Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Monitoring Earth’s climate variables with satellite laser altimetry

Nature Reviews Earth & Environment volume 5, pages 120–136 (2024)Cite this article

Subjects

Abstract

Satellite laser altimetry measures accurate elevations of the Earth’s surface and precise changes with time, monitoring key climate variables. These observations have transformed understanding of the Earth System, revealing changes and dynamics across spheres. In this Review, we highlight the Earth and climate science contributions from three NASA satellite laser altimeter missions: Ice, Cloud and land Elevation Satellite (ICESat; 2003–2009), ICESat-2 (2018 to present) and Global Ecosystem Dynamics Investigation (GEDI; 2018 to present). Over two decades of observations, satellite altimetry revealed cryosphere decline, including a loss of 320 Gt yr−1 in global land ice from Greenland and Antarctica, and a 30% decrease in volume of winter sea ice in the Arctic between 2003 and 2021. Observations have also been key to understanding ecosystems on land, providing data on the hydrosphere (showing that 57% of the Earth’s seasonal terrestrial water storage variability comes from human-managed reservoirs) and biosphere (showing that forest carbon stocks have globally increased owing to growth, despite a loss of the equivalent of ~8 Gt CO2 from land use). In the atmosphere, the data have enabled assessment of the global vertical cloud distribution, aerosol fraction, and dust and smoke transport. There is currently no planned satellite laser altimeter mission to continue from ICESat-2 and GEDI, jeopardizing critical data collection that supports decision-making and environmental management.

Key points

  • NASA’s three satellite laser altimeter missions (ICESat, GEDI and ICESat-2) have provided surface elevation data for monitoring essential climate variables across the Earth system, at high spatial and temporal resolution.

  • In the cryosphere, ICESat and ICESat-2 observations revealed a decline in sea ice thickness in the Arctic and a loss of land ice from glaciers, the Greenland Ice Sheet and the Antarctic Ice Sheet, and provided insights into the drivers of loss.

  • In the biosphere, ICESat, ICESat-2 and GEDI measured vegetation structure and ground heights across all ecosystems to better quantify changes to the biosphere in response to climatic and anthropogenic forces.

  • In the hydrosphere, ICESat, ICESat-2 and GEDI inventoried and monitored global water reservoirs and sea level changes including in the Arctic Ocean. ICESat-2 provides nearshore bathymetry for benthic mapping and coastal geomorphology.

  • In the atmosphere, ICESat and ICESat-2 have provided vertical structure of global clouds and aerosol layers critical to modelling radiative fluxes. They have also revealed substantial climate events associated with dust storms and fire disturbances.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Key climate variables monitored by satellite laser altimetry.
Fig. 2: Changes in sea ice and land ice in the polar regions from ICESat and ICESat-2.
Fig. 3: Global woody aboveground biomass density (AGBD) from GEDI and ICESat-2.
Fig. 4: Nearshore shallow water bathymetry mapping with ICESat-2.
Fig. 5: The vertical structure of the atmospheric layer from laser altimetry.

Similar content being viewed by others

References

  1. The Status of the Global Climate Observing System 2021: The GCOS Status Report (WMO, 2021).

  2. Dutton, A. et al. Sea-level rise due to polar ice-sheet mass loss during past warm periods. Science 349, aaa4019 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Goosse, H. et al. Quantifying climate feedbacks in polar regions. Nat. Commun. 9, 1919 (2018).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  4. Duncanson, L. et al. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sens. Environ. 270, 112845 (2022).

    Article  Google Scholar 

  5. Ryan, J. C., Smith, L. C., Cooley, S. W., Pitcher, L. H. & Pavelsky, T. M. Global characterization of inland water reservoirs using ICESat‐2 altimetry and climate reanalysis. Geophys. Res. Lett. https://doi.org/10.1029/2020GL088543 (2020).

  6. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) on-orbit performance, data discoveries and early science. Geophys. Res. Lett. https://doi.org/10.1002/(ISSN)1944-8007.ICESAT2 (2022).

  7. Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D. & DiMarzio, J. P. Overview of the ICESat mission. Geophys. Res. Lett. 32, L21S01 (2005).

    Article  Google Scholar 

  8. Markus, T. et al. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2): science requirements, concept, and implementation. Remote Sens. Environ. 190, 260–273 (2017).

    Article  ADS  Google Scholar 

  9. Dubayah, R. et al. The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).

    Article  Google Scholar 

  10. Shan, J. & Toth, C. K. Topographic Laser Ranging and Scanning: Principles and Processing (CRC Press, 2018).

  11. Zhou, H., Chen, Y., Hyyppä, J. & Li, S. An overview of the laser ranging method of space laser altimeter. Infrared Phys. Technol. 86, 147–158 (2017).

    Article  ADS  Google Scholar 

  12. Fu, L.-L. & Cazenave, A. Satellite Altimetry and Earth Sciences: A Handbook of Techniques and Applications (Elsevier, 2000).

  13. Harding, D. J. ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophys. Res. Lett. 32, L21S10 (2005).

    Article  Google Scholar 

  14. Degnan, J. J. Photon-counting multikilohertz microlaser altimeters for airborne and spaceborne topographic measurements. J. Geodyn. 34, 503–549 (2002).

    Article  Google Scholar 

  15. Robin, G. D. Q., Drewry, D. J. & Squire, V. A. Satellite observations of polar ice fields. Phil. Trans. R. Soc. Lond. A 309, 447–461 (1983).

    Article  ADS  Google Scholar 

  16. Fu, L.-L. et al. TOPEX/POSEIDON mission overview. J. Geophys. Res. 99, 24369 (1994).

    Article  ADS  Google Scholar 

  17. Brooks, R. L., Campbell, W. J., Ramseier, R. O., Stanley, H. R. & Zwally, H. J. Ice sheet topography by satellite altimetry. Nature 274, 539–543 (1978).

