cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26037/yareta:zm7lw7tnznccrf4zoc2ioys4sy
title: Landsat 7 Surface Reflectance Analysis Ready Data 2021

description: Data for 2021
This dataset is the Landsat 7 Surface Reflectance Analysis Ready Data (ARD) collection archive for Switzerland, generated by USGS (https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance).

Detailed description about the Landsat 7 mission can be found in the CEOS Earth Observation Handbook: http://database.eohandbook.com/database/missionsummary.aspx?missionID=349.

The Landsat archive is extremely interesting because this is the longest EO program, initiated in 1972, it has provided continual and consistent observations for almost 50 years. Since 2008, the complete data archive has been provided under a free and open access policy. This has enabled dense time-series analysis, moving beyond simple diachronic comparison of a set of images, therefore dramatically improving capabilities to monitor environmental changes. To cover the whole of Switzerland, it requires eight Landsat 7 scenes (Path/Row: 193/027, 194/027, 195/027, 196/027, 193/028, 194/028, 195/028, 196/028) representing an area of latitude 44.9 to 48.7 and longitude 4.1 to 12.8. Data are downloaded as Collection 1/Tier 1 – Level 2 Surface Reflectance encompassing a surface of approximately 185km by 180km. Collection 1/Tier 1 scenes are data with the highest available data quality (e.g., geometric and radiometric corrections) and considered suitable for time-series analysis (https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1). Level 2 corresponds to surface reflectance (i.e., the estimate based on Landsat sensor observations of the fraction of incoming solar radiation reflected from Earth’s surface). These data are corrected for atmospheric perturbations (e.g., aerosol scattering, thin clouds) enabling direct comparison between multiple images and dates. This corresponds to the ARD level.

The sensor’s Scan-Line Corrector (SLC) failed in July 2003 and approximately 225 of the pixels per scene have since then not been scanned. However, the spatial and spectral quality of the remaining 78% of pixels images remain valid. Landsat data are provided by USGS with Quality Assessment (QA) information to help users to determine their suitability for specific applications. An 8-bit LandsatLook Quality Image and 16-bit Quality Assessment Band40 are also included. Details on each file are described at: https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band. Level 2 products are generated by the USGS from level 1 product and using the official LEDAPS/LASRC algorithm. "A Pixel Quality Assurance (pixel_qa) band is provided with all Landsat Surface Reflectance-derived Spectral Indices. The band is in unsigned 16-bit format, values are bit-packed and provide information pertaining to a pixel condition of fill, clear, water, cloud shadow, snow, cloud (yes/no), cloud confidence and cirrus cloud confidence (Landsat 8 only)" https://www.usgs.gov/land-resources/nli/landsat/landsat-sr-derived-spectral-indices-pixel-quality-band.
DOI: 10.26037/yareta:2g5kpiewlvfnnmumpqmyxstmka
title: Moisture Stress Index (MSI) - Switzerland [2016]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Moisture Stress Index (MSI) computed from Sentinel-2 data. MSI is a reflectance measurement, sensitive to increases in leaf water content using the forumla MSI=b11/b8. See Rock et al. (1985) DOI: 

As water content in vegetation canopy leaves increases, the absorbtion at wavelengths around 1599 nm also increases. Absorption at 819nm is used as a reference, since it’s nearly unaffected by changes in water content. Applications of the MSI include canopy stress analysis, productivity prediction and modelling, fire hazard analysis, and studies of ecosystem physiology. The index is inverted relative to the other water vegetation indices; higher values indicate greater water stress and less water content. 

