Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,138
datasets available to search
ShareScore release 0.9.0
Dataset results
1,138 results for “Modis”
Normalized Difference Water Index for Douro Valley based on MODIS
<p><strong>Normalized Difference Water Index </strong>(NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. </p> <p>The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001).</p> <p><a href="https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf">https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf</a></p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp3_douro_MOD09A1_ndwi</p> <p> </p>
Normalized Multi-band Drought Index for Douro Valley based on MODIS
<p><strong>Normalized Multi-band Drought Index</strong> (NMDI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the nmdi was calculated. </p> <p>NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture.( <a href="https://agupubs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Wang%2C+Lingli">Lingli Wang</a>, 2007)</p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021</a></p> <p> </p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp3_douro_MOD09A1_nmdi</p>
Normalized Difference Vegetation Index for Douro Valley based on MODIS
<p><strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. </p> <p>NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation.</p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wpe_douro_MOD09A1_ndvi</p> <p> </p>
Normalized Multi-band Drought Index for Andalusia Region based on MODIS
<p><strong>Normalized Multi-band Drought Index </strong>(NMDI) was calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the nmdi was calculated. </p> <p> </p> <p>NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture.(Lingli Wang, 2007)</p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021</a></p> <p> </p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_nmdi</p> <p> </p>
Normalized Difference Water Index for Andalusia Region based on MODIS
<p><strong>Normalized Difference Water Index</strong> (NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. </p> <p>The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001).</p> <p><a href="https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf">https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf</a></p> <p> </p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_ndwi</p>
An Optimized North America MODIS Leaf Area Index (LAI) Dataset for Air Quality Modeling
<p>Air Quality Research Division, Environment and Climate Change Canada,</p> <p>4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada</p> <p>Email: Junhua.zhang@ec.gc.ca</p> <p> </p> <p>Leaf Area Index (LAI) is used in air quality models for land surface processes and for calculating biogenic emissions. MODIS LAI product provided by NASA (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php) has been widely used in the air quality modeling community for such purposes. However, limitations of MODIS LAI product have been seen for some geographic areas, particularly unreasonably low LAI over the evergreen needleleaf boreal forests in the northern hemisphere during wintertime due to snow cover and low sun angle. Missing retrievals over urban areas and areas with persistent cloud cover are also seen. Considerable efforts have been made to improve the MODIS LAI product. However, some issues are still persistent, such as the very low LAI over boreal forests during wintertime. In order to solve these issues for supporting regional air quality modelling, the 8-day MODIS Collection 6 (C6) LAI product at 500m resolution (MCD15A2H) was examined for North America. Statistics were calculated by month and by land cover type defined in the “Land Cover Type 1” science data set (SDS) of the Collection 6 MODIS Land Cover (MCD12Q1) product. Comparisons with LAI calculated from the EPA’s Biogenic Emissions Landuse Database, version 4 (BELD4, https://www.epa.gov/air-emissions-modeling/biogenic-emission-sources) were also done (Zhang et al., 2020). Based on the analysis, an updated monthly LAI dataset was calculated based on 1) 17-year (2003-2019) average of MODIS summer-time peak LAI, 2) fraction of evergreen and deciduous for each pixel from BELD4, and 3) monthly profiles of LAI for evergreen and deciduous vegetation species from MODIS LAI (Zhang et al., 2021). This is the final LAI dataset for North America compiled using the 17 years of MODIS LAI product complemented by information from BELD4.</p> <p> </p> <p>REFERENCES:</p> <p>Zhang, J., M. D. Moran, P. A. Makar, and S. Kharol, 2020. Examination of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 19th CMAS Conference, 26-30 Oct., Virtual [see https://www.cmascenter.org/conference/2020/slides/ZhangJ_MODIS_LAI_CMAS_2020.pdf].</p> <p>Zhang, J., P. A. Makar, S. Kharol, M. D. Moran, and C. McLinden, 2021. Examination and Processing of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 2021 Meteorology and Climate - Modeling for Air Quality Conference, Sep 14-17, 2021, Virtual</p> <p> </p>
