Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

Monitoring recent changes in the Beaufort Sea coast using very high resolution remote sensing

Arctic permafrost coasts are major carbon (Schuur et al., 2015) and mercury pools (Schuster et al., 2018). They represent about 34% of the Earth’s coastline, with long sections affected by high erosion rates (Fritz et al, 2017), increasingly threatening coastal communities. Year-round reduction in Arctic sea ice is forecasted and by the end of the 21st century, models indicate a decrease in sea ice area from 43 to 94% in September and from 8 to 34% in February (IPCC, 2014). An increase of the sea-ice free season leads to a longer exposure of coasts to wave action. Further, climate warming is also expected to modify the contribution of terrestrial erosion (Fritz et al., 2015, Ramage et al., 2018, Irrgang et al., 2018). Within the project EU Horizon2020 project NUNATARYUK, we are updating the mapping of the Arctic coast, with the Canadian Beaufort coast as a case-study. The surveying methodology includes: i. a high resolution update of the coastline mapping and change rates using Pleiades (CNES) satellite acquisitions from 2018, ii. a survey using RTK-UAV aerial imagery of long-term monitoring sites from the Canada-US border to King Point, and iii. the experimental use of TerraSAR-X staring spotlight scenes and PAZ at key sites to monitor intraseasonal dynamics of cliff edge retreat. This research is funded by the EC H2020 Project NUNATARYUK. Support on remote sensing imagery access by the WMO Polar Space Task Group.

opencc-by-4.0Jul 2024View details →

Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city

This dataset contains the processed data and analysis code used in the article: <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. Quarterly Journal of the Royal Meteorological Society, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div>LST_data contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> UM100_output contains model output from initial and final runs for the four study days The python script plot.py can be used to generate the figures shown in this article.

opencc-by-4.0Sep 2024View details →

DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images

This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. Figure 8 shows the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability.

