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1,138 results for “Modis”
Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Remaining bands of Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Remaining bands of Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Monthly MODIS LST data related to the article: A new fully gap-free time series of land surface temperature from MODIS LST data
<p>Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps.</p> <p>[1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333</p> <p>LICENSE: Open Data Commons Open Database License (ODbL) http://opendatacommons.org/licenses/odbl/</p> <p>Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006.</p> <p><strong>The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min</strong> (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), <strong>aggregated to monthly data</strong>. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL:<br> <br> GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid",<br> DATUM["Not specified (based on Clarke 1866 spheroid)",<br> SPHEROID["Clarke 1866",6378206.4,294.9786982138982,<br> AUTHORITY["EPSG","7008"]]],<br> PRIMEM["Greenwich",0],<br> UNIT["degree",0.0174532925199433]]<br> </p> <p><strong>File name</strong> abbreviations:</p> <ul> <li>avg = average of daily averages</li> <li>min = minimum of daily minima</li> <li>max = maximum of daily maxima</li> </ul> <p>Meaning of <strong>pixel values</strong>:</p> <ul> <li>The <strong>pixel values</strong> are coded in <strong>degree Celsius * 100</strong> (hence, to obtain °C divide the pixel values by 100.0).</li> </ul> <p>Version <strong>changelog</strong>:</p> <ul> <li>V1.1.0: GeoTIFF metadata updated.</li> <li>V1.0.0: original upload</li> </ul>
MUSES Leaf Area Index (LAI) Derived from MODIS Data Monthly Global 0.05º Geographic Grid Since 2000
<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 LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). It is provided on Geographic grid and spans from 2000 to 2019 (continuously updated). The MUSES LAI 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: 2000 – 2019</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: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Leaf Area Index (LAI) Derived from MODIS Data 8-Day Global 0.05º Geographic Grid Since 2000
<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>The MUSES LAI product at 0.05º spatial resolution and 8-day temporal resolution was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). It is provided on Geographic grid and spans from 2000 to 2023 (continuously updated). The MUSES LAI 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: 2000 – 2023</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: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Modis and GA 7.0 Cluster Analysis Results
<p>Data output associated<strong><em> </em></strong>with Schuddeboom et al. 2018.</p>
A 1-km resolution monthly mean air temperature (Ta) dataset across the Tibetan Plateau during 2001-2015, related with the article "Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods".
<p>We present a 1-km resolution monthly mean air temperature (Ta) dataset across the Tibetan Plateau from 2001 to 2015. It ranges from 25°-45°N, 70°-105°E, covering a total area of ~7,045,000 km2. To develop this dataset, 10 machine learning algorithms were applied to 11 environmental variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data and topographic index data. The best model generated by Cubist algorithm was finally selected to calculate monthly mean Ta, and achieved an overall accuracy of RMSE= 1.00 °C and MAE= 0.73 °C. To get details of this dataset, please refer to the manuscript "Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods". This Ta dataset provides spatially continuous coverage compared with station observed data, and has much higher accuracy and spatial resolution than reanalysis datasets, making it a useful dataset for climate change and environmental studies in the Tibetan Plateau.</p> <p>Xu Y., Knudby A., Shen Y., Liu Y., Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(2): 345-354. (DOI: 10.1109/jstars.2017.2787191).</p> <p>The Ta dataset is provided in ENVI standard format. The coordinate system is WGS84 Geographic Coordinate System.</p>
Global Seasonal Mountain Snow Mask from MODIS MOD10A2
<p>Seasonal Mountain Snow (SMS) mask derived from MODIS MOD10A2 snow cover extent and GTOPO30 digital elevation model produced at 30 arcsecond spatial resolution.</p> <p>Three datasets are provided: the Seasonal Mountain Snow mask (MODIS_mtnsnow_classes), a seasonal snow cover classification (MODIS_snow_classes), and cool-season cloud percentages (MODIS_clouds). The classification systems are as follows:</p> <p>MODIS_snow_classes:</p> <ul> <li>0: Little-to-no snow</li> <li>1: Indeterminate due to clouds</li> <li>2: Ephemeral snow</li> <li>3: Seasonal snow</li> </ul> <p>MODIS_mtnsnow_classes:</p> <ul> <li>0: Mountains with little-to-no snow</li> <li>1: Indeterminate due to clouds</li> <li>2: Mountains with ephemeral snow</li> <li>3: Mountains with seasonal snow</li> </ul> <p>MODIS_clouds</p> <ul> <li>0: < 5% of clouds during the cool season (defined Oct.-Mar. for Northern Hemisphere and Apr.-Sep. for Southern Hemisphere)</li> <li>1: 5% cool-season days with cloud cover</li> <li>2: 10% cool-season days with cloud cover</li> <li>3: 20% cool-season days with cloud cover</li> <li>4: 30% cool-season days with cloud cover</li> <li>5: 40% cool-season days with cloud cover</li> <li>6: 50% cool-season days with cloud cover</li> </ul> <p> </p> <p>For manuscript "Characterizing biases in mountain snow accumulation from global datasets" submitted to WRR</p>
