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769 results for “MOSAIC”
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories
<p>CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories of anthropogenic CO2 emissions developed in the framework of the CoCO2 project (<a href="https://coco2-project.eu/">https://coco2-project.eu/</a>). CoCO2-MOSAIC 1.0 provides gridded (0.1˚×0.1˚) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors: energy_s (super-emitting sources above 7.9e-6 kg/m2/s), energy_a (average emitters), manufacturing, settlements, transport, aviation land/take-off (LTO) and other. The regional inventories included are CAMS-GHG-REG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (South-East Asia) and VULCAN 3.0 (USA). EDGAR 6.0 and CAMS-GLOB-SHIP 3.1 are used for gap-filling missing sectors and regions. CAMS-GLOB-TEMPO 3.1 is used for temporal disaggregation of inventories providing annual emissions. Aviation emissions from climb, descent, and cruise are not covered by regional inventories and are provided as a separate file. Note that 2015 is the only year when all regional inventories are simultaneously available. </p> <p>Compared to global inventories, CoCO2-MOSAIC 1.0 includes all the regional information available without the limitation of providing spatially consistent emissions. Therefore, CoCO2-MOSAIC 1.0 can be used as a global baseline inventory due to the higher level of detail, higher spatial resolution, and country-specific information included by regional inventories. </p> <p>For further details see Urraca et al. 2023 (ESSD submitted). The paper (i) describes the CoCO2-MOSAIC methodology and (ii) uses the mosaic to inter-compare the most widely used global inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0/7.0, ODIAC v2020b, and CEDS v2020_04_24.</p>
MODIS MCD12Q1 Land Cover and Land Use Time Series Global Mosaics 2001-2022 (500 m)
<p><strong>General Description</strong></p> <p>The yearly land use and land cover dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD12Q1"><abbr title="MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m">MCD12Q1 v061</abbr></a>. This data provides an yearly mosaics of land use and land cover data from 2001 to 2022 in cloud optimized Geotiff (COG) format. This dataset includes layers of land cover type 1 (t1), 2 (t2), and 5 (t5), land cover property 1 (p1) and 2 (p2), land cover property assessment 1 (p1a) and 2 (p2a), and land cover quality control (qc). </p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> 2001–2022</li> <li><strong>Type of data:</strong> Land cover and land use</li> <li><strong>How the data was collected or derived:</strong> Derived from MCD12Q1 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>.</li> <li><strong>Statistical methods used:</strong> None</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km)</li> <li><strong>Image size:</strong> 86,400 x 35,849</li> <li><strong>File format:</strong> Cloud optimized Geotiff.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, 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">GitLab Issues</a></li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li> </ul> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: </p> <ol> <li>generic variable name: lc = Land cover</li> <li>variable procedure combination: mcd12q1v061.t1 = MCD12Q1 v061 LC Type1 band</li> <li>Position in the probability distribution / variable type: c = class | p = probability</li> <li>Spatial support: 500m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20010101 = 2001-01-01</li> <li>Time reference end time: 20011231 = 2001-12-31</li> <li>Bounding box: go = global (without Antarctica)</li> <li>EPSG code: epsg.4326 = EPSG:4326</li> <li>Version code: v20230818 = creation date</li> </ol>
FCE Redlands 2008 Slope Mosaic, Miami-Dade County, South Florida
Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.
