Airborne imaging spectroscopy surveys of Arctic and boreal Alaska and northwestern Canada 2017–2023


  • Miller, C. E. et al. The ABoVE L-band and P-band airborne synthetic aperture radar surveys, Earth Syst. Sci. Data 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024 (2024).

    Article 

    Google Scholar
     

  • Rantanen, M. et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun Earth Environ 3, 168, https://doi.org/10.1038/s43247-022-00498-3 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Myers-Smith, I. H. et al. Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities. Environmental Research Letters 6(4), 045509, https://doi.org/10.1088/1748-9326/6/4/045509 (2011).

    Article 
    ADS 

    Google Scholar
     

  • Heijmans, M. M. P. D. et al. Tundra vegetation change and impacts on permafrost. Nat Rev Earth Environ 3, 68–84, https://doi.org/10.1038/s43017-021-00233-0 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Jones, B. M. et al. Lake and drained lake basin systems in lowland permafrost regions. Nat Rev Earth Environ 3, 85–98, https://doi.org/10.1038/s43017-021-00238-9 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Miner, K. R. et al. Permafrost carbon emissions in a changing Arctic. Nat Rev Earth Environ 3, 55–67, https://doi.org/10.1038/s43017-021-00230-3 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Miller, C. E. et al. An overview of ABoVE airborne campaign data acquisitions and science opportunities. Environmental Research Letters 14(8), 080201, https://doi.org/10.1088/1748-9326/ab0d44 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Stavros, E. N. et al. Designing an observing system to study the Surface Biology and Geology (SBG) of the Earth in the 2020s. Journal of Geophysical Research: Biogeosciences 128, e2021JG006471, https://doi.org/10.1029/2021JG006471 (2023).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Raiho, A. M. et al. Exploring mission design for imaging spectroscopy retrievals for land and aquatic ecosystems. Journal of Geophysical Research: Biogeosciences 128(4), e2022JG006833, https://doi.org/10.1029/2022JG006833 (2023).

    Article 
    ADS 
    MathSciNet 

    Google Scholar
     

  • Nieke, J. et al. The copernicus hyperspectral imaging mission for the environment (CHIME): an overview of its mission, system and planning status. Sensors, Systems, and Next-Generation Satellites XXVII 12729: 21-40. https://doi.org/10.1117/12.2679977 (2023).

  • Thompson, D. R. et al. On-orbit calibration and performance of the EMIT imaging spectrometer. Remote Sensing of Environment 303, 113986, https://doi.org/10.1016/j.rse.2023.113986 (2024).

    Article 

    Google Scholar
     

  • Coleman, R. W. et al. An accuracy assessment of the surface reflectance product from the EMIT imaging spectrometer. Remote Sensing of Environment 315, 114450, https://doi.org/10.1016/j.rse.2024.114450 (2024).

    Article 

    Google Scholar
     

  • Gorman, E. T. et al. The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission: an emerging era of global, hyperspectral Earth system remote sensing. In Sensors, systems, and next-generation satellites XXIII (Vol. 11151, pp. 78-84). SPIE. https://doi.org/10.1117/12.2537146 (2019, October).

  • Buongiorno, M. F. et al. Asi-Prisma Hyperspectral Mission for the Analysis of Geophysical Phenomena. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 8099-8102). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553103 (2021, July).

  • Guanter, L. et al. The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sensing 7(7), 8830–8857, https://doi.org/10.3390/rs70708830 (2015).

    Article 
    ADS 

    Google Scholar
     

  • Montesano, P. M. et al. Patterns of regional site index across a North American boreal forest gradient. Environmental Research Letters 18(7), 075006, https://doi.org/10.1088/1748-9326/acdcab (2023).

    Article 
    ADS 

    Google Scholar
     

  • Pitcher, L. H. et al. Advancing field-based GNSS surveying for validation of remotely sensed water surface elevation products. Frontiers in Earth Science 8, 278, https://doi.org/10.3389/feart.2020.00278 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Sweeney, C. et al. Using atmospheric trace gas vertical profiles to evaluate model fluxes: a case study of Arctic-CAP observations and GEOS simulations for the ABoVE domain. Atmos. Chem. Phys. 22, 6347–6364, https://doi.org/10.5194/acp-22-6347-2022 (2022).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Fayne, J. V. et al. Airborne observations of arctic-boreal water surface elevations from AirSWOT Ka-Band InSAR and LVIS LiDAR. Environmental Research Letters, 15(10), p.105005, https://doi.org/10.1088/1748-9326/abadcc (2020).

  • Wang, C. et al. Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta. Remote Sensing of Environment 294, 113646, https://doi.org/10.1016/j.rse.2023.113646 (2023).

    Article 

    Google Scholar
     

  • Mederer, D. et al. Plant trait retrieval from hyperspectral data: Collective efforts in scientific data curation outperform simulated data derived from the PROSAIL model. ISPRS Open Journal of Photogrammetry and Remote Sensing 15, 100080, https://doi.org/10.1016/j.ophoto.2024.100080 (2025).