    Article  ADS  Google Scholar 

  18. Brooks, R. L., Williams, R. S. Jr, Ferrigno, J. G. & Krabill, W. B. in Antarctic Earth Science (eds Oliver, R. L. et al.) 441–445 (Cambridge Univ. Press, 1983).

  19. Benveniste, J. Strategy for ENVISAT radar altimetry cross-calibration and validation. In Proc. IGARSS 2000. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment 2730–2732 (IEEE, 2000).

  20. Wingham, D. J., Ridout, A. J., Scharroo, R., Arthern, R. J. & Shum, C. K. Antarctic elevation change from 1992 to 1996. Science 282, 456–458 (1998).

    Article  CAS  PubMed  ADS  Google Scholar 

  21. Laxon, S., Peacock, N. & Smith, D. High interannual variability of sea ice thickness in the Arctic region. Nature 425, 947–950 (2003).

    Article  CAS  PubMed  ADS  Google Scholar 

  22. Brenner, A. C., DiMarzio, J. P. & Zwally, H. J. Precision and accuracy of satellite radar and laser altimeter data over the continental ice sheets. IEEE Trans. Geosci. Remote Sens. 45, 321–331 (2007).

    Article  ADS  Google Scholar 

  23. Scott, R. F. et al. A comparison of the performance of the ice and ocean tracking modes of the ERS-1 radar altimeter over non-ocean surfaces. Geophys. Res. Lett. 21, 553–556 (1994).

    Article  ADS  Google Scholar 

  24. Davis, C. H. & Moore, R. K. A combined surface- and volume-scattering model for ice-sheet radar altimetry. J. Glaciol. 39, 675–686 (1993).

    Article  ADS  Google Scholar 

  25. Magruder, L., Neuenschwander, A. & Klotz, B. Digital terrain model elevation corrections using space-based imagery and ICESat-2 laser altimetry. Remote Sens. Environ. 264, 112621 (2021).

    Article  Google Scholar 

  26. Giles, K. A., Laxon, S. W. & Ridout, A. L. Circumpolar thinning of Arctic sea ice following the 2007 record ice extent minimum. Geophys. Res. Lett. 35, L22502 (2008).

    Article  ADS  Google Scholar 

  27. Drinkwater, M. R., Francis, R., Ratier, G. & Wingham, D. J. The European Space Agency’s Earth Explorer Mission CryoSat: measuring variability in the cryosphere. Ann. Glaciol. 39, 313–320 (2004).

    Article  ADS  Google Scholar 

  28. Zwally, H. J. et al. ICESat’s laser measurements of polar ice, atmosphere, ocean, and land. J. Geodyn. 34, 405–445 (2002).

    Article  Google Scholar 

  29. Spinhirne, J. D., Palm, S. P. & Hart, W. D. Antarctica cloud cover for October 2003 from GLAS satellite lidar profiling. Geophys. Res. Lett.https://doi.org/10.1029/2005GL023782 (2005).

    Article  Google Scholar 

  30. Abshire, J. B. et al. Geoscience laser altimeter system (GLAS) on the ICESat mission: on-orbit measurement performance. Geophys. Res. Lett. 32, L21S02 (2005).

    Article  Google Scholar 

  31. Schenk, T. & Csatho, B. A new methodology for detecting ice sheet surface elevation changes from laser altimetry data. IEEE Trans. Geosci. Remote Sensing 50, 3302–3316 (2012).

    Article  ADS  Google Scholar 

  32. Fricker, H. A. Assessment of ICESat performance at the salar de Uyuni, Bolivia. Geophys. Res. Lett. 32, L21S06 (2005).

    Article  Google Scholar 

  33. Magruder, L., Silverberg, E., Webb, C. & Schutz, B. In situ timing and pointing verification of the ICESat altimeter using a ground-based system. Geophys. Res. Lett. 32, L21S04 (2005).

    Article  Google Scholar 

  34. Gardner, A. S. et al. A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science 340, 852–857 (2013).

    Article  CAS  PubMed  ADS  Google Scholar 

  35. Pritchard, H. D., Arthern, R. J., Vaughan, D. G. & Edwards, L. A. Extensive dynamic thinning on the margins of the Greenland and Antarctic ice sheets. Nature 461, 971–975 (2009).

    Article  CAS  PubMed  ADS  Google Scholar 

  36. Pritchard, H. D. et al. Antarctic ice-sheet loss driven by basal melting of ice shelves. Nature 484, 502–505 (2012).

    Article  CAS  PubMed  ADS  Google Scholar 

  37. Fricker, H. A., Scambos, T., Bindschadler, R. & Padman, L. An active subglacial water system in West Antarctica mapped from space. Science 315, 1544–1548 (2007).

    Article  CAS  PubMed  ADS  Google Scholar 

  38. Kwok, R. et al. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008. J. Geophys. Res. 114, C07005 (2009).

    ADS  Google Scholar 

  39. Kurtz, N. T. & Markus, T. Satellite observations of Antarctic sea ice thickness and volume. J. Geophys. Res. https://doi.org/10.1029/2012JC008141 (2012).

  40. Lefsky, M. A. et al. Estimates of forest canopy height and aboveground biomass using ICESat. Geophys. Res. Lett. 32, GL023971 (2005).

    Article  Google Scholar 

  41. Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  42. Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  43. Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).

    Article  CAS  ADS  Google Scholar 

  44. Wang, L. & Dessler, A. E. Instantaneous cloud overlap statistics in the tropical area revealed by ICESat/GLAS data. Geophys. Res. Lett. 33, L15804 (2006).

    Article  ADS  Google Scholar 

  45. O’Loughlin, F. E., Neal, J., Yamazaki, D. & Bates, P. D. ICESat-derived inland water surface spot heights. Water Resour. Res. 52, 3276–3284 (2016).