MOSAiC Cloudnet issue data set
<p>This data set contains information on possible data issues caused by external drivers (e.g. tethered balloon artefacts in the observations) related to the MOSAiC Cloudnet data set. Flagged data must be handled with care and should be excluded from statistical analyses. Issues tracking flags are identified by tethered balloon operation periods and experienced-eye observations of MOSAiC staff.</p>
ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC
<p>This data set is used for the training, validation and evaluation of retrievals of temperature and specific humidity profiles, as well as integrated water vapour from simlulated or measured microwave brightness temperatures (TBs), which are described in <strong>[1]</strong>.</p> <p>The data set consists of yearly files (2001-2018, 6-hourly resolution) that include data from the European Centre for Medium-Range Weather Forecasts's ERA5 reanalysis <strong>[2]</strong> and simulated TBs in the microwave spectrum. TB simulations were performed with PAMTRA <strong>[3,4]</strong> on the native ERA5 model level resolution at frequencies of a low frequency Humidity and Temperature Profiler (HATPRO, 22-58 GHz) and of a Low Humidity Profiler (LHUMPRO-243-340, aka MiRAC-P, 175-340 GHz). Afterwards, the ERA5 model level data has been interpolated to a new height grid (dimension 'z'), of which the lowest 43 indices equal the height grid of the retrieval that is developed with this data set. The upper 11 indices are included for additional TB simulations needed for the information content estimation performed and are not used for the retrievals to avoid the tropopause.</p> <p>The trained retrieval is applied to observations from the HATPRO and MiRAC-P that were installed onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition.</p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.</p> <p><strong>[3]:</strong> Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geoscientific Model Development, 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.</p> <p><strong>[4]:</strong> Mech, M., Maahn, M., Ori, D., Kneifel, S., and Orlandi, E.: PAMTRA Package – Passive and Active Microwave TRANsfer, available at: https://github.com/igmk/pamtra (last access: 6 September 2020), 2019c.</p>
Orthophoto mosaic from UAV of a Pinna nobilis population within the Venice lagoon
<p>Orthophoto mosaic from a UAV survey on a tidal flat within the Venice lagoon (Italy) colonized by Pinna nobilis and Cymodocea nodosa. On June 23<sup>rd</sup>, 2020 at 06:05 a.m. (GMT) (07:05 solar local time) UAV images were collected at low tide with a DJI Zenmuse X4S camera (20 Mpixels, focal length 8.8 mm, 1-inch CMOS Sensor) mounted on a professional quadcopter DJI Matrice210v2. A total of 228 images were collected and the Agisoft Metashape Pro v 1.6.2 software was then used to produce an ortho-photo mosaic through Structure from Motion photogrammetric technique.</p>
CTX DTM and ORI Mosaics over Sakarya Vallis, Gale Crater, Mars
<p>Local digital terrain model (DTM) and orthorectified image (ORI) mosaics over Sakarya Vallis, west of Aeolis Mons in Gale crater, Mars. The two constituent DTMs were processed using the CASP-GO suite described in Tao et al. (2018); the ORIs were processed using Ames Stereo Pipeline. The DTMs were then co-registered to an HRSC DTM mosaic (Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808354) and each other using Ames Stereo Pipeline, and then cropped and mosaicked.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> DTM grid-spacing: 18 m/pixel<br> ORI resolution: 6 m/pixel</p> <p>Stereo pairs (from Grindrod and Davis, 2018):</p> <ul> <li>P04_002675_1746_XI_05S222W, B21_017786_1746_XN_05S222W</li> <li>D02_027834_1748_XN_05S222W, G04_019698_1747_XI_05S222W</li> </ul> <p>Image IDs of the ORIs: P04_002675_1746_XI_05S222W, D02_027834_1748_XN_05S222W</p>
30-m HRSC DTM Mosaic of Gale Crater, Mars
<p>Digital terrain model (DTM) mosaic of Gale crater, Mars, processed from High-Resolution Stereo Camera (HRSC) stereo images using the modification of DLR-VICAR described by Kim and Muller (2009).</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> Grid-spacing: 30 m/pixel<br> Terrain reference: 200-m MOLA and HRSC blended global DTM (Fergason et al. 2018)</p> <p>HRSC source images: H1938_0000, H1927_0000, and H1916_0000</p>
MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference 2000-2017
<p>MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference in GPP for the period 2000-2017. Changes in GPP could be used to estimate land degradation or similar. Derived using <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD17A2H">the data.table package and quantile function in R</a>. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod17a2h_v006. Antartica is not included.</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> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>veg = theme: vegetation,</li> <li>gpp = variable: gross primary productivity in kg C m<sup>2</sup>,</li> <li>mod17a2h.oct = determination method: MOD17A2H product, GPP values for October,</li> <li>d = median value / difference = difference between periods / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>500m = spatial resolution / block support: 500 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
Orthorectfied photo mosaics derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains orthorectified mosaic photographs of each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The mosaics were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive orthorectified photo mosaics. The raw images are not provided but can be made available via project PIs. This data package includes one centimeter resolution orthomosaic images of all 15 sites as geotiff raster files. Other derived products from these UAV missions, including digital elevation models, digital terrain models, and sparse point clouds, are available in other EDI data packages (knb-lter-jrn.210543002, knb-lter-jrn.210543003, and knb-lter-jrn.210543004, respectively). This study is complete.
