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Dataset results
17 results for “L-band”
GLAB-VOD: Global L-band AI-Based Vegetation Optical Depth Dataset Based on Machine Learning and Remote Sensing
<p>GLAB VOD is a Global L-band Ai-Based vegetation optical depth dataset with 18-day temporal and 25 km spatial resolution, covering 2002 to 2020. The dataset is created using a neural network with SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB) VOD product as a target (over 2015-2020) and brightness temperatures (TB) from the SMOS, AMSR-E, and AMSR-2 spaceborne missions alongside with a novel soil moisture dataset (CASM) as inputs. The GLAB-VOD dataset was created using a recently developed methodology previously used to create a long-term consistent soil moisture dataset CASM, adapted to the VOD retrievals. First, the TB and VOD signals were divided into fixed seasonal cycle and residuals, where the residual part of the signal contains sub-seasonal periodic signals, trends, extremes, and noise. Then, a multi-staged neural network training scheme was used to achieve internally consistent predictions by merging data from different sources without introducing biases or compromising data distribution. A side-product of this project is GLAB TB - a global long-term brightness temperature dataset that matches SMOS TB quality and spawns back to 2002. GLAB TB has daily temporal resolution and 25 km spatial resolution. </p>
Data for "Broadband thulium fiber amplifier for spectral region located beyond the L-band"
<p>Includes data for absorption and emission measurements, profiles of refractive index, and data for spectral dependence of amplifier output, as well as typical output spectra presented in the graphs.</p> <p> </p>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
Data sets for distributed ionospheric L-band scintillation and TEC observations made in the American sector during the March 23-24, 2023 geomagnetic storm
<p>These data sets contain the scintillation measurements presented in the manuscript titled, "On the extraordinary L-band scintillation event observed in the American sector during the March 23-24, 2023 geomagnetic storm".</p> <p><br>The HDF5 files are organized by constellations and satellites. Each satellite includes the following parameters: Azimuth (AZIM), Elevation (ELEV), Number of Samples (NOS), Amplitude Scintillation Index (S4), 1-minute average SNR (SNR), relative Total Electron Content (PTEC), and Time of Week in seconds (S_TW)</p>
Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry
<p>This repository contains the dataset associated with the analyses presented in the following study:</p> <p>Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: <em>Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland Ice Sheet using L-band radiometry</em>, <strong>The Cryosphere</strong>, 19, 4237–4258, <a href="https://doi.org/10.5194/tc-19-4237-2025" target="_new">https://doi.org/10.5194/tc-19-4237-2025</a>, 2025.</p> <p>In this study, we demonstrated the capability of NASA's Soil Moisture Active Passive (SMAP) L-band radiometer to estimate surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland Ice Sheet. The article presents our initial retrieval algorithm, validation results, and highlights the potential for developing a Greenland-wide LWA data product.</p> <p><strong>Contents of this Repository</strong></p> <p>This repository includes:</p> <ul> <li><strong>SMAP-retrieved daily, vertically integrated LWA gridded initial data products</strong> (2015–2023), derived from enhanced-resolution SMAP TB observations. These data include spatial coordinates, acquisition dates, and a melt flag indicator.</li> <ul> <li>SMAP_LWA_time_series_AWS contains daily time series at a AWS location (point observation)</li> <li>Samimi_EBM_LWA_time_series_AWS contains corresponding time series of LWA estimated by Samimi model forced by PROMICE AWS.</li> <li>GEMB_LWA_time_series_AWS contains corresponding time series of LWA estimated by GEMB model forced by PROMICE AWS</li> <li>The locations and name ID of the AWS are given in AWS.txt/xls file</li> <li>L_band_LWA_yyyy.nc files contain daily LWA and TB data over the entire percolation zone</li> </ul> <li><strong>Corresponding vertically polarized brightness temperature (TBV) data</strong>, including their winter mean and standard deviation.</li> <li><strong>Model-based LWA estimates used for validation</strong>, including outputs from:</li> <ul> <li>The locally calibrated <strong>Energy and Mass Balance (EMB)</strong> model.</li> <li>The <strong>Glacier Energy and Mass Balance (GEMB)</strong> model within NASA’s <strong>Ice-sheet and Sea-level System Model (ISSM)</strong>.