    Article  ADS  Google Scholar 

  46. National Research Council. Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. https://nap.nationalacademies.org/read/11820/chapter/1 (National Academies Press, 2007).

  47. Martino, A. J., Neumann, T. A., Kurtz, N. T. & McLennan, D. ICESat-2 mission overview and early performance. In Sensors, Systems, and Next-Generation Satellites XXII (eds Neeck, S. P. et al.) 11 (SPIE, 2019).

  48. Abdalati, W. et al. The ICESat-2 laser altimetry mission. Proc. IEEE 98, 735–751 (2010).

    Article  Google Scholar 

  49. Smith, B. et al. Pervasive ice sheet mass loss reflects competing ocean and atmosphere processes. Science 368, 1239–1242 (2020).

    Article  CAS  PubMed  ADS  Google Scholar 

  50. Dubayah, R. et al. GEDI launches a new era of biomass inference from space. Environ. Res. Lett. 17, 095001 (2022).

    Article  ADS  Google Scholar 

  51. Brunt, K. M., Neumann, T. A. & Smith, B. E. Assessment of ICESat‐2 ice sheet surface heights, based on comparisons over the interior of the Antarctic Ice Sheet. Geophys. Res. Lett. 46, 13072–13078 (2019).

    Article  ADS  Google Scholar 

  52. Kwok, R. et al. Surface height and sea ice freeboard of the arctic ocean from ICESat‐2: characteristics and early results. J. Geophys. Res. Oceans 124, 6942–6959 (2019).

    Article  ADS  Google Scholar 

  53. Farrell, S. L., Duncan, K., Buckley, E. M., Richter‐Menge, J. & Li, R. Mapping sea ice surface topography in high fidelity with ICESat‐2. Geophys. Res. Lett. https://doi.org/10.1029/2020GL090708 (2020).

  54. Neuenschwander, A. L. & Magruder, L. A. Canopy and terrain height retrievals with ICESat-2: a first look. Remote Sensing 11, 1721 (2019).

    Article  ADS  Google Scholar 

  55. Thomas, N. et al. Space‐borne cloud‐native satellite‐derived bathymetry (SDB) models using ICESat‐2 and Sentinel‐2. Geophys. Res. Lett. https://doi.org/10.1002/essoar.10504452.1 (2021).

  56. Fricker, H. A. et al. ICESat‐2 meltwater depth estimates: application to surface melt on amery ice shelf, East Antarctica. Geophys. Res. Lett. https://doi.org/10.1029/2020GL090550 (2021).

  57. Zhu, X. et al. Consistency analysis of forest height retrievals between GEDI and ICESat-2. Remote Sens. Environ. 281, 113244 (2022).

    Article  Google Scholar 

  58. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H. O. et al.) (Cambridge Univ. Press, 2022).

  59. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2023).

  60. Parkinson, C. L. Global sea ice coverage from satellite data: annual cycle and 35-yr trends. J. Clim. 27, 9377–9382 (2014).

    Article  ADS  Google Scholar 

  61. Worby, A. P. et al. Thickness distribution of Antarctic sea ice. J. Geophys. Res. 113, C05S92 (2008).

    Google Scholar 

  62. Rothrock, D. A., Yu, Y. & Maykut, G. A. Thinning of the Arctic sea-ice cover. Geophys. Res. Lett. 26, 3469–3472 (1999).

    Article  ADS  Google Scholar 

  63. Haas, C. et al. Reduced ice thickness in Arctic transpolar drift favors rapid ice retreat. Geophys. Res. Lett. 35, L17501 (2008).

    Article  ADS  Google Scholar 

  64. Kacimi, S. & Kwok, R. Arctic snow depth, ice thickness, and volume from ICESat‐2 and CryoSat‐2: 2018–2021. Geophys. Res. Lett. https://doi.org/10.1029/2021GL097448 (2022).

  65. Ricker, R. et al. Evidence for an increasing role of ocean heat in Arctic winter sea ice growth. J. Clim. 34, 1–42 (2021).

    Article  Google Scholar 

  66. Eicken, H., Lovecraft, A. L. & Druckenmiller, M. L. Sea-ice system services: a framework to help identify and meet information needs relevant for observing networks. Arctic 62, 119–136 (2009).

    Article  Google Scholar 

  67. Kwok, R., Zwally, H. J. & Yi, D. ICESat observations of Arctic sea ice: a first look. Geophys. Res. Lett. 31, 2004GL020309 (2004).

    Article  Google Scholar 

  68. Thoman, R. L. et al. The Arctic. Bull. Am. Meteorol. Soc. 103, S257–S306 (2022).

    Article  ADS  Google Scholar 

  69. Meier, W. N. et al. Arctic sea ice in transformation: a review of recent observed changes and impacts on biology and human activity. Rev. Geophys. 52, 185–217 (2014).

    Article  ADS  Google Scholar 

  70. Farrell, S. L., Laxon, S. W., McAdoo, D. C., Yi, D. & Zwally, H. J. Five years of Arctic sea ice freeboard measurements from the Ice, Cloud and land Elevation Satellite. J. Geophys. Res. 114, C04008 (2009).

    ADS  Google Scholar 

  71. Comiso, J. C., Parkinson, C. L., Gersten, R. & Stock, L. Accelerated decline in the Arctic sea ice cover. Geophys. Res. Lett. 35, L01703 (2008).

    Article  ADS  Google Scholar 

  72. Kurtz, N. T., Markus, T., Farrell, S. L., Worthen, D. L. & Boisvert, L. N. Observations of recent Arctic sea ice volume loss and its impact on ocean–atmosphere energy exchange and ice production. J. Geophys. Res. 116, C04015 (2011).