WPE01 Assessing the value added of NEON for using machine learning to quantify vegetation mosaics and woody plant encroachment at Konza Prairie
Woody encroachment, or invasion of woody plants, is rapidly shifting tallgrass prairie into shrub and evergreen dominated ecosystems, mainly due to exclusion of fire. Tracking the pace and extent of woody encroachment is difficult because shrubs and small trees are much smaller than the coarse resolution (>10m2) of common remote sensed images. However, the US government has been investing in finer resolution (<2m2) remote sensing through USDA NAIP and the National Ecological Observatory Network (NEON), both of which cost multi-million dollars each year and contain different remote sensed products. We compared two methods of classification (random forests and support vector machines) with these two freely available remotely sensed aerial images to determine if and how much NEON adds to classification accuracy and determine which method of machine learning was more accurate. All models have very high overall classification accuracy (>91%), with the NEON image a few percent more accurate than NAIP. The NEON image significantly relies on canopy height (LiDAR) to make classifications, but the importance of bands is more evenly distributed during NAIP classification. Lastly, accuracy for Eastern Red Cedar specifically is high with NEON (78-84%), compared to the relatively low classification accuracy using NAIP imagery (55-61%).
Band Ratio Mosaics from Airborne Hyperspectral Data at Aramo, Spain
<p> </p> <table> <tbody> <tr> <td> <h2>Metadata information</h2> </td> <td> </td> </tr> <tr> <td><strong>Full Title</strong></td> <td>Band Ratio Mosaics from Airborne Hyperspectral Data at Aramo, Spain</td> </tr> <tr> <td><strong>Abstract</strong></td> <td> <p>This dataset comprises results from the S34I Project, derived from processing airborne hyperspectral data acquired at the Aramo pilot site in Spain. Spectral Mapping Services (SMAPS Oy) conducted the airborne data acquisition in May 2024 using the Specim AisaFENIX sensor (covering VNIR-SWIR spectral ranges) over 17 flight lines. SMAPS performed geometric correction, radiometric calibration to reflectance, and atmospheric correction of the data. Subsequent processing steps included spectral smoothing with a Savitzky-Golay filter, cloud masking, bad pixel corrections, and hull correction (continuum removal).</p> <p>Manual processing and interpretation of hyperspectral data is a challenging, time-consuming, and subjective task, necessitating automated or semi-automated approaches. Therefore, we present a semi-automated workflow for large-scale interpretation of hyperspectral data, based on a combination of state-of-the-art methodologies. This dataset results from the calculation of a series of band ratios applied to the images and their subsequent mosaicking into a TIFF file. The mosaics are delivered as georeferenced TIFF files that cover approximately 97 km² with a spatial resolution of 1.2 m per pixel. The NoData value is set to -9999, representing areas of cloud removal or missing flight lines. The projected coordinate system is UTM Zone 30 Northern Hemisphere WGS 1984, EPSG 4326.</p> <p>Hyperspectral band ratios involve applying mathematical operations (such as division, subtraction, addition, or multiplication) among the reflectance values of different spectral bands. This technique enhances subtle variations in how materials absorb and reflect light across the electromagnetic spectrum. These variations are caused by electronic transitions, vibrations of chemical bonds (including -OH, Si-O, Al-O, and others), and lattice vibrations within the material's crystal structure.