Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
153
datasets available to search
ShareScore release 0.9.0
Dataset results
153 results for “SMAP”
SMAP L1B Brightness Temperatures Arctic
<p>This is a data set of polarised brightness temperatures (TBs) from the L-band (1.4 GHz) passive microwave sensor flying onboard the Soil Moisture Active Passive (SMAP) satellite. The data set was produced to enable a consistent combination of TBs from SMAP with those measured by the SMOS (Soil Moisture and Ocean Salinity) mission.</p> <p>It is based on the version 3 SMAP L1B brightness temperatures (Piepmeier et al., 2016), which are not corrected with respect to solar and cosmic radiation or atmospheric effects. The data are aggregated daily and 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, SMAP and SMOS 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 SMOS 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<br> <strong>Tbh</strong> - brightness temperatures at horizontal polarisation<br> <strong>Tbv_std</strong> - weighted standard error of Tbv<br> <strong>Tbh_std</strong> - weighted standard error of Tbh<br> <strong>nmp</strong> - effective number of measurements used for averaging</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
SM2RAIN test dataset with ASCAT and SMAP satellite soil moisture (plus ERA5 evapotranspiration)
<p>Are you looking for a research contest?</p> <p>Here [SM_RAIN_EVAP_1009points.nc] you can find a 5-year dataset at 1009 points in Italy, the United States, India and Australia of co-located in space and time:</p> <ol> <li>satellite soil moisture (from ASCAT, Wagner et al., 2013, doi:10.1127/0941-2948/2013/0399)</li> <li>evapotranspiration (from ERA5 reanalysis by ECMWF: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview)</li> <li>ground-based rainfall.</li> </ol> <p>and a ~3-year dataset at the same points including soil moisture from SMAP (April-2015 --> December 2017) [SM_SMAP_ASCAT_ETERA5_Pobs_1009opints.nc]</p> <p>The dataset can be used for testing multiple approaches for rainfall estimation from soil moisture, as done in <a href="https://www.linkedin.com/feed/hashtag/?keywords=%23SM2RAIN">#SM2RAIN</a> algorithm (<a href="http://hydrology.irpi.cnr.it/research/sm2rain/">http://hydrology.irpi.cnr.it/research/sm2rain/</a>).</p> <p>The global dataset we have developed is available here: <a href="https://zenodo.org/record/3635932">https://zenodo.org/record/3635932</a></p> <p>The NetCDF file contains all the data, and the figures (PNG files) represent an example of the results we have obtained in the paper and of the new dataset including SMAP.</p> <p><strong>Reference</strong><br> Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. <em>Earth System Science Data</em>, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>.</p> <p>For clarifications and support contact me at <a href="mailto:luca.brocca@irpi.cnr.it?subject=SM2RAIN%20test%20dataset">luca.brocca@irpi.cnr.it</a> </p>
A Deep Neural Network Based SMAP Soil Moisture Product
<p>The soil moisture datasets here are based on a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The bias is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables include polarized SMAP brightness temperatures, incidence angles, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.</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>
SMAP Daily Seamless Soil Moisture Products from 2015 to 2022 (Physics-constrained Gap-filling Method,PhyFill)
<p>The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address this issue, this dataset presents a physics-constrained gap-filling method, shortly named PhyFill. The PhyFill method employs a partial convolutional neural network to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous daily soil moisture data on a global scale. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSE compared with the original SMAP soil moisture data. The PhyFill method can generate globally continuous, high-accuracy soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.</p>
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
A Deep Neural Network Based SMAP Soil Moisture Product
<p>It is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas -- when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.</p>
Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model
<p>This repository contains:</p> <ol> <li>A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (<a href="../api/records/13309165/draft/files/SMAP_NN_36km_20150331_20220326.nc/content" target="_blank" rel="noopener noreferrer">SMAP_NN_36km_20150331_20220326.nc</a>)</li> <li>Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (<a href="../api/records/13309165/draft/files/SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc/content" target="_blank" rel="noopener noreferrer">SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc</a>)</li> </ol> <p>Summary:</p> <p>The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted.</p> <p>The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals. </p> <p>Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3.</p> <p>The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing.</p> <p>For more details, please contact me (wotp12@unist.ac.kr)</p>
SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States
<p><a href="https://waterai.earth/smaphb/">SMAP-HydroBlocks (SMAP-HB)</a> is a hyper-resolution satellite-based surface soil moisture product that combines NASA's Soil Moisture Active-Passive (SMAP) L3 Enhance product, hyper-resolution land surface modeling, radiative transfer modeling, machine learning, and in-situ observations. The dataset was developed over the continental United States at 30-m 6-hourly resolution (2015–2019), and it reports the top 5-cm surface soil moisture in volumetric units (m3/m3).</p> <p>This repository contains the following two versions of the SMAP-HydroBlocks dataset:</p> <ol> <li><strong>SMAP-HB_hru_6h.zip</strong>: SMAP-HydroBlocks data in the Hydrological Response Unit (HRU) space. Storing the data in the HRU space enables the entire 30-m 6-h dataset to be compressed to 33.8 GB. A python script and instructions to post-process and remap the data from the HRU-space into geographic coordinates (latitude, longitude) is provided at <a href="https://github.com/NoemiVergopolan/SMAP-HydroBlocks_postprocessing">GitHub</a>. After post-processed, files are stored in netCDF4 format with a Plate Carrée projection.</li> <li><strong>SMAP-HB_1km_6h.zip</strong>: SMAP-HydroBlocks data at 1-km 6-h resolution. This aggregated version is already post-processed, and thus it is already in geographic coordinates (latitude, longitude), stored in netCDF4 format, with a Plate Carrée projection, and comprising 31.5 GB of data. </li> </ol> <p>Different subsets of the original dataset can be made available on request from Noemi Vergopolan (noemi.v.rocha@gmail.com). Data visualization, updates, and more information is available at <a href="http://waterai.earth/smaphb/">https://waterai.earth/smaphb/</a> </p> <p> </p> <p>Please cite the following paper when using the dataset in any publication:</p> <p>Vergopolan, N., Chaney, N.W., Pan, M. <em>et al.</em> SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US. <em>Sci Data</em> 8<strong>, </strong>264 (2021). <a href="https://doi.org/10.1038/s41597-021-01050-2">https://doi.org/10.1038/s41597-021-01050-2</a></p> <p>Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan, S., & Wood, E. F. (2020). Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 242, 111740. <a href="https://doi.org/10.1016/j.rse.2020.111740">https://doi.org/10.1016/j.rse.2020.111740</a></p> <p> </p> <p>To download all the files via the command line, please try <a href="https://zenodo.org/record/1261813">zenodo_get</a>:</p> <pre><code>pip install zenodo-get zenodo_get 5206725</code></pre>
ALEXI evapotranspiration product (v10E) at 36 km SMAP grid for CONUS
