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1,138 results for “Modis”
2010_2022_MODIS_Day_Night_Dekadal_Monthly_LandSurfaceTemperature
<p>MODIS daily, dekadal and monthly of 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. Itis designed for use with administraytive level analysis which need to use covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive (MOD11C1 , MOD11C2, and MOD11C3) and windowed for the E4warning study area.</p> <p> </p> <p><strong>File names:</strong></p> <p>DLST refers to day land surface temperature </p> <p>NLST: refers to night land surface temperature </p>
Data from: Snow-cover seasonality in Kyrgyzstan: Variation and change over 20 years (2001-2021) as observed by the MODIS Terra snow product
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Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019
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eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
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Phenological metrics for Protected Area "OhridPrespa", MODIS aqua 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;
Supplementary Information to: Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high–resolution visible channel: Evaluation of candidate approaches with MODIS observations
<p>This repository contains the Python programmes and datasets used for</p> <p>the research described in the following paper:<br> https://doi.org/10.5194/amt-2019-334</p> <p>It has been prepared as supplementary information to the final paper.<br> <br> Note that the actual Cloud Property Retrieval (CPP) which would be<br> required for full reproducability of the results cannot be made<br> available by the authors, that the code included here is based on Python2, and that<br> paths to the dataset have to be adapted in the code.<br> <br> The repository consists of three separate parts/directories:<br> <br> * method: Python routines for generating the cloud property retrieval<br> input based on the Meteosat and MODIS data<br> <br> * analysis: Python routines for analysing the different experiments<br> from the CPP outputs, producing the figures and calculating the<br> comparison statistics<br> <br> * datasets: the various input and output datasets used for the<br> analyses of the paper</p>
Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm
<p>This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.</p>
MOSEV: A global burn severity database from MODIS (2000-2020)
<p>To advance in the fire discipline as well as in the study of CO<sub>2</sub> emissions it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. We present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products scenes since November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the Normalized Burn Ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR), and its relativized version (RdNBR), as well as the post-burn NBR which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR and the date of burning.</p>
Monthly Global NDVI at 5 km based on MODIS and AVHRR products - 1982 to 2019
<p>Long-term monthly NDVI calculated through daily images of AVHRR (<a href="https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5?hl=en">NOAA/CDR/AVHRR/NDVI/V5</a>) and MOD09GA.006 (<a href="https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GA">MODIS/006/MOD09GA</a>), which were cloud screened and aggregated by percentile 90th on <a href="https://earthengine.google.com/">Google Earth Engine</a>-GEE. All the images were exported from GEE to R environment (MOD09GA data were downsampled to 5km using the average), where the follow procedures were executed on a pixel-basis:</p> <ol> <li><strong>Outlier removal</strong> based on a temporal moving window, used to identify and remove NDVI outlier values (<em>i.e.</em> greater or less than 1.5 standard deviation calculated on 60-months time window) </li> <li><strong>Smoothing and gap filling</strong> approach implemented by the <a href="https://greenbrown.r-forge.r-project.org/">greenbrown package</a> (TSGFssa method)</li> <li><strong>Temporal harmonization</strong> using MOD09GA NDVI as target variable to train a model <em>[modis.ndvi ~ poly(avhrr.ndvi, 2) + max.temp + min.temp]</em> able to predict the MODIS NDVI before 2000</li> </ol> <p>The final product is composed by 456 NDVI global images without gaps, which were harmonized, from 1982 to 1999, to match with the expected MODIS NDVI values from 2000 onwards. For each month, an auxiliary image indicates which pixels were:</p> <ul> <li><strong>Disregarded</strong>: value 0 (nodata)</li> <li><strong>Gapfilled</strong>: value 1</li> <li><strong>smoothed</strong>: value 2</li> </ul> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>Publication in preparation.</p> <p>All files were internally compressed using "COMPRESS=DEFLATE" creation option in GDAL.<br> File naming convention <em>(veg_ndvi_avhrr.mod09ga_p90_5km_s0..0cm_20000101..20000131_v1.0.tif)</em>:</p> <ul> <li>veg = Vegetation theme</li> <li>ndvi = Normalized Difference Vegetation Index </li> <li>avhrr.mod09ga = NOAA CDR AVHRR V5 and MOD09GA version 6 products</li> <li>p90 = Percentile 90th to aggregate the daily images</li> <li>5km = 5 km of spatial resolution</li> <li>s0..0cm = land surface as vertical reference</li> <li>20000101..20000131 = from 2000-01-01 to 2000-01-31 (time reference)</li> <li>v1.0 = version number</li> </ul>
Summer land surface temperature from MODIS Aqua and Terra satellites for Houston in 2014 and Phoenix in 2003 at 1km resolution