MODIS tree cover change of North American boreal forests 2000-2019
<p>The published files are two maps of North American boreal forest tree cover trends between 2000 and 2019. Pixel values are annual trends in tree cover expressed as % change per year. The trends are based on annual tree cover estimates from the MODIS Vegetation Continuous Field version 6 product. We quantified tree cover trends per pixel through Theil-Sen's slope estimation using the 'zyp' package in R. We followed the Yue-Pilon pre-whitening method to account for temporal autocorrelation. We created a trend map for all data points within the boreal biome boundary following Gauthier et al. 2015, Science (<a href="https://doi.org/10.1126/science.aaa9092">DOI: 10.1126/science.aaa9092</a>) and added a 120km buffer around it (tcchange_all_points_clipped.tif). We also produced a map where we masked out non-significant trends based on a Mann-Kendall-test (tcchange_significant_points.tif). Both maps have a spatial resolution of around 1,000m.</p> <p>The map forms the key results in our manuscript: Rotbarth et al. 2023. 'North American boreal forests: Northern expansion is not compensating for southern declines'. Nature Communications</p>
Supporting Data for MODIS Aqua L2 SST Correction Project
<p>This dataset consists of two parts:</p> <p>A) Three data objects needed to ‘fix’ several issues associated with the L2 sea surface temperature (SST) dataset obtained from the MODIS Aqua instrument by the Ocean Biology Processing Group (OBPG) at NASA’s Goddard Space Flight Center. These files are required by the Matlab script, build_and_fix_orbits.m, in the GitHub repo git@github.com:pcornillon/MODIS_L2.git. This script ‘unmasks’ pixels improperly flagged as bad in the vicinity of large SST gradients and those flagged as bad because the difference between the retrieved SST and a reference field exceeds a given threshold. The script also regrids the L2 fields to compensate for the bow-tie effect, which tends to jumble the pixel locations in the along-track direction. The files are:</p> <p>1) weights_and_locations_from_31191.mat - used to correct for the bow-tie effect.</p> <p>2) SST_Range_for_Declouding.mat - are monthly climatologies of the minimum and maximum temperatures in 5x5 degree cells determined from the MODIS Aqua L2 SST dataset for 2002-2019. These fields are used to unmask pixels flagged as cloudy based on the reference SST test. The original test used was for a fixed SST range independent of time-of-year and location.</p> <p>3) Separation_and_Angle_Arrays.n4 - a file containing several fields, one for the along-track separation of pixels at each pixel location, a second for the along-scan separation and a third for the angle the perpendicular to the scan line makes counterclockwise of east. These data are used to determine the eastward and northward components of the SST gradient determined from the 'fixed' SST fields.</p> <p>B) A set of files used to test build_and_fix_orbits.m. These files are contained in four folders zipped into one file, MODIS_R2019.zip, plus the output file, AQUA_MODIS.20100619T052031.L2.SST.nc4, written by build_and_fix_orbits.m. The .zip file contains the data for one complete MODIS Aqua orbit beginning at 05h20 GMT on 19 June 2010. The folders in this file are: </p> <p>1) Data_from_OBPG_for_PO-DAAC - metadata files copied from a portion of the OBPG SST granules.</p> <p>2) Day - daytime granules obtained from the OBPG’s web site.</p> <p>3) Night - nighttime granules obtained from the OBPG’s web site.</p> <p>4) Orbits - a files indicating which OBPG granules make up individual orbits for June 2010</p> <p> </p>
Extreme values and exceedances of chlorophyll-a calculated from the OBGP MODIS AQUA v2018 dataset's daily files in European seas between 2003 and 2021.
<p>This dataset includes all the files generated to extract the extreme values of chlorophyll-a in European Seas between 2003 and 2021.</p> <p>The thresholds used are the overall's period and the monthly 90th percentiles.</p> <p>These files support a publication recently submitted.</p> <p> </p>
BGC-Argo radiometry matchups with L2 satellite images from MODIS, VIIRS and OLCI sensors
<p> Diffuse attenuation coefficients(Kd) were computed from measured downwelling irradiance measurements from BGC-Argo floats. Matchups between satellite images and float profiles were then performed. Estimates of Kd at two different wavelengths and<br> band-integrated (PAR) were obtained from Remote Sensing Reflectance using different published algorithms developed for open ocean waters spanning in type from explicit-empirical, semi-analytical and implicit-empirical and applied to data from spectral radiometers on board six different satellites (MODIS-Aqua, MODIS-Terra, VIIRS–SNPP, VIIRS-JPSS, OLCI-Sentinel 3A and OLCI-Sentinel 3B).</p>
