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

Soil surface texture 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 texture is determined by the percentage and size distribution of mineral (sand, silt and clay) particles of the soil finer than 2mm, carried out in the field. This soil surface texture raster data represents a modelled dataset of soil texture for the major part of the A horizons (surface soil) and is derived from field measured site data and environmental covariates. The soil texture classes are based on the field texture classes of the National Committee on Soil and Terrain 2009 (NCST) texture descriptions. Data values are: 1 Sandy, 2 Loamy, 3 Silty, 4 Clayey and the texture groupings behind these values are supplied in the word document READ_ME_Texture_Classes. Soil surface texture is a parameter used in land suitability assessments of soil physical factors and affects; water infiltration, water holding capacity, permeability, drainage, water and wind erosion, workability (soil adhesiveness), trafficability and soil nutrients levels. 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 surface texture 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 surface texture 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

土壤表层质地是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，通过数字土壤制图（Digital Soil Mapping, DSM）流程实现。土壤质地由野外实测的、粒径小于2mm的土壤矿物颗粒（砂粒、粉粒和黏粒）的占比及粒径分布决定。本土壤表层质地栅格数据为A层（表层土壤）主体区域的土壤质地模拟数据集，基于野外实测样点数据与环境协变量生成。土壤质地分类依据2009年国家土壤与地形委员会（National Committee on Soil and Terrain, NCST）的野外质地分类标准。数据取值为：1 砂质、2 壤质、3 粉壤质、4 黏质，对应分类的详细说明可查阅配套Word文档"READ_ME_Texture_Classes"。
土壤表层质地是评价土壤物理因子的土地适宜性参数之一，其影响包括水分入渗、持水能力、渗透性、排水性、水蚀与风蚀、土壤可耕作性（土壤黏着性）、通行性以及土壤养分水平。本栅格数据提供了更精准的土壤信息，可为一系列可持续区域发展方案提供支撑、识别发展机遇并推动开展详细调研，该数据集由澳大利亚联邦科学与工业研究组织（Commonwealth Scientific and Industrial Research Organisation, CSIRO）ROWRA项目的“土地适宜性”研究活动生成。本数据集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谱系部分。处理代码以R语言脚本形式提供，属性建模采用随机森林（Random Forest）方法。DSM流程的详细说明可参阅CSIRO ROWRA项目发表的报告《北领地罗珀集水区土壤与土地适宜性》，该报告为CSIRO提交给澳大利亚政府的罗珀河水资源评估技术报告。罗珀河水资源评估全面概述并综合评估了北领地罗珀集水区水产养殖与农业开发的可行性，以及开发活动带来的生态、社会与文化（原住民水权、权利与诉求）影响。
谱系：本土壤表层质地数据集由多类输入数据与处理步骤生成，概述如下。如需更多信息，请参阅CSIRO ROWRA项目发表的报告，尤其是《北领地罗珀集水区土壤与土地适宜性》这份提交给澳大利亚政府的罗珀河水资源评估技术报告。
1. 整合现有数据（涵盖土壤、气候、地形、自然资源、遥感影像等多种类型，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，通过条件拉丁超立方统计抽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土壤样本用于分析，并加深对地貌与景观过程的认知。
4. 进行数据库分析，按照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，利用ranger R包实现的随机森林方法进行预测学习，构建模型。
6. 生成土壤表层质地数字土壤制图（DSM）属性栅格数据集。DSM数据是一类地理参考数据集，通过野外观测与实验室数据，结合定量关系与环境协变量数据生成。其采用土壤计量学（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应用。
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. 通过三种方法开展DSM属性数据的质量评估（Quality Assessment, QA）：
方法1：针对模型与输入数据的统计（定量）评估。利用未参与模型训练的留存数据测试DSM模型质量，以袋外误差（Out-of-Bag, OOB）与混淆矩阵结果呈现，实现模型预测可靠性的估算，相关结果已随数据集提供。
方法2：针对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，可靠性估算采用混淆指数法，该数据已随数据集提供。
方法3：采集独立的外部验证样点数据，并结合野外验证考察期间的实地专家（定性）检查结果开展评估。在每个研究区域，基于属性可靠性数据，通过条件拉丁超立方抽样的随机抽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考察，将模拟得到的DSM属性值与实地实测值进行对比。相关评估结果已发表于本元数据记录引用的报告中。