Marine satellite image test collections (AIMS)

This dataset consists of collections of satellite image composites (Sentinel 2 and Landsat 8) that are created from manually curated image dates for a range of projects. These images are typically prepared for subsequent analysis or testing of analysis algorithms as part of other projects. This dataset acts as a repository of reproducible test sets of images processed from Google Earth Engine using a standardised workflow.Details of the algorithms used to produce the imagery are described in the GEE code and code repository available on GitHub (https://github.com/eatlas/World_AIMS_Marine-satellite-imagery).Project test image sets:As new projects are added to this dataset, their details will be described here:- NESP MaC 2.3 Benthic reflection estimation (projects/CS_NESP-MaC-2-3_AIMS_Benth-reflect): This collection consists of six Sentinel 2 image composites in the Coral Sea and GBR for the purpose of testing a method of determining benthic reflectance of deep lagoonal areas of coral atolls. These image composites are in GeoTiff format, using 16-bit encoding and LZW compression. These images do not have internal image pyramids to save on space. [Status: final and available for download]- NESP MaC 2.3 Oceanic Vegetation (projects/CS_NESP-MaC-2-3_AIMS_Oceanic-veg):This project is focused on mapping vegetation on the bottom of coral atolls in the Coral Sea. This collection consists of additional images of Ashmore Reef. The lagoonal area of Ashmore has low visibility due to coloured dissolved organic matter, making it very hard to distinguish areas that are covered in vegetation. These images were manually curated to best show the vegetation. While these are the best images in the Sentinel 2 series up to 2023, they are still not very good. Probably 80 - 90% of the lagoonal benthos is not visible.[Status: final and available for download]- NESP MaC 3.17 Australian reef mapping (projects/AU_NESP-MaC-3-17_AIMS_Reef-mapping): This collection of test images was prepared to determine if creating a composite from manually curated image dates (corresponding to images with the clearest water) would produce a better composite than a fully automated composite based on cloud filtering. The automated composites are described in https://doi.org/10.26274/HD2Z-KM55. This test set also includes composites from low tide imagery. The images in this collection are not yet available for download as the collection of images that will be used in the analysis has not been finalised. [Status: under development, code is available, but not rendered images]- Capricorn Regional Map (projects/CapBunk_AIMS_Regional-map): This collection was developed for making a set of maps for the region to facilitate participatory mapping and reef restoration field work planning.[Status: final and available for download] - Default (project/default): This collection of manual selected scenes are those that were prepared for the Coral Sea and global areas to test the algorithms used in the developing of the original Google Earth Engine workflow. This can be a good starting point for new test sets. Note that the images described in the default project are not rendered and made available for download to save on storage space.[Status: for reference, code is available, but not rendered images]Filename conventions:The images in this dataset are all named using a naming convention. An example file name is `Wld_AIMS_Marine-sat-img_S2_NoSGC_Raw-B1-B4_54LZP.tif`. The name is made up of: - Dataset name (`Wld_AIMS_Marine-sat-img`), short for World, Australian Institute of Marine Science, Marine Satellite Imagery. - Satellite source: `L8` for Landsat 8 or `S2` for Sentinel 2.- Additional information or purpose: `NoSGC` - No sun glint correction, `R1` best reference imagery set or `R2` second reference imagery.- Colour and contrast enhancement applied (`DeepFalse`, `TrueColour`,`Shallow`,`Depth5m`,`Depth10m`,`Depth20m`,`Raw-B1-B4`), - Image tile (example: Sentinel 2 `54LZP`, Landsat 8 `091086`)Limitations:Only simple atmospheric correction is applied to land areas and as a result the imagery only approximates the bottom of atmosphere reflectance.For the sentinel 2 imagery the sun glint correction algorithm transitions between different correction levels from deep water (B8) to shallow water (B11) and a fixed atmospheric correction for land (bright B8 areas). Slight errors in the tuning of these transitions can result in unnatural tonal steps in the transitions between these areas, particularly in very shallow areas.For the Landsat 8 image processing land areas appear as black from the sun glint correction, which doesn't separately mask out the land. The code for the Landsat 8 imagery is less developed than for the Sentinel 2 imagery.The depth contours are estimated using satellite derived bathymetry that is subject to errors caused by cloud artefacts, substrate darkness, water clarity, calibration issues and uncorrected tides. They were tuned in the clear waters of the Coral Sea. The depth contours in this dataset are RAW and contain many false positives due to clouds. They should not be used without additional dataset cleanup. Change log:As changes are made to the dataset, or additional image collections are added to the dataset then those changes will be recorded here.2nd Edition, 2024-06-22: CapBunk_AIMS_Regional-map1st Edition, 2024-03-18: Initial publication of the dataset, with CS_NESP-MaC-2-3_AIMS_Benth-reflect, CS_NESP-MaC-2-3_AIMS_Oceanic-veg and code for AU_NESP-MaC-3-17_AIMS_Reef-mapping and Default projects.Data Format:GeoTiff images with LZW compression. Most images do not have internal image pyramids to save on storage space. This makes rendering these images very slow in a desktop GIS. Pyramids should be added to improve performance.Data Location:This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Wld-AIMS-Marine-sat-img

本数据集包含多组卫星影像合成集（哨兵2号（Sentinel 2）、陆地卫星8号（Landsat 8）），影像日期均经过人工甄选，服务于多项科研项目。此类影像通常用于后续分析或算法测试，作为其他项目的组成部分。本数据集作为可复现测试集的存储库，所有影像均通过标准化工作流在谷歌地球引擎（Google Earth Engine，GEE）中处理生成。用于生成影像的算法细节已在GitHub（https://github.com/eatlas/World_AIMS_Marine-satellite-imagery）公开的GEE代码及代码仓库中详述。