</p> <p>By creating these mathematical combinations, specific absorption features are emphasized, generating unique spectral fingerprints for different materials. However, these fingerprints alone cannot definitively identify a mineral, as different minerals may share similar absorption features due to common chemical bonds or crystal structures. Spectral geologists use band ratios as a tool to highlight potential areas of interest, but they must integrate this information with other geological knowledge and analyses to accurately interpret the mineralogy of an area. </p> <p>This dataset includes nine spectral band ratios. The mathematical formulas used to calculate each ratio are provided below:</p> <p> </p> <p>BR1 target Carbonate / Chlorite / Epidote</p> <p>BR1 = ((C7 + C9) / (C8))</p> <p>C7= Mean of bands between 2246.6 and 2257.55 nm</p> <p>C8= Mean of bands between 2339 and 2345 nm</p> <p>C9= Mean of bands between 2400 and 2410 nm</p> <p> </p> <p>BR2 target Chlorite</p> <p>BR2 = ((Cl1 + Cl2) / (Cl2))</p> <p>Cl1 = Mean of bands between 2191.93 and 2197.4 nm</p> <p>Cl2 = Mean of bands between 2246.63 and 2257.55 nm</p> <p> </p> <p>BR3 target Clay</p> <p>BR3 = ((C1 + C2) / (C2))</p> <p>C1 = Mean of bands between 1590.32 and 1612.56 nm</p> <p>C2 = Mean of bands between 2191.93 and 2208.35 nm</p> <p> </p> <p>BR4 target Dolomite</p> <p>BR4 = ((C6 + C8) / (C7))</p> <p>C6= Mean of bands between 2186 and 2191 nm</p> <p>C7= Mean of bands between 2246.6 and 2257.55 nm</p> <p>C8= Mean of bands between 2339 and 2345 nm</p> <p> </p> <p>BR5 target Fe2</p> <p>BR5 = ((Fe2n + Fe2d) / (Fe2d))</p> <p>Fe2n = Mean of bands between 721.85 and 742.48 nm</p> <p> </p> <p>BR6 target Fe3</p> <p>BR6 = ((Fe3n - Fe3d) / (Fe3n + Fe3d))</p> <p>Fe3n = Mean of bands between 776.87 to 811.26 nm</p> <p>Fe3d = = Mean of 3 bands around 610 nm</p> <p> </p> <p>BR7 target = Kaolinite / clays</p> <p>BR7 = ((K1 + K2) / (K3 + K4))</p> <p>K1 = Mean of bands between 2082.27 and 2104.23 nm</p> <p>K2 = Mean of bands between 2104.23 and 2115.2 nm</p> <p>K3 = Mean of bands between 2159.07 and 2164.55 nm</p> <p>K4 = Mean of bands between 2202.88 and 2208.35 nm</p> <p> </p> <p>BR8 target Kaolinite2 / clays</p> <p>BR8 = ((K1_2 + K2_2) / (K2_2))</p> <p>K1_2 = Mean of bands between 2197.4 and 2219.29 nm</p> <p>K2_2 = Mean of bands between 2159.07 and 2170.03 nm</p> <p> </p> <p>BR9 target NDVI (Normalized Difference Vegetation Index)</p> <p>BR9 = ((NIR - Red) / (NIR + Red))</p> <p>NIR= Mean of bands between 776.87 and 811.26 nm</p> <p>Red = Mean of bands between 666.87 to 680.6 nm</p> </td> </tr> <tr> <td>Keywords</td> <td>Earth Observation, Remote Sensing, Hyperspestral Imaging, Automated Processing, Hyperspectral Data Processing, Mineral Exploration, Critical Raw Materials</td> </tr> <tr> <td>Pilot area</td> <td>Aramo</td> </tr> <tr> <td>Language</td> <td> <p>English</p> </td> </tr> <tr> <td>URL Zenodo</td> <td>https://zenodo.org/uploads/14193286</td> </tr> <tr> <td><strong>Temporal reference</strong></td> <td> </td> </tr> <tr> <td>Acquisition date (dd.mm.yyyy)</td> <td>01.05.2024</td> </tr> <tr> <td>Upload date (dd.mm.yyyy)</td> <td>20.11.2024</td> </tr> <tr> <td><strong>Quality and validity</strong></td> <td> </td> </tr> <tr> <td>Fromat</td> <td>GeoTiff</td> </tr> <tr> <td>Spatial resolution</td> <td>1.2m </td> </tr> <tr> <td>Positional accuracy</td> <td>0.5m </td> </tr> <tr> <td>Coordinate system</td> <td>EPGS 4326</td> </tr> <tr> <td><strong>Access and use constrains</strong></td> <td> </td> </tr> <tr> <td>Use limitation</td> <td>None</td> </tr> <tr> <td>Access constraint</td> <td>None</td> </tr> <tr> <td>Public/Private</td> <td>Public</td> </tr> <tr> <td><strong>Responsible organisation</strong></td> <td> </td> </tr> <tr> <td>Responsible Party</td> <td>Beak Consultants GmbH</td> </tr> <tr> <td>Responsible Contact</td> <td>Roberto De La Rosa</td> </tr> <tr> <td><strong>Metadata on metadata</strong></td> <td> </td> </tr> <tr> <td>Contact</td> <td>Roberto.delarosa@beak.de</td> </tr> <tr> <td>Metadata language</td> <td>English</td> </tr> </tbody> </table>
