FGARA Digital Soil Mapping Output - Soil Surface Condition

Soil surface condition is one of 19 attributes of soils chosen to underpin the land suitability assessment of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project through the digital soil mapping process (DSM). This raster data (in GeoTIFF format) represents a modelled surface of the condition of the surface of the soil using codes from the Australian Soil and Land Survey Field Handbook (ASLSFH) and is derived from measured site data and environmental covariates. The data is used in assessment of soil physical factors eg water infiltration, seedling establishment and machinery workability.\nCodes are: 1 Cracking and/or self mulching, 2 Loose and/or soft, 3 Firm and/or hardsetting, 4 Surface crust.\nThe attribute data file is named "ConditionClasses.tif". \nAlso included are data reflecting confidence of the main dataset. This file is named "Condition_CI.tif". "CI" represents "confusion index".\nThe DSM process is described in the technical report: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO.\nThis raster data provides improved soil information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the “Land Suitability” component of FGARA projects.\nLineage: This data has been created from a range of inputs and processing steps. Below is an overview. Broadly, the steps were to: \n1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc.). \n2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. \n3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. \n4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. \n5. Create Soil Surface Condition Digital Soil Mapping (DSM) key attribute output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental 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.\nQuality assessment of the attribute data is mapped spatially as a function of the model output by evaluating the rigour of the DSM attribute data using non-parametric bootstrapping of the DSM modelling. For more information refer to “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

土壤表层状况是为支撑弗林德斯与吉尔伯特农业资源评估（Flinders and Gilbert Agricultural Resource Assessment, FGARA）项目的土地适宜性评价而选取的19项土壤属性之一，该评估通过数字土壤制图（Digital Soil Mapping, DSM）流程开展。
本栅格数据（格式为GeoTIFF）基于《澳大利亚土壤与土地调查野外手册（Australian Soil and Land Survey Field Handbook, ASLSFH）》中的编码，对土壤表层状况进行建模表征，其数据来源于实测样点数据与环境协变量。
该数据可用于评估土壤物理因子，例如水分入渗、幼苗定植以及农机作业可行性。
编码规则如下：1 龟裂型/自覆型；2 松散/松软型；3 紧实/硬结成块型；4 地表结壳型。
该属性数据文件命名为"ConditionClasses.tif"。
本次数据集还包含反映主数据集置信度的相关数据，其文件命名为"Condition_CI.tif"，其中"CI"即"混淆指数（confusion index）"。
数字土壤制图流程的详细说明可参考以下技术报告：Bartley R、Thomas MF、Clifford D、Phillip S、Brough D、Harms D、Willis R、Gregory L、Glover M、Moodie K、Sugars M、Eyre L、Smith DJ、Hicks W 与 Petheram C（2013）《土地适宜性：技术方法》，为澳大利亚政府针对弗林德斯与吉尔伯特农业资源评估（FGARA）项目撰写的技术报告，由CSIRO出品。
本栅格数据提供了优化后的土壤信息，可用于识别开发机遇并推动针对各类可持续发展方案的详细调研，其隶属于FGARA项目的"土地适宜性"模块。
数据溯源：本数据集通过一系列输入数据与处理流程生成，整体流程概述如下：
1. 整合现有数据：涵盖气候、地形、土壤、自然资源、遥感等多类型数据，以及各类格式的报告、空间矢量、空间栅格等数据。
2. 基于协变量空间，通过拉丁超立方统计抽样法选取补充的土壤与属性样点数据。
3. 开展野外调查，补充采集土壤与属性数据并明确地貌与景观特征。
4. 依托RuleFit3软件中的规则集成预测学习方法，基于选取的输入数据与协变量数据构建模型。
5. 生成土壤表层状况数字土壤制图（DSM）核心属性输出数据。数字土壤制图是指通过定量关系，结合野外与实验室观测数据与环境数据，构建并填充地理参考数据库的过程。其依托土壤计量学（pedometrics）方法——即运用数学与统计模型，将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所包含的信息进行融合。
属性数据的质量评估通过将DSM建模的非参数自助法用于DSM属性数据的严谨性评价，以模型输出为变量实现空间制图。如需了解更多细节，请参考《土地适宜性：技术方法》，即澳大利亚政府针对弗林德斯与吉尔伯特农业资源评估（FGARA）项目的技术报告。