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21 results for “satellite remote sensing”
Satellite-based remote sensing of water clarity in the shallow coastal lagoons of Virginia 2013-2021
This dataset contains raw data, analysis products and code for a study of satellite-based estimation of water clarity. The files are: Match-up.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2. Satellite overpasses occurred +/- 0-1 days within in situ sampling. Valid remote sensing reflectance values (Rrs) from NASA SeaDAS (not masked by quality flags) were recovered at 12 of 17 in situ sampling sites: 6 ocean inlet sites, 2 lagoon site, and 3 mainland tidal creek sites. Therefore, there are 12 in situ sites available for comparison with satellite estimates. compare_L8S2.csv: Satellite data and water clarity estimates from 150 randomly sampled sites across 5 clear day images in the Virginia Coast Reserve, 2021. Satellite data are from Landsat-8 and Sentinel-2 and processed/atmospherically-corrected using NASA SeaDAS 8.2. The Virginia Coast Reserve is a coastal lagoon system located in Virginia, USA, near the southern tip of the Delmarva Peninsula. Due to low nitrogen inputs and frequent exchange with the Atlantic Ocean via inlets between barrier islands, water quality is high relative to many other coastal bays in the United States and worldwide. Spatial_averaging_analysis.csv: Secchi depths at in situ water quality sites at 10 m resolution (Sentinel-2 only), 30 m resolution (Landsat-8 and Sentinel-2), and 90 m resolution (Landsat-8 and Sentinel-2) where there are in situ match-ups. atmocorrect.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2 and ACOLITE Version 2022022.00. L8_ALL.csv: All Landsat-8 Secchi depth data available between 2013-2021 at in situ water quality sites. S2_ALL.csv: All Sentinel-2 Secchi depth data availab
Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data
<p>This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.<br> 1. Initial data used<br> We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).<br> 2. The calculation method<br> The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.<br> 3. Data sets<br> The data set contains two files:<br> - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)<br> - the yearly average of the EI (period 01/2003 - 12/2018)<br> <br> Two images are joined with this data set:<br> - a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)<br> (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).</p> <p> - a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).<br> (file: Figure-enrichment-index.pdf)<br> <br> An improved version of this index will be available in a near future.</p>
Satellite remote sensing dataset for urban climate in Bergen and Prague (TURBAN-D09)
<p><span>Shared dataset contains remote sensing data necessary for a land surface temperature (LST) calculation. Layers were processed for two cities; Bergen (Norway) and Prague (Czech Republic). Original data were downloaded from the U.S. Geological Survey (https://doi.org/10.5066/P975CC9B). For a LST calculation, a land surface emissivity (LSE) algorithm was used.</span></p> <h3><span>Processing of LANDSAT-8 and LANDSAT-9 data</span></h3> <ol> <li><span>Reading metadata file for each scene (*MTL.txt)</span></li> <li><span>Reprojection of scene (note: Bergen scenes have two UTM Zones; 31N and 32N)</span></li> <li><span>Cloud cover raster (see folder 01_CloudCover)</span></li> <li><span>Calculation Top-Of-Atmosphere (TOA) reflectance for bands 10 and 11 (TB_10 and TB_11), saving to folder 02_TOA-reflectance</span></li> <li><span>Calculating of NDVI and Fractional Vegetation Cover (FVC), saving to folder 03_FVC-NDVI</span></li> <li><span>Calculating of LSE for both bands, same as different and mean LSE (folder 04_LSE)</span></li> <li><span>Calculating of LST</span></li> <li><span>Saving of metadata file (see *metadata.txt)</span></li> </ol>
MCR LTER: Reference: Satellite Remote Sensing: Landsat ETM from 2001 to 2010
This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data collected between 2001 and 2010 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat ETM+ scene 20010429-LE70540722001119EDC00, LPGS_11.2.1, USGS, Sioux Falls, 04/29/2001.
MCR LTER: Reference: Satellite Remote Sensing: Landsat 7 in 1999 and 2000
This LTER Remote Sensing spatial raster dataset consists of Landsat 7 Thematic Mapper image data collected in 1999 and 2000 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2001, Landsat ETM+ scene RNL71054072_07220000426, Unspecified processing, USGS, Sioux Falls, 04/26/2000. NASA Landsat Program, 2003, Landsat ETM+ scene L7054072_19990830, Ortho product, USGS, Sioux Falls, 08/30/1999.