The values of this index range from 0 to more than 3. The common range for green vegetation is 0.4 to 2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:a2t3haktwvdqhm6s62h77hvxxq
title: Data

description: Data
DOI: 10.26037/yareta:a6767rcmgza6vo2zw4wi4rajum
title: Leaf Area Index (LAI) - Switzerland [2010-2019, Landsat]

description: This dataset is an time-serie of Landsat Analysis Ready Data (ARD)- derived Leaf Area Index (LAI) computed from Landsat 5-7-8 data. LAI is designed to analyze the foliage surface of our planet and it estimates the quantity of leaves in a specific region using the forumla LAI= 3.618*EVI - 0.118. For more details see Boegh et al. (2002) DOI: 10.1016/S0034-4257(01)00342-X

LAI is a unitless measure that is calculated as the ratio of the one-sided (illuminated) foliage area to the soil surface it can cover. This vegetation index is important to monitor crop and forest health, the environment, and climatic conditions. 

Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)
 
Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:b7uhgu4nhveclp74a67zh3kym4
title: Moisture Stress Index (MSI) - Switzerland [2018]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Moisture Stress Index (MSI) computed from Sentinel-2 data. MSI is a reflectance measurement, sensitive to increases in leaf water content using the forumla MSI=b11/b8. See Rock et al. (1985) DOI: 

As water content in vegetation canopy leaves increases, the absorbtion at wavelengths around 1599 nm also increases. Absorption at 819nm is used as a reference, since it’s nearly unaffected by changes in water content. Applications of the MSI include canopy stress analysis, productivity prediction and modelling, fire hazard analysis, and studies of ecosystem physiology. The index is inverted relative to the other water vegetation indices; higher values indicate greater water stress and less water content. 

The values of this index range from 0 to more than 3. The common range for green vegetation is 0.4 to 2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:bxfqfi5mrzhmrdok62d4kd4em4
title: Leaf Area Index (LAI) - Switzerland [2018, Sentinel-2]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Leaf Area Index (LAI) computed from Sentinel-2 data. LAI is designed to analyze the foliage surface of our planet and it estimates the quantity of leaves in a specific region using the forumla LAI= 3.618*EVI - 0.118. For more details see Boegh et al. (2002) DOI: 10.1016/S0034-4257(01)00342-X

LAI is a unitless measure that is calculated as the ratio of the one-sided (illuminated) foliage area to the soil surface it can cover. This vegetation index is important to monitor crop and forest health, the environment, and climatic conditions. 

Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)
 
Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:comyimu5ufc4zk2qolrscg5bge
title: Moisture Stress Index (MSI) - Switzerland [2020]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Moisture Stress Index (MSI) computed from Sentinel-2 data. MSI is a reflectance measurement, sensitive to increases in leaf water content using the forumla MSI=b11/b8. See Rock et al. (1985) DOI: 

As water content in vegetation canopy leaves increases, the absorbtion at wavelengths around 1599 nm also increases. Absorption at 819nm is used as a reference, since it’s nearly unaffected by changes in water content. Applications of the MSI include canopy stress analysis, productivity prediction and modelling, fire hazard analysis, and studies of ecosystem physiology. The index is inverted relative to the other water vegetation indices; higher values indicate greater water stress and less water content. 

The values of this index range from 0 to more than 3. The common range for green vegetation is 0.4 to 2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:coqdu6sgyvfyngit55u53cwpom
title: Anthocyanin Reflectance Index (ARI) - Switzerland [2021]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Anthocyanin Reflectance Index (ARI) computed from Sentinel-2 data. ARI is is a reflectance measurement, sensitive to vegetation pigment content using the forumla ARI = (1/b3) - (1/b5). See Gitelson et al. (2009) DOI: 10.3732/ajb.0800395. 

Anthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses.
The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.

ARI values for the examined trees in this original article ranged in values from 0 to 0.2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:e7kqfyrtw5epdfz6zru4am46me
title: Sentinel-2 Surface Reflectance Analysis Ready Data 2020-Q2

description: Data for 2020-Q2
This dataset is the Sentinel-2 Surface Reflectance Analysis Ready Data (ARD) archive for Switzerland, generated sen2cor 2.5 (http://step.esa.int/main/third-party-plugins-2/sen2cor/).

