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80 results for “DMSP”
A Consistent and Corrected Nighttime Light dataset (CCNL 1992-2013) from DMSP-OLS data
<p>DMSP-OLS provides the longest observations of NTL information, from 1992 to 2013, an unparalleled dataset for studying historical artificial lights. Version 4 of the DMSP-OLS Nighttime Lights Time Series is widely used ( Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency ). However, it suffers from three main problems: inter-annual inconsistency, saturation, and blooming effect.</p> <p>We used a series of methods to mitigate the impact and improve data quality. After processing, we get consistent and corrected nighttime light dataset (CCNL).</p> <p>The version 1 products span the globe from 75N latitude to 65S. The products are produced in 30 arc resolution and are made available in GeoTIFF format. Pixel Unit: 'DN'(Digital Number).</p> <p>Each GeoTIFF filename has 4 filename fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below using this example filename:</p> <p>CCNL_DMSP_1992_V1</p> <p>Field 1: CCNL(Consistent and Corrected Nighttime Light dataset)</p> <p>Field 2: Platform "DMSP"</p> <p>Field 3: Year “1992”</p> <p>Field 4: version “V1”</p>
Nighttime Lights PC1-4 based on the Version 4 DMSP-OLS Nighttime Lights Time Series 1997–2014
<p>Nighttime Lights PC1-4 based on the Version 4 <a href="https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html">DMSP-OLS Nighttime Lights Time Series</a> 1997–2014. Derived using SAGA GIS Principal Component analysis. Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul>
Code for producing a consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data
<p>The DMSP-OLS NTL product suffers from three main problems, i.e.inter-annual inconsistency, saturation, and blooming effect which will affect the accuracy of urban extraction and the estimation of the social-economic indexes. To address these problems, we adopted three correction methods to rectify inter-annual inconsistency, saturation, and blooming effects.<br> The code is written based on the Javascript API provided by the Google Earth Engine platform(https://earthengine.google.com/)</p>
A Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2024) Combining DMSP-OLS and NPP-VIIRS
<h1><strong>Updated to 2024! Welcome to download!</strong></h1> <p>We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL).</p> <p>Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21/V22 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels.</p> <p>PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.</p> <p> </p> <p><strong>Please refer to the paper for detailed information.</strong></p> <div> <div>Li, S., Cao, X*., Zhao, C., Jie, N., Liu, L., Chen, X., Cui, X., (2023). Developing a Pixel-Scale Corrected Nighttime Light Dataset (PCNL, 1992–2021) Combining DMSP-OLS and NPP-VIIRS. <em>Remote Sensing,</em> 15, 3925. <a href="https://doi.org/10.3390/rs15163925" target="_blank" rel="noopener">https://doi.org/10.3390/rs15163925</a></div> </div> <p> </p> <p><strong>Other recent publications using PCNL:</strong></p> <p>Li, S., Cao, X.*, (2024). Monitoring the modes and phases of global human activity development over 30 years: Evidence from county-level nighttime light. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 126, 103627. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jag.2023.103627" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jag.2023.103627</a></p> <div> <div> <div>Li, S., Cao, X.*, Liu, L., Li, A., (2025). Inequality of divided and shared socio-economic resources in 15-minute cities of China. <em>Geography and Sustainability, </em>100337. <a href="https://doi.org/10.1016/j.geosus.2025.100337" target="_blank" rel="noopener">https://doi.org/10.1016/j.geosus.2025.100337</a></div> </div> </div>
DMSP F15/F16/F18 2011-2014 Binned Poynting Flux Data