opencc-by-4.0Dec 2022View details →

High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula

Abstract: Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017. -  - - - - - - - - - - - - - - -  - - - - - - - - - - - - - - This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below: Raster value             Land cover/use category 1                                  Forest 2                                  Marsh 3                                  Salt patch 4                                  Built 5                                  Open water 6                                  Farmland 7                                  Bare soil 8                                  Other vegetation   Input Data: These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE. Methods: NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel. NDVI = (Near-infrared – Red) / (Near-infrared + Red) NDWI = (Green – Near-infrared) / (Green + Near-infrared) SI = (256 – Blue) * (256 + Blue) In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel. EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1) A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands. Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits. Accuracy assessment: A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below. <table> <tbody> <tr> <td> Delaware 2013 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.46% </td> <td> 90.89% </td> <td> 0.90 </td> <td> 86.37% </td> <td> 0.83 </td> </tr> <tr> <td> Marsh </td> <td> 84.03% </td> <td> 80.13% </td> <td> 0.82 </td> </tr> <tr> <td> Salt patch </td> <td> 97.02% </td> <td> 71.18% </td> <td> 0.82 </td> </tr> <tr> <td> Built </td> <td> 94.56% </td> <td> 95.06% </td> <td> 0.95 </td> </tr> <tr> <td> Water </td> <td> 91.01% </td> <td> 96.63% </td> <td> 0.94 </td> </tr> <tr> <td> Farmland </td> <td> 83.16% </td> <td> 86.19% </td> <td> 0.85 </td> </tr> <tr> <td> Bare Soil </td> <td> 87.70% </td> <td> 87.30% </td> <td> 0.88 </td> </tr> <tr> <td> Other Vegetation </td> <td> 82.46% </td> <td> 84.20% </td> <td> 0.83 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Delaware 2017 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 95.07% </td> <td> 90.30% </td> <td> 0.93 </td> <td> 91.37% </td> <td> 0.90 </td> </tr> <tr> <td> Marsh </td> <td> 88.86% </td> <td> 92.44% </td> <td> 0.91 </td> </tr> <tr> <td> Salt patch </td> <td> 91.82% </td> <td> 85.59% </td> <td> 0.89 </td> </tr> <tr> <td> Built </td> <td> 87.58% </td> <td> 93.54% </td> <td> 0.90 </td> </tr> <tr> <td> Water </td> <td> 92.68% </td> <td> 90.48% </td> <td> 0.92 </td> </tr> <tr> <td> Farmland </td> <td> 91.61% </td> <td> 93.61% </td> <td> 0.93 </td> </tr> <tr> <td> Bare Soil </td> <td> 95.67% </td> <td> 87.67% </td> <td> 0.91 </td> </tr> <tr> <td> Other Vegetation </td> <td> 91.16% </td> <td> 90.24% </td> <td> 0.91 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Maryland 2011 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.32% </td> <td> 90.97% </td> <td> 0.90 </td> <td> 87.20% </td> <td> 0.85 </td> </tr> <tr> <td> Marsh </td> <td> 87.14% </td> <td> 82.08% </td> <td> 0.85 </td> </tr> <tr> <td> Salt patch </td> <td> 96.74% </td> <td> 78.76% </td> <td> 0.87 </td> </tr> <tr> <td> Built </td> <td> 89.02% </td> <td> 88.50% </td> <td> 0.89 </td> </tr> <tr> <td> Water </td> <td> 92.74% </td> <td> 96.10% </td> <td> 0.94 </td> </tr> <tr> <td> Farmland </td> <td> 84.31% </td> <td> 89.83% </td> <td> 0.87 </td> </tr> <tr> <td> Bare Soil </td> <td> 87.53% </td> <td> 90.05% </td> <td> 0.89 </td> </tr> <tr> <td> Other Vegetation </td> <td> 86.11% </td> <td> 80.57% </td> <td> 0.83 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Maryland 2017 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.66% </td> <td> 88.66% </td> <td> 0.89 </td> <td> 87.34% </td> <td> 0.84 </td> </tr> <tr> <td> Marsh </td> <td> 87.65% </td> <td> 88.35% </td> <td> 0.88 </td> </tr> <tr> <td> Salt patch </td> <td> 93.29% </td> <td> 68.30% </td> <td> 0.79 </td> </tr> <tr> <td> Built </td> <td> 93.44% </td> <td> 86.92% </td> <td> 0.90 </td> </tr> <tr> <td> Water </td> <td> 92.36% </td> <td> 92.36% </td> <td> 0.92 </td> </tr> <tr> <td> Farmland </td> <td> 83.84% </td> <td> 94.55% </td> <td> 0.89 </td> </tr> <tr> <td> Bare Soil </td> <td> 92.86% </td> <td> 82.61% </td> <td> 0.87 </td> </tr> <tr> <td> Other Vegetation </td> <td> 86.56% </td> <td> 76.00% </td> <td> 0.81 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Virginia 2012 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 84.65% </td> <td> 91.18% </td> <td> 0.88 </td> <td> 86.88% </td> <td> 0.84 </td> </tr> <tr> <td> Marsh </td> <td> 87.03% </td> <td> 84.39% </td> <td> 0.86 </td> </tr> <tr> <td> Salt patch </td> <td> 97.67% </td> <td> 72.41% </td> <td> 0.83 </td> </tr> <tr> <td> Built </td> <td> 90.97% </td> <td> 86.24% </td> <td> 0.89 </td> </tr> <tr> <td> Water </td> <td> 94.17% </td> <td> 87.39% </td> <td> 0.91 </td> </tr> <tr> <td> Farmland </td> <td> 86.87% </td> <td> 91.81% </td> <td> 0.89 </td> </tr> <tr> <td> Bare Soil </td> <td> 85.82% </td> <td> 83.04% </td> <td> 0.84 </td> </tr> <tr> <td> Other Vegetation </td> <td> 83.60% </td> <td> 80.59% </td> <td> 0.82 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Virginia 2016 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 84.65% </td> <td> 86.00% </td> <td> 0.85 </td> <td> 85.83% </td> <td> 0.83 </td> </tr> <tr> <td> Marsh </td> <td> 86.25% </td> <td> 90.72% </td> <td> 0.88 </td> </tr> <tr> <td> Salt patch </td> <td> 90.61% </td> <td> 62.12% </td> <td> 0.74 </td> </tr> <tr> <td> Built </td> <td> 88.70% </td> <td> 77.72% </td> <td> 0.83 </td> </tr> <tr> <td> Water </td> <td> 92.12% </td> <td> 84.41% </td> <td> 0.88 </td> </tr> <tr> <td> Farmland </td> <td> 85.17% </td> <td> 90.36% </td> <td> 0.88 </td> </tr> <tr> <td> Bare Soil </td> <td> 83.30% </td> <td> 88.54% </td> <td> 0.86 </td> </tr> <tr> <td> Other Vegetation </td> <td> 84.49% </td> <td> 84.17% </td> <td> 0.84 </td> </tr> </tbody> </table> While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface.   Data format: The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis. Code: Sample code is available at <a href="https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806">https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806</a>. The user would need to upload study area boundaries and reference points in order to successfully run these codes. Datasets for download: <ul> <li>Two zipped data layers for Delaware:</li> </ul> <ol> <li>DE_3counties_2013</li> <li>DE_3counties_2017</li> </ol> These data layers cover 3 counties: Kent, New Castle, Sussex. <ul> <li>Two zipped data layers for Maryland:</li> </ul> <ol> <li>MD_9counties_2011</li> <li>MD_9counties_2017</li> </ol> These data layers cover 9 counties: Caroline, Cecil, Dorchester, Kent, Queen Anne's, Somerset, Talbot, Wicomico, Worcester. <ul> <li>Two zipped data layers for Virginia:</li> </ul> <ol> <li>VA_2counties_2012</li> <li>VA_2counties_2016</li> </ol> These data layers cover 2 counties: Accomack, Northampton. We also provided a color map (DELMARVA_ColorMap.clr) that can be used with these data files.  Data citation: Mondal, P., Walter, M., Miller, J., Epanchin-Niell, R., Yawatkar, V., Nguyen, E., Gedan, K. and Tully, K. 2022. High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula. Available at: 10.5281/zenodo.6685695. Accessed DAY MONTH YEAR.