    Article 

    Google Scholar
     

  • Smith, C. W., Panda, S. K., Bhatt, U. S. & Meyer, F. J. Improved boreal forest wildfire fuel type mapping in interior alaska using aviris-ng hyperspectral data. Remote Sensing 13(5), 897, https://doi.org/10.3390/rs13050897 (2021).

    Article 
    ADS 

    Google Scholar
     

  • Badola, A. et al. Hyperspectral data simulation (sentinel-2 to aviris-ng) for improved wildfire fuel mapping, Boreal Alaska. Remote Sensing 13(9), 1693, https://doi.org/10.3390/rs13091693 (2021).

    Article 
    ADS 

    Google Scholar
     

  • Badola, A. et al. A novel method to simulate AVIRIS-NG hyperspectral image from Sentinel-2 image for improved vegetation/wildfire fuel mapping, boreal Alaska. International Journal of Applied Earth Observation and Geoinformation 112, 102891, https://doi.org/10.1016/j.jag.2022.102891 (2022).

    Article 

    Google Scholar
     

  • Badola, A. et al. Estimation and Validation of Sub-Pixel Needleleaf Cover Fraction in the Boreal Forest of Alaska to Aid Fire Management. Remote Sensing 15(10), 2484, https://doi.org/10.3390/rs15102484 (2023).

    Article 
    ADS 

    Google Scholar
     

  • Elder, C. D. et al. Airborne mapping reveals emergent power law of arctic methane emissions. Geophysical Research Letters 47(3), e2019GL085707, https://doi.org/10.1029/2019GL085707 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Elder, C. D. et al. Characterizing methane emission hotspots from thawing permafrost. Global Biogeochemical Cycles 35(12), e2020GB006922, https://doi.org/10.1029/2020GB006922 (2021).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Baskaran, L. et al. Geomorphological patterns of remotely sensed methane hot spots in the Mackenzie Delta, Canada. Environmental Research Letters 17(1), 015009, https://doi.org/10.1088/1748-9326/ac41fb (2022).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Clark, J. A. et al. Do beaver ponds increase methane emissions along Arctic tundra streams? Environ. Res. Lett. 18, 075004, https://doi.org/10.1088/1748-9326/acde8e (2023).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Yoseph, E. et al. Tundra fire increases the likelihood of methane hotspot formation in the Yukon–Kuskokwim Delta, Alaska, USA. Environmental Research Letters 18(10), 104042, https://doi.org/10.1088/1748-9326/acf50b (2023).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Borchardt, J. et al. Detection and quantification of CH4 plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data. Atmos. Meas. Tech. 14, 1267–1291, https://doi.org/10.5194/amt-14-1267-2021 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Nelson, P. R. et al. Remote sensing of tundra ecosystems using high spectral resolution reflectance: opportunities and challenges. Journal of Geophysical Research: Biogeosciences 127(2), e2021JG006697, https://doi.org/10.1029/2021JG006697 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Yang, D. et al. Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska. Remote Sensing of Environment 286, 113430, https://doi.org/10.1016/j.rse.2022.113430 (2023).

    Article 

    Google Scholar
     

  • Pierrat, Z. A. et al. Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests. Ecology 105(10), e4402, https://doi.org/10.1002/ecy.4402 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Pierrat, Z. A. et al. Proximal remote sensing: an essential tool for bridging the gap between high‐resolution ecosystem monitoring and global ecology. New Phytologist. https://doi.org/10.1111/nph.20405 (2025).

  • Green, R. O. & Team, C. New measurements of the earth’s spectroscopic diversity acquired during the AVIRIS-NG campaign to India. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3066-3069). IEEE. https://doi.org/10.1109/IGARSS.2017.8127646 (2017, July).

  • Bhattacharya, B. K. et al. An overview of AVIRIS-NG airborne hyperspectral science campaign over India. Current Science 116(7), 1082–1088, https://www.jstor.org/stable/27138000 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Duren, R. M. et al. California’s methane super-emitters. Nature 575, 180–184, https://doi.org/10.1038/s41586-019-1720-3 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hamlin, L. et al. Imaging spectrometer science measurements for terrestrial ecology: AVIRIS and new developments. In 2011 Aerospace conference (pp. 1-7). IEEE. https://doi.org/10.1109/AERO.2011.5747395 (2011, March).

  • Green, R. O. et al. Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3). In 2022 IEEE Aerospace Conference (AERO) (pp. 1-10). IEEE. https://www.doi.og/10.1109/AERO53065.2022.9843565 (2022, March).

  • Chapman, J. W. et al. Spectral and radiometric calibration of the next generation airborne visible infrared spectrometer (AVIRIS-NG). Remote Sensing 11(18), 2129, https://doi.org/10.3390/rs11182129 (2019).

    Article 

    Google Scholar
     

  • Eckert, R. et al. AVIRIS-3: Next-Generation Imaging Spectroscopy Calibration and First Results. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 289-291). IEEE. https://doi.org/10.1109/IGARSS53475.2024.10641513 (2024, July).