<p>Satellite remote-sensing is used to collect important atmospheric and geophysical data at various spatial resolutions, providing insight into spatiotemporal surface and climate variability globally. These observations are often plagued with missing spatial and temporal information of Earth's surface due to (1) cloud cover at the time of a satellite passing and (2) infrequent passing of polar-orbiting satellites. While many methods are available to model missing data in space and time, in the case of land surface temperature (LST) from thermal infrared remote sensing, these approaches generally ignore the temporal pattern called the 'diurnal cycle' which physically constrains temperatures to peak in the early afternoon and reach a minimum at sunrise. In order to infill an LST dataset, we parameterize the diurnal cycle into a functional form with unknown spatiotemporal parameters. Using multiresolution spatial basis functions, we estimate these parameters from sparse satellite observations to reconstruct an LST field with continuous spatial and temporal distributions. These estimations may then be used to better inform scientists of spatiotemporal thermal patterns over relatively complex domains. The methodology is demonstrated using data collected by MODIS on NASA's Aqua and Terra satellites over both Houston, TX and Phoenix, AZ USA.</p>
All-weather 1km land surface temperature at global scale from 2000-2020 from MODIS data
<p>All-weatherLand Surface Temperature product (2000-2020): LSTs from Moderate Resolution Imaging Spectroradiometer(MODIS)/Terra have been produced. The LST data were generated by integrating multiple data from MODIS, reanalysis, and ground in situ measurements using meachine learning method. </p> <ul> <li>The dataset is organized by year.</li> <li>The data is stored in tif format.</li> </ul>
Alkenone Unsaturation Index, UK'37-Calculated SST and Observational SST (Aqua MODIS and WOA13) from samples of the South Brazilian Bight
<p>The file contains data on the alkenone unsaturation index from samples retrieved in the SW Brazilian margin (-23 °S to -27 °S), the calculated SST using five equations, observational SST data from the NASA Aqua MODIS Mission, and SST data from the World Ocean Atlas (2013) from the same sites, and the residuals between the calculated SST and the observational SST.</p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua and Terra
<p>The dataset integrates seamless POC concentration daily products for the global ocean, derived from MODIS-Aqua and Terra XGBoost satellite retrieval products. It covers the time span from 2017 to 2020. 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-Terra
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2009 to 2016, derived from MODIS-Terra‘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 and Terra
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2021 to 2022 with a 9-km resolution, derived from MODIS-Aqua and Terra 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 *10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration monthly products derived from MODIS-Aqua and Terra
<p>The dataset integrates seamless POC concentration daily products with a 9-km resolution for the global ocean from 2003 to 2022, derived from MODIS-Aqua and Terra 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 *10000 and then rounded using int32.</p>
Outputs of the Jupyter Notebook - MODIS MOD021KM and FIRMS
<p>The dataset contains the outputs of the notebook "MODIS MOD021KM and FIRMS" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samuel Jackson (author), Science & Technology Facilities Council, <a href="https://github.com/samueljackson92">@samueljackson92</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <p>MOD021KM</p> <ul> <li> <p>MODIS Characterization Support Team (MCST)</p> </li> <li> <p>MODIS Adaptive Processing System (MODAPS)</p> </li> </ul> <p>Firms</p> <ul> <li> <p>University of Maryland</p> </li> </ul> <p><em>Dataset authors</em></p> <p>MOD021KM</p> <ul> <li> <p>MODIS Science Data Support Team (SDST)</p> </li> </ul> <p>Firms</p> <ul> <li> <p>NASA’s Applied Sciences Program</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Louis Giglio, Wilfrid Schroeder, Joanne V. Hall, and Christopher O. Justice. MODIS Collection 6 Active Fire Product User’s Guide Revision B. Technical Report, NASA, 2018. URL: <a href="https://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf">https://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf</a>.</p> </li> </ul> <p> </p>
Regridded subset of MODIS chlorophyll, OC-CCI chlorophyll, MODIS sea surface temperature; basin bathymetric depth
<p>The North Atlantic phytoplankton bloom depends on a confluence of environmental factors that drive transient periods of exponential phytoplankton growth and interannual variability in bloom magnitude. I analyze interannual bloom variability in the North Atlantic via extreme value theory where the Generalized Extreme Value Distribution (GEVD) is fitted spatially to annual maxima of satellite-measured surface chlorophyll. I find excellent agreement between the observed distribution of interannual bloom maxima and those predicted from the GEVD. The spatial distribution of fitted GEVD parameters closely follows basin bathymetry where the largest extremes and heaviest distribution tails are found on the continental shelves and slopes. Trend analyses suggest weak evidence for changes in GEVD parameters, despite regional trends in mean chlorophyll levels and sea surface temperature. These results provide a framework to quantify interannual bloom variability and call for further work examining how extreme blooms propagate through food webs and contribute to carbon export.</p>
MODIS_6km_res
<p>MODIS image classified 19th Feb 2017</p>
Remaining bands of Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Remaining bands of 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>
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