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Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling

Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe.

opencc-zeroDec 2016View details →

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GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images

GDCLD : A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., and Catani, F.: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-239, in review, 2024.   Data description:   The training dataset and the validation dataset are composed of UAV, PlanetScope, Gaofen-6 and Map World images of the 5 earthquake regions of Luding, Nippes, Hokkaido, Jiuzhaigou and Mainling. There is no overlapping area in each TIFF. The training dataset and the validation dataset are randomly divided at a ratio of approximately 0.75:0.25.   train_dataset: train_data: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF). train_label: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).   Validation_dataset val_data: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF). val_label: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).   Test_dataset (Lushan, Sumatra, Mesetas and Palu dataset) This package contains the original files of remote sensing images from three sources: UAV, Map World, and PlaneScope belonging to the Lushan, Sumatra, Mesetas and Palu earthquake regiones, which are used to display the test area. Future work: The future work includes additional landslide data that the authors will continue to upload. In this 2.0 version update, we have added UAV imagery and interpreted data for loess landslides triggered by the December 2023 M6.2 earthquake in Gansu, China, with a resolution of 0.1 m. Due to authorization constraints, we can only provide PNG files without geographic coordinates. Additionally, this update includes PlanetScope imagery of landslides induced by heavy rainfall in Guangdong, China, in 2024, as well as PlanetScope imagery and landslide labels for events triggered by the Hualien earthquake in Taiwan. Please note that this landslide dataset is publicly available exclusively for scientific research purposes and must not be used for commercial purposes.

opencc-by-4.0Aug 2024View details →

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Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling

Open the record for dataset details and reuse information.

publicMay 2018View details →

zenodo28/100

Restore Centre of Excellence: High-resolution mapping of Louisina vegetation remote sensing data for surge modelling

Satellite derived Leaf Area Index map of the Louisiana coast, translated into plant dimensions using field data from CMRS stations and dedicated project sampling. Plant dimensions have been used to prescribe hydraulic roughness fields for a hydrodynamic model (Delft3D) used to asses the effect of wetlands on storm surge levels.

opencc-by-4.0Dec 2019View details →

dryad28/100

High-resolution residual dry matter (RDM) map for a California oak savanna/annual grassland derived from drone multispectral remote sensing imagery and in-situ grass biomass data

Open the record for dataset details and reuse information.

publicJun 2023View details →

DISCOVER-AQ Colorado Deployment B200 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL-2) Data

DISCOVERAQ_Colorado_AircraftRemoteSensing_B200_HSRL-2_Data contains remotely sensed data collected by the High Spectral Resolution Lidar-2 (HSRL-2) onboard NASA's B-200 aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