  • Bohn, N., Brodrick, P., Montgomery, J. & Thompson, D. Advances in Imaging Spectrometer Atmospheric Correction with the Open-Source ISOFIT Codebase. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 1253–1256). IEEE. https://doi.org/10.1109/IGARSS52108.2023.10282637 (2023, July).

  • Eckert, R., Mauceri, S., Thompson, D. R., Fahlen, J. E. & Brodrick, P. G. Spatially constrained atmosphere and surface retrieval for imaging spectroscopy. Remote Sensing of Environment 300, 113902, https://doi.org/10.1016/j.rse.2023.113902 (2024b).

    Article 

    Google Scholar
     

  • Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S. & Martonchik, J. V. Reflectance quantities in optical remote sensing—Definitions and case studies. Remote sensing of environment 103(1), 27–42, https://doi.org/10.1016/j.rse.2006.03.002 (2006).

    Article 
    ADS 

    Google Scholar
     

  • Thompson, D. R. et al. Optimal estimation for imaging spectrometer atmospheric correction. Remote sensing of environment 216, 355–373, https://doi.org/10.1016/j.rse.2018.07.003 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Brodrick, P. G. et al. Generalized radiative transfer emulation for imaging spectroscopy reflectance retrievals. Remote Sensing of Environment 261, 112476, https://doi.org/10.1016/j.rse.2021.112476 (2021).

    Article 

    Google Scholar
     

  • Berk, A. & Hawes, F. Validation of MODTRAN® 6 and its line-by-line algorithm. Journal of Quantitative Spectroscopy and Radiative Transfer 203, 542–556, https://doi.org/10.1016/j.jqsrt.2017.03.004 (2017).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Thompson, D. R. et al. Spectroscopic imaging of sub-kilometer spatial structure in lower-tropospheric water vapor. Atmos. Meas. Tech. 14, 2827–2840, https://doi.org/10.5194/amt-14-2827-2021 (2021).

    Article 

    Google Scholar
     

  • Schaepman, M. E. et al. Earth system science related imaging spectroscopy – an assessment. Remote Sens. Environ. 113, S123–S137, https://doi.org/10.1016/j.rse.2009.03.001 (2009).

    Article 

    Google Scholar
     

  • Miller, C. E. et al. ABoVE: AVIRIS-NG Imaging Spectroscopy for Alaska, Canada, and Iceland, 2017-2022, V3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2362 (2024).

  • Miller, C. E. et al. ABoVE: Hyperspectral Imagery AVIRIS-NG, Alaskan and Canadian Arctic, 2017-2019 V2. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2009 (2022).

  • Eckert, R. et al. AVIRIS-3 L1B Calibrated Radiance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2356 (2024).

  • Hestir, E. L., Wilson, A., Cardoso, A., Slingsby, J. & Forbes, C. The NASA Biodiversity Survey of the Cape (BioSCape). In Hyperspectral/Multispectral Imaging and Sounding of the Environment (pp. HM2C-3). Optica Publishing Group. https://doi.org/10.1364/HMISE.2023.HM2C.3 (2023, July).

  • Carmon, N. et al. Unified topographic and atmospheric correction for remote imaging spectroscopy. Frontiers in Remote Sensing 3, 916155, https://doi.org/10.3389/frsen.2022.916155 (2022).

    Article 

    Google Scholar
     

  • Carmon, N. et al. Shape from spectra. Remote Sensing of Environment 288, 113497, https://doi.org/10.1016/j.rse.2023.113497 (2023).

    Article 

    Google Scholar
     

  • Cavender-Bares, J., Gamon, J. A. & Townsend, P. A. eds. Remote sensing of plant biodiversity (pp. 581). Springer Nature. https://library.oapen.org/handle/20.500.12657/39986 (2020).

  • Harris, J. A. et al. Understanding the climate impacts on decadal vegetation change in northern Alaska. Arctic Science 8(3), 878–898, https://doi.org/10.1139/as-2020-0050 (2021).

    Article 

    Google Scholar
     



  • Source link

    More From Forest Beat

    Impact of transfer learning methods and dataset characteristics on generalization in...

    The data processing, methodology, and evaluation workflow for this study are outlined in Fig. 1.(Left) Distribution of the number of recordings per species in...
    Biodiversity
    18
    minutes

    Global intraspecific diversity of marine forests of brown macroalgae predicted by...

    Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433. https://doi.org/10.1038/nrg.2016.58 (2016).Maggs, C. A. et al. Evaluating signatures of...
    Biodiversity
    9
    minutes

    Insect trafficking poses a risk to wildlife and human health

    Four men were recently arrested and fined for attempting to smuggle more than 5,000 ants out of Kenya. Aiming...
    Biodiversity
    3
    minutes

    Reexamination of honey bee Africanization in Mexico and other regions of...

    Ruttner, F. Biogeography and Taxonomy of Honeybees (Springer, 1988).Book  ...
    Biodiversity
    9
    minutes
    spot_imgspot_img