Soil permeability DSM data of the Roper catchment NT generated by the Roper River Water Resource Assessment

Soil permeability 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). Soil permeability describes the ability of a soil to transmit water internally by its least permeable layer (rate of water movement into and through the soil profile). This soil permeability raster data represents a modelled dataset of permeability as described by the National Committee on Soil and Terrain 2009 (NCST) and is derived from field measured site data and environmental covariates. Data values are: 1 Very slowly permeable, 2 Slowly permeable, 3 Moderately permeable, 4 Highly permeable. Soil permeability is a parameter used in land suitability assessments of irrigation efficiency and soil wetness (in combination with soil drainage indicating site and soil conditions that result in poor soil aeration for plant growth). 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 permeability 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 permeability 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

土壤渗透性是遴选用于通过数字土壤制图（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项土壤属性之一。土壤渗透性指的是土壤通过其渗透性最弱的土层实现内部水分传输的能力，即水分进入并穿过土壤剖面的速率。本土壤渗透性栅格数据为依据2009年《国家土壤与地形委员会（National Committee on Soil and Terrain, NCST）》相关标准构建的渗透性模拟数据集，其数据源自野外实测样点数据与环境协变量。数据取值如下：1 极慢渗透性，2 慢渗透性，3 中等渗透性，4 高渗透性。

土壤渗透性是评估灌溉效率与土壤湿度的土地适宜性评价参数之一，常与土壤排水性结合使用，以指示影响植物生长的土壤通气不良的样点与土壤条件。本栅格数据提供了优化后的土壤信息，用于支撑一系列可持续区域发展方案的机遇识别与深化调研，该数据集由澳大利亚联邦科学与工业研究组织（CSIRO）下属ROWRA项目的"土地适宜性"工作模块所生成。本数据集配套的可靠性统计数据集亦同步提供，相关说明可参见本元数据记录的谱系章节。

处理相关信息可通过ranger R脚本获取，土壤属性的建模采用随机森林（Random Forest）方法。数字土壤制图流程的详细说明可参见CSIRO ROWRA发布的技术报告《北领地罗珀流域土壤与土地适宜性》，该报告为CSIRO提交给澳大利亚政府的罗珀河水资源评估专项技术报告。罗珀河水资源评估项目对北领地罗珀流域水产养殖与农业开发的可行性开展了全面综述与综合评估，并同步分析了开发活动带来的生态、社会及文化（原住民水权、用水权益与发展诉求）影响。

### 谱系说明
本土壤渗透性数据集由多类输入数据与一系列处理步骤生成，以下为流程概览。如需获取更多细节，可参阅CSIRO ROWRA发布的相关报告，尤其是前述《北领地罗珀流域土壤与土地适宜性》专项技术报告（即CSIRO提交给澳大利亚政府的罗珀河水资源评估专项报告）。
1. 整合现有数据：涵盖土壤、气候、地形、自然资源、遥感影像等多类数据源，格式包含报告文件、空间矢量数据、空间栅格数据等。
2. 选取补充样点：基于协变量数据空间，通过条件拉丁超立方统计采样方法筛选新增土壤与土地属性样点位置。
3. 开展野外调研：采集新增属性数据与分析用土壤样品，并建立对研究区地貌与景观过程的认知。
4. 数据库分析：依据属性建模所需的特定筛选标准，从数据库中提取对应数据。
5. 建模计算：采用R统计编程环境开展属性计算，通过ranger R包实现的随机森林（Random Forest）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. 生成土壤渗透性数字土壤制图（DSM）属性栅格数据集：DSM数据为地理参考数据集，通过定量关联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。其采用土壤计量学（pedometrics）方法，即通过数学与统计模型，融合土壤观测数据与相关环境变量、遥感影像及部分地球物理测量数据中的信息。
7. 生成配套可靠性预测数据：基于构建的500个独立随机森林属性模型生成配套的可靠性预测数据集。
8. 质量评估：本DSM属性数据采用三种方法开展质量评价。
方法1：模型与输入数据统计（定量）分析方法：采用模型训练时预留的袋外数据测试DSM模型质量，以袋外误差（Out-of-Bag, OOB）与混淆矩阵结果表征模型预测可靠性，相关测试结果已随本数据集一并提供。
方法2：空间属性输出数据统计（定量）评估方法：针对以栅格形式输出的"可靠性"属性数据开展评估。通过随机森林模型的500棵独立决策树生成500个属性数据集，以此估算各属性的模型可靠性；对于分类属性，采用混淆指数（Confusion Index）进行可靠性估算，相关评估数据已同步提供。
方法3：独立外部验证样点数据结合野外验证实地专家（定性）核查方法：在每个研究区内，基于属性可靠性数据通过条件拉丁超立方采样设计生成全新的验证样点集，随后开展为期两周的野外验证调研，将DSM模拟的属性值与实地实测值进行对比评估。相关评估结果已发表于本元数据记录引用的技术报告中。