<p>This dataset, created using the <a href="https://github.com/lkilcommons/esabin">esabin</a> Python library, comprises 9 spacecraft-years of electrodynamics measurements from Defense Meteorology Satellite Program F15, F16 and F18 spacecraft. <strong>Users of this data should prefer files which do not contain 'idm_only' in their filenames, as these do not use both components of DMSP ion drift vector measurements.</strong> They are included here to support reproducibility of a upcoming publication.</p> <p>The HDF5 files herein organize this data in equal area bins in magnetic coordinates. Each Group in the files represents one bin. Each Dataset contains the data for one crossing of that bin by a DMSP spacecraft. The name of each Dataset is the approximate time of the crossing as Julian date. Hourly NASA OMNIWeb solar wind and geomagnetic activity parameters are included as attributes for each Dataset.</p> <p>The electrodynamic parameters herein are magnetic perturbation (dB), electric field (E) and ion drift velocity (V). These quantities are scaled from satellite altitude (~850 km) where they were observed to ionospheric altitude (~110 km) using Modified Magnetic Apex coordinates. In the filenames, 'e' represents magnetic eastward and 'n' represents magnetic northward. Also included are geomagnetic-main-field-aligned Poynting flux also scaled to 110km altitude (files with 'poynting' in the name).</p>
35S-DMSP uptake by chemosensitive dinoflagellates
<p>Dataset comprising uptake data of 35S-DMSP by three cultured dinoflagellates (<em>K. armiger, O. marina </em>and <em>G. dominans</em>) during incubations of 24-48h. This data was used in the article: <strong>The distinctive chemotactic responses of three marine herbivore protists to DMSP and related compounds.</strong></p>
Conjugate observation data between DMSP satellites and all-sky imager of Chinese Yellow River station at Ny-Ålesund, Svalbard from January 2005 to December 2009
<p>Chinese Arctic Yellow River Station (YRS) locates at Ny-Ålesund, Svalbard with the geographic coordinates (78.92°N, 11.93°E) and the corrected geomagnetic latitude 76.24°. The relation between local time and universal time is MLT≈UT+3hr. In November 2003, a set of monochromatic auroral observation system was installed at YRS, which is consisted of three identical all-sky imageries (ASIs) with the filters at 427.8nm, 557.7nm and 630.0nm, respectively. DMSP (Defense Meteorological Satellite Program) satellites are a collection of polar-orbit weather satellites launched by the U.S. department of defense. This series of satellites is sun synchronous satellite, which takes about 101 minutes to orbit the earth, has an altitude of about 835-850km, an inclination of about 96°, and crosses the equatorial plane daily from south to north (ascending segment) and from north to south (descending segment) at a fixed time (DMSP F13 is at 05:45LT and 17:45LT, and F15 is at 09:30LT and 21:30LT). DMSP satellite is equipped with Special Sensor for Particle Flux (SSJ/4), which can measure the fluxes of downgoing electrons and ions with energies between from 30eV to 30keV in 19 energy steps (34, 49, 71, 101, 150, 218, 320, 460, 670, 960 eV, and 1.4, 2.1, 3.0, 4.4, 6.5, 9.5, 14.0, 20.5, 29.5 keV), with a time resolution of 1 second. According to the orbit of the DMSP satellites and the observation of the ASIs of YRS, we obtained the conjugate observation periods of the satellite flying over the ASI.</p> <p>During the period from January 2005 to December 2009, a total of 136 conjugate observation events were obtained. Moreover, according to the morphological characteristics of discrete aurora in the all-sky image, 136 events are classified according to four typical forms of dayside discrete auroras, namely 27 events of drapery dayside corona (DDC), 24 events of radial dayside corona (RDC), 37 events of hot-spot aurora (HSA), and 48 events of arc aurora (ARC). The event list of 136 events is recorded in the “asi&dmsp@YRS03-09-v2.xlsx” file. The EPS file gives the trajectory of the DMSP satellite crossing the ASI in each conjugate observation event, while the JPG files are the corresponding all-sky images.</p>
Secondary_data_Maximum chemotactic indices (Icmax) to DMSP, DMS and acrylate
<p>Dataset containing secondary data regarding Chemotactic Indexes (Ic). This data was used for the generation the plot about Ic related to the article: <strong>The distinctive chemotactic responses of three marine herbivore protists to DMSP and related compounds.</strong> Therefore, provides information about the chemotaxis indexes obtained three cultured dinoflagellates (<em>Karlodinium armiger, Gyrodinium dominans</em> and<em> Oxyrrhis marina</em>) responding to gradients of DMSP, DMS and and acrylate diffusing from a microcapillary. </p> <p>Nomenclature:</p> <p>Cap: Capillary (C=control, E=experimental (with the chemical cue).</p> <p>D=DMSP/ M=DMS/ A=ACRYLATE</p> <p>2=2uM / 20= 20uM / 200= 200uM</p> <p>_1, _2, _3 = replicates 1, 2 or 3 </p>
DMSP_data-2018GL077381R
<p>DMSP electron temperature data used for Geophysical Research Letter paper 2018GL077381R, Hairston et al., Topside ionospheric electron temperature observations of the 21 August 2017 eclipse by DMSP spacecraft</p>
