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99 results for “Vegetation Index”
A long-term 250-m resolution Normalized Difference Vegetation Index (NDVI) product for 1982–2020 in Idaho
<ol> <li>We developed a novel spatio-temporal fusion method to downscale the AVHRR NDVI products to the Moderate-resolution Imaging Spectroradiometer (MODIS) resolution. The algorithm effectively combines the high spatial variability of the MODIS NDVI data and the long-term temporal information of the AVHRR NDVI data. Finally, we successfully generated a monthly global long-term (since 1982) and high-resolution (250m) NDVI database.</li> <li>Here we provide the downscaled NDVI dataset of Idaho from 1982 to 2020.</li> <li>Datasets for other regions can be easily produced by the GEE platform with the code provided in the github (https://github.com/babyfoal/downsclaed_NDVI/tree/main).</li> <li>The spatial distribution and temporal variation of this dataset have been both well validated by the simulated and real-data experiments. </li> </ol>
MUSES Normalized Difference Vegetation Index (NDVI) 16-Day 30m Geographic Grid over Beijing Since 1984
<p>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>).</p> <p>This dataset is the MUSES NDVI product at 30 m spatial resolution and 16-day temporal resolution over Beijing. The MUSES NDVI product is provided on Geographic grid and spans from 1984 to 2021 (continuously updated). It was generated from the Landsat collection 2 surface reflectance data using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 115.416599º E – 117.508219º E, 39.441929º N – 41.059283º N</li> <li>Temporal Coverage: 1984 – 2021</li> <li>Spatial Resolution: 0.000269469º (approximately 30 m)</li> <li>Temporal Resolution: 16 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.0001</li> <li>Valid Range: 0 – 10000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p> <p> </p>
8-day Vegetation optical depth (VOD) and normalised difference vegetation index (NDVI)- based estimated degree of curing (DOC) for Australia
<p>This is a gridded degree of curing (DOC) dataset over Australia based on vegetation optical depth (VOD) and normalised difference vegetation index (NDVI) that can reasonably reproduce groundbased observations in space and time.</p> <p>The gridded DOC data is produced via estimation models using the VOD dataset from AMSR-E (0.1 degree; 8-day) and NDVI dataset from MODIS Terra MOD09A1 (0.005 degree; 8-day). The estimation models are derived from the calibration and evaluation of VOD and NDVI datset with field observed DOC over Australia. Matlab was used for the calibration and evaluation of these models.</p> <p>There are 2 variations based on the following estimation models: DOC_M1 = 145.57-260.82(NDVI)+137.19(VOD)(NDVI) DOC_M2 = 48.70+147.60(VOD)-259.95(VOD)(NDVI) The domain covered is Australia with a 0.05 degree spatial resolution. Temporal resolution is 8-day composites from 04/07/2002 to 26/06/2011 .</p> <p>These experiments were executed by Waisin Chaivaranont of the ARC Centre of Excellence for Climate System Science (ARCCSS) research program "The role of land surface forcing and feedbacks for regional climate".</p>
MUSES Normalized Difference Vegetation Index (NDVI) Monthly Global 0.05º Geographic Grid Since 1982
<p>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>).</p> <p>This dataset is the MUSES global NDVI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES NDVI product is provided on Geographic grid and spans from 1982 to 2015 (continuously updated). It was generated from the Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1982 – 2015</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.0001</li> <li>Valid Range: 0 – 10000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Normalized Difference Vegetation Index (NDVI) 8-Day Global 0.05º Geographic Grid Since 1982
<p>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>).</p> <p>This dataset is the MUSES global NDVI product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES NDVI product is provided on Geographic grid and spans from 1982 to 2015 (continuously updated). It was generated from the Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1982 – 2015</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: 0.0001</li> <li>Valid Range: 0 – 10000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Data from: The utility of normalized difference vegetation index for predicting African buffalo forage quality
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Modified Soil Adjusted Vegetation Index, (M)SAVI image of 2003 ASTER image
Modified Soil-Adjusted vegetation index (MSAVI) produced from the 2003 ASTER image. MSAVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is specifically designed for areas with low vegetation, such as arid lands, where soil reflectance in the signal is high. The index is intended to minimize the effect of bare soil.