Digital elevation model mosaic of the Hypanis region, Mars
<p><strong>This dataset accompanies the following papers:</strong></p> <p>Adler et al. (2019) Hypotheses for the origin of the Hypanis fan-shaped deposit at the edge of the Chryse escarpment, Mars: Is it a delta? Icarus, 319, 885-908. doi: https://doi.org/10.1016/j.icarus.2018.05.021</p> <p>Adler et al. (2022) Regional Geology of the Hypanis Valles System, Mars. JGR: Planets, doi: 10.1029/2021JE006994</p> <p><strong>Contents:</strong></p> <p>- DEM mosaic of the Hypanis Valles and deposit region constructed from CTX, HRSC, and MOLA elevation data (geotiff).</p> <p>- Coverage map of CTX, HRSC, and MOLA footprints used (shapefile).</p> <p>- Previews of DEM (greyscale and color) and of coverage map (png)</p> <p><strong>Description:</strong></p> <p>We constructed a regional elevation mosaic (~17 m/pixel) in Adler et al. (2019) archived here. This mosaic incorporates 10 CTX digital elevation models (DEMs) of high resolution, 3 HRSC digital elevation models of medium resolution, and 1 MOLA global DEM of low resolution. Individual CTX DEMs were generated from the stereopairs listed below. Some individual products were calibrated and formatted with the USGS Integrated Software for Imagers and Spectrometers (ISIS) and then Ames Stereo Pipeline. Other products were generated with SOCET SET. All products were controlled to MOLA shot elevation data.</p> <p><strong>Data incorporated:</strong></p> <p><strong>CTX stereopairs:</strong></p> <p><em>ID (nadir-most), ID (resolution [m/pix])</em></p> <p>P07_003631_1920, B09_013296_1920 (17.7 m/pixel)</p> <p>B17_016408_1913, G06_020443_1916 (18.5 m/pixel)</p> <p>J03_046104_1918, F05_037783_1918 (24.0 m/pixel)</p> <p>P08_004264_1912, B06_011951_1916 (17.7 m/pixel)</p> <p>G09_021788_1918, G11_022434_1918 (18.4 m/pixel)</p> <p>B17_016474_1915, B19_017186_1915 (20.2 m/pixel)</p> <p>D19_034816_1921, F01_036293_1920 (24.0 m/pixel)</p> <p>P13_006176_1918, F01_036293_1920 (18.2 m/pixel)</p> <p>D07_029845_1921, D07_029990_1921 (20.2 m/pixel)</p> <p>G21_026601_1918, P04_002774_1922 (20.2 m/pixel)</p> <p><strong>HRSC DA4:</strong></p> <p>H2134 (75 m/pixel)</p> <p>H2145 (50 m/pixel)</p> <p>H0894 (75 m/pixel)</p> <p><strong>MOLA Elevation:</strong></p> <p>128 ppd Elevation (463 m/pixel)</p> <p><strong>Funding:</strong></p> <p>The work to create individual CTX stereopair DEMs was funded by UK Space Agency (UK SA) grants ST/ K502388/1, ST/R002355/1, ST/L00643X/1, and ST/R001413/1. We thank the Science and Technology Facilities Council for supporting science relating to ExoMars Rover landing site selection activities. The work to create a mosaic using these products and others was supported by grants from the NASA Mars Odyssey Project under a subcontract to ASU administered by the Jet Propulsion Laboratory/California Institute of Technology.</p>
Retrieval coefficients for HATPRO observations during MOSAiC
