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1,138 results for “Modis”
MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution
<p>Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS data for Europe at 1 km resolution.</p> <p>Source data:<br> - MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MOD13A1 v006): https://lpdaac.usgs.gov/products/mod13a1v006/<br> - MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MYD13A1 v006): https://lpdaac.usgs.gov/products/myd13a1v006/</p> <p><br> The MOD/MYD13A1 Version 6 product provide Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.</p> <p>For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 16 days. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data".</p> <p>File naming:<br> productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif<br> example: MOD13A1.A2020305_mosaic_1000m_16_days_NDVI.tif</p> <p>The date is Year and Day of Year.</p> <p>Values are NDVI/EVI * 10000. Example: Value 6473 = 0.6473</p> <p>Projection + EPSG code:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG: 3035)</p> <p>Spatial extent:<br> north: 72N<br> south: 30S<br> west: -52W<br> east: 49E</p> <p>Spatial resolution:<br> 1 km</p> <p>Temporal resolution:<br> 16 days</p> <p>Pixel values:<br> NDVI/EVI * 10000 (scaled to Integer; example: value 6473 = 0.6473)</p> <p>Software used:<br> GRASS GIS 8.0</p> <p>Original dataset license:<br> All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/myd13a1v006/</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
MODIS MNDWI, 8-day time series for Europe at 1 km resolution
<p>Modified Normalized Difference Water Index (MNDWI) from MODIS data for Europe at 1 km resolution.</p> <p>Source data:<br> - MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v006): https://lpdaac.usgs.gov/products/mod09a1v006/</p> <p>The corresponding MODIS/Aqua product (MYD09A1 v006) could not be used due to the fact that the Aqua satellite has a number of broken detectors resulting in unreliable data for band 6 (SWIR) measurements.</p> <p>The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.</p> <p>For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 8 days. Bad quality pixels (cloud, cloud shadow, dead detector, solar zenith angle too large, etc.) have been masked using the provided quality assurance (QA) layers and appear as "no data".</p> <p>File naming:<br> productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif<br> example: MOD09A1.A2016353_mosaic_1000m_8_days_MNDWI.tif</p> <p>The date is Year and Day of Year.</p> <p>Values are MNDWI * 10000. Example: Value -5099 = -0.5099</p> <p>Projection + EPSG code:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)</p> <p>Spatial extent:<br> north: 72N<br> south: 30S<br> west: -52W<br> east: 49E</p> <p>Spatial resolution:<br> 1 km</p> <p>Temporal resolution:<br> 8 days</p> <p>Pixel values:<br> MNDWI * 10000 (scaled to Integer; example: value -5099 = -0.5099)</p> <p>Software used:<br> GRASS GIS 8.0</p> <p>Original dataset license:<br> All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/mod09a1v006/</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
2006_2022_MODIS _EnhancedVegetationIndex
<p>MODIS decadal and monthly of Enhanced Vegetation Index, 5km, 2006-2022. </p> <p>Abstract: this is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for use with administraytive level analysis which need to used covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive (MOD13C1 and MOD13C2) and windowed for the E4warning study area.</p>
2015_Min_RelativeHumidity_Modis_FourirerProcess
<p>This is a set of images produced by Temporal Fourier Analysis (TFA) of Modis minimum Relative humidity data:</p> <p>Modis: Minimum Relative Humidity</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of Modis data, processed according to Scharlemann et al (2008), has been updated to include imagery for 2015.</p> <p> </p> <h4>Process:</h4> <p>Image values were extracted from MODIS( Min Relative Humidity) 5 km imagery for 2015. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>) <br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic (WGS84). The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p> </p> <p>This new RH Modis Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <h4>File names:</h4> <p><br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p>
MODIS NDVI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023).</p> <p>Source data:<br>- MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p><br>The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.</p> <p>For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84.</p> <p>File naming:<br><code>ndvi_filt_YYYY_MM_01T00_00_00.tif</code><br>e.g.: <code>ndvi_filt_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28N<br>south: 14N<br>west: 18W<br>east: 4W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
