily as a communication tool. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO ROWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO ROWRA published report ‘Soils and land suitability for the Roper catchment, Northern Territory’. A technical report from the CSIRO Roper River Water Resource Assessment to the Government of Australia. The Roper River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Roper catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. \nLineage: This soil generic group dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO ROWRA published reports and in particular ' Soils and land suitability for the Roper catchment, Northern Territory’. A technical report from the CSIRO Roper River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil generic group Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and confusion matrix results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For categorical attributes the method for estimating reliability is the Confusion Index. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.\n

土壤通用组（Soil Generic Group, SGG）是通过数字土壤制图（Digital Soil Mapping, DSM）流程支撑罗珀河水资源评估（Roper River Water Resource Assessment, ROWRA）土地适宜性评价的18个土壤属性之一。SGG数据集旨在简化单个土壤及其属性的复杂信息，以服务于推广、规划与管理工作。该数据集同时承载多重功能：一是具备描述性，助力非专业人士开展土壤与资源相关的交流；二是与农业生产潜力相契合；三是在可行范围内与澳大利亚土壤分类系统（Australian Soil Classification, ASC）的分类等级保持一致（Isbell与国家土壤与地形委员会，2016）。本SGG栅格数据集为包含13个类别的建模产物，其通过将规则应用于实测样点数据，并结合环境协变量进行建模生成。本数据集附带13个SGG类别的说明、对应规则及空间数据值的详细描述。SGG制图虽作为次要输入项纳入土地适宜性评价框架，但核心功能为交流工具。本栅格数据提供了优化后的土壤信息，用于支撑并识别发展机遇，推动针对一系列可持续区域发展方案开展详细调研，其产出隶属于CSIRO（澳大利亚联邦科学与工业研究组织）ROWRA项目的“土地适宜性”工作模块。本数据集还附带反映数据可靠性的配套数据集与统计结果，相关说明可参见本元数据记录的数据溯源部分。数据处理相关信息已在ranger R脚本中提供，属性建模采用随机森林（Random Forest）方法。DSM流程的详细说明可参见CSIRO ROWRA发布的技术报告"Soils and land suitability for the Roper catchment, Northern Territory"，该报告为CSIRO提交给澳大利亚政府的罗珀河水资源评估技术报告之一。罗珀河水资源评估对北领地罗珀流域的水产养殖与农业开发可行性开展了全面综述与综合评估，并同步分析了开发带来的生态、社会及文化（原住民水价值观、权利与诉求）影响。
数据溯源：本土壤通用组数据集由一系列输入数据与处理步骤生成，以下为流程概览。如需获取更多信息，请参考CSIRO ROWRA发布的相关报告，尤其是前述"Soils and land suitability for the Roper catchment, Northern Territory"技术报告，该报告为CSIRO提交给澳大利亚政府的罗珀河水资源评估技术报告之一。
1. 整合现有数据：涵盖土壤、气候、地形、自然资源、遥感数据等多种类型，格式包括报告、空间矢量、空间栅格等。
2. 基于协变量数据空间，采用条件拉丁超立方统计采样方法选取额外的土壤与土地属性样点位置。
3. 开展野外工作，采集新的属性数据与用于分析的土壤样本，并构建对地貌与景观过程的认知。
4. 开展数据库分析，按照属性建模所需的特定筛选标准提取数据。
5. 采用R统计编程环境开展属性计算，通过ranger R包中实现的随机森林预测学习方法，基于筛选后的输入数据与协变量数据构建模型。
6. 生成土壤通用组数字土壤制图（DSM）属性栅格数据集。DSM数据为地理参考数据集，通过定量关系将野外观测数据、实验室分析数据与环境协变量数据相结合生成，其应用了土壤计量学（pedometrics）——即结合土壤观测信息与相关环境变量、遥感影像及部分地球物理测量信息的数学与统计模型方法。
7. 基于构建的500个独立随机森林属性模型，生成配套的预测可靠性数据。
8. 质量保证（QA）：本DSM属性数据采用三种方法开展质量评估。方法1：模型与输入数据的统计（定量）评估。通过留出的模型计算数据对DSM模型质量进行测试，结果以袋外误差（Out-of-Bag, OOB）与混淆矩阵形式呈现，用于估算模型预测的可靠性，相关结果已随数据集一并提供。方法2：空间属性输出数据的统计（定量）评估，以属性“可靠性”栅格形式呈现。该方法利用属性随机森林（RF）模型的500棵独立决策树生成500个属性数据集，用于估算每个属性的模型可靠性；对于分类属性，可靠性估算方法采用混淆指数（Confusion Index）。相关数据已随数据集一并提供。方法3：收集独立的外部验证样点数据，并结合验证野外考察期间的专家实地（定性）检查开展评估。在每个研究区域，均开展了为期两周的验证野外考察，所用的新验证样点集基于属性可靠性数据，通过条件拉丁超立方采样的随机抽样设计生成。将建模得到的DSM属性值与实地实测值进行对比，相关结果已发表于本元数据记录引用的报告中。