Analysis and quantification of ENSO linked changes in the tropical Atlantic cloud vertical distribution using 14 years of MODIS observations
<p>Time series of monthly means and anomolies of the cloud vertical distribution and other parameters used in this study.</p>
DINEOF reconstructed MODIS-terra 8-day composite of surface chlorophyll data
<p>Sea surface chlorophyll data are the 8–day composite of MODIS–terra chlorophyll products from 2001 to 2019. The L3 data with a 4 km spatial resolution were extracted from 42°N-44.5°N and 65.5°W-71°W and interpolated spatially onto a 0.025°*0.025° grid. Cloud–free chlorophyll data were reconstructed using the Data INterpolation Empirical Orthogonal Function (DINEOF) method, which has been applied to fill the spatial gaps of satellite products without lowering their qualities. The original 8–day composite of MODIS–terra chlorophyll products are available at https://oceancolor.gsfc.nasa.gov. Only those spatial points without data due to clouds or sun glint are constructed using the DINEOF. chl_GOM_origin_and_DINEOF.mat is the surface chlorophyll data. lon_GOM_2D.mat and lat_GOM_2D.mat are longitude and latitude information. The data can be loaded directly using MATLAB (.mat file).</p>
EOS AQUA, MODIS l1b data, HDF4, 2019-02-26
<p>Data covers western Europe, from south of Greece and Italy, up to Greenland</p> <p>Received in SMHI ground station in Norrköping, Sweden on the 26th of February 2019. Processed from raw to level 1 with Seadas</p>
Random Forest fused MODIS and Landsat snow cover from spectral mixture analysis in the Sierra Nevada, USA
<p>This data is snow cover fraction from the Snow Covered Area and Grain Size (SCAG) model for Landsat OLI and Terra MODIS and well as a 2-stage random forest model to fuse the 2 datasets for improved temporal/spatial resolution. There are 170 scenes in 2001 to 2012. It was used in the a publication for Remote Sensing of the Environment titled: Multi-sensor fusion using random forests for daily fractional snow cover at 30 m, doi: to be assigned.</p> <p><strong>Inputs</strong>: [YYYYMMDD is year month day of month, $num is 5 or 7 for Landsat platform, $sens is sensor TM or ETM+]</p> <p>Landsat.zip:</p> <p>Snow cover from Landsat: SSN.p042r034_YYYYMMDD.Landsat$num-$sens.canopyadjusted_mask.v01.tif </p> <p> </p> <p>MODIS.zip:</p> <p>Snow cover from MODIS: SSN.SN_W$YYYYMMDD_$YYYYMMDD.Terra-MODIS.snow_cover_percent.v01.tif</p> <p> </p> <p>Predictors.zip<strong> </strong></p> <p>Static predictors (see RSE publication Table 2): SouthernSierraNevada*.tif [* here is the variable name]</p> <p><strong>Outputs [</strong> [YYYYMMDD is year month day of month]</p> <p>ProbabilityNot0Not100.zip</p> <p>SSN.prob.btwn.YYYYMMDD.v3.tif - from classification random forest, probability of being between 0 and 100</p> <p> </p> <p>Probability100fSCA</p> <p>SSN.pro.hundred.YYYYMMDD.v3.tif - from classification random forest, probability of being 100</p> <p> </p> <p>RegressionResult.zip</p> <p>SSN.regression.YYYYMMDD.v3.tif - from prediction random forest</p> <p> </p> <p>Final_Downscaled.zip</p> <p>SSN.downscaled.YYYYMMDD.v3.3e+05.tif - final product (combination of classification and prediction)</p>
Sea salt aerosol AOD at 550nm derived from MODIS
<p>This dataset uses MODIS MOD08_M3 and MYD08_M3 data to derive monthly sea salt aerosol optical depth, using the Angstrom exponent as a filtering criterion. This dataset was created for the publication "The representation of sea salt aerosols and their role in polar climate within CMIP6", JGR: Atmospheres, 2023. The methodology for building this dataset is described in the publication, and the scripts used to do so can be found on GitHub at https://github.com/rlapere/CMIP6_SSA_Paper. The time period covered is 2005-2014, with a monthly time step. Warning: this dataset is only valid for polar regions, where sea salt dominate coarse mode aerosols. outside of 60-90N and 60-90S this product should not be used.</p>
Transitioning from MODIS to VIIRS Global Water Reservoir Product
<p>The 8-day (VNP28C2/VJ128C2) and monthly (VNP28C3/VJ128C3) VIIRS global water reservoir (GWR) product data are available in the respective folders. While the SNPP (VNP) data is available from 2012-2021, the JPSS1 (VJ1) data is provided from March 2020 to December 2021. Please note that VIIRS (Collection 2) GWR products for the full mission period of SNPP and JPSS-1 will be launched by NASA later this year and will be operational. Hence, VIIRS GWR data for the full-time period in the future can be downloaded from NASA LPDAAC. More information about the VIIRS GWR products will be provided at <a href="https://viirsland.gsfc.nasa.gov/Products/NASA/GWR.html">https://viirsland.gsfc.nasa.gov/Products/NASA/GWR.html</a>.</p>
Switching From Insulin to Sulfonylurea in Diabetes Associated With Variants in MODY Genes
ClinicalTrials.gov study NCT04239586. IPD Sharing: YES. Countries: 1. Publications: 17.
Usefulness of Continuous Glucose Monitoring in MODY Diagnosis
ClinicalTrials.gov study NCT05918484. IPD Sharing: YES. Countries: 1. Publications: 5.
The Effects of GLP-1 in Maturity-Onset Diabetes of The Young (MODY)
ClinicalTrials.gov study NCT01610934. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pathophysiological Implications of the Incretin Hormones in Maturity Onset of Diabetes of the Young (MODY)
ClinicalTrials.gov study NCT01342939. IPD Sharing: Not stated. Countries: 1. Publications: 1.
MODY in Young-onset Diabetes in Different Ethnicities
ClinicalTrials.gov study NCT02082132. IPD Sharing: NO. Countries: 1. Publications: 4.
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