<p>Retrieval files required to run the IDL program MWR_PRO <strong>[1]</strong> script 'pl_mk_pol.sh' to process raw HATPRO brightness temperatures and retrieve integrated water vapour (IWV, also called prw), liquid water path (LWP, also called clwvi), zenith humidity profiles (hze), zenith temperature profiles (tze) and boundary layer temperature profiles (tel).</p> <p> </p> <p>The retrieval files contain the coefficients acquired via regression with linear only (tel) or also quadratic terms (prw, clwvi, hze, tze) based on Ny-Alesund radiosonde measurements. The coefficients have been determined by Nomokonova et al. <strong>[2]</strong>.<br> <br> <strong>[1]</strong>: Walbröl, Andreas. (2022). Codes for: Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC (v2.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.6673957">https://doi.org/10.5281/zenodo.6673957</a></p> <p><strong>[2]</strong>: Nomokonova, T., Ebell, K., Löhnert, U., Maturilli, M., Ritter, C., and O'Connor, E.: Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund using ground-based sensor synergy, Atmos. Chem. Phys., 19, 4105–4126, <a href="https://doi.org/10.5194/acp-19-4105-2019">https://doi.org/10.5194/acp-19-4105-2019</a>, 2019.</p>
Resistance test to bean common mosaic virus (BCMV) in common bean
<p>This video is part of a series of videos prepared by SERIDA partner for the BRESOV project (GA 774244) The video briefly describes a test for resistance to BCMV, a common disease in bean crops</p> <p> </p> <p>https://www.youtube.com/watch?v=ukEVm_yC26Q</p>
Simulated microwave brightness temperatures based on two radiosoundings performed during the MOSAiC expedition
<p>This data set contains simulated brightness temperatures in the microwave spectrum for a summer case and a winter case based on radiosoundings performed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition <strong>[1]</strong>. The simulations cover the frequencies 1-400 GHz and have been performed with the Passive and Active Microwave radiative TRAnsfer model (PAMTRA, <strong>[2]</strong>). Dimensions 'grid_x', 'grid_y', 'outlevel' can be truncated as they have the length 1. To get simulated brightness tempeartures (TBs) of a zenith-looking microwave radiometer, chose the last index of the dimension 'angles' (which is zenith angle 0°) and average over the 'passive_polarization' dimension. The data has been used to generate Fig. 1 of <strong>[3]</strong>.</p> <p> </p> <p><strong>[1]:</strong> Maturilli, M., Holdridge, D. J., Dahlke, S., Graeser, J., Sommerfeld, A., Jaiser, R., Deckelmann, H., Schulz, A.: Initial radiosonde data from 2019-10 to 2020-09 during project MOSAiC [dataset publication series]. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.928656, 2021.</p> <p><strong>[2]:</strong> Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geoscientific Model Development, 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.</p> <p><strong>[3]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p>
Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020
<p>Seasonal composites of <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> imagery created as part of the <a href="https://opendatascience.eu/geo-harmonizer/">Geo-harmonizer project</a>, containing median of the blue, green, red, NIR, SWIR1 and SWIR2 bands, as well as pixel counts per season, produced in the ETRS89-extended / LAEA Europe (<a href="https://epsg.io/3035">EPSG:3035</a>) spatial reference system. Mosaics were produced from winter 2017 to spring 2020, with the imaging intervals per season being:</p> <ul> <li>winter: 02/12 of previous year to 20/03</li> <li>spring: 21/03 to 24/06</li> <li>summer: 25/06 to 12/09</li> <li>fall: 13/09 to 01/12</li> </ul> <p>Seamlessness of the composites was achieved through overlapping pixel averaging weighted by distance from the suborbital track.</p> <p>The data are provided as UINT8 values and were scaled with a common threshold (13712) chosen to minimize compression loss across the dataset. Data at the original (UINT16) scale can be obtained as follows:</p> <p><span>\(x_{\text{uint16}} = 13712 {x_{\text{uint8}} \over 254}\)</span></p> <p>For any additional questions regarding the data please contact the authors at <a href="mailto:multione@multione.hr?subject=S2L2A%20Europe%20mosaics">multione[at]multione.hr</a>.</p>
