Soil surface salinity DSM data of the Fitzroy catchment WA, Darwin catchments and Mitchell catchment Qld generated by the Northern Australia Water Resource Assessment

Soil surface salinity is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Northern Australia Water Resource Assessment (NAWRA) through the digital soil mapping process (DSM). Soil salinity represents the salt content of the soil. This raster data represents a modelled dataset of salinity at the soil surface and is derived from field measured and laboratory analysed site data, and environmental covariates. Data values are: 1 Surface salinity present, 4 Surface salinity absent. Soil surface salinity is a parameter used in land suitability assessments as it hinders seed establishment and retards plant growth. This raster data provides improved soil information used to 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 NAWRA. 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.\nThe DSM process is described in the CSIRO NAWRA published report ‘Digital soil mapping of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment to the Government of Australia'. The land suitability assessment this dataset underpins is described in the CSIRO NAWRA published report ‘Land suitability of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment to the Government of Australia'.\nLineage: This soil surface salinity dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO NAWRA published reports and in particular 'Digital soil mapping of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments. CSIRO, Australia 2018'. 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 surface salinity 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 R squared 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.

土壤表层盐度（Soil Surface Salinity）是为支撑北澳大利亚水资源评估（Northern Australia Water Resource Assessment, NAWRA）的土地适宜性评估而选取的18项土壤属性之一，相关工作通过数字土壤制图（Digital Soil Mapping, DSM）流程开展。土壤盐度即土壤中的盐分含量。本栅格数据为土壤表层盐度的建模数据集，源于野外实测、实验室分析的点位数据以及环境协变量。数据取值为：1代表存在表层盐度，4代表不存在表层盐度。土壤表层盐度是土地适宜性评估中的关键参数，因其会阻碍种子建植并抑制植物生长。本栅格数据提供了优化后的土壤信息，可用于识别发展机遇并推动针对各类可持续区域开发方案的详细调研，由澳大利亚联邦科学与工业研究组织（CSIRO）主导的NAWRA“土地适宜性”工作模块生成。本数据集的配套数据集与反映数据可靠性的统计信息亦已同步提供，相关说明可参见本元数据记录的谱系（Lineage）章节。处理信息以ranger R脚本形式提供，属性建模采用随机森林（Random Forest）方法。

数字土壤制图流程的详细说明见于CSIRO发布的NAWRA技术报告《菲茨罗伊、达尔文与米切尔集水区数字土壤制图——澳大利亚联邦科学与工业研究组织北澳大利亚水资源评估致澳大利亚政府的技术报告》。本数据集所支撑的土地适宜性评估相关内容，见于CSIRO发布的NAWRA技术报告《菲茨罗伊、达尔文与米切尔集水区土地适宜性——澳大利亚联邦科学与工业研究组织北澳大利亚水资源评估致澳大利亚政府的技术报告》。

谱系说明：本土壤表层盐度数据集由多类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发布的NAWRA相关报告，尤其是《菲茨罗伊、达尔文与米切尔集水区数字土壤制图——澳大利亚联邦科学与工业研究组织北澳大利亚水资源评估（国家水利基础设施发展基金水资源评估子项目），2018年，澳大利亚CSIRO》。
1. 整合现有数据：涵盖土壤、气候、地形、自然资源、遥感等多类数据，格式包含报告、空间矢量、空间栅格等。
2. 基于条件拉丁超立方统计采样方法，在协变量数据空间中选取额外的土壤与土地属性点位数据采集位置。
3. 开展野外作业，采集新增属性数据与土壤分析样本，并梳理地貌与景观过程相关认知。
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，基于筛选后的输入数据与协变量数据，借助ranger R包实现的随机森林预测学习方法构建模型。
6. 生成土壤表层盐度数字土壤制图（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、实验室数据与环境协变量数据相结合生成。该数据集采用土壤计量学（Pedometrics）方法——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（Quality Assurance, QA）：本DSM属性数据采用三种方法开展质量评估。
方法1：针对模型与输入数据的统计（定量）评估方法。利用模型训练过程中的袋外（Out-of-Bag, OOB）样本与决定系数（R²）结果对DSM模型质量进行测试，以此估算模型预测的可靠性，相关评估结果已同步提供。
方法2：针对空间属性输出数据的统计（定量）评估方法，该输出为属性“可靠性”栅格。利用属性随机森林模型中的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，可靠性估算采用混淆指数（Confusion Index）方法，相关数据已同步提供。
方法3：收集独立外部验证点位数据，并结合验证野外考察中的实地专家（定性）检查结果开展评估。在各研究区域内，基于属性可靠性数据，采用条件拉丁超立方采样的随机抽样设计生成全新验证点位集，随后开展为期两周的验证野外考察。将建模得到的DSM属性值与实地实测值进行对比评估，相关评估结果已见于本元数据记录引用的报告中。