    ADS  Google Scholar 

  73. Laxon, S. W. et al. CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophys. Res. Lett. 40, 732–737 (2013).

    Article  ADS  Google Scholar 

  74. Fredensborg Hansen, R. M., Rinne, E., Farrell, S. L. & Skourup, H. Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2. Cryosphere 15, 2511–2529 (2021).

    Article  ADS  Google Scholar 

  75. Buckley, E. M. et al. Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2. The Cryosphere 17, 3695–3719 (2023).

    Article  ADS  Google Scholar 

  76. Duncan, K. & Farrell, S. L. Determining variability in Arctic Sea ice pressure ridge topography with ICESat‐2. Geophys. Res. Lett. https://doi.org/10.1029/2022GL100272 (2022).

  77. Tilling, R., Kurtz, N. T., Bagnardi, M., Petty, A. A. & Kwok, R. Detection of melt ponds on Arctic summer sea ice from ICESat‐2. Geophys. Res. Lett. https://doi.org/10.1029/2020GL090644 (2020).

  78. Eayrs, C., Li, X., Raphael, M. N. & Holland, D. M. Rapid decline in Antarctic sea ice in recent years hints at future change. Nat. Geosci. 14, 460–464 (2021).

    Article  CAS  ADS  Google Scholar 

  79. Zwally, H. J., Yi, D., Kwok, R. & Zhao, Y. ICESat measurements of sea ice freeboard and estimates of sea ice thickness in the Weddell Sea. J. Geophys. Res. 113, C02S15 (2008).

    ADS  Google Scholar 

  80. Fons, S. W. & Kurtz, N. T. Retrieval of snow freeboard of Antarctic sea ice using waveform fitting of CryoSat-2 returns. Cryosphere 13, 861–878 (2019).

    Article  ADS  Google Scholar 

  81. Yi, D., Zwally, H. J. & Robbins, J. W. ICESat observations of seasonal and interannual variations of sea-ice freeboard and estimated thickness in the Weddell Sea, Antarctica (2003–2009). Ann. Glaciol. 52, 43–51 (2011).

    Article  ADS  Google Scholar 

  82. Kacimi, S. & Kwok, R. The Antarctic sea ice cover from ICESat-2 and CryoSat-2: freeboard, snow depth, and ice thickness. Cryosphere 14, 4453–4474 (2020).

    Article  ADS  Google Scholar 

  83. Horvat, C., Blanchard‐Wrigglesworth, E. & Petty, A. Observing waves in sea ice with ICESat‐2. Geophys. Res. Lett. https://doi.org/10.1029/2020GL087629 (2020).

  84. Bisson, K. M. & Cael, B. B. How are under ice phytoplankton related to sea ice in the Southern Ocean? Geophys. Res. Lett. https://doi.org/10.1029/2021GL095051 (2021).

  85. Otosaka, I. N. et al. Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020. Earth Syst. Sci. Data 15, 1597–1616 (2023).

    Article  ADS  Google Scholar 

  86. Hugonnet, R. et al. Accelerated global glacier mass loss in the early twenty-first century. Nature 592, 726–731 (2021).

    Article  CAS  PubMed  ADS  Google Scholar 

  87. Shepherd, A. et al. A reconciled estimate of ice-sheet mass balance. Science 338, 1183–1189 (2012).

    Article  CAS  PubMed  ADS  Google Scholar 

  88. The IMBIE team. Mass balance of the Antarctic Ice Sheet from 1992 to 2017. Nature 558, 219–222 (2018).

    Article  ADS  Google Scholar 

  89. Shepherd, A., Fricker, H. A. & Farrell, S. L. Trends and connections across the Antarctic cryosphere. Nature 558, 223–232 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  90. Csatho, B. M. et al. Laser altimetry reveals complex pattern of Greenland Ice Sheet dynamics. Proc. Natl Acad. Sci. USA 111, 18478–18483 (2014).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  91. Porter, D. F. et al. Bathymetric control of tidewater glacier mass loss in northwest Greenland. Earth Planet. Sci. Lett. 401, 40–46 (2014).

    Article  CAS  ADS  Google Scholar 

  92. Korsgaard, N. J. et al. Digital elevation model and orthophotographs of Greenland based on aerial photographs from 1978–1987. Sci. Data 3, 160032 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Spada, G. et al. Greenland uplift and regional sea level changes from ICESat observations and GIA modelling: Greenland uplift and sea level change. Geophys. J. Int. 189, 1457–1474 (2012).

    Article  ADS  Google Scholar 

  94. Larour, E. et al. Inferred basal friction and surface mass balance of the Northeast Greenland Ice Stream using data assimilation of ICESat (Ice Cloud and land Elevation Satellite) surface altimetry and ISSM (Ice Sheet System Model). Cryosphere 8, 2335–2351 (2014).

    Article  ADS  Google Scholar 

  95. Moholdt, G., Padman, L. & Fricker, H. A. Basal mass budget of Ross and Filchner‐Ronne ice shelves, Antarctica, derived from Lagrangian analysis of ICESat altimetry. J. Geophys. Res.: Earth Surface 119, 2361–2380 (2014).

    Article  ADS  Google Scholar 

  96. Horgan, H. J., Walker, R. T., Anandakrishnan, S. & Alley, R. B. Surface elevation changes at the front of the Ross Ice Shelf: implications for basal melting. J. Geophys. Res. 116, C02005 (2011).

    ADS  Google Scholar 

  97. Fricker, H. A. & Padman, L. Ice shelf grounding zone structure from ICESat laser altimetry. Geophys. Res. Lett. 33, L15502 (2006).

    Article  ADS  Google Scholar 

  98. Brunt, K. M., Fricker, H. A. & Padman, L. Analysis of ice plains of the Filchner–Ronne Ice Shelf, Antarctica, using ICESat laser altimetry. J. Glaciol. 57, 965–975 (2011).

    Article  ADS  Google Scholar 

  99. Marsh, O. J. et al. High basal melting forming a channel at the grounding line of Ross Ice Shelf, Antarctica. Geophys. Res. Lett. 43, 250–255 (2016).