High-resolution sea ice drift and deformation example data derived from Sentinel-1 in the Arctic Ocean during MOSAiC
<p>This data set contains two high-resolution sea ice drift and deformation fields from 30/31 December 2019 and 20/21 June 2021. They were acquired in the Transpolar Drift along the drift track of the research campaign "Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Drift fields were calculated from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. The time step between two sequential images is approximately one day. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Outliers in the velocity data were reduced by a 3x3 point running median filter covering an area of 2.1x2.1 km. For the deformation estimates, we calculated deformation using a linear approximation based on Green's Theorem that relates the double integral over a plane to the line integral along a simple curve surrounding the plane. We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices of the grid cells. This work contains modified Copernicus Sentinel data (2020)</p> <p>Related publications:</p> <p><strong>von Albedyll, L., Haas, C., and Dierking, W.</strong>: Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations, The Cryosphere, 15, 2167–2186, <a href="https://doi.org/10.5194/tc-15-2167-2021">https://doi.org/10.5194/tc-15-2167-2021</a>, 2021.</p> <p><strong>Hollands, Thomas; Dierking, Wolfgang (2011):</strong> Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results. <em>Annals of Glaciology</em>, <strong>52(57)</strong>, 311-317, <a href="https://doi.org/10.3189/172756411795931462">https://doi.org/10.3189/172756411795931462</a></p> <p><strong>Thomas, Mani; Geiger, Cathleen A; Kambhamettu, Chandra (2008):</strong> High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. <em>Cold Regions Science and Technology</em>, <strong>52(2)</strong>, 207-223, <a href="https://doi.org/10.1016/j.coldregions.2007.06.006">https://doi.org/10.1016/j.coldregions.2007.06.006</a></p> <p><strong>Thomas, Mani; Kambhamettu, Chandra; Geiger, Cathleen A (2011):</strong> Motion Tracking of Discontinuous Sea Ice. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>49(12)</strong>, 5064-5079, <a href="https://doi.org/10.1109/TGRS.2011.2158005">https://doi.org/10.1109/TGRS.2011.2158005</a></p>
RAW SEM images mosaics dataset for the HRDIC strain localization study in shot peened Ni superalloy
<p>We used a FEI Magellan HR 400L FE-SEM with a theoretical resolution of ≤ 0.9 nm at <1kV and ≤ 0.8 nm at ≥ 5kV to take backscattered electron images of the fine, homogeneous distributed gold speckle pattern obtained by remodelling of a thin gold layer previously deposited on the polished sample surface. The images were obtained at a working distance of 3.5 mm, 5 kV and 0.8 nA beam current. Mosaics of 30x15 images were used to cover 950x420 µm<sup>2</sup>. Each image contains 2048 x 1768 pixels and has a horizontal field of view of 43 µm. The images were overlapped by 20% to enable easy stitching prior to the digital image correlation. We obtained 7 mosaics, one before tensile testing and 6 after each deformation step.</p> <p>This set of images at different strain steps is coupled with the EBSD data set in https://doi.org/10.5281/zenodo.4730184, the HRDIC strain maps in http://doi.org/10.5281/zenodo.4728016 and data visualisation scripts in http://doi.org/10.5281/zenodo.4727939</p> <p>0_def corresponds to the undeformed sample, while 1_def to 6_def were obtained after each deformation step.</p>
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