HAP02 - Percentage Working and Median Income and P25 and P75 of HAP Households. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Percentage Working and Median Income and P25 and P75 of HAP Households...

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 Antar

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 Antar

Objective biomarkers for amyotrophic lateral sclerosis would facilitate the discovery of new treatments. The common neurotrophin receptor p75 is up regulated and the extracellular domain cleaved from injured neurons and peripheral glia in amyotrophic lateral sclerosis. We have tested the hypothesis that urinary levels of extracellular neurotrophin receptor p75 serve as a biomarker for both human motor amyotrophic lateral sclerosis and the SOD1G93A mouse model of the disease. The extracellular domain of neurotrophin receptor p75 was identified in the urine of amyotrophic lateral sclerosis patients by an immuno-precipitation/western blot procedure and confirmed by mass spectrometry. An ELISA was established to measure urinary extracellular neurotrophin receptor p75. The mean value for urinary extracellular neurotrophin receptor p75 from 28 amyotrophic lateral sclerosis patients measured by ELISA was 7.9±0.5 ng/mg creatinine and this was significantly higher (p<0.001) than 12 controls (2.6±0.2 ng/mg creatinine) and 19 patients with other neurological disease (Parkinson's disease and Multiple Sclerosis; 4.1±0.2 ng/mg creatinine). Pilot data of disease progression rates in 14 MND patients indicates that p75NTRECD levels were significantly higher (p = 0.0041) in 7 rapidly progressing patients as compared to 7 with slowly progressing disease. Extracellular neurotrophin receptor p75 was also readily detected in SOD1G93A mice by immuno-precipitation/western blot before the onset of clinical symptoms. These findings indicate a significant relation between urinary extracellular neurotrophin receptor p75 levels and disease progression and suggests that it may be a useful marker of disease activity and progression in amyotrophic lateral sclerosis.

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 Antar

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 Antar

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 Antar

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

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 Antar

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 Antar

Data information

  <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2003. 
  The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. 
  The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; 
  as well as two spectral indices: NDVI and NDWI.</p>
  <h2><strong>As a part of a Data Cube</strong></h2>
  <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. 
  For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p>
  <ul>
  <li>To cite this dataset, refer to the DOI available on the landing page.</li>
  <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li>
  </ul>
  <h2><strong>Support</strong></h2>
  <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Data information This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2004. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. As a part of a Data Cube This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. To cite this dataset, refer to the DOI available on the landing page. To access other data layers in the data cube, use the navigation catalog on the landing page as well. Support If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

Data information This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2011. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. As a part of a Data Cube This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. To cite this dataset, refer to the DOI available on the landing page. To access other data layers in the data cube, use the navigation catalog on the landing page as well. Support If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

Data information This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2016. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. As a part of a Data Cube This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. To cite this dataset, refer to the DOI available on the landing page. To access other data layers in the data cube, use the navigation catalog on the landing page as well. Support If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

Data information This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2007. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. As a part of a Data Cube This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. To cite this dataset, refer to the DOI available on the landing page. To access other data layers in the data cube, use the navigation catalog on the landing page as well. Support If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

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 Antar

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 Antar

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 Antar

Data information

  <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2019. 
  The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. 
  The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; 
  as well as two spectral indices: NDVI and NDWI.</p>
  <h2><strong>As a part of a Data Cube</strong></h2>
  <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. 
  For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p>
  <ul>
  <li>To cite this dataset, refer to the DOI available on the landing page.</li>
  <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li>
  </ul>
  <h2><strong>Support</strong></h2>
  <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>