    Article  ADS  Google Scholar 

  100. Smith, B. E., Fricker, H. A., Joughin, I. R. & Tulaczyk, S. An inventory of active subglacial lakes in Antarctica detected by ICESat (2003–2008). J. Glaciol. 55, 573–595 (2009).

    Article  ADS  Google Scholar 

  101. Gudmundsson, G. H., Paolo, F. S., Adusumilli, S. & Fricker, H. A. Instantaneous Antarctic ice sheet mass loss driven by thinning ice shelves. Geophys. Res. Lett. 46, 13903–13909 (2019).

    Article  ADS  Google Scholar 

  102. Taubenberger, C. J., Felikson, D. & Neumann, T. Brief communication: preliminary ICESat-2 (Ice, Cloud and land Elevation Satellite-2) measurements of outlet glaciers reveal heterogeneous patterns of seasonal dynamic thickness change. Cryosphere 16, 1341–1348 (2022).

    Article  ADS  Google Scholar 

  103. Clem, K. R. et al. Antarctica and the Southern Ocean. Bull. Am. Meteorol. Soc. 103, S307–S340 (2022).

    Article  Google Scholar 

  104. Smith, B. E. et al. Evaluating Greenland surface-mass-balance and firn-densification data using ICESat-2 altimetry. Cryosphere 17, 789–808 (2023).

    Article  ADS  Google Scholar 

  105. Adusumilli, S., Fish, M., Fricker, H. A. & Medley, B. Atmospheric river precipitation contributed to rapid increases in surface height of the West Antarctic Ice Sheet in 2019. Geophys. Res. Lett. https://doi.org/10.1029/2020GL091076 (2021).

  106. Herzfeld, U. C., Trantow, T., Lawson, M., Hans, J. & Medley, G. Surface heights and crevasse morphologies of surging and fast-moving glaciers from ICESat-2 laser altimeter data — application of the density-dimension algorithm (DDA-ice) and evaluation using airborne altimeter and Planet SkySat data. Sci. Remote Sens. 3, 100013 (2021).

    Article  Google Scholar 

  107. Becker, M. K. et al. Buoyancy‐driven flexure at the front of Ross Ice Shelf, Antarctica, observed with ICESat‐2 laser altimetry. Geophys. Res. Lett. https://doi.org/10.1029/2020GL091207 (2021).

  108. Walker, C. C., Becker, M. K. & Fricker, H. A. A high resolution, three‐dimensional view of the D‐28 calving event from Amery Ice Shelf with ICESat‐2 and satellite imagery. Geophys. Res. Lett. https://doi.org/10.1029/2020GL091200 (2021).

  109. Li, T., Dawson, G., Chuter, S. & Bamber, J. A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry. Earth Syst. Sci. Data 14, 535–557 (2022).

    Article  ADS  Google Scholar 

  110. Freer, B. I. D., Marsh, O. J., Hogg, A. E., Fricker, H. A. & Padman, L. Modes of Antarctic tidal grounding line migration revealed by ICESat-2 laser altimetry. The Cryosphere 17, 4079–4101 (2023).

    Article  ADS  Google Scholar 

  111. Chartrand, A. M. & Howat, I. M. Basal channel evolution on the Getz Ice Shelf, West Antarctica. J. Geophys. Res.: Earth Surface https://doi.org/10.1029/2019JF005293 (2020).

  112. Narkevic, A., Csatho, B. & Schenk, T. Rapid basal channel growth beneath Greenland’s longest floating ice shelf. Geophys. Res. Lett. 50, e2023GL103226 (2023).

    Article  ADS  Google Scholar 

  113. Warner, R. C. et al. Rapid formation of an ice doline on Amery Ice Shelf, East Antarctica. Geophys. Res. Lett. 48, e2020GL091095 (2021).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  114. Wild, C. T. et al. Weakening of the pinning point buttressing Thwaites Glacier, West Antarctica. Cryosphere 16, 397–417 (2022).

    Article  ADS  Google Scholar 

  115. Siegfried, M. R. & Fricker, H. A. Illuminating active subglacial lake processes with ICESat‐2 laser altimetry. Geophys. Res. Lett. https://doi.org/10.1029/2020GL091089 (2021).

  116. Datta, R. T. & Wouters, B. Supraglacial lake bathymetry automatically derived from ICESat-2 constraining lake depth estimates from multi-source satellite imagery. Cryosphere 15, 5115–5132 (2021).

    Article  ADS  Google Scholar 

  117. Jin, S., Zhang, T. Y. & Zou, F. Glacial density and GIA in Alaska estimated from ICESat, GPS and GRACE measurements: glacial density and GIA in Alaska. J. Geophys. Res. Earth Surf. 122, 76–90 (2017).

    Article  ADS  Google Scholar 

  118. Kääb, A., Berthier, E., Nuth, C., Gardelle, J. & Arnaud, Y. Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature 488, 495–498 (2012).

    Article  PubMed  ADS  Google Scholar 

  119. Bolch, T. et al. Mass loss of Greenland’s glaciers and ice caps 2003–2008 revealed from ICESat laser altimetry data. Geophys. Res. Lett. 40, 875–881 (2013).

    Article  ADS  Google Scholar 

  120. Moholdt, G., Wouters, B. & Gardner, A. S. Recent mass changes of glaciers in the Russian High Arctic. Geophys. Res. Lett. https://doi.org/10.1029/2012GL051466 (2012).

  121. Nuth, C., Moholdt, G., Kohler, J., Hagen, J. O. & Kääb, A. Svalbard glacier elevation changes and contribution to sea level rise. J. Geophys. Res. 115, 2008JF001223 (2010).

