Enhanced barest earth Landsat imagery for soil and lithological modelling: Dataset

Please Note: The data related to this Abstract can be obtained by contacting Manager Client Services and quoting Catalogue number 144231. The data are arranged by regions, so please download the Data Description document found in the Downloads tab to determine your area of interest.Remotely sensed datasets provide fundamental information for understanding the chemical, physical and temporal dynamics of the atmosphere, lithosphere, biosphere and hydrosphere. Satellite remote sensing has been used extensively in mapping the nature and characteristics of the terrestrial land surface, including vegetation, rock, soil and landforms, across global to local-district scales. With the exception of hyper-arid regions, mapping rock and soil from space has been problematic because of vegetation that either masks the underlying substrate or confuses the spectral signatures of geological materials (i.e. diagnostic mineral spectral features), making them difficult to resolve. As part of the Exploring for the Future program, a new barest earth Landsat mosaic of the Australian continent using time-series analysis significantly reduces the influence of vegetation and enhances mapping of soil and exposed rock from space. Here, we provide a brief background on geological remote sensing and describe a suite of enhanced images using the barest earth Landsat mosaic for mapping surface mineralogy and geochemistry. These geological enhanced images provide improved inputs for predictive modelling of soil and rock properties over the Australian continent. In one case study, use of these products instead of existing Landsat TM band data to model chromium and sodium distribution using a random forest machine learning algorithm improved model performance by 28–46%.

请注意：本摘要相关数据可通过联系客户服务经理并援引目录编号144231获取。数据按区域划分，请下载下载选项卡中的《数据说明（Data Description）》文档以确定您关注的研究区域。遥感数据集可为理解大气圈、岩石圈、生物圈与水圈的化学、物理及时间动态提供基础支撑信息。卫星遥感已被广泛应用于全球至局地区域尺度下的陆地地表自然属性与特征制图，涵盖植被、岩石、土壤与地貌。除超干旱区外，通过太空遥感绘制岩石与土壤分布图一直存在难题：植被要么会掩盖下伏基质，要么会干扰地质物质的光谱特征（即具有诊断性的矿物光谱特征），导致其难以被分辨。作为“未来勘探（Exploring for the Future）”计划的一部分，研究人员采用时序分析方法生成了澳大利亚大陆裸地表（Barest Earth）Landsat镶嵌影像，该影像可显著降低植被的干扰影响，并提升太空遥感绘制土壤与裸露岩石分布图的精度。本文简要介绍了地质遥感的相关背景，并阐述了基于该裸地表Landsat镶嵌影像生成的一系列增强影像，用于地表矿物学与地球化学制图。这些地质增强影像可为澳大利亚大陆范围内的土壤与岩石属性预测建模提供更优质的输入数据。在一项案例研究中，相较于现有Landsat专题制图仪（Landsat Thematic Mapper, TM）波段数据，使用本研究生成的产品结合随机森林（random forest）机器学习算法对铬与钠分布进行建模，可将模型性能提升28%至46%。