DEVOTE UC-12 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL) Data

DEVOTE_ AircraftRemoteSensing_UC12_HSRL_Data are remotely sensed data collected by the High Spectral Resolution Lidar (HSRL) onboard the UC-12 aircraft as part of the Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) sub-orbital project. Data collection is complete.The Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) project investigated aerosols and clouds with the specific goals of satellite validation and the improvement of satellite data retrieval algorithms. Conducted in September and October 2011, DEVOTE scientists collected measurements of aerosols and cloud optical and microphysical properties using airborne sensors over ground sites and along satellite overpasses to demonstrate the use of airborne platforms in future scientific measurement campaigns. These measurements were used to validate and improve satellite data retrieval algorithms from missions including the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and the Aerosol, Cloud, Ecosystems (ACE) Decadal Survey mission.DEVOTE scientists conducted eleven science flights based at the NASA Langley Research Center throughout the campaign. The flight plans were specifically designed to coordinate with CALIPSO satellite overpasses and to fly over the Aerosol Robotic Network (AERONET) ground network sites. The DEVOTE sampling strategy required two aircraft dedicated to remote sensing and in-situ observations, which flew in coordinated flight patterns. This was implemented through use of the NASA UC-12 and the NASA B-200 airborne platforms. The UC-12 had the following remote sensing payload: the Research Scanning Polarimeter (RSP) and High Spectral Resolution Lidar (HSRL) instruments. The B-200 had an in-situ payload including the Polarized Imaging Nephelometer (PI-Neph), the Diode Laser Hygrometer (DLH), and Langley Aerosol Research Group Experiment (LARGE) instruments for aerosol microphysical and optical properties.DEVOTE was partly funded through the Hands-On Project Experience (HOPE) initiative. HOPE was a NASA development program designed to offer early career scientist opportunities to design, implement, and analyze small missions offering hands-on experience. Opportunities are increasingly limited for principal investigators, program managers, and system engineers to obtain mission life cycle training, and HOPE provides opportunities to those early on in their career or who are transitioning to a different field. Thus, DEVOTE had a focus on providing hands-on training in the mission life cycle to early career scientists in addition to its primary objective of using cloud and aerosol data collected from airborne sensors to validate and improve satellite data retrieval algorithms. Additionally, the information obtained from DEVOTE research was used to prepare for the implementation of ACE.

ACEPOL High Spectral Resolution Lidar 2 (HSRL-2) Remotely Sensed Data

ACEPOL High Spectral Resolution Lidar 2 (HSRL-2) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_HSRL2_Data) are remotely sensed measurements collected by the High-Spectral Resolution Lidar (HSRL-2) onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA’s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.

TRACER-AQ JSC G-V Aircraft Remotely Sensed High Spectral Resolution Lidar-2 (HSRL-2) Data

TRACERAQ_AircraftRemoteSensing_GV_HSRL2_Data is the remotely sensed High Spectral Resolution Lidar-2 (HSRL-2) data collected onboard the Johnson Space Center (JSC) Gulfstream V (G-V) aircraft during the TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) field study. Data collection is complete.The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign is a field study co-sponsored by NASA and TCEQ (Texas Commission on Environmental Quality), with partners from DOE (Department of Energy) TRacking Aerosol Convection ExpeRiment (TRACER), and several academic institutions. This synergistic effort aims to gain an updated understanding in photochemistry and meteorological impact on ozone formation in the Houston region, particularly around the Houston Ship Channel, Galveston Bay, and the Gulf of America; and provide observations for evaluating air quality models and satellite observations.The primary TRACER-AQ field observations period lasted from mid-August to late September 2021, coinciding with the peak ozone season in East Texas, with a second deployment in summer 2022 with a subset of ground-based assets. The observing system includes airborne remote sensing, mobile (boat/vehicle) laboratories, and stationary ground-based assets.The airborne component was based on the NASA Gulfstream V aircraft instrumented with GCAS (GEOCAPE Airborne Simulator) for making measurements of column NO2 and HCHO as well as a lidar system, HSRL-2 (High Spectral Resolution Lidar-2), to measure O3 and aerosol vertical profiles over the course of 12 flight days. Ground-based assets include ground-based ozone lidars from the Tropospheric Ozone Lidar Network (TOLNet), ceilometers, Pandora spectrometers, AErosol RObotic NETwork (AERONET) remote sensors, ozonesondes, and stationary and mobile laboratories of in situ air quality and meteorological observations. This coordinated observing system provides updated or unseen perspectives in spatial and temporal distribution of the key photochemical species and atmospheric structure information, particularly with a focus on the temporal evolution of observations throughout the daytime in preparation for upcoming geostationary satellite air quality observations.

FIREX-AQ DC-8 High-Spectral Resolution Lidar (HSRL) Remotely Sensed Data

FIREXAQ_HSRL_AircraftRemoteSensing_DC8_Data are remotely sensed data collected by the High-Spectral Resolution Lidar (HSRL) onboard the DC-8 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.

DISCOVER-AQ Maryland Deployment UC-12 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL) Data

DISCOVERAQ_Maryland_AircraftRemoteSensing_UC12_HSRL_Data contains remotely sensed data collected by the High Spectral Resolution Lidar (HSRL) onboard NASA's UC-12 aircraft during the Maryland deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

DISCOVER-AQ Texas Deployment B200 Aircraft Remotely Sensed High Spectral Resolution Lidar (HSRL-2) Data

DISCOVERAQ_Texas_AircraftRemoteSensing_B200_HSRL2_Data contains remotely sensed data collected by the High Spectral Resolution Lidar (HSRL-2) onboard NASA's UC-12 aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.