Soil surface salinity DSM data of the Southern Gulf catchments (NT and Qld) generated by the Southern Gulf Water Resource Assessment

Soil surface salinity is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Southern Gulf Water Resource Assessment (SOGWRA) 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 absent, 2 Surface salinity present. 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 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 SOGWRA. 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 SOGWRA published report ‘Soils and land suitability for the Southern Gulf catchments’. A technical report from the CSIRO Southern Gulf Water Resource Assessment to the Government of Australia. The Southern Gulf Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Southern Gulf catchments NT and Qld as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. \nLineage: The 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 SOGWRA published reports and in particular ' Soils and land suitability for the Southern Gulf catchments’. A technical report from the CSIRO Southern Gulf 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 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 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: A workshop was conducted in March 2023 to review DSM soil attribute and land suitability products and facilitated an alternative to the field external validation carried out in other northern Australia water resource assessments. Stakeholders from the NT and Qld jurisdictions reviewed, evaluated and discussed the soundness of the data and processes. The workshop desk top assessment approach provided recommendations for acceptance, improvement and re-modelling of attributes based on expert knowledge and understanding of the soil distribution and landscape in the study area and available data.\n

土壤表层盐度是支撑南湾水资源评估（Southern Gulf Water Resource Assessment, SOGWRA）土地适宜性评估的18项选定土壤属性之一，通过数字土壤制图（Digital Soil Mapping, DSM）流程生成。土壤盐度指土壤中的盐分含量。本栅格数据为土壤表层盐度的建模数据集，源于野外实测与实验室分析的样点数据及环境协变量。数据取值为：1 无土壤表层盐度，2 存在土壤表层盐度。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、延缓植物生长。本栅格数据提供了优化的土壤信息，用于支撑并识别可持续区域发展的各类机遇，推动开展针对性详细调研，其研发工作隶属于澳大利亚联邦科学与工业研究组织（Commonwealth Scientific and Industrial Research Organisation, CSIRO）SOGWRA的“土地适宜性”项目。本数据集配套的数据与反映数据可靠性的统计结果亦已提供，相关说明可参见本元数据记录的谱系（Lineage）章节。相关处理信息可通过ranger R脚本获取，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 SOGWRA发布的技术报告《南湾集水区土壤与土地适宜性》，该报告为CSIRO向澳大利亚政府提交的南湾水资源评估技术报告。南湾水资源评估全面概述并综合评估了北领地（Northern Territory, NT）与昆士兰州（Queensland, Qld）南湾集水区的水产养殖与农业开发可行性，以及开发活动带来的生态、社会与文化（原住民水权、权益与诉求）影响。

谱系：本土壤表层盐度数据集由多类输入数据与处理流程生成，概述如下。如需更多信息，请参考CSIRO SOGWRA发布的相关报告，尤其是《南湾集水区土壤与土地适宜性》这份提交给澳大利亚政府的技术报告。
1. 整合现有数据：涵盖土壤、气候、地形、自然资源、遥感等多类格式数据，包括报告、空间矢量、空间栅格等。
2. 选取额外的土壤与土地属性样点位置：采用条件拉丁超立方统计抽样方法，基于协变量数据空间进行选取。
3. 开展野外工作：采集新的属性数据与土壤样本用于分析，同时加深对地貌与景观过程的理解。
4. 数据库分析：按照建模所需的属性筛选标准提取数据。
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，通过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与实验室数据与环境协变量数据相结合，其依托土壤计量学（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应用方法。
7. 由构建的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. 采用三种方法对本DSM属性数据开展质量评估（Quality Assessment, QA）：
方法1：模型与输入数据的统计（定量）评估。使用未参与模型计算的预留数据测试DSM模型质量，以袋外（Out-of-Bag, OOB）结果与混淆矩阵结果进行表达，以此估算模型预测的可靠性，相关结果已提供。
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。利用随机森林模型的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），相关数据已提供。
方法3：2023年3月举办专题研讨会，对DSM土壤属性与土地适宜性产品进行评审，作为澳大利亚北部其他水资源评估中开展的野外外部验证的替代方案。来自北领地与昆士兰州辖区的利益相关方对数据与流程的合理性进行了审查、评估与讨论。本次桌面研讨会评估方法基于专家知识、对研究区土壤分布与景观的理解以及可用数据，为属性数据的验收、改进与重新建模提供了建议。