Landsat-based soil spectral indices for pan-EU 2000-2022: Annual Landsat P75 (2006)

Description This data is part of the Soil Health data cube (EU, 30m) dataset. Check the related identifiers section below to access other parts of the dataset. General Description This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. This data cube includes: Long-term trend (2000-2022): The long term trend data includes 4 pan-European trend maps: NDVI P50 trend, NDWI P50 trend, BSF trend, and minNDTI trend. They are calculated from the corresponding annual indices from 2000 to 2022. Annual Landsat P25: Derived from bimonthly Landsat surface reflectance bands, this data provides an annually aggregated P25 from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, thermal bands, and 2 indices NDVI and NDWI. Annual Landsat P50: Similar to annual Landsat bands P25, but is aggregated as P50 instead. This data includes annual P50 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI. Annual Landsat P75: Similar to annual Landsat bands P25, but is aggregated as P75 instead. This data includes annual P75 aggregation of red, green, blue, nir, swir1, swir2, thermal, NDVI, and NDWI. Annual aggregated indices: This dataset includes minimum NDTI, BSF, NOS and CDR. Each of them are annually aggregated from bimonthly NDVI time series within the corresponding year, through time analysis and statistics calculation. Bimonthly Landsat bands: Derived from Landsat ARD v2 to analysis-ready, cloud-optimized bimonthly Landsat surface reflectance bands, spanning from 2000 to 2022. The bands include red, green, blue, nir, swir1, swir2, and thermal bands. Landsat ARD v2 provides spatial data of these bands, as well as the quality band at 16 days (23 layers of each year) interval from 2000 to 2023. Only pixels with clear sky according to quality band are kept. The gaps are firstly reduced by aggregating the 16 days interval data to bimonthly. The left gaps are then be gapfilled with SWAG method. Bimonthly spectral indices: This dataset is derived from bimonthly Landsat surface reflectance bands through band operation, including NDVI, BSI, NDTI, NDSI, SAVI, NDWI, and FAPAR. Related identifiers Long-term trend: 2000-2022 Annual Landsat P25: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Annual Landsat P50: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Annual Landsat P75: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Annual aggregated indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Bimonthly Landsat bands: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Bimonthly spectral indices: 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Data Details Time period: 2000–2022 Type of data: soil health data cube, with selected indices relevant to soil health monitoring. How the data was collected or derived: Derived from Landsat ARD v2. Cloudy pixels were removed and only clear sky values were considered in further processing. The time-series gap-filling and time-series aggregation were computed using the Scikit-map Python package. Statistical methods used: band operation, time series analysis and statistics