Land suitability for Rice for the FGARA project

This land suitability for Rice raster data (in GeoTIFF format) represents areas of potential suitability for this crop and its specific irrigation management systems in the Flinders and Gilbert catchments of North Queensland. \nThe data is coded 1-5: \n1 - Suitable with no limitations; \n2 - Suitable with minor limitations; \n3 - Suitable with moderate limitations; \n4 - Marginal; \n5 - Unsuitable. \nThe land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO).\nThis data is part of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project and is designed to support sustainable regional development in Australia being of importance to Australian Governments and agricultural industries. The project identifies new opportunities for irrigation development in these remote areas by providing improved soil and land evaluation data to identify opportunities and promote detailed investigation.\nA companion dataset exists, “Confidence of suitability data for the FGARA project”. A link to this dataset can be found in the “related materials” section of this metadata record.\nLineage: These suitability raster data for Rice and its individual irrigation management systems have been created from a range of inputs and processing steps. Below is an overview. For more information refer to the CSIRO FGARA published reports and in particular: 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. 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 Digital Soil Mapping (DSM) key attributes 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.\n6. Choose land management options and create suitability rules for DSM attributes.\n7. Run suitability rules to produce limitation datasets using a modification on the FAO methods.\n8. Create final suitability data for all land management options.\nTwo companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

水稻土地适宜性栅格数据（GeoTIFF格式）反映了北昆士兰弗林德斯和吉尔伯特流域内该作物及其特定灌溉管理系统的潜在适宜区域。
该数据采用1-5编码：
1 - 无限制适宜；
2 - 轻微限制适宜；
3 - 中度限制适宜；
4 - 边缘适宜；
5 - 不适宜。
生成该数据所采用的土地适宜性评价方法是对联合国粮食及农业组织（FAO）方法的改良版本。
本数据是弗林德斯和吉尔伯特农业资源评估（FGARA）项目的一部分，旨在支持澳大利亚区域可持续发展，对澳大利亚政府及农业产业具有重要意义。该项目通过提供改进的土壤与土地评价数据，识别偏远地区灌溉发展的新机遇，并推动详细调研。
存在一个配套数据集——《FGARA项目适宜性数据置信度》，其链接可在本元数据记录的“相关材料”部分找到。
数据谱系：水稻及其各灌溉管理系统的适宜性栅格数据由一系列输入数据及处理步骤生成。以下为概述，更多信息请参考CSIRO发布的FGARA报告，特别是：Bartley R等人（2013）《土地适宜性：技术方法——澳大利亚政府弗林德斯和吉尔伯特农业资源评估（FGARA）项目技术报告》（CSIRO）。大致步骤如下：
1. 整理现有数据（包括气候、地形、土壤、自然资源、遥感等各类格式数据；报告、空间矢量、空间栅格等）。
2. 采用拉丁超立方统计抽样法在协变量空间中选取额外的土壤及属性站点数据。
3. 开展野外工作，收集额外土壤及属性数据，并了解地貌与景观特征。
4. 利用RuleFit3软件中的规则集成预测学习方法，基于选定的输入数据和协变量数据构建模型。
5. 生成数字土壤制图（DSM）关键属性输出数据。DSM指通过定量关系将野外与实验室观测数据与环境数据相结合，创建并填充地理参考数据库的过程。它运用土壤计量学——即运用数学和统计模型，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数据中的信息相结合。
6. 选择土地管理方案，并针对DSM属性制定适宜性规则。
7. 运用改良的FAO方法运行适宜性规则，生成限制数据集。
8. 为所有土地管理方案生成最终适宜性数据。
本数据集存在两个配套数据集：第一个链接位于本元数据记录的“相关材料”部分，标题为《FGARA项目适宜性数据置信度》；第二个（由CSIRO土地与水资源部门持有）包含关于景观过程或条件的专家意见与知识，这些过程或条件会影响该流域的农业发展潜力，但未在建模过程中得到充分捕捉（以及马氏距离法（Mahanabolis method）低估置信度的专家意见区域）。需特别关注的两个景观特征是：上弗林德斯流域中未被协变量数据充分捕捉的玄武岩露头，以及中弗林德斯流域的次生盐渍化风险。更多信息请参考报告《土地适宜性：技术方法——澳大利亚政府弗林德斯和吉尔伯特农业资源评估（FGARA）项目技术报告》。