A High-Quality Reprocessed MODIS Leaf Area Index Dataset (HiQ-LAI)
<p>The High-Quality Leaf Area Index (HiQ-LAI) is derived from reprocessed MODIS LAI C6.1 product by SpatioTemporal Information Compositing Algorithm (STICA). This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and original observations, to improve the raw MODIS LAI retrievals with poor quality. The HiQ-LAI covers the period from 2000 to 2022, with spatial resolutions of 500m/5km for global vegetation area and temporal resolutions of 8 days.</p> <p> </p> <p>Ground-based verification results show that HiQ-LAI performs better than the original MODIS product (MOD15A2H C6.1). Time series curves of the HiQ-LAI exhibit reduced abnormal fluctuations and better alignment with expected phenological patterns. Additionally, the agreement with ground measurements increases gradually as raw data quality decreases. HiQ-LAI was found to be more continuous and consistent than MODIS LAI on a global scale from both spatial and temporal perspectives, especially in the equatorial regions where optical remote sensing usually cannot achieve good performance. Thus, We anticipate that HiQ-LAI with better spatio-temporal continuity will better support varying global LAI time series applications.</p> <p> </p> <p>Here, we offer a product version with a spatial resolution of 5km and a temporal resolution of 8 days. Another version has a spatial resolution of 500 meters and is available through Google Earth Engine (https://code.earthengine.google.com/?asset=projects/verselab-398313/assets/HiQ_LAI/wgs_500m_8d).</p> <p>More details about HiQ-LAI can be found at https://github.com/tiramisu18/HiQ-LAI</p>
MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)
<p>The Asia Water Tower region, with the Qinghai-Tibet Plateau at its core, is the most widespread region of snow cover on Earth, except for the North and South Poles. The topographic heterogeneity of the Asian Water Tower region is so great that the snow cover is thin and patchy, resulting in a highly time-varying snow cover in the region, and therefore daily-scale fractional snow cover data are urgently needed. This dataset is based on the MODIS surface reflectance product MO/YD09GA product, and the MODIS daily cloud-free fractional snow cover dataset for the Asian Water Tower region from 2000 to 2022 was produced using the MESMA-AGE algorithm and the MSTI algorithm. The high spatial resolution Landsat-8 image was taken as the "ground truth", the RMSE was 0.16, and the MAE was 0.10. This dataset has a time series from 26 February 2000 to 31 December 2022 with a spatial resolution of 0.005°, which can provide quantitative snow cover information on the spatial distribution of snow for mountain hydrological models, land surface models, numerical weather forecasts, etc.</p>
SPIReS-MODIS-ParBal snow water equivalent reconstruction: Western USA, water years 2001–2024
Open the record for dataset details and reuse information.
MODIS sea ice leads detections using a U-Net
Open the record for dataset details and reuse information.
MODIS annual maximum NDVI for Tanana-Yukon Uplands Ecoregion from 2000-2012
This dataset contains maximum NDVI from the MODIS sensor onboard Aqua and Terra satellites. The NDVI has been filtered to contain only NDVI values exceeding 0.4 which typically represent vegetated pixels in the boreal region of Alaska. Scaling factor of 10,000 was applied to convert 4-byte floating point to 2-byte integer NDVI values (for example 0.4 to 40000,0.9 to 9000)
MODIS Leaf Area Index estimates for Alaska: 2002
The MODIS Leaf Area Index (LAI) Product is a global product produced every 8 days. The Leaf Area Index is estimated by a radiative transfer model assuming a given distribution of biome types within each pixel. The LAI Product was evaluated for the period of May 2002 to September 2002. The temporal pattern of spring greenup and fall senescence appeared reasonable across a large latitudinal transect from the Kenai Peninsula to the Arctic Coastal Plain. The temporal pattern also appeared reasonable across an elevational transect from Bonanza Creek Experimental Forest to Caribou Poker Creek Research Watershed to Eagle Summit. The positional accuracy and spatial pattern LAI was judged excellent by comparing the M2002 maximum LAI for the Survey Line Burn with a Landsat ETM+ image. However, there were two consistent problems with the LAI index at all spatial scales. First, a dip in maximum LAI during the green-up period most likely indicated cloud contamination of pixels. Second, the maximum 2002 LAI estimate was unrealistically high (>6.5) in many areas of Alaska. The accuracy of global estimates of leaf area and vegetation indices are suspect for high latitude areas due to several factors: 1) There is no tundra or taiga biome used in the leaf area index radiative transfer model. 2) Although a cloud-screen algorithm is applied on the front-end of processing, sub-pixel cloud contamination may occur over much of Alaska. 3) Subpixel broadleaf shrubs may lead to an overestimate of leaf area index and inflate vegetation indices.This dataset contains MOD15 leaf area index (LAI) and fraction of photosynthetically absorbed radiation (FPAR) for most of Alaska during the 2002 growing season. The data are in hdf format, with one file for each MODIS tile, for each 8-day composite period. The original quality control bits are included in each hdf file. The data are in the integerized sinusoidal projection with approzimately 1-km pixel size. The files were submitted in winzip format. T