MCR LTER: Reference: Satellite Remote Sensing: Landsat MSS in 1979
This LTER Remote Sensing spatial raster dataset consists of Landsat Multi-Spectral Scanner (MSS) image data collected in 1979 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2010, Landsat MSS scene L3057072_07219790206_MTL, LPGS_11.2.1, USGS, Sioux Falls, 1979-02-06.
Remote Sensing Satellite Video Dataset for Super-resolution
<p>This is a satellite video super-resolution dataset generated from "Jilin-1" video satellite.</p> <p>Training set: 189 clips; Test set: 12 clips.</p> <p>More details can be found in our paper published in IEEE TGRS: https://ieeexplore.ieee.org/document/9530280</p> <p>If you find our work helpful, please cite our paper. Thank you very much!</p>
New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities
<p>The dataset is the vegetation type map for India as per <a href="https://www.sciencedirect.com/science/article/pii/S0303243415000574?via%3Dihub">Roy et 2015 "<span>New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities". </span></a></p> <p><span>The dataset consistes of two files- (1) a raster GIS file at 60m spatial resolution (EPSG 32643 WGS 84/ UTM Zone 34) in which each pixel value means a vegetation class as defined and mapped in Roy et al., 2015 and (2) a csv file which contains information matching the pixel value with the vegetation type. <br><br>For all additional information, please refer to the pper reviewed publication. </span></p>
Satellite remote sensing dataset of Sentinel-2 for phenology metrics extraction from sites in Bulgaria and France
<p><strong>Site Description:</strong></p> <p>In this dataset, there are seventeen production crop fields in Bulgaria where winter rapeseed and wheat were grown and two research fields in France where winter wheat – rapeseed – barley – sunflower and winter wheat – irrigated maize crop rotation is used. The full description of those fields is in the database "In-situ crop phenology dataset from sites in Bulgaria and France" (doi.org/10.5281/zenodo.7875440).</p> <p> </p> <p><strong>Methodology and Data Description:</strong></p> <p>Remote sensing data is extracted from Sentinel-2 tiles 35TNJ for Bulgarian sites and 31TCJ for French sites on the day of the overpass since September 2015 for Sentinel-2 derived vegetation indices and since October 2016 for HR-VPP products. To suppress spectral mixing effects at the parcel boundaries, as highlighted by Meier et al., 2020, the values from all datasets were subgrouped per field and then aggregated to a single median value for further analysis.</p> <p>Sentinel-2 data was downloaded for all test sites from CREODIAS (https://creodias.eu/) in L2A processing level using a maximum scene-wide cloudy cover threshold of 75%. Scenes before 2017 were available in L1C processing level only. Scenes in L1C processing level were corrected for atmospheric effects after downloading using Sen2Cor (v2.9) with default settings. This was the same version used for the L2A scenes obtained intermediately from CREODIAS. </p> <p>Next, the data was extracted from the Sentinel-2 scenes for each field parcel where only SCL classes 4 (vegetation) and 5 (bare soil) pixels were kept. We resampled the 20m band B8A to match the spatial resolution of the green and red band (10m) using nearest neighbor interpolation. The entire image processing chain was carried out using the open-source Python Earth Observation Data Analysis Library (EOdal) (Graf et al., 2022).</p> <p>Apart from the widely used Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), we included two recently proposed indices that were reported to have a higher correlation with photosynthesis and drought response of vegetation: These were the Near-Infrared Reflection of Vegetation (NIRv) (Badgley et al., 2017) and Kernel NDVI (kNDVI) (Camps-Valls et al., 2021). We calculated the vegetation indices in two different ways: </p> <p>First, we used <strong>B08</strong> as near-infrared (NIR) band which comes in a native spatial resolution of 10 m. <strong>B08</strong> (central wavelength 833 nm) has a relatively coarse spectral resolution with a bandwidth of 106 nm.</p> <p>Second, we used <strong>B8A</strong> which is available at 20 m spatial resolution. <strong>B8A</strong> differs from B08 in its central wavelength (864 nm) and has a narrower bandwidth (21 nm or 22 nm in the case of Sentinel-2A and 2B, respectively) compared to B08.