Normalized Difference Vegetation Index (NVDI) image of 2000 Landsat Enhanced Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 2000 Enhanced Landsat Thematic Mapper(ETM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows: (NIR-RED) / (NIR+RED), where NIR (Near Infra-Red) is the ETM band 4 (0.76-0.9 micrometers) and RED is band 3 (0.78-0.82 micrometers).
Soil Adjusted Vegetation Index (SAVI) image of 2000 Landsat Enhanced Thematic Mapper (ETM) image
Soil Adjusted Vegetation Index (SAVI) produced from the 2000 Enhanced Landsat Thematic Mapper(ETM) image. SAVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows:( (NIR-RED) / (NIR+RED+L))*(1+L), where NIR (Near Infra-Red) is the ETM band 4 (0.76-0.9 micrometers), RED is band 3 (0.78-0.82 micrometers), and L is the correction factor whose values range from 0 (high vegetation cover) to 1 (low vegetation). L=0.5 was used. The index has been designed to correct for high soil reflectance in arid regions.
NDVI (Normalized difference vegetation index) Image of 1975 Landsat MSS Image
Normalized difference vegetation index (NDVI) produced from the 1975 Landsat MSS image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
SAVI (Soil Adjusted Vegetation Index) Image of 1975 Landsat MSS Image
SAVI (Soil Adjusted Vegetation Index) map produced from the 1975 Landsat MSS image. SAVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
NDVI (Normalized difference vegetation index) Image of 1980 Landsat MSS Image
Normalized difference vegetation index (NDVI) produced from the 1980 Landsat MSS image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
SAVI (Soil Adjusted Vegetation Index) Image of 1980 Landsat MSS Image
SAVI (Soil Adjusted Vegetation Index) map produced from the 1980 Landsat MSS image. SAVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
SAVI (Soil Adjusted Vegetation Index) image of central Arizona-Phoenix from a 2005 Landsat Thematic Mapper image
Soil Adjusted Vegetation Index (SAVI) produced from the 2005 Landsat Thematic Mapper(ETM) image. SAVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows:( (NIR-RED) / (NIR+RED+L))*(1+L), where NIR (Near Infra-Red) is the TM band 4 (0.76-0.9 micrometers), RED is band 3 (0.78-0.82 micrometers), and L is the correction factor whose values range from 0 (high vegetation cover) to 1 (low vegetation). L=0.5 was used. The index has been designed to correct for high soil reflectance in arid regions.
NDVI (Normalized difference vegetation index) image of central Arizona-Phoenix from a 2005 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 2005 Landsat Thematic Mapper (TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows: (NIR-RED) / (NIR+RED), where NIR (Near Infra-Red) is the TM band 4 (0.76-0.9 micrometers) and RED is band 3 (0.78-0.82 micrometers).
NDVI (Normalized difference vegetation index) image of central Arizona-Phoenix from a 1998 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 1998 Landsat Thematic Mapper(TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
NDVI (Normalized difference vegetation index) image of central Arizona-Phoenix from a 1993 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 1993 Landsat Thematic Mapper(TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
NDVI (Normalized difference vegetation index) image of central Arizona-Phoenix from a 1985 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 1985 Landsat Thematic Mapper(TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
SAVI (Soil Adjusted Vegetation Index) image of central Arizona-Phoenix from a 1990 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 1990 Landsat Thematic Mapper(TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
SAVI (Soil Adjusted Vegetation Index) Image of 1993 Landsat Thematic Mapper Image for the Central Arizona-Phoenix area
SAVI (Soil Adjusted Vegetation Index) map produced for the Central Arizona-Phoenix area from a 1993 Enhanced Landsat Thematic Mapper image.
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