calculation Limitations or exclusions in the data: The dataset does not include data for Svalbard. Coordinate reference system: EPSG:3035 Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000) Spatial resolution: 30m Image size: 216,700P x 153,400L File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: ndti.min.slopes = the long term slope of minNDTI variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment Spatial support: 30m Depth reference: s = surface Time reference begin time: 20000101 = 2000-01-01 Time reference end time: 20221231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.3035 Version code: v20231218 = 2023-12-18 (creation date)数据集说明 本数据集隶属于土壤健康数据立方体（Soil Health data cube, EU, 30m），如需获取该数据集的其他分支，请参阅下文的相关标识符章节。 数据集总览 本数据集覆盖泛欧洲区域，涵盖乌克兰、英国与土耳其。该数据立方体可应用于泛欧洲范围内的土壤属性制图与全域土壤健康综合评估等场景。本数据立方体包含以下内容： 长期趋势数据（2000-2022年） 长期趋势数据包含4张泛欧洲趋势图：归一化差异植被指数（Normalized Difference Vegetation Index, NDVI）P50趋势、归一化差异水体指数（Normalized Difference Water Index, NDWI）P50趋势、BSF趋势以及minNDTI趋势。上述趋势图均基于2000至2022年的对应年度指数计算得到。 年度Landsat P25数据 该数据源自双月尺度的Landsat地表反射率波段，提供2000至2022年的年度聚合P25值。涵盖波段包括红光、绿光、蓝光、近红外、短波红外1（SWIR1）、短波红外2（SWIR2）、热红外波段，以及NDVI和NDWI。 年度Landsat P50数据 与年度Landsat P25数据处理逻辑一致，仅聚合方式为P50。该数据包含2000至2022年红光、绿光、蓝光、近红外、SWIR1、SWIR2、热红外波段以及NDVI、NDWI的年度P50聚合值。 年度Landsat P75数据 与年度Landsat P25数据处理逻辑一致，仅聚合方式为P75。该数据包含2000至2022年红光、绿光、蓝光、近红外、SWIR1、SWIR2、热红外波段以及NDVI、NDWI的年度P75聚合值。 年度聚合指数数据 本数据集包含minNDTI、BSF、NOS与CDR四类指数。所有指数均通过时序分析与统计计算，基于对应年份内的双月尺度NDVI时间序列进行年度聚合得到。 双月尺度Landsat波段数据 该数据源自已预处理且支持云优化的Landsat ARD v2（Analysis-Ready, Cloud-optimized）产品，涵盖2000至2022年的双月尺度Landsat地表反射率波段，包括红光、绿光、蓝光、近红外、SWIR1、SWIR2及热红外波段。Landsat ARD v2提供上述波段的空间数据，以及2000至2023年每16天（每年共计23层）的质量波段。仅保留质量波段标记为晴空的像素；首先通过将16天间隔数据聚合为双月尺度以填补部分数据间隙，剩余间隙则通过SWAG方法进行间隙填充。 双月尺度光谱指数数据 本数据集通过波段运算从双月尺度Landsat地表反射率波段计算得到，涵盖NDVI、BSI、NDTI、NDSI、SAVI、NDWI与FAPAR共7类指数。 相关标识符 长期趋势数据（2000-2022年） 年度Landsat P25：2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 年度Landsat P50：2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 年度Landsat P75：2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 年度聚合指数数据：2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 双月尺度Landsat波段数据：2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 双月尺度光谱指数数据：2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 数据详情 时间范围：2000-2022年 数据类型：土壤健康数据立方体，包含与土壤健康监测相关的精选指数 数据获取与衍生方式：源自Landsat ARD v2产品。已剔除云污染像素，仅保留晴空像素用于后续处理；时序间隙填充与时序聚合操作通过Scikit-map Python工具包完成计算 所用统计方法：波段运算、时序分析与统计计算 数据局限性与排除项：本数据集未包含斯瓦尔巴群岛的相关数据 坐标参考系统：EPSG:3035 边界框（最小X、最小Y、最大X、最大Y）：(900,000, 899,000, 7,401,000, 5,501,000) 空间分辨率：30米 图像尺寸：216,700像素 × 153,400行 文件格式：云优化GeoTIFF（Cloud Optimized Geotiff, COG）格式 技术支持 若您发现数据存在错误、异常或不一致之处，或有相关疑问，请提交GitHub议题：GitLab Issues（待确认） 命名规范 为确保项目内部及跨项目的一致性与易用性，本数据集遵循Ai4SoilHealth与Open-Earth-Monitor标准命名规范。该规范通过10个字段描述数据的关键属性，使用户可无需打开文件即可实现文件检索、数据分析准备等操作。各字段说明如下： 通用变量名：ndti.min.slopes = minNDTI的长期趋势斜率 变量-处理组合：glad.landsat.ard2.seasconv.yearly.min.theilslopes = 基于NDTI年度最小值计算得到的Theil-Sen斜率 概率分布位置/变量类型：m=均值 | sd=标准差 | n=观测样本数 | qa=质量评估 空间分辨率：30米 深度参考：s=地表 时间参考起始时间：20000101 = 2000-01-01 时间参考结束时间：20221231 = 2022-12-31 边界框：go=全球（不含南极洲） EPSG代码：epsg.3035 版本代码：v20231218 = 2023-12-18（数据创建日期）