Detailed description about the Sentinel-2 A & B missions can be found in the CEOS Earth Observation Handbook:
http://database.eohandbook.com/database/missionsummary.aspx?missionID=552 
http://database.eohandbook.com/database/missionsummary.aspx?missionID=553 

Sentinel-2 (S2) is a constellation of two sun-synchronous satellites operated by ESA respectively from 2015 to 2022 (Sentinel-2A) and 2017-2024 (Sentinel-2B). Switzerland is covered by twelve Sentinel-2 scenes (ID: 31TGM, 31TGN, 32TLR, 32TLS, 32TLT, 32TMR, 32TMS, 32TMT, 32TNS, 32TNT, 32TPS, 32TPT). This corresponds to a spatial extent of latitude 45.0 to 47.9N, longitude 5.5 to 11.8E. Data are downloaded as L1C products corresponding to 100x100km2 geometrically corrected Top Of the Atmosphere (TOA) tiles.It supports land monitoring related services, including generation of generic land cover maps, risk mapping and fast images for disaster relief, generation of leaf coverage leaf chlorophyll content and leaf water content42,43. Both satellites carry the MultiSpectral Instrument (MSI).

Sentinel-2 processed to level 2A with Sen2Cor comes with a scene classification (similar to the pixel_qa band provided by USGS for Landsat data) with 12 classes as described at: https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm. It helps identifying pixels that are saturated or defective as well as cloud and cloud shadows that may affect the quality of the images. In addition, ESA provides a monthly status of the quality of Sentinel-2 data through Data Quality Reports (DQR). These reports document geometric and radiometric performances against initial sensor specifications together with observed anomalies and issues. DQR are available at: https://sentinels.copernicus.eu/web/sentinel/data-product-quality-reports.
DOI: 10.26037/yareta:f6jnl43zdzgtdonjhmahdnkxpq
title: Moisture Stress Index (MSI) - Switzerland [2019]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Moisture Stress Index (MSI) computed from Sentinel-2 data. MSI is a reflectance measurement, sensitive to increases in leaf water content using the forumla MSI=b11/b8. See Rock et al. (1985) DOI: 

As water content in vegetation canopy leaves increases, the absorbtion at wavelengths around 1599 nm also increases. Absorption at 819nm is used as a reference, since it’s nearly unaffected by changes in water content. Applications of the MSI include canopy stress analysis, productivity prediction and modelling, fire hazard analysis, and studies of ecosystem physiology. The index is inverted relative to the other water vegetation indices; higher values indicate greater water stress and less water content. 

The values of this index range from 0 to more than 3. The common range for green vegetation is 0.4 to 2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:fhdeskus6nd45ijwelr3bika7u
title: Leaf Area Index (LAI) - Switzerland [2017, Sentinel-2]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Leaf Area Index (LAI) computed from Sentinel-2 data. LAI is designed to analyze the foliage surface of our planet and it estimates the quantity of leaves in a specific region using the forumla LAI= 3.618*EVI - 0.118. For more details see Boegh et al. (2002) DOI: 10.1016/S0034-4257(01)00342-X

LAI is a unitless measure that is calculated as the ratio of the one-sided (illuminated) foliage area to the soil surface it can cover. This vegetation index is important to monitor crop and forest health, the environment, and climatic conditions. 

Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)
 
Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:g2pr7i67krfy5e4uklc3nxxdei
title: Moisture Stress Index (MSI) - Switzerland [2021]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Moisture Stress Index (MSI) computed from Sentinel-2 data. MSI is a reflectance measurement, sensitive to increases in leaf water content using the forumla MSI=b11/b8. See Rock et al. (1985) DOI: 

As water content in vegetation canopy leaves increases, the absorbtion at wavelengths around 1599 nm also increases. Absorption at 819nm is used as a reference, since it’s nearly unaffected by changes in water content. Applications of the MSI include canopy stress analysis, productivity prediction and modelling, fire hazard analysis, and studies of ecosystem physiology. The index is inverted relative to the other water vegetation indices; higher values indicate greater water stress and less water content. 