</li> </ul> </ul> <p><strong>Retrieval and Validation Codebase</strong></p> <p>The MATLAB scripts and tools used for the microwave retrieval algorithm, radiative transfer modeling, inversion process, and comparative validation with in situ AWS-driven model outputs are available at the following GitHub repository:</p> <p>🔗 <a href="https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS" target="_new">https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS</a><br><em>(Last accessed: 17 September 2025)</em></p> <p>The codebase includes:</p> <ul> <li>Preprocessing routines for SMAP TB data.</li> <li>Implementation of the radiative transfer forward model.</li> <li>Inversion and threshold-based detection algorithms.</li> <li>Validation scripts for comparison against AWS-forced EMB and GEMB model outputs.</li> </ul> <p><strong>Relevance</strong></p> <p>These data and methods support ongoing efforts to improve surface mass balance (SMB) estimates and enhance projections of Greenland’s contribution to global sea level rise.</p> <p> </p>
Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI
<p><span>The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in </span><span><span>(Yu et al., 2023)</span></span><span> by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts </span><span><span>(Lei et al., 2018, Lei and Siqueira, 2022)</span></span><span>, this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.<span> </span>This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (</span><span>JAXA’s MOLI, NASA’s GEDI, China’s TECIS</span><span>) and InSAR missions (</span><span>NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1</span><span>). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.</span></p>
3C286 with eMERLIN at L-band
<p>Short scan of 3C286 with eMERLIN at L-band as a measurement set (.ms). This is a test dataset for the RadioNet RINGS project, but also a test upload to Zenodo. Further flagging should ideally be done to this dataset before further testing, which will be uploaded as a new version.</p>
Radio and gamma-ray timing of TRAPUM L-band Fermi pulsar survey discoveries - Supplementary material
<p>Timing ephemerides for the redback pulsar J1803-6707 (published in <em>"Radio and gamma-ray timing of TRAPUM L-band \textit{Fermi} pulsar survey discoveries"</em>, Burgay et al 2024, A&A) covering 15 years of Fermi data. The ephemeris are provided both in "ORBIFUNC" (to be used with timing software <a href="https://bitbucket.org/psrsoft/tempo2/src/master/" target="_blank" rel="noopener">tempo2</a>) and "ORBWAVES" (with <a href="https://github.com/nanograv/PINT" target="_blank" rel="noopener">PINT</a>) formats. The ORBIFUNC parameters are a time-domain interpolating function describing the orbital phase variations over time, while the ORBWAVES parameters are the Fourier-domain representation of the same function.</p>
Data from: L-band InSAR SWE retrievals over Northern Colorado
Open the record for dataset details and reuse information.
L-band InSAR snow water equivalent retrievals over Fraser Experimental Forest, Colorado
Open the record for dataset details and reuse information.
L-band sounding data
<p>L-band sounding data</p>
ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019
This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions.
Summary of the ABoVE L-band and P-band Airborne SAR Surveys, 2012-2022
This dataset contains tables containing Airborne flight metadata from synthetic aperture radar (SAR) surveys from 2012 to 2022 in Alaska and Canada. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne SAR surveys of over 120,000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. Legacy lines acquired between 2012 and 2015 by other projects are included for completeness and to enable longer times series creation. The data files and companion file contain L-band and P-band airborne SAR metadata acquired during the ABoVE airborne campaigns. Included are detailed descriptions of ~80 SAR flight lines and how each fits into the ABoVE experimental design. Extensive maps, tables, and hyperlinks give direct access to every flight plan as well as individual flight lines. This entry is a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Data sets for "On the quiet-time occurrence rates, severity and origin of L-band ionospheric scintillations observed from low-to-mid latitude sites located in Puerto Rico" by Gomez Socola et al.
<p>These data sets contain the night-time scintillation index (S4) of the geomagnetically quiet days. </p>
Severe L-band scintillation over low-to-mid latitudes caused by an extreme equatorial plasma bubble: joint observations from ground-based monitors and GOLD
<p>Data from ScintPis 3.0 that was used in the manuscript.</p>
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>
AIRSAR_TOPSAR_L-BAND_STOKES
AIRSAR topographic SAR digital elevation model L_Stokes product
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