2001_2021_ MODIS_FourierProcessed_1k_ER
<p><strong>Overview: </strong></p> <p>This is a set of images produced by Temporal Fourier Analysis of global MODIS data</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p> </p> <p><strong>Abstract: </strong></p> <p>MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.</p> <p>This is an update of the 2001-2019 series, which adds to 2021 and is then processed by a temporal Fourier processing algorithm.</p> <p>A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)<br> Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format in order to give data users more flexibility<br> </p> <p><strong>File naming scheme:</strong> </p> <p> The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 11 refers to the year timeline from 2001-2021.<br> <br> The next two characters identify the channel:<br> 03 - middle infra-red<br> 07 - daytime land surface temperature<br> 08 - nighttime land surface temperature<br> 14 - NDVI: Normalised Difference Vegetation Index<br> 15 - EVI: Enhanced Vegetation Index<br> <br> The last two characters of each file name denote the output from Fourier processing:<br> a0 - mean<br> mn - minimum<br> mx - maximum<br> a1 - amplitude of annual cycle<br> a2 - amplitude of bi-annual cycle<br> a3 - amplitude of tri-annual cycle<br> p1 - phase of annual cycle<br> p2 - phase of bi-annual cycle<br> p3 - phase of tri-annual cycle<br> d1 - variance in annual cycle<br> d2 - variance in bi-annual cycle<br> d3 - variance in tri-annual cycle<br> da - combined variance in annual, bi-annual, and tri-annual cycles<br> vr - variance in raw data</p> <p> </p> <h4>Files can be accessed <a href="https://tinyurl.com/tfamodis01211k" target="_blank" rel="noopener">here</a> as well (including Global files). </h4> <p><br> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) <br><strong>Temporal resolution:</strong><br> 8-day and 16-day from 2001 to 2021</p> <p><br><strong>Pixel values</strong></p> <p>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <p><br><strong>Source: </strong><br>MODIS NASA :MOD11A2 and MOD13A2</p> <p><br><strong>Software used:</strong><br>Codes for modelling are in Python and C++<br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
2001_2019_MODIS_Fourier Processed_1k_ER
<p><strong>Overview:</strong></p> <p>This is a set of images produced by Temporal Fourier Analysis of global MODIS data</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p> </p> <p><strong>Abstract: </strong></p> <p>MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.</p> <p>This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.</p> <p>A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)<br> Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility<br> </p> <p><strong>File naming scheme:</strong></p> <p><br> The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.<br> <br> The next two characters identify the channel:<br> 03 - middle infra-red<br> 07 - daytime land surface temperature<br> 08 - nighttime land surface temperature<br> 14 - NDVI: Normalised Difference Vegetation Index<br> 15 - EVI: Enhanced Vegetation Index<br> <br> The last two characters of each file name denote the output from Fourier processing:<br> a0 - mean<br> mn - minimum<br> mx - maximum<br> a1 - amplitude of annual cycle<br> a2 - amplitude of bi-annual cycle<br> a3 - amplitude of tri-annual cycle<br> p1 - phase of annual cycle<br> p2 - phase of bi-annual cycle<br> p3 - phase of tri-annual cycle<br> d1 - variance in annual cycle<br> d2 - variance in bi-annual cycle<br> d3 - variance in tri-annual cycle<br> da - combined variance in annual, bi-annual, and tri-annual cycles<br> vr - variance in raw data</p> <p><br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) <br><strong>Temporal resolution:</strong><br> 8-day and 16-day from 2001 to 2019</p> <p><br><strong>Pixel values</strong></p> <p>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <p><br><strong>Source: </strong><br>MODIS NASA :MOD11A2 and MOD13A2</p> <p><br><strong>Software used:</strong><br>Codes for modelling are in Python and C++<br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
2015_MODIS_RelativeHumidity_FourierProcessed