    Article  Google Scholar 

  122. Khan, S. A. et al. Accelerating ice loss from peripheral glaciers in North Greenland. Geophys. Res. Lett. https://doi.org/10.1029/2022GL098915 (2022).

  123. Wang, J., Yang, Y., Wang, C. & Li, L. Accelerated glacier mass loss over Svalbard derived from ICESat-2 in 2019–2021. Atmosphere 13, 1255 (2022).

    Article  ADS  Google Scholar 

  124. Sochor, L., Seehaus, T. & Braun, M. H. Increased ice thinning over Svalbard measured by ICESat/ICESat-2 laser altimetry. Remote Sens. 13, 2089 (2021).

    Article  ADS  Google Scholar 

  125. Wang, Q., Yi, S. & Sun, W. Continuous estimates of glacier mass balance in high mountain Asia based on ICESat‐1,2 and GRACE/GRACE follow‐on data. Geophys. Res. Lett. https://doi.org/10.1029/2020GL090954 (2021).

  126. Wang, Q. & Sun, W. Seasonal cycles of high mountain Asia glacier surface elevation detected by ICESat‐2. J. Geophys. Res. Atmos. https://doi.org/10.1029/2022JD037501 (2022).

  127. Chen, W. et al. Towards ice-thickness inversion: an evaluation of global digital elevation models (DEMs) in the glacierized Tibetan Plateau. Cryosphere 16, 197–218 (2022).

    Article  ADS  Google Scholar 

  128. Ye, Q. et al. Monitoring glacier thinning rate in Rongbuk catchment on the northern slope of Mt. Qomolangma from 1974 to 2021. Ecol. Indic. 144, 109418 (2022).

    Article  Google Scholar 

  129. Friedlingstein, P. et al. Global carbon budget 2022. Earth System Science Data 14, 4811–4900 (2022).

    Article  ADS  Google Scholar 

  130. Herold, M. et al. The role and need for space-based forest biomass-related measurements in environmental management and policy. Surv. Geophys. 40, 757–778 (2019).

    Article  ADS  Google Scholar 

  131. Yamazaki, D. et al. A high-accuracy map of global terrain elevations: accurate global terrain elevation map. Geophys. Res. Lett. 44, 5844–5853 (2017).

    Article  ADS  Google Scholar 

  132. Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. 116, G04021 (2011).

    ADS  Google Scholar 

  133. Duncanson, L. I., Niemann, K. O. & Wulder, M. A. Estimating forest canopy height and terrain relief from GLAS waveform metrics. Remote Sens. Environ. 114, 138–154 (2010).

    Article  ADS  Google Scholar 

  134. Hilbert, C. & Schmullius, C. Influence of surface topography on ICESat/GLAS forest height estimation and waveform shape. Remote Sens. 4, 2210–2235 (2012).

    Article  ADS  Google Scholar 

  135. Lang, N. et al. Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sens. Environ. 268, 112760 (2022).

    Article  Google Scholar 

  136. Healey, S. P., Yang, Z., Gorelick, N. & Ilyushchenko, S. Highly local model calibration with a new GEDI LiDAR asset on Google Earth Engine reduces Landsat forest height signal saturation. Remote Sens. 12, 2840 (2020).

    Article  ADS  Google Scholar 

  137. Schlund, M., Wenzel, A., Camarretta, N., Stiegler, C. & Erasmi, S. Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13933 (2022).

  138. Choi, C. et al. Large-scale forest height mapping by combining TanDEM-X and GEDI data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 2374–2385 (2023).

    Article  ADS  Google Scholar 

  139. Liu, X. et al. Neural network guided interpolation for mapping canopy height of China’s forests by integrating GEDI and ICESat-2 data. Remote Sens. Environ. 269, 112844 (2022).

    Article  Google Scholar 

  140. Li, Y. et al. Retrieving low and sparse vegetation heights in desert ecosystems using ICESat-2 ATL03 photon-counting LiDAR data. IEEE Geosci. Remote Sens. Lett. https://doi.org/10.1109/LGRS.2022.3194877 (2022).

    Article  PubMed  Google Scholar 

  141. Milenković, M. et al. Dataset for assessing Amazon rainforest regrowth with GEDI and ICESat-2 data. https://doi.org/10.5281/ZENODO.6480488 (2022).

  142. Neuenschwander, A., Guenther, E., White, J. C., Duncanson, L. & Montesano, P. Validation of ICESat-2 terrain and canopy heights in boreal forests. Remote Sens. Environ. 251, 112110 (2020).

    Article  Google Scholar 

  143. Feng, T. et al. A systematic evaluation of multi-resolution ICESat-2 ATL08 terrain and canopy heights in boreal forests. Remote Sens. Environ. 291, 113570 (2023).

    Article  Google Scholar 

  144. Ni, W., Zhang, Z. & Sun, G. Assessment of slope-adaptive metrics of GEDI waveforms for estimations of forest aboveground biomass over mountainous areas. J. Remote Sens. 2021, 2021/9805364 (2021).

    Article  Google Scholar 

  145. Rosenqvist, J., Rosenqvist, A. & McDonald, K. C. An analysis of ICESat-2, PALSAR-2 and Sentinel-1 data for the assessment of inundation characteristics in the Amazon Basin. In IGARSS 2020 — 2020 IEEE International Geoscience and Remote Sensing Symposium, 5081–5084 (IEEE, 2020).

  146. Thomas, N. et al. Seasonal flooding provides limitations and opportunities for ecosystem carbon accounting from space. Environ. Res. Lett. 18, 081002 (2023).

    Article  ADS  Google Scholar 

  147. Cobb, A. R. et al. Improved terrain estimation from spaceborne lidar in tropical peatlands using spatial filtering. Sci. Remote Sens. 7, 100074 (2023).

