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11 results for “SMOS”
Circumpolar mid-winter thaw and refreeze based on fusion of Metop ASCAT and SMOS, 2011/2012 - 2021/2022
<p>Rain-on-Snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snow pack properties are changing and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event.</p> <p>Active and passive microwave data have been combined to identify events over land North of 65°N (Bartsch et al. 2023). In a first step Metop ASCAT (C-Band radar) was used to identify potential sudden snow structure change. In a second step, results have been masked for coincident observation of wet snow within +- 3 days based on SMOS (derived from Centre Aval de Traitement des Données SMOS (CATDS) level 3 product). Note that the SMOS retrievals can have data gaps due to radio frequency interferences (RFI) what leads to gaps in the event detection.</p> <p>The dataset is structured by centre points of the hexagonal grid of the used Metop ASCAT product (EUMETSAT, approximately 12.5 km nominal resolution). Attributes include point ID (GPI), latitude, longitude and</p> <ul> <li>aggregated number of events for the months November to February, 2011/12 to 2021/22, and their sum per winter (referred to as annual), or</li> <li>in case of daily results (date in file name) the magnitude of ASCAT backscatter change in dB (DSigma0; no data value is '0.0').</li> </ul> <p>The dataset extents Seawinds QuikScat (Ku-band) based results for 2000-2009 (Bartsch 2010, Freund and Bartsch 2020).</p>
SMOS Brightness Temperatures at 40° incidence angle Arctic
<p>This is a data set of polarised brightness temperatures (TBs) at 40° incidence angle from the L-band (1.4 GHz) passive microwave sensor flying onboard the Soil Moisture and Ocean Salinity (SMOS) mission. The data set was produced to enable a consistent combination of TBs from SMOS with those measured by the SMAP (Soil Moisture Active Passive) satellite.</p> <p>It is based on the version v620 SMOS L1C brightness temperatures, which are not corrected with respect to solar and cosmic radiation or atmospheric effects. A fitting function is applied to the daily multi-angular SMOS data (see Zhao et al., 2015 and Schmitt and Kaleschke, 2018) to obtain brightness temperature values at the SMAP incidence angle of 40°. The data are gridded to a north polar EASE-grid 2.0 (Brodzik et al. 2012) with a grid size of 12.5 km. The data were produced within the framework of the EU Horizon2020 project SPICES and therefore only covers the period from the first available SMAP data to the end of the project (1 April 2015 to 31 May 2018).</p> <p>Within SPICES, SMOS and SMAP data were combined to a homogenized data set, which was then used to estimate sea ice thickness. For details see Schmitt and Kaleschke (2018) and the related data sets of SMAP TBs and SMOS/SMAP sea ice thickness.</p> <p>The files contain the following data fields:<br> <strong>Tbv</strong> - brightness temperatures at vertical polarisation at 40° incidence angle<br> <strong>Tbh</strong> - brightness temperatures at horizontal polarisation at 40° incidence angle<br> <strong>RMSE_v</strong> - root-mean-squared-error of fitting function for horizontal polarisation<br> <strong>RMSE_h</strong> - root-mean-squared-error of fitting function for horizontal polarisation<br> <strong>nmp</strong> - number of incidence angles used for the fit<br> <strong>dataloss</strong> - fraction of discarded data</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
Dry matter databases derived by crossing burned areas databases with above ground estimations from SMOS sensor
<p>Estimates of the the fuel consumed during a fire (dry-matter, DM) is derived by combining burned areas (from two different fire inventories) with above ground biomass (derived from SMOS L Band Vegetation Optical Depth). Six new datasets are provided on a 25 km grid for the years 2010-2017.</p> <p>The portion of vegetation that is consumed during biomass burning is expressed as:<br> <strong><span class="math-tex">\(DM= AGB * BA*\beta\)</span></strong></p> <p>where BA is the burned area; AGB is the fuel load, the amount of biomass or organic matter an ecosystem contains per unit area; <span class="math-tex">\(\beta\)</span> is the combustion completeness or burning efficiency, which is the fraction of fuel actually consumed during the fire.