<p><span>The ALEXI surface energy balance model is designed to estimate E for continental to global scale applications (Anderson et al., 2007, 2011). It maintains a physically realistic representation of land-atmosphere exchange over a wide range of landscapes, while being relatively independent of ancillary meteorological or plant functional information. Importantly for the present analysis, it does not require any knowledge of soil moisture conditions. The main diagnostic inputs to the ALEXI model are land surface temperature, net radiation (and its components), leaf area index (to inform partitioning of radiation between soil and canopy), windspeed, and the early morning lapse rate profile. The internal model structure solves the energy balance for the soil and canopy separately. ALEXI uses an atmospheric boundary layer model to translate the change in temperature over the morning hours into a self-consistent boundary layer temperature and to constrain the sensible heat flux. </span></p> <p><span>The product shared here is regridded from the 4 km CONUS ALEXI E daily product (v10E) for the 2015-2022 period to the coarser 36 km grid of the SMAP data. <span>In addition, the corresponding downwelling short wave radiation (R<sub>sw</sub>), obtained from the Climate Forecast System Reanalysis (Saha et al. 2014) is provided. <br></span></span></p>
SMAP_Level4_CONUS_May2019_Dataset
<p>Surface soil Moisture data collected from the NASA SMAP L4 products (https://nsidc.org/data/spl4smgp/versions/7) over CONUS for May 2019.</p>
Soil moisture maps of Ukraine based on SMAP satellite data, vegetation season 2018
<p>A set of soil moisture maps of Ukraine based on SMAP satellite data</p> <p>Product: SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture V001</p> <p>Vegetation season 2018</p> <p> </p>
Soil Moisture Active/Passive (SMAP) Level 4 Carbon (L4C) Nature Run version 7.2
<p>The Soil Moisture Active/ Passive (SMAP) Level 4 Carbon (L4C) product is a daily, global, terrestrial carbon budget driven, in part, by soil moisture estimates from the Level 4 Soil Moisture (L4SM) product and, in turn, on brightness temperature observations from the SMAP satellite [1,2]. The SMAP L4C operational product's record begins on March 31, 2015, shortly after the launch of SMAP, and continues to the present, with an average latency of 9 days [3]. SMAP L4C data are posted to a global, 9-km equal-area EASE-Grid 2.0 [4].</p> <p>In order to improve the longitudinal coverage of the SMAP L4C record, a model-only "Nature Run" was devised, with daily carbon budget estimates beginning January 1, 2000. The Nature Run differs from the SMAP L4C operational product in the following ways:</p> <p>- The SMAP L4C Nature Run uses the MERRA-2 re-analysis dataset for meteorological driver data, instead of the GEOS-5 FP driver data used in the operational product.<br> - The SMAP L4C Nature Run uses soil moisture and soil temperature estimates from the L4SM Nature Run, which is a model-only version of the operational L4SM product that does not assimilate SMAP brightness temperature data.</p> <p>This repository contains the full README for the data. The data can be downloaded from:</p> <p>http://files.ntsg.umt.edu/data/SMAP_L4C_NatureRun/NRv7.2/</p>
Soil Moisture from SMAP, Sentinel 1 and in-situ observations dataset
<p>Each folder contains the dataset for the entire CONUS over the corresponding time period.</p> <p>For folder contains two classes of GeoTIFF images, namely the NW_Day_XXX_9km_Signals.tif and NW_Day_XXX_1km_Signals.tif, corresponding to data at 9 and 1 km spatial resolution respectively (XXX encodes the specific date e.g. 20180601)</p> <p>Each *9km_Signals.tif image contains 7 bands that correspond to the following variables:</p> <ol> <li> <p>SMAP Tb, Horizontal polarization</p> </li> <li> <p>SMAP Tb, Vertical polarization</p> </li> <li> <p>Soil Moisture (estimation)</p> </li> <li> <p>Clay</p> </li> <li> <p>Bulk Density</p> </li> <li> <p>Latitude</p> </li> <li> <p>Longitude</p> </li> </ol> <p> </p> <p>Each *1km_Signals.tif image contains 12 bands that correspond to the following variables:</p> <ol> <li> <p>Sentinel 1 σ0, Vertical polarization</p> </li> <li> <p>Sentinel 1 σ0, Horizontal polarization</p> </li> <li> <p>SMAP TB, vertical polarization;</p> </li> <li> <p>In-Situ Soil Moisture value</p> </li> <li> <p>Soil Moisture (estimated) at 1km;</p> </li> <li> <p>Vegetation Water Content</p> </li> <li> <p>Surface Temperature</p> </li> <li> <p>Precipitation</p> </li> <li> <p>Land cover map</p> </li> <li> <p>In-Situ location name</p> </li> <li> <p>Latitude</p> </li> <li> <p>Longitude</p> </li> </ol> <p> </p>
An Integrated Approach for enhanced SMAP Soil Moisture Retrieval: Multi-Source Data Fusion and Data-Driven Machine Learning
