Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

Data from: Substrate composition impacts long-term vegetation development on blue-green roofs: Insights from an experimental roof and greenhouse study

Data from: van der Kolk et al. (2022). Substrate composition impacts long-term vegetation development on blue-green roofs: Insights from an experimental roof and greenhouse study. Ecological Engineering.   See the main article on how data was collected.   The dataset contains three tables: greenhouse_biomass.csv: The biomass (in g dry weight) harvested from each container in the greenhouse experiment. greenhouse_percentage_share.csv: The estimated percentage that each species contributes to the total harvested biomass in the greenhouse experiment. roof_cover.csv: The cover (in %) of all species per plot for all surveys.

opencc-by-4.0Dec 2022View details →

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 02 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 02 and day 02 after drought stress application.

opencc-by-4.0Dec 2022View details →

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 0.05º Geographic Grid Since 1981

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on Geographic grid and spans from 1981 to 2018 (continuously updated). It was generated from the MUSES LAI product at 0.05º resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. Dataset Characteristics: <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N;</li> <li>Temporal Coverage: 1981 – 2018;</li> <li>Spatial Resolution: 0.05º (approximately 5 km);</li> <li>Temporal Resolution: 8 days;</li> <li>Projection: Geographic;</li> <li>Data Format: HDF;</li> <li>Scale: 0.004;</li> <li>Valid Range: 0 – 250.</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2019 (185–361)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2019 (185–361). Please <a href="https://zenodo.org/record/7498056#.Y7OSfdVBw2x">click here</a> to download the MUSES FVC product in 2019 (001–177). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019 (185–361)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2016 (001–177)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2016 (001–177). Please <a href="https://zenodo.org/record/7511224#.Y7oJM_5Bw2x">click here</a> to download the MUSES FVC product in 2015 (185–361), and <a href="http://zenodo.org/record/7508632#.Y7gpc31ByUk">click here</a> to download the MUSES FVC product in 2016 (185–361). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2016 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2011 (001–177)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2011 (001–177). Please <a href="https://zenodo.org/record/7533423#.Y8HkE3ZByUk">click here</a> to download the MUSES FVC product in 2010 (185–361), and <a href="http://zenodo.org/record/7528428#.Y8CZcnZByUk">click here</a> to download the MUSES FVC product in 2011 (185–361). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2011 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2010 (185–361)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2010 (185–361). Please <a href="https://zenodo.org/record/7535796#.Y8Jq2XZByUk">click here</a> to download the MUSES FVC product in 2010 (001–177) and <a href="https://zenodo.org/record/7531951#.Y8Ec8HZByUk">click here</a> to download the MUSES FVC product in 2011 (001–177). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2010 (185–361)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

opencc-by-4.0Aug 2022View details →

Fast Modelling of Vegetated Flow and Sediment Transport over Mobile Beds Using Shallow Water Equations with Anisotropic Porosity

The data are the numerical solutions and errors in different cases by the present model.

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2008 (001–177)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2008 (001–177). Please <a href="https://zenodo.org/record/7540561#.Y8c995hBw2x">click here</a> to download the MUSES FVC product in 2007 (185–361), and <a href="http://zenodo.org/record/7538189#.Y8SRkv5ByYl">click here</a> to download the MUSES FVC product in 2008 (185–361). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2008 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

opencc-by-4.0Sep 2022View details →

Directional aspects of vegetation linear and circular polarization biosignatures.

This contains the experimental data collected by the authors for the article "Directional aspects of vegetation linear and circular polarization biosignatures", published in Astrobiology volume 22 issue 9 on September 5, 2022 (https://doi.org/10.1089/ast.2021.0156). A preprint of the article is freely available on ArXiv: https://arxiv.org/abs/2207.08125 (https://doi.org/10.48550/arXiv.2207.08125). The article provides the methodology for the experimental aquisitions, discussion of the errors and uncertainties, analysis of the spectra and their implications. The file is in csv format and the content of each column is indicated in the first line (header).

dryad28/100

Simultaneous selection on vegetative and reproductive phenology in a perennial herb

The timing of different life history events are often correlated, and selection might only rarely be exerted independently on the timing of a single event. In plants, phenotypic selection has often been shown to favour earlier flowering. However, little is known about to what extent this selection acts directly vs. indirectly via vegetative phenology, and if selection on the two traits is correlational. We estimated direct, indirect and correlational phenotypic selection on vegetative and reproductive phenology over three years for flowering individuals of the perennial herb Lathyrus vernus. Direct selection favoured earlier flowering and shorter timespans between leaf-out and flowering in all years. However, early flowering was associated with early leaf-out, and the direction of selection on leaf-out day varied among years. As a result, selection on leaf-out weakened selection for early flowering in one of the study years. We found no evidence of correlational selection. Our results highlight the importance of including temporally correlated traits when exploring selection on the phenology of seasonal events.