Code for Producing a Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2021) Combining DMSP-OLS and NPP-VIIRS
<p>We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL, 1992-2021).</p> <p>Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels.</p> <p>PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.</p>
SSUSI Images acquired by Ultraviolet Spectrographic Imager aboard DMSP satellite from October to December during 2004-2006
<p>SSUSI is a far ultraviolet scanning imaging spectrometer, which can detect 5 spectral bands of ultraviolet aurora (HI-Lyman α@121.6 nm、 OI@130.4nm、 OI@135.6 nm、 N2-LBHS@140-160 nm and N2-LBHL@160-180 nm) for synchronous scanning imaging observation, its mirror rotates perpendicular to the satellite orbit, and each scan produces an image of 16 ×156pixels. Due to the DMSP satellites need 20-30 minutes to fly over the polar regions, SSUSI can obtain an image covering 1/3 to 1/2 of the auroral oval with a spatial resolution of about 10 × 10 km for each polar flight.In order to reduce the influence of dayglow on SSUSI image data, we only use data from October to December 2004 to 2006 for the study of substorm detection (the aurora oval region in the northern hemisphere during this period is in the polar night region, and the aurora image is weakly affected by dayglow and solar light).<br> The zip file SSUSIdata_mat contains two folders, the plain folder contains the data without the westbound surge structure and the wts folder contains the files with the westbound surge structure.</p>
DMSP F16, F17, and F18 Polar Cap Crossings during 2015-2023
<p>Lists of DMSP F16, F17, and F18 northern hemisphere polar cap crossings (called <em>events</em> in the file names) during the years 2015-2023. The lists include the time at which the satellite reaches the highest latitude, as well as the times at which it enters and exits the polar cap, respectively. The entrance and exit times are estimated using DMSP measurements of the ion energy flux.</p>
Defense Meteorology Satellite Program (DMSP) Electron Precipitation (SSJ) Auroral Boundaries, 2010-2014
<p>This dataset contains comma seperated value (CSV) files describing the equatorward and poleward edges of the auroral oval in magnetic and geocentric coordinates as determined in Kilcommons et al. 2017 (https://doi.org/10.1002/2016JA023342), using the ssj_auroral_boundary Python package version 1 (https://doi.org/10.5281/zenodo.3267414) This dataset spans 15 satellite years 2010-2014, DMSP spacecraft flights 16, 17 and 18.</p>
NDUI+: A fused DMSP-VIIRS based multidecadal, high-resolution global normalized difference urban index (NDUI) dataset
<p>M. Singh and S. Ghosh are equal contributors to this work and are designated as co-first authors</p>
RSS SSM/I OCEAN PRODUCT GRIDS 3-DAY AVERAGE FROM DMSP F11 NETCDF V7
The RSS SSM/I Ocean Product Grids 3-Day Average from DMSP F11 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F11 for 3-day averages.
NASA MEASURES Precipitation Ensemble based on SSMIS DMSP F18 NASA PPS L1C V05 Tbs 1-orbit L2 Swath 12x12km V1 (PRECIP_SSMIS_F18) at GES DISC
The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave Imager Sounder (SSMIS) flown on the US Defense Meteorological Satellite Program (DMSP) F18 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2010 to 2020 with one file per orbit.
RSS SSM/I OCEAN PRODUCT GRIDS WEEKLY AVERAGE FROM DMSP F8 NETCDF V7
The RSS SSM/I Ocean Products Grid Weekly Average from DMSP F8 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F8 forweekly average.
RSS SSM/I OCEAN PRODUCT GRIDS 3-DAY AVERAGE FROM DMSP F14 NETCDF V7
The RSS SSM/I Ocean Product Grids 3-Day Average from DMSP F14 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F14 for a 3-day average.
RSS SSM/I OCEAN PRODUCT GRIDS WEEKLY AVERAGE FROM DMSP F11 NETCDF V7
The RSS SSM/I Ocean Product Grids Weekly Average from DMSP F11 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefully intercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F11 for weekly averages.
NASA MEASURES Precipitation Ensemble based on SSMIS DMSP F17 NASA PPS L1C V05 Tbs 1-orbit L2 Swath 12x12km V1 (PRECIP_SSMIS_F17) at GES DISC
The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Special Sensor Microwave Imager Sounder (SSMIS) flown on the US Defense Meteorological Satellite Program (DMSP) F17 mission. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2008 to 2020 with one file per orbit.
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