Dataset used for PARASOL/GRASP aerosol products validation with AERONET and comparison with MODIS
<p><strong>Dataset used for PARASOL/GRASP aerosol products validation with AERONET and comparison with MODIS </strong></p> <p>1. PARASOL/GRASP vs. AERONET for 2005-2013</p> <ul> <li>AOD at 443, 490, 550, 565, 670, 865 and 1020 nm</li> <li>AE (440/870)</li> <li>AODF and AODC at 550 nm</li> <li>SSA at 440, 675, 870, and 1020 nm</li> <li>AAOD at 550 nm</li> </ul> <p>2. PARASOL/GRASP, PARASOL/Operational, MODIS (DT, DB and MAIAC) vs. AERONET for year 2008</p> <ul> <li>AOD 550 nm</li> <li>AE (440/670) and AE (440/870)</li> <li>AODF and AODC 550 nm</li> </ul> <p>3. Inter-comparison of daily 0.1 degree grided PARASOL and MODIS aerosol products for year 2008</p> <ul> <li>Daily 0.1 x 0.1 degree grided PARASOL and MODIS AOD 550 nm for 2008 <ul> <li>PARASOL/HP; PARASOL/Models; MODIS/DT; MODIS/DB; MODIS/MAIAC</li> </ul> </li> </ul> <p> </p> <p><strong><em>Please follow the data policy of each data source:</em></strong></p> <p>PARASOL/GRASP: GRASP-OPEN (<a href="https://www.grasp-open.com/products/">https://www.grasp-open.com/products/</a>)</p> <p>PARASOL/Operational: ICARE (<a href="http://www.icare.univ-lille1.fr">http://www.icare.univ-lille1.fr</a>)</p> <p>MODIS/DT and DB C6 MYD04_L2: ICARE (<a href="http://www.icare.univ-lille1.fr">http://www.icare.univ-lille1.fr</a>)</p> <p>MODIS/MAIAC MAC19A2: NASA LAADS (<a href="https://ladsweb.modaps.eosdis.nasa.gov">https://ladsweb.modaps.eosdis.nasa.gov</a>)</p> <p>AERONET: <a href="http://www.aeronet.gsfc.nasa.gov">http://www.aeronet.gsfc.nasa.gov</a></p> <p> </p> <p>Details can be found in manuscript:</p> <p>Chen, C., O. Dubovik, D. Fuertes, P. Litvinov, T. Lapyonok, A. Lopatin, F. Ducos, Y. Derimian, M. Herman, D. Tanré, L. A. Remer, A. Lyapustin, A. M. Sayer, R. C. Levy, N. C. Hsu, J. Descloitres, L. Li, B. Torres, Y. Karol, M. Herrera, M. Herreras., M. Aspetsberger, M. Wanzenboeck, L. Bindreiter, D. Marth, A. Hangler, and Federspiel C., Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring, submitted to ESSD, <a href="https://essd.copernicus.org/preprints/essd-2020-224/">https://essd.copernicus.org/preprints/essd-2020-224/</a>, 2020. </p>
MODIS-VIIRS Cloud Regimes
<p>The MODIS-VIIRS Cloud Regimes are a discrete classification of cloud fields at the mesoscale as observed by the VIIRS sensors aboard the Suomi-NPP and NOAA-20 satellites, as well as the MODIS sensor aboard the Aqua satellite using the Continuity algorithm. Derived by applying the <i>k</i>-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. This MODIS-VIIRS Cloud Regimes dataset is an independently derived dataset which can be viewed as an extension of the work that produced the MODIS Cloud Regimes (Cho et al. 2021, Oreopoulos 2021).</p><p>The MODIS-VIIRS Cloud Regimes are derived similarly by applying a <i>k</i>-means clustering algorithm to the joint-histograms at equal-area grids of 110 km every 3-h generated from granule (Level-2) cloud products available from the Continuity Algorithm "CLDPROP" applied on observations by the MODIS Aqua, VIIRS Suomi-NPP, and VIIRS NOAA-20 sensors during the period of March 2018 to February 2023. A cluster number of <i>k</i> = 11 was selected, which when including a clear sky regime gives a total of 12 MODIS-VIIRS Cloud Regimes. Each histogram is then assigned to the nearest <i>k</i>-means centroid to obtain its regime membership. Two variants of the VIIRS Cloud Regimes are available: (1) a 3-h product in the original 110-km native resolution of its derivation, and (2) a 3-h product derived from the 110-km product by nearest neighbor interpolation to 1°.</p><p>Cho, N., J. Tan, and L. Oreopoulos, 2021: Classifying planetary cloudiness with an updated set of MODIS Cloud Regimes. <i>J. Appl. Meteorol. Climatol.</i>, <strong>60</strong>, 981–997, https://doi.org/10.1175/JAMC-D-20-0247.1.</p><p>Oreopoulos, L., 2021: MODIS CR Equal Area 3-Hour. https://doi.org/10.5067/MEASURES/MODISCR/EQAR3H/DATA301.</p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2011 to 2016, derived from MODIS-Aqua‘s XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2003 to 2010, derived from MODIS-Aqua‘s XGBoost satellite retrieval products. It covers the time span from 2003 to 2010. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.