opencc-zeroJan 2023View details →

opencc-by-4.0Feb 2023View details →

Microhabitat selection of meadow and steppe vipers enlightened by digital photography and image processing to describe grassland vegetation structure

Dataset <ol> <li> Understanding animals’ selection of microhabitats is important in both ecology and biodiversity conservation. However, there is no generally accepted methodology for the characterisation of microhabitats, especially for vegetation structure. </li> <li> We studied microhabitat selection of Vipera snakes by comparing grassland vegetation structure between viper occurrence points and random points in three grassland ecosystems: V. graeca in mountain meadows of Albania, V. renardi in loess steppes of Ukraine, and V. ursinii in sand grasslands in Hungary. We quantified vegetation structure in an objective manner by automated processing of images taken of the vegetation against a vegetation profile board under standardised conditions. We developed an R script for automatic calculation of four vegetation structure variables derived from raster data obtained in the images: leaf area (LA), height of closed vegetation (HCV), maximum height of vegetation (MHC), and foliage height diversity (FHD). </li> <li> Generalized linear mixed models revealed that snake occurrence was positively related to HCV in V. graeca, to LA in V. renardi and to LA and MHC in V. ursinii, and negatively to to HCV in V. ursinii. </li> <li> Our results demonstrate that vegetation structure variables derived from automated image processing significantly influence viper microhabitat selection. Our method minimises the risk of subjectivity in measuring vegetation structure, allows upscaling if neighbouring pixels are combined, and is suitable for comparison of or extrapolation across different grasslands, vegetation types or ecosystems. </li> </ol>

dryad28/100

Retaining natural vegetation to safeguard biodiversity and humanity

Humanity is on a pathway of unsustainable loss of the natural systems upon which we, and all life, rely. To date, global efforts to deliver internationally-agreed goals to reduce carbon emissions, halt biodiversity loss, and retain essential ecosystem services, have been poorly integrated. All these goals rely in part on preserving natural (e.g. native, largely unmodified) and semi-natural (e.g. under some form of low-intensity/sustainable human use) forests, woodlands and grasslands. Here, we show how to unify these goals by empirically deriving spatially explicit, quantitative area-based targets for the retention of natural and semi-natural (e.g. native) terrestrial vegetation. We found that at least 67 million km2 of Earth's terrestrial vegetation (∼79% of the area of vegetation remaining) requires retention – via sustainable and appropriate land use and management – to contribute to biodiversity, climate, soil and freshwater objectives under four United Nations Resolutions. This equates to retaining natural and semi-natural vegetation across at least 50% of the total terrestrial (excluding Antarctica) surface of Earth. Our results show where retention efforts could contribute to multiple goals simultaneously. Such management can and should co-occur alongside and be driven by the people who live in and rely on places where natural and sustainably managed vegetation remains in situ, and must be complemented by restoration and appropriate management of more human-modified environments, if global goals are to be realised.

opencc-zeroFeb 2023View details →

opencc-by-4.0Dec 2022View details →

Effects of elevated ozone exposure on regional meteorology and air quality in China through ozone-vegetation coupling

Main data and code used in the study.

opencc-zeroMar 2023View details →

Supplementary material 1 from: Tájek P, Tenčík A, Konvička M, John V (2023) Vegetation changes at oligotrophic grasslands managed for a declining butterfly. Nature Conservation 52: 23-46. https://doi.org/10.3897/natureconservation.52.90452

Ordination scores from the indirect DCA analysis species

Urban environments provide new perspectives for forecasting vegetation phenology responses under climate warming

The temperature sensitivity of 343 cities in China

MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2002

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 500 m spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500 m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2002. Please <a href="https://zenodo.org/record/7901748#.ZFXohnZBypo">click here</a> to download the MUSES FVC product in 2001, and <a href="https://zenodo.org/record/7898122#.ZFT6yHZByUl">click here</a> to download the MUSES FVC product in 2003. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2002</li> <li>Spatial Resolution: 500 m</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2006

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2006. Please <a href="https://zenodo.org/record/7920127#.ZFwWGnZBypo">click here</a> to download the MUSES FVC product in 2005, and <a href="https://zenodo.org/record/7917483#.ZFuCgxFBw2x">click here</a> to download the MUSES FVC product in 2007. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2006</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2004

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2004. Please <a href="https://zenodo.org/record/7922793#.ZFx3xHZBypo">click here</a> to download the MUSES FVC product in 2003, and <a href="https://zenodo.org/record/7920127#.ZFwXNxFBw2x">click here</a> to download the MUSES FVC product in 2005. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2004</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2006

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2006. Please click here to download the MUSES FVC product in 2005, and <a href="https://zenodo.org/record/7931558#.ZF87cxFBw2x">click here</a> to download the MUSES FVC product in 2007. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2006</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).