</p> <p> </p> <p>The High Resolution Vegetation Phenology and Productivity (<strong>HR-VPP</strong>) dataset from Copernicus Land Monitoring Service (CLMS) has three 10-m set products of Sentinel-2: vegetation indices, vegetation phenology and productivity parameters and seasonal trajectories (Tian et al., 2021). Both vegetation indices, Normalized Vegetation Index (NDVI) and Plant Phenology (PPI) and plant parameters, Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) and Leaf Area Index (LAI) were computed for the time of Sentinel-2 overpass by the data provider. </p> <p>NDVI is computed directly from B04 and B08 and PPI is computed using Difference Vegetation Index (DVI = B08 - B04) and its seasonal maximum value per pixel. FAPAR and LAI are retrieved from B03 and B04 and B08 with neural network training on PROSAIL model simulations. The dataset has a quality flag product (QFLAG2) which is a 16-bit that extends the scene classification band (SCL) of the Sentinel-2 Level-2 products. A “medium” filter was used to mask out QFLAG2 values from 2 to 1022, leaving land pixels (bit 1) within or outside cloud proximity (bits 11 and 13) or cloud shadow proximity (bits 12 and 14). </p> <p>The <strong>HR-VPP</strong> daily raw vegetation indices products are described in detail in the user manual (Smets et al., 2022) and the computations details of PPI are given by Jin and Eklundh (2014). Seasonal trajectories refer to the 10-daily smoothed time-series of PPI used for vegetation phenology and productivity parameters retrieval with TIMESAT (Jönsson and Eklundh 2002, 2004).</p> <p>HR-VPP data was downloaded through the WEkEO Copernicus Data and Information Access Services (DIAS) system with a Python 3.8.10 harmonized data access (HDA) API 0.2.1. Zonal statistics [’min’, ’max’, ’mean’, ’median’, ’count’, ’std’, ’majority’] were computed on non-masked pixel values within field boundaries with rasterstats Python package 0.17.00.</p> <p> </p> <p>The Start of season date (SOSD), end of season date (EOSD) and length of seasons (LENGTH) were extracted from the annual Vegetation Phenology and Productivity Parameters (<strong>VPP</strong>) dataset as an additional source for comparison. These data are a product of the Vegetation Phenology and Productivity Parameters, see (https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity/vegetation-phenology-and-productivity) for detailed information.</p> <p> </p> <p><strong>File Description:</strong></p> <p>4 datasets:</p> <p>1_senseco_data_S2_B08_Bulgaria_France; 1_senseco_data_S2_B8A_Bulgaria_France; 1_senseco_data_HR_VPP_Bulgaria_France; 1_senseco_data_phenology_VPP_Bulgaria_France</p> <p>3 metadata:</p> <p>2_senseco_metadata_S2_B08_B8A_Bulgaria_France; 2_senseco_metadata_HR_VPP_Bulgaria_France; 2_senseco_metadata_phenology_VPP_Bulgaria_France</p> <p> </p> <p>The dataset files “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” concerns all vegetation indices (EVI, NDVI, kNDVI, NIRv) data values and related information, and metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France” describes all the existing variables. Both “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” have the same column variable names and for that reason, they share the same metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France”.</p> <p>The dataset file “1_senseco_data_HR_VPP_Bulgaria_France” concerns vegetation indices (NDVI, PPI) and plant parameters (LAI, FAPAR) data values and related information, and metadata file “2_senseco_metadata_HRVPP_Bulgaria_France” describes all the existing variables. </p> <p>The dataset file “1_senseco_data_phenology_VPP_Bulgaria_France” concerns the vegetation phenology and productivity parameters (LENGTH, SOSD, EOSD) values and related information, and metadata file “2_senseco_metadata_VPP_Bulgaria_France” describes all the existing variables.</p> <p> </p> <p><strong>Bibliography</strong></p> <p>G. Badgley, C.B. Field, J.A. Berry, Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv. 3 (2017) e1602244. https://doi.org/10.1126/sciadv.1602244.</p> <p>G. Camps-Valls, M. Campos-Taberner, Á. Moreno-Martínez, S. Walther, G. Duveiller, A. Cescatti, M.D. Mahecha, J. Muñoz-Marí, F.J. García-Haro, L. Guanter, M. Jung, J.A. Gamon, M. Reichstein, S.W. Running, A unified vegetation index for quantifying the terrestrial biosphere, Sci. Adv. 7 (2021) eabc7447. https://doi.org/10.1126/sciadv.abc7447.</p> <p>L.V. Graf, G. Perich, H. Aasen, EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data, Comput. Electron. Agric. 203 (2022) 107487. https://doi.org/10.1016/j.compag.2022.107487.</p> <p>H. Jin, L. Eklundh, A physically based vegetation index for improved monitoring of plant phenology, Remote Sens. Environ. 152 (2014) 512–525. https://doi.org/10.1016/j.rse.2014.07.010.