<p><span>Accurate satellite-based soil moisture (SM) retrieval is essential for hydrometeorological and agroecological applications, yet traditional physical models for L-band SM retrieval are hindered by uncertainties stemming from inaccuracies in prior parameters. This work combines multi-source data fusion and a physically-guided machine learning framework to develop a Soil Moisture Active Passive (SMAP) SM retrieval model (Fusion-LightGBM, F-LGB) that bypasses the need for static prior parameters, resulting in a new SM product. The retrieval benchmark is a new seamless SM data constructed by combining Triple Collection correlation coefficients (TC-R) and the Maximized-R method, which demonstrates superior temporal correlation on 20 International Soil Moisture Network (ISMN) <em>in-situ</em> networks compared to existing SM data, including ECMWF Reanalysis v5-Land (ERA5-Land), SMAP Level 4 (SMAP L4), and Global Land Data Assimilation System (GLDAS) Noah. The machine learning model incorporates input variables that represent the Tau-Omega model’s radiative transfer process, including brightness temperature, vegetation optical depth, soil temperature, and an external variable for precipitation. In the 2015-2020 validation set, F-LGB demonstrated the highest correlation (mean R = 0.72, significantly surpassing the second-best SMAP-INRAE-BORDEAUX (SMAP-IB) SM and deep neural network (DNN) SM at 0.67) and the lowest ubRMSE (mean value of 0.052 m<sup>3</sup>/m<sup>3</sup>, better than 0.055 m<sup>3</sup>/m<sup>3</sup> for both DNN and SMAP-IB). F-LGB performed well across diverse land covers, vegetation densities, and climates, with SHAP analysis showing H-polarized brightness temperature as crucial, especially in areas with low to moderate vegetation. This new machine learning-based SMAP SM product may improve global satellite-based SM estimation capabilities.</span></p>
Datasets for "Reconstructing Global High Quality 3–day Surface Soil Moisture from ESA CCI and SMAP product from 2015 to 2021 using Conditional Variational Auto-Encoder"
<p>These are supporting datasets for the paper titled, "<strong>Reconstructing Global High Quality 3–day Surface Soil Moisture from ESA CCI and SMAP product during 2015–2021 using Conditional Variational Auto-Encoder</strong>"</p>
Quantifying the linkage between SMAP soil moisture and fully-integrated hydrologic simulations
<p>This dataset contains soil moisture (SM) (over multiple layers between 0‒100 cm depth) and groundwater (GW) storage simulated by a high resolution fully-integrated surface water - groundwater model HydroGeoSphere (HGS) over the South Nation Watershed (SNW) in eastern Canada. The dataset also provides the near-surface soil moisture (SSM) and Soil Water Index (SWI) derived from the Soil Moisture Active and Passive (SMAP) enhanced L3 radiometer 9 km EASE-grid SM (SPL3SMP_E) version 5 product. The temporal coverage for all data is from March 31, 2015 to December 31, 2017.</p>
SMAP-derived Perennial Firn Aquifer and Ice Slab Extents 2015-2019 Version 1
<p>SMAP-derived Perennial Firn Aquifer and Ice Slab Extents 2015-2019 Version 1</p>
RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset
The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.<br><br>The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. The RSS SMAP L3 monthly product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM. Each data file is available in netCDF-4 file format and is averaged over one-month time intervals with about 7-day latency (after the end of the averaging period). Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.
RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.0 Validated Dataset
The version 5.0 SMAP-SSS level 3, 8-Day running mean gridded product is based on the fifth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS). The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. Daily data files for this product are based on SSS averages spanning an 8-day moving time window. SMAP data begins on April 1,2015 and is ongoing. L3 products are global in extent with a default spatial resolution of approximately 70KM. The datasets are gridded at 0.25degree x 0.25degree. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.