The values of this index range from 0 to more than 3. The common range for green vegetation is 0.4 to 2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:hddmt753jrbi7c5qcyp5kgv3am
title: Leaf Area Index (LAI) - Switzerland [2016, Sentinel-2]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Leaf Area Index (LAI) computed from Sentinel-2 data. LAI is designed to analyze the foliage surface of our planet and it estimates the quantity of leaves in a specific region using the forumla LAI= 3.618*EVI - 0.118. For more details see Boegh et al. (2002) DOI: 10.1016/S0034-4257(01)00342-X

LAI is a unitless measure that is calculated as the ratio of the one-sided (illuminated) foliage area to the soil surface it can cover. This vegetation index is important to monitor crop and forest health, the environment, and climatic conditions. 

Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)
 
Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:jusdzhzr75ckbnhwaxgg2ipcfi
title: Anthocyanin Reflectance Index (ARI) - Switzerland [2016]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Anthocyanin Reflectance Index (ARI) computed from Sentinel-2 data. ARI is is a reflectance measurement, sensitive to vegetation pigment content using the forumla ARI = (1/b3) - (1/b5). See Gitelson et al. (2009) DOI: 10.3732/ajb.0800395. 

Anthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses.
The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.

ARI values for the examined trees in this original article ranged in values from 0 to 0.2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:mlrtcoojvbatvbomgyup2hk3qy
title: Sentinel-2 Surface Reflectance Analysis Ready Data 2020-Q3

description: Data for 2020-Q3
This dataset is the Sentinel-2 Surface Reflectance Analysis Ready Data (ARD) archive for Switzerland, generated sen2cor 2.5 (http://step.esa.int/main/third-party-plugins-2/sen2cor/).

Detailed description about the Sentinel-2 A & B missions can be found in the CEOS Earth Observation Handbook:
http://database.eohandbook.com/database/missionsummary.aspx?missionID=552 
http://database.eohandbook.com/database/missionsummary.aspx?missionID=553 

Sentinel-2 (S2) is a constellation of two sun-synchronous satellites operated by ESA respectively from 2015 to 2022 (Sentinel-2A) and 2017-2024 (Sentinel-2B). Switzerland is covered by twelve Sentinel-2 scenes (ID: 31TGM, 31TGN, 32TLR, 32TLS, 32TLT, 32TMR, 32TMS, 32TMT, 32TNS, 32TNT, 32TPS, 32TPT). This corresponds to a spatial extent of latitude 45.0 to 47.9N, longitude 5.5 to 11.8E. Data are downloaded as L1C products corresponding to 100x100km2 geometrically corrected Top Of the Atmosphere (TOA) tiles.It supports land monitoring related services, including generation of generic land cover maps, risk mapping and fast images for disaster relief, generation of leaf coverage leaf chlorophyll content and leaf water content42,43. Both satellites carry the MultiSpectral Instrument (MSI).

Sentinel-2 processed to level 2A with Sen2Cor comes with a scene classification (similar to the pixel_qa band provided by USGS for Landsat data) with 12 classes as described at: https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm. It helps identifying pixels that are saturated or defective as well as cloud and cloud shadows that may affect the quality of the images. In addition, ESA provides a monthly status of the quality of Sentinel-2 data through Data Quality Reports (DQR). These reports document geometric and radiometric performances against initial sensor specifications together with observed anomalies and issues. DQR are available at: https://sentinels.copernicus.eu/web/sentinel/data-product-quality-reports.
DOI: 10.26037/yareta:mrzvtlmqifbh7fhmov4qxib4sq
title: Anthocyanin Reflectance Index (ARI) - Switzerland [2017]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Anthocyanin Reflectance Index (ARI) computed from Sentinel-2 data. ARI is is a reflectance measurement, sensitive to vegetation pigment content using the forumla ARI = (1/b3) - (1/b5). See Gitelson et al. (2009) DOI: 10.3732/ajb.0800395. 

Anthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses.
The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.

ARI values for the examined trees in this original article ranged in values from 0 to 0.2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project