</p> <p>P. Jonsson, L. Eklundh, Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Trans. Geosci. Remote Sens. 40 (2002) 1824–1832. https://doi.org/10.1109/TGRS.2002.802519.</p> <p>P. Jönsson, L. Eklundh, TIMESAT—a program for analyzing time-series of satellite sensor data, Comput. Geosci. 30 (2004) 833–845. https://doi.org/10.1016/j.cageo.2004.05.006.</p> <p>J. Meier, W. Mauser, T. Hank, H. Bach, Assessments on the impact of high-resolution-sensor pixel sizes for common agricultural policy and smart farming services in European regions, Comput. Electron. Agric. 169 (2020) 105205. https://doi.org/10.1016/j.compag.2019.105205.</p> <p>B. Smets, Z. Cai, L. Eklund, F. Tian, K. Bonte, R. Van Hoost, R. Van De Kerchove, S. Adriaensen, B. De Roo, T. Jacobs, F. Camacho, J. Sánchez-Zapero, S. Else, H. Scheifinger, K. Hufkens, P. Jönsson, HR-VPP Product User Manual Vegetation Indices, 2022.</p> <p>F. Tian, Z. Cai, H. Jin, K. Hufkens, H. Scheifinger, T. Tagesson, B. Smets, R. Van Hoolst, K. Bonte, E. Ivits, X. Tong, J. Ardö, L. Eklundh, Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe, Remote Sens. Environ. 260 (2021) 112456. https://doi.org/10.1016/j.rse.2021.112456.</p>
Locating Manning-based Satellite Gauging Reach for River Discharge Estimation from Remotely Sensed Imagery
<p>River discharge is critical for understanding river hydrological condition and water resource management. With the advance of earth observation technologies, estimating river discharge through remote sensing using Manning's Equation has become more and more popular for filling the gaps in gauging observations. However, finding a Manning-based Satellite Gauging Reach (MSGR) that can successfully transfer satellite signals into river discharge based on the Equation is not easy and lacks proper guidance. Theredore, we provide a practical approach for locating MSGR.<br> The manuscript is currently under review by Water Resources Research.<br> This repositry is the data for reproducing the figures in our manuscript.</p> <p>This repositry consists of four part. They are the corresponding data and results at MSGR, CR_FSE, CR_SCT locations, and elevation values along river centerline for calculating riverbed slope.</p> <p>MSGR: Manning-based Satellite Gauging Reach<br> CR_FSE: comparison reach with unsatisfactory FSE<br> CR_SCT: comparison reach with unsatisfactory SCT<br> FSE: Fluctuation degree of surface water extent<br> SCT: Stability of Channel Terrain</p>
Data from: Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations
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Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)
<p>The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variables operating at distinct scales are considered. These include climate, but also ecosystem functioning attributes (EFAs) related to energy–matter exchanges that characterize land cover types under various environmental settings, at finer scales. Here, we used satellite-based EFAs and climate variables in species distribution models (SDMs) to assess how variables related to ecosystem functioning improve our understanding of current distributions and the identification of suitable areas for White Oak species in Iberia. We developed consensus ensemble SDMs targeting a set of thirteen oaks, including both narrow endemic and widespread taxa. Models combining EFAs and climate variables obtained a higher performance and predictive ability (true-skill statistic (TSS): 0.88, sensitivity: 99.6, specificity: 96.3), in comparison to the climate-only models (TSS: 0.86, sens.: 96.1, spec.: 90.3) and EFA-only models (TSS: 0.73, sens.: 91.2, spec.: 82.1). Overall, narrow endemic species obtained higher predictive performance using combined models (TSS: 0.96, sens.: 99.6, spec.: 96.3) in comparison to widespread oaks (TSS: 0.80, sens.: 92.6, spec.: 87.7). The Iberian White Oaks show a high dependence on precipitation and the inter-quartile range of Normalized Difference Water Index (NDWI) (i.e., seasonal water availability) which appears to be the most important EFA variable. Spatial projections of climate–EFA combined models contribute to identify the major diversity hotspots for White Oaks in Iberia, holding higher values of cumulative habitat suitability and species richness. We discuss the implications of these findings for guiding the long-term conservation of IberianWhite Oaks and provide spatially explicit geospatial information about each oak species (or set of species) relevant for developing biogeographic conservation frameworks.</p>
Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite
<p>Scripts and results for our "Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite" manuscript.</p> <p>Consult the README file for a more detailed description of the data, results, and scripts.</p>
NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)
<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>
Constellations of small commercial earth remote sensing satellites (2011 - 2025)
<p>Spacecraft are classified as small if their mass ranges from a few kilograms (CubeSat 3U) to 1 ton. All the satellite constellations considered belong to private companies. One constellation included satellites belonging to one private company-operator that have identical or similar imaging equipment characteristics at a given orbital altitude.</p>
Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm
<p><strong>Aims</strong>: Remote sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because it is cost-effective and allows for coverage of large areas over a short period. This study investigated the relationship between multispectral remote sensing data from Landsat 8 and Sentinel 2 and species richness and diversity in mountainous and protected grasslands.</p> <p><strong>Locations</strong>: Golden Gate Highlands National Park, Free State, South Africa. </p> <p><strong>Methods</strong>: In-situ data of plant species composition and cover from 142 plots with 16 releves each were distributed across the study site and used to calculate species richness and Shannon-wiener species diversity index (species diversity. We used a machine-learning random forest algorithm to optimise the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetation indices were used to estimate species richness through random forest regression. </p> <p><strong>Results</strong>: This research found weak relationships between remote sensing vegetation indices and the diversity metrics, but significant relationships were found between some spectral bands and diversity metrics. Moreover, using machine learning random forest, the multispectral datasets exhibited strong predictive powers. In this investigation, for both sensors, near-infrared (NIR) seemed to be the most selected band to explain species diversity in mountainous grasslands.</p> <p><strong>Main</strong> <strong>conclusions</strong>: This finding further ascertains the efficiency of using NIR in vegetation mapping. This research shows that NIR, SAVI and EVI are the most adequate for predicting species richness and diversity in mountainous grasslands with relatively good accuracies.</p>
Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm
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Development and Evolution of NASA Satellite Remote Sensing for Ecology
This dataset provides a presentation that highlights the role NASA research and researchers played in developing a wide range of significant, quantitative ecological applications of satellite data. The presentation by Dr Diane E. Wickland, former NASA Terrestrial Ecology Program Manager and Lead for NASA Carbon Cycle and Ecosystems Focus Area, provides a top-level overview from her perspective of the development and evolution of the program. Dr Wickland joined NASA in 1985 to manage a newly formed Terrestrial Ecosystems Program. Along with other NASA program managers, she was charged with reorienting the program to be less empirical and have a greater focus on first principles, and to prepare for a next generation of earth-observing satellites. As an ecologist, she thought that focusing on important ecological questions and recruiting practicing ecologists to the program would facilitate such a change in directions. The presentation emphasizes the early years of U.S. satellite remote sensing and covers a few highlights after 2005.
SMEX02 European Remote Sensing Satellite (ERS-2) AMI Data, Iowa, Version 1
This data set consists of browse images acquired by the C-band Active Microwave Instrument (AMI) onboard the European Remote Sensing Satellite 2 (ERS-2) and provides only a general quality assessment of the ERS-2 AMI data.
FIREX-AQ ER-2 Remotely Sensed National Polar - Orbiting Operational Environmental Satellite System Airborne Sounder Testbed - Interferometer (NAST-I) Data
FIREXAQ_ TraceGasAircraftRemoteSensing_ER2_NASTI_Data are remotely sensed measurements collected by the National Polar-Orbiting Operational Environmental Satellite System Airborne Sounder Testbed-Interferometer (NAST-I) onboard the ER-2 aircraft during FIREX-AQ. Data collection for this product is complete.Completed during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA’s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite’s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.
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.