<p>This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS Relative Humidity data:</p> <p><strong>Abstract :</strong></p> <p>This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MID) derived from the MOD11A2 Product from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.<br>MODIS is a sensor on two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture.</p> <p> </p> <p>The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data</p> <p> </p> <p><br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) <br><strong>Temporal resolution:</strong><br> Decadal </p> <p><strong>Pixel values</strong></p> <p>Parameter Fourier Variable Image values are<br>RH values A0, A1, A2, A3, Min, Max, Vr Reflectance values </p> <p>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <p><br><strong>Source: </strong><br>Middle Infra Red (MID) derived from the MOD11A2(MODIS NASA) Product from USGS</p> <p><br><strong>Software used:</strong><br>Codes for modelling are in Python and C++<br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
2006_2022_MODIS_EnhancedVegetationIndex_5k_ER
<p>MODIS 16-day Enhanced Vegetation Index, 5km, 2006-2022. </p> <p><strong>Abstract: </strong></p> <p>This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for administrative-level analysis that uses covariate data that temporally matches the modeled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>There are two zip files. One includes data from 2006 to 2021, and the second is for 2022. </p> <p>the files name are: moeve5km16day + year + day (out of 365) : moeve5km16day2022049.tif</p> <p> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>5k<br><strong>Temporal resolution:</strong><br>16-day from 2006 to 2022</p> <p><br><strong>Pixel values:</strong></p> <p>Vegetation Index</p> <p><strong>Source: </strong><br>MODIS NASA : MOD13C1</p> <p><br><strong>Software used:</strong><br> <br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0</p> <p><br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p> <p> </p> <p> </p> <p> </p> <p> </p>
2010_2022_MODIS_LandSurfaceTemperature_5k_ER
<p>MODIS daily, decadal, and monthly land surface Temperature, 5km, 2010-2022. </p> <p><strong>Abstract:</strong></p> <p>This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for administrative-level analysis that uses covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>Monthly Day LST : 2022 <a href="../api/records/13122960/draft/files/MOODMonthlyDLST2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMonthlyDLST2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122960/draft/files/MOODMonthlyDLST20102021.zip/content" target="_blank" rel="noopener noreferrer">MOODMonthlyDLST20102021.zip</a></p> <p>Monthly Night LST 2022 <a href="../api/records/13122960/draft/files/MOODMonthlyNLST2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMonthlyNLST2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122960/draft/files/MOODMonthlyNLST20102021.zip/content" target="_blank" rel="noopener noreferrer">MOODMonthlyNLST20102021.zip</a></p> <p>Decadal Day LST: 2022 <a href="../api/records/13122960/draft/files/MOODMOD11c2Dekadaldlst2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMOD11c2Dekadaldlst2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122960/draft/files/MOODMOD11C2DEKADALDLST20102021.zip/content" target="_blank" rel="noopener noreferrer">MOODMOD11C2DEKADALDLST20102021.zip</a></p> <p>Decadal Night LST: 2022 <a href="../api/records/13122960/draft/files/MOODMODC11dekadalnlst2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMODC11dekadalnlst2022.zip</a> ; 2010 to 2021 <a href="../api/records/13122960/draft/files/MOODMOD11C2DEKADALNLST20102021.zip/content" target="_blank" rel="noopener noreferrer">MOODMOD11C2DEKADALNLST20102021.zip</a></p> <p>Daily Day LST: 2022 <a href="../api/records/13122960/draft/files/MOODMOD11C1DAILYDLST2022.zip/content" target="_blank" rel="noopener noreferrer">MOODMOD11C1DAILYDLST2022.zip</a> ; 2010 to 2021 <a href="13122960" target="_blank" rel="noopener noreferrer">MOODMOD11C1DAILYDLST20102021.zip</a></p> <p>Daily Night LST: 2022 <a href="13122960" target="_blank" rel="noopener noreferrer">MOODMOD11C1DAILYNLST2022.zip</a> ; 2010 to 2021 <a href="13122960" target="_blank" rel="noopener noreferrer">MOODMOD11C1DAILYNLST20102021.zip</a></p> <p><br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>5k<br><strong>Temporal resolution:</strong><br>Daily, Decadal, and monthly from 2010 to 2022</p> <p><strong>Pixel values</strong></p> <p>Temperature Degree</p> <p><br><strong>Source: </strong><br>MODIS NASA : MOD11A1</p> <p><br><strong>Software used:</strong><br> <br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p> <p> </p>
Northern Italy gap-filled MODIS Land Surface Temperature 1km daily
<p>Northern Italy Land Surface Temperature 1km daily Celsius gap-filled dataset, LST daily average, 2014 - 2018.</p> <p>The dataset is stored as a GRASS GIS project/mapset, in ZIP compressed format.</p> <ul> <li>Spatial resolution: 1 km</li> <li>Temporal resolution: 1 day</li> <li>Temporal extent: 2014-2018</li> <li>Units: Celsius</li> <li>Aggregation method: average</li> <li>Format: stored as a <a href="https://grass.osgeo.org/">GRASS GIS</a> 8+ project</li> <li>Software used: GRASS GIS 8.4.0</li> </ul> <p>Reference:<br><br>Metz, M.; Andreo, V.; Neteler, M. <em>A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data</em>. Remote Sens. 2017, 9, 1333. <a href="https://doi.org/10.3390/rs9121333">https://doi.org/10.3390/rs9121333</a></p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod09a1v006/">https://lpdaac.usgs.gov/products/mod09a1v006/</a></p> <p>Data provided by:</p> <p>mundialis GmbH & Co. KG<br>Koelnstrasse 99<br>53111 Bonn, Germany<br><a href="https://www.mundialis.de">https://www.mundialis.de</a></p>
MODIS Snow-Cover Frequency Maps
<p>The 365 global snow cover frequency maps were derived from MODIS MOD10C1 data. There is one map for each day of the year (leap year days are excluded). Each map displays the frequency of snow cover for the 20-year time series, Hydrological Years 2000 - 2020, on a per-grid cell basis. Each grid cell is approximately 5 X 5 km. The dataset is described in detail in Riggs et al. (in press).</p>
eSIF product: enhanced SIF by combining TROPOSIF, MODIS reflectance and ERA-5 shorwave radiation
<p>The eSIF product is generaged by combining SIF and NIRvP data from different data sources. The original SIF data (TROPOSIF v2.1 L2B) are from the TROPOMI instrument onboard the Sentinel-5P mission, whereas NIRvP data are obtained from MODIS spectral reflectance and ERA5 reanalysis data. The resulting eSIF product has a spatial resolution of 0.05° and a temporal resolution of 8 days.</p> <p>For details, please refer to:</p> <p>Liu, X., Liu, L., Bacour, C., Guanter, L., Chen, J., Ma, Y., Chen, R., & Du, S. (2023). A simple approach to enhance the TROPOMI solar-induced chlorophyll fluorescence product by combining with canopy reflected radiation at near-infrared band. <em>Remote Sensing of Environment, 284</em>, 113341, <a href="http://doi.org/10.1016/j.rse.2022.113341">http://doi.org/10.1016/j.rse.2022.113341</a></p>
Daily lake area delineated from MODIS using GEEDiT for the year 2019
<p>Daily lake area delineated from MODIS using GEEDiT for the year 2019</p>
Data for "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion"
<p>Data used in generating results for the paper <a href="https://doi.org/10.1029/2023JG007457">"Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion."</a></p> <ol> <li>VIIRS_MOD16_MOD17_tower_site_drivers_v9.h5</li> <li>MOD17_5km_global_simulation.zip</li> <li>VNP17_5km_global_simulation.zip</li> </ol> <p>[1] is an HDF5 file containing surface meteorological drivers, MODIS/ VIIRS vegetation fPAR and LAI, and other data necessary for calibrating and validating the MOD17 and VNP17 GPP models at FLUXNET towers. It also contains driver data and field-based NPP data for calibrating and validating MOD17/ VNP17 NPP models.</p> <p>[2] and [3] are the global, 5-km GPP and NPP simulations using the updated MOD17 parameters and new VNP17 model parameters. Other than their 5-km resolution, these global, annual GeoTIFF files are formatted the same as MOD17A3H data; <a href="https://lpdaac.usgs.gov/products/mod17a3hgfv061/">see the User Guide</a> for more information. The same scale factors apply to recover geophysical units: multiply the values by 0.0001 to obtain [kg C m-2 year-1].</p> <p>Please cite the peer-reviewed paper:</p> <blockquote> <p>Endsley, K.A., M. Zhao, J.S. Kimball, S. Devadiga. 2023. "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion." <em>Journal of Geophysical Research: Biogeosciences</em> 128(9).</p> </blockquote>
Fire INventory from NCAR (FINN) v2.5(MODIS), MOZART VOC speciation
<p>The Fire INventory from NCAR (FINN) provides daily global fire emissions at high spatial resolution. The FINN model uses satellite detection of active fires (thermal anomalies) and the land cover type to determine the emission estimates. These emission estimates are based on MODIS active fire detection. Other versions of FINNv2.5 use MODIS+VIIRS fire detections. Please find additional VOC speciations and gridded emissions files at: https://doi.org//10.5065/XNPA-AF09</p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</p>
Phenological metrics for Protected Area "GranParadiso", MODIS terra tile h18v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "HighTatra", MODIS terra tile h19v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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