    Article  Google Scholar 

  148. Urbazaev, M. et al. Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types. Sci. Remote Sens. 6, 100067 (2022).

    Article  Google Scholar 

  149. Tang, H., Dubayah, R., Brolly, M., Ganguly, S. & Zhang, G. Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS/ICESat). Remote Sens. Environ. 154, 8–18 (2014).

    Article  ADS  Google Scholar 

  150. Chen, J. M., Rich, P. M., Gower, S. T., Norman, J. M. & Plummer, S. Leaf area index of boreal forests: theory, techniques, and measurements. J. Geophys. Res. Atmos. 102, 29429–29443 (1997).

    Article  ADS  Google Scholar 

  151. Drake, J. B. et al. Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Glob. Ecol. Biogeogr. 12, 147–159 (2003).

    Article  Google Scholar 

  152. Fatoyinbo, T. E. & Simard, M. Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM. Int. J. Remote Sens. 34, 668–681 (2013).

    Article  Google Scholar 

  153. Dolan, K., Masek, J. G., Huang, C. & Sun, G. Regional forest growth rates measured by combining ICESat GLAS and Landsat data. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2008JG000893 (2009).

  154. García, M. et al. Characterization of canopy fuels using ICESat/GLAS data. Remote Sens. Environ. 123, 81–89 (2012).

    Article  ADS  Google Scholar 

  155. Shendryk, Y. Fusing GEDI with Earth observation data for large area aboveground biomass mapping. Int. J. Appl. Earth Obs. Geoinf. 115, 103108 (2022).

    Google Scholar 

  156. Bullock, E. L. et al. Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory. Environ. Res. Lett. 18, 085001 (2023).

    Article  ADS  Google Scholar 

  157. Duncanson, L. et al. The effectiveness of global protected areas for climate change mitigation. Nat. Commun. 14, 2908 (2023).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  158. Hoffrén, R. et al. Assessing GEDI-NASA system for forest fuels classification using machine learning techniques. Int. J. Appl. Earth Obs. Geoinf. 116, 103175 (2023).

    Google Scholar 

  159. Liu, M. & Popescu, S. Estimation of biomass burning emissions by integrating ICESat-2, Landsat 8, and Sentinel-1 data. Remote Sens. Environ. 280, 113172 (2022).

    Article  Google Scholar 

  160. Francini, S., D’Amico, G., Vangi, E., Borghi, C. & Chirici, G. Integrating GEDI and landsat: spaceborne lidar and four decades of optical imagery for the analysis of forest disturbances and biomass changes in Italy. Sensors 22, 2015 (2022).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  161. Liang, M., Duncanson, L., Silva, J. A. & Sedano, F. Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat. Remote Sens. Environ. 284, 113367 (2023).

    Article  Google Scholar 

  162. Hakkenberg, C. R., Tang, H., Burns, P. & Goetz, S. J. Canopy structure from space using GEDI lidar. Front. Ecol. Environ. 21, 55–56 (2023).

    Article  Google Scholar 

  163. Crockett, E. T. H. et al. Structural and species diversity explain aboveground carbon storage in forests across the United States: evidence from GEDI and forest inventory data. Remote Sens. Environ. 295, 113703 (2023).

    Article  Google Scholar 

  164. Torresani, M. et al. LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems. Ecol. Inform. 76, 102082 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  165. Marselis, S. M., Keil, P., Chase, J. M. & Dubayah, R. The use of GEDI canopy structure for explaining variation in tree species richness in natural forests. Environ. Res. Lett. 17, 045003 (2022).

    Article  ADS  Google Scholar 

  166. Doughty, C. E. et al. Tropical forests are mainly unstratified especially in Amazonia and regions with lower fertility or higher temperatures. Environ. Res. Ecol. 2, 035002 (2023).

    Article  Google Scholar 

  167. Smith, A. B. et al. Spaceborne LiDAR and animal–environment relationships: an assessment for forest carnivores and their prey in the Greater Yellowstone ecosystem. For. Ecol. Manag. 520, 120343 (2022).

    Article  Google Scholar 

  168. Burns, P. et al. Incorporating canopy structure from simulated GEDI lidar into bird species distribution models. Environ. Res. Lett. 15, 095002 (2020).

    Article  ADS  Google Scholar 

  169. Ma, L. et al. Global evaluation of the Ecosystem Demography model (ED v3.0). Geosci. Model Dev. 15, 1971–1994 (2022).

    Article  ADS  Google Scholar 

  170. Duncanson, L. et al. Forest aboveground biomass estimation with GEDI and ICESat-2 in boreal forests. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 670–672 (IEEE, 2021).

  171. Xu, N. et al. Deriving highly accurate shallow water bathymetry from Sentinel-2 and ICESat-2 datasets by a multitemporal stacking method. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 6677–6685 (2021).

    Article  ADS  Google Scholar 

  172. Buzzanga, B., Heijkoop, E., Hamlington, B. D., Nerem, R. S. & Gardner, A. An assessment of regional ICESat‐2 sea‐level trends. Geophys. Res. Lett. https://doi.org/10.1029/2020GL092327 (2021).

  173. Cooley, S. W., Ryan, J. C. & Smith, L. C. Human alteration of global surface water storage variability. Nature 591, 78–81 (2021).

    Article  CAS  PubMed  ADS  Google Scholar 

  174. Scherer, D., Schwatke, C., Dettmering, D. & Seitz, F. ICESat‐2 based river surface slope and its impact on water level time series from satellite altimetry. Water Resour. Res. 58, e2022WR032842 (2022).

    Article  ADS  Google Scholar 

  175. Parrish, C. E. et al. Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS’s bathymetric mapping performance. Remote Sens. 11, 1634 (2019).

    Article  ADS  Google Scholar 

  176. Teutschbein, C. & Seibert, J. Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J.Hydrol. 456–457, 12–29 (2012).

    Article  Google Scholar 

  177. Alsdorf, D. E., Rodríguez, E. & Lettenmaier, D. P. Measuring surface water from space. Rev. Geophys. 45, RG2002 (2007).