</p> <p>Monthly maps of AGB are converted from SMOS L-band vegetation optical depth (VOD) through an algorithm described in [1] and obtained looking at the relationship between three static AGB benchmark maps from the works of [2],[3] and [4]. Each static map provide three L-VOD to AGB conversion curves fitting the 5th, 50th and 95th percentiles of the data using a logistic regression([1]) In the following, the three different databases are named AGB-BA (Baccini), AGB-SA (Saatchi) and AGB-AV (Avitabile) </p> <p>Monthly total of burned areas are available from two sources. The first one, called hereinafter BA-GFED, is available though GEFD4.1s [5] and is based on MODIS MCD64A1 product. It has a 500m pixel resolution and is also available on a regular grid of 0.25 deg. The second BA product, named hereinafter BA-CCI, is a multi-sensors product provided by the European Space Agency Climate Change Initiative (ESA-CCI) [6] We use version FireCCI5.1 which is calculated using a two-phase algorithm, where MODIS active fire locations are used to identify seed pixels corresponding to high confidence burned areas. These areas are then grown using Medium Resolution Imaging Spectrometer (MERIS) vegetation input data.</p> <p>Combustion completeness, is a taken from table 4 of [7]. </p> <p>By selectively combining any AGB estimations with the two available BA datasets, a total of 6 products are generated.</p> <p> </p> <p><strong>References </strong></p> <p>[1] https://doi.org/10.5194/bg-15-4627-2018</p> <p>[2] <a href="https://doi.org/10.1126/science.aam5962">DOI: 10.1126/science.aam5962</a></p> <p>[3]<a href="https://cce.nasa.gov/veg3dbiomass/saatchi_tgrs07.pdf">https://cce.nasa.gov/veg3dbiomass/saatchi_tgrs07.pdf</a></p> <p>[4] <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.13139">https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.13139</a></p> <p>[5] <a href="https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html">https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html</a></p> <p>[6] <a href="https://geogra.uah.es/fire_cci/firecci51.php">https://geogra.uah.es/fire_cci/firecci51.php</a></p> <p>[7] <a href="https://acp.copernicus.org/articles/6/3423/2006/acp-6-3423-2006.pdf">https://acp.copernicus.org/articles/6/3423/2006/acp-6-3423-2006.pdf</a></p> <p> </p>
Combined SMOS and SMAP sea ice thickness Arctic
<p>This data set contains Arctic sea ice thicknesses derived from L-band passive microwave brightness temperatures. For this purpose, brightness temperatures at 40° incidence angle from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites were combined to a homogenised data set (see also related data sets). Sea ice thicknesses were then derived using the algorithm described in Tian-Kunze et al. (2014).</p> <p>Data are generally produced for the freeze-up period from 15 October to 15 April. This data set contains data from on 1 April 2015 (first available SMAP data) to 15 April 2015 and for the winter seasons 2015/16 to 2017/18.</p> <p>A detailed description of the data set can be found Schmitt and Kaleschke (2018) and in the document <em>1_Documentation_Combined_SIT.pdf</em>.</p> <p><strong>Please note</strong>: Sea ice thicknesses from L-band are most suitable for thin ice and reach saturation for thicker sea ice (above 0.5 m - 1 m, depending on ice salinity and temperature). Thickness values with a saturation ratio of 100 % should be discarded (depending on the application)!</p> <p>Version 1.0 of this data set is based on SMOS version v620 and SMAP version 3 data.</p> <p>The files contain the following data fields:<br> <strong>sea_ice_thickness</strong> - sea ice thickness with post-processing to account for the thickness distribution<br> <strong>plane_layer_thickness</strong> - sea ice thickness using the plane layer assumption<br> <strong>thickness_uncertainty_upper</strong> - upper limit of the sea ice thickness uncertainty interval<br> <strong>thickness_uncertainty_lower</strong> - lower limit of the sea ice thickness uncertainty interval<br> <strong>saturation_ratio</strong> - ratio of plane layer thickness and maximum retrievable thickness (%)<br> <strong>TB_intensity</strong> - brightness temperature intensity combined from SMOS and SMAP at 40° incidence angle</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
PrISM satellite rainfall product (2010-2021) based on SMOS soil moisture measurements in Africa (3h, 0.1°)
<p>The PrISM product is a satellite precipitation product available for Africa over a regular grid at 0.1° (about 10x10 km²) and every 3 hours. It is obtained from the synergy of SMOS satellite soil moisture measurements and IMERG-Early Run precipitation product through the PrIMS algorithm (<em>Pellarin et al., 2009, 2013, 2020, 2022, Louvet et al., 2015, </em>Román-Cascón et al. 2017). </p>
Exemplary validation of SMOS Level 3 version 339 Descending vs ERA5-Land v20190904 vs ISMN 20230110 global for EGU24
Exemplary QA4SM validation using FRMs for EGU24: SMOS Level 3 version 339 Descending vs ERA5-Land v20190904 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/7f27889e-95e4-4e67-9a85-2e472cbb935c. Produced on QA4SM (https://qa4sm.eu)
Validation of H-SAF ASCAT SSM CDR H119 vs SMOS Level 2 v700 vs ERA5 v20190613
QA4SM validation: H-SAF ASCAT SSM CDR H119 vs SMOS Level 2 v700 vs ERA5 v20190613. URL: https://qa4sm.eu/ui/validation-result/033a3604-b935-4864-93a7-0497e1f2e61b. Produced on QA4SM (https://qa4sm.eu)
SMOS Level 2 v700 vs ERA5 v20190613
QA4SM validation: SMOS Level 2 v700 vs ERA5 v20190613. URL: https://qa4sm.eu/ui/validation-result/5a7439de-9dee-4b81-87e6-0599de0ffbb2. Produced on QA4SM (https://qa4sm.eu)
SMOS-Based SMAP L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V001
The Soil Moisture Active Passive (SMAP, launched in 2015) and the Soil Moisture and Ocean Salinity (SMOS, launched in 2009) missions are each L-band satellites that provide brightness temperature and soil moisture estimates and vegetation optical depth approximately every 2-3 days with a spatial resolution of ~40 km. By integrating brightness temperature observations from both satellites, these data products reduce the revisit time to about one day, improving the ability to monitor fast-response processes such as groundwater drainage and recharge, early dry-down after storms, and pre-storm soil moisture conditions for runoff determination. <br> The Integrated SMAP and SMOS Soil Moisture Data is available in two products: <ul> <li>SMOS-Based SMAP L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture (NSIDC-0799): This product is generated by processing half-orbit granules (either 6 AM ascending or 6 PM descending) from the pre-processed SMOS product. The L2 product consists solely of SMOS-based data, incorporating information after SMOS brightness temperatures (TBs) at a 40° incidence angle were inter-calibrated and the soil moisture (SM) and vegetation optical depth (VOD) were retrieved using the SMAP Dual Channel Algorithm (DCA). </li> <li>SMAP/SMOS L3 Radiometer 9 km EASE-Grid Soil Moisture (NSIDC-0800): To generate this product, processing software ingests one day’s worth of granules from both the SMAP SPL2SMP_E product and the SMOS-derived L2 data. The L3 product contains both SMAP and SMOS descending (6:00 AM/PM) and ascending (6:00 PM/AM) data stored in separate arrays. </li> </ul>
SMAP/SMOS L3 Radiometer 9 km EASE-Grid Soil Moisture V001
The Soil Moisture Active Passive (SMAP, launched in 2015) and the Soil Moisture and Ocean Salinity (SMOS, launched in 2009) missions are each L-band satellites that provide brightness temperature and soil moisture estimates and vegetation optical depth approximately every 2-3 days with a spatial resolution of ~40 km. By integrating brightness temperature observations from both satellites, these data products reduce the revisit time to about one day, improving the ability to monitor fast-response processes such as groundwater drainage and recharge, early dry-down after storms, and pre-storm soil moisture conditions for runoff determination. <br> The Integrated SMAP and SMOS Soil Moisture Data is available in two products: <ul> <li>SMOS-Based SMAP L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture (NSIDC-0799): This product is generated by processing half-orbit granules (either 6 AM ascending or 6 PM descending) from the pre-processed SMOS product. The L2 product consists solely of SMOS-based data, incorporating information after SMOS brightness temperatures (TBs) at a 40° incidence angle were inter-calibrated and the soil moisture (SM) and vegetation optical depth (VOD) were retrieved using the SMAP Dual Channel Algorithm (DCA). </li> <li>SMAP/SMOS L3 Radiometer 9 km EASE-Grid Soil Moisture (NSIDC-0800): To generate this product, processing software ingests one day’s worth of granules from both the SMAP SPL2SMP_E product and the SMOS-derived L2 data. The L3 product contains both SMAP and SMOS descending (6:00 AM/PM) and ascending (6:00 PM/AM) data stored in separate arrays. </li> </ul>
IB-AGC: Annual 25 km global live biomass carbon product from SMOS L-band passive microwave vegetation optical depth
<p>The IB AGC dataset provides global aboveground biomass carbon (AGC) estimates for 2010-2020, derived from SMOS-IC L-band vegetation optical depth (L-VOD) using improved calibration methods and corrections for vegetation water effects. The accuracy assessments revealed that the IB L-VOD-derived AGC shows a very good spatial and temporal consistency with national forest inventory data and forest disturbance events, when compared to other mainstream satellite products. The dataset is stored in netCDF4 format and projected on the global cylindrical Equal-Area Scalable Earth Grid version 2.0 (EASE-Grid 2.0), with dimensions of 584 by 1388 and a grid resolution of 25 km. It includes eight layers, featuring the AGC density map and its associated uncertainty layer, representing the standard error of calibration. Additionally, a sub-product of total biomass carbon (BC) density is provided, calculated using a ratio method. The associated uncertainty, derived via error propagation, accounts for calibration errors in AGC and the AGC-to-BGC ratio.</p>
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