### 项目测试影像集
随着新项目加入本数据集，其详情将在此处更新：
1. NESP MaC 2.3 底栖反射估算（projects/CS_NESP-MaC-2-3_AIMS_Benth-reflect）：该数据集包含珊瑚海与大堡礁区域的6组哨兵2号（Sentinel 2）影像合成集，用于测试珊瑚环礁潟湖深水区底栖反射率的估算方法。影像合成集采用GeoTIFF（GeoTiff）格式，16位编码与LZW压缩，未内置影像金字塔以节省存储空间。[状态：已定稿，可下载]
2. NESP MaC 2.3 海洋植被（projects/CS_NESP-MaC-2-3_AIMS_Oceanic-veg）：本项目聚焦珊瑚海珊瑚环礁海底植被制图，该数据集包含阿什莫尔礁的补充影像。阿什莫尔礁潟湖因有色溶解有机物导致水体能见度较低，难以区分植被覆盖区域。本次甄选的影像旨在最大化凸显植被信息，尽管为2023年之前哨兵2号序列中的最优影像，整体质量仍有限，约80%-90%的潟湖底栖生物仍不可见。[状态：已定稿，可下载]
3. NESP MaC 3.17 澳大利亚礁体制图（projects/AU_NESP-MaC-3-17_AIMS_Reef-mapping）：本测试影像集旨在验证：基于人工甄选影像日期（选取水体最清晰的影像）生成的合成影像，是否优于仅基于云过滤的全自动合成影像。全自动合成影像的详情见https://doi.org/10.26274/HD2Z-KM55。本测试集还包含落潮时段的影像合成集。当前本数据集的影像尚未开放下载，因为分析所用的影像集合尚未最终确定。[状态：开发中，代码已公开，但未生成渲染影像]
4. 卡普里科恩区域制图（projects/CapBunk_AIMS_Regional-map）：本数据集为该区域制图工作开发，用于辅助参与式制图与礁体修复野外作业规划。[状态：已定稿，可下载]
5. 默认项目（project/default）：该数据集包含人工甄选的影像场景，用于测试谷歌地球引擎原始工作流开发过程中所用的算法，覆盖珊瑚海与全球区域，可作为新测试集的良好起点。需注意：默认项目中的影像未渲染并开放下载，以节省存储空间。[状态：仅供参考，代码已公开，但未生成渲染影像]

### 文件名约定
本数据集所有影像均遵循统一命名规范，示例文件名：`Wld_AIMS_Marine-sat-img_S2_NoSGC_Raw-B1-B4_54LZP.tif`。文件名由以下部分组成：
- 数据集名称（`Wld_AIMS_Marine-sat-img`）：为World（全球）、Australian Institute of Marine Science（澳大利亚海洋科学研究所）、Marine Satellite Imagery（海洋卫星影像）的缩写。
- 卫星数据源：`L8`代表陆地卫星8号（Landsat 8），`S2`代表哨兵2号（Sentinel 2）。
- 附加信息或用途：`NoSGC`表示未进行太阳耀斑校正，`R1`代表最优参考影像集，`R2`代表次优参考影像集。
- 应用的色彩与对比度增强方式：包括`DeepFalse`（深假彩色）、`TrueColour`（真彩色）、`Shallow`（浅水区增强）、`Depth5m`（5米深度标识）、`Depth10m`（10米深度标识）、`Depth20m`（20米深度标识）、`Raw-B1-B4`（原始波段1-4）。
- 影像瓦片编号：例如哨兵2号（Sentinel 2）的`54LZP`、陆地卫星8号（Landsat 8）的`091086`。

### 局限性
仅对陆地区域应用了简易大气校正，因此影像仅近似表征地表大气反射率。
针对哨兵2号（Sentinel 2）影像，太阳耀斑校正算法在深水（B8波段）与浅水（B11波段）校正等级间切换，并对陆地（B8波段亮区）应用固定大气校正。该校正过渡参数的细微误差可能导致区域间色调突变，尤其在极浅水区更为明显。
针对陆地卫星8号（Landsat 8）影像，太阳耀斑校正未单独掩膜陆地，导致陆地区域显示为黑色，其处理代码的完善程度低于哨兵2号（Sentinel 2）影像。
深度等值线通过卫星测深法估算，受云伪影、底质暗度、水体透明度、校准问题与未校正潮汐等因素影响存在误差。该校准基于珊瑚海的清澈水域完成，本数据集中的深度等值线为原始数据，因云干扰存在大量假阳性结果，未经额外数据集清理不得直接使用。

### 变更日志
本数据集的更新或新增影像集合的变更记录将在此处留存。
- 第二版，2024-06-22：新增CapBunk_AIMS_Regional-map项目
- 第一版，2024-03-18：数据集首次发布，包含CS_NESP-MaC-2-3_AIMS_Benth-reflect、CS_NESP-MaC-2-3_AIMS_Oceanic-veg两个项目，以及AU_NESP-MaC-3-17_AIMS_Reef-mapping与Default项目的代码。

### 数据格式
影像均为采用LZW压缩的GeoTIFF（GeoTiff）格式，多数影像未内置影像金字塔以节省存储空间，这会导致在桌面GIS中渲染速度极慢，建议添加金字塔以提升性能。

### 数据存储位置
本数据集存储于eAtlas永久数据仓库中，路径为：datacustodian2020-2029-AIMSWld-AIMS-Marine-sat-img