    Article  ADS  Google Scholar 

  178. Luo, S. et al. Satellite laser altimetry reveals a net water mass gain in global lakes with spatial heterogeneity in the early 21st century. Geophys. Res. Lett. 49, e2021GL096676 (2022).

    Article  ADS  Google Scholar 

  179. Birkett, C. M. & Beckley, B. Investigating the performance of the Jason-2/OSTM radar altimeter over lakes and reservoirs. Mar. Geod. 33, 204–238 (2010).

    Article  ADS  Google Scholar 

  180. Song, C., Huang, B., Ke, L. & Richards, K. S. Seasonal and abrupt changes in the water level of closed lakes on the Tibetan Plateau and implications for climate impacts. J. Hydrol. 514, 131–144 (2014).

    Article  ADS  Google Scholar 

  181. Song, L. et al. Integrating ICESat-2 altimetry and machine learning to estimate the seasonal water level and storage variations of national-scale lakes in China. Remote Sens. Environ. 294, 113657 (2023).

    Article  Google Scholar 

  182. Liu, A., Cheng, X. & Chen, Z. Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals. Remote Sens. Environ. 264, 112571 (2021).

    Article  Google Scholar 

  183. Zhang, Z., Bo, Y., Jin, S., Chen, G. & Dong, Z. Dynamic water level changes in Qinghai Lake from integrating refined ICESat-2 and GEDI altimetry data (2018–2021). J. Hydrol. 617, 129007 (2023).

    Article  Google Scholar 

  184. Urban, T. J. & Schutz, B. E. ICESat sea level comparisons. Geophys. Res. Lett. 32, L23S10 (2005).

    Article  Google Scholar 

  185. Farrell, S. L. et al. Mean dynamic topography of the Arctic Ocean. Geophys. Res. Lett. 39, 1601 (2012).

    Article  ADS  Google Scholar 

  186. Forsberg, R. & Skourup, H. Arctic Ocean gravity, geoid and sea-ice freeboard heights from ICESat and GRACE. Geophys. Res. Lett. 32, L21502 (2005).

    Article  ADS  Google Scholar 

  187. McAdoo, D. C. et al. Gravity of the Arctic Ocean from satellite data with validations using airborne gravimetry: oceanographic implications. J. Geophys. Res. Oceans 118, 917–930 (2013).

    Article  ADS  Google Scholar 

  188. Padman, L. & Fricker, H. A. Tides on the Ross Ice Shelf observed with ICESat. Geophys. Res. Lett. https://doi.org/10.1029/2005GL023214 (2005).

    Article  Google Scholar 

  189. Padman, L., Erofeeva, S. Y. & Fricker, H. A. Improving Antarctic tide models by assimilation of ICESat laser altimetry over ice shelves. Geophys. Res. Lett. 35, L22504 (2008).

    Article  ADS  Google Scholar 

  190. Crooks, S. The effect of sea-level rise on coastal geomorphology: sea-level rise and coastal geomorphology. Ibis 146, 18–20 (2004).

    Article  Google Scholar 

  191. Herrmann, J., Magruder, L. A., Markel, J. & Parrish, C. E. Assessing the ability to quantify bathymetric change over time using solely satellite-based measurements. Remote Sens. 14, 1232 (2022).

    Article  ADS  Google Scholar 

  192. Le Quilleuc, A., Collin, A., Jasinski, M. F. & Devillers, R. Very high-resolution satellite-derived bathymetry and habitat mapping using Pleiades-1 and ICESat-2. Remote Sens. 14, 133 (2021).

    Article  ADS  Google Scholar 

  193. Syrakov, D., Djolov, G. & Yordanov, D. Incorporation of planetary boundary layer dynamics in a numerical model of long-range air-pollutant transport. Bound. -Layer Meteorol. 26, 1–13 (1983).

    Article  ADS  Google Scholar 

  194. Kulmala, M. et al. Aerosols, clusters, greenhouse gases, trace gases and boundary-layer dynamics: on feedbacks and interactions. Bound.-Lay. Meteorol. 186, 475–503 (2023).

    Article  ADS  Google Scholar 

  195. Stephens, G. L. et al. An update on Earth’s energy balance in light of the latest global observations. Nat. Geosci. 5, 691–696 (2012).

    Article  CAS  ADS  Google Scholar 

  196. Eguchi, N., Yokota, T. & Inoue, G. Characteristics of cirrus clouds from ICESat/GLAS observations. Geophys. Res. Lett. https://doi.org/10.1029/2007GL029529 (2007).

  197. Palm, S. P. et al. ICESat‐2 atmospheric channel description, data processing and first results. Earth Space Sci. https://doi.org/10.1029/2020EA001470 (2021).

  198. Palm, S. P., Selmer, P., Yorks, J., Nicholls, S. & Nowottnick, E. Planetary boundary layer height estimates from ICESat-2 and CATS backscatter measurements. Front. Remote Sens. 2, 716951 (2021).

    Article  Google Scholar 

  199. Herzfeld, U. et al. Detection and height measurement of tenuous clouds and blowing snow in ICESat‐2 ATLAS data. Geophys. Res. Lett. https://doi.org/10.1029/2021GL093473 (2021).

  200. Duda, D. P., Spinhirne, J. D. & Eloranta, E. W. Atmospheric multiple scattering effects on GLAS altimetry. I. Calculations of single pulse bias. IEEE Trans. Geosci. Remote Sens. 39, 92–101 (2001).

    Article  ADS  Google Scholar 

  201. Yang, Y., Marshak, A., Palm, S. P., Varnai, T. & Wiscombe, W. J. Cloud impact on surface altimetry from a spaceborne 532-nm micropulse photon-counting lidar: system modeling for cloudy and clear atmospheres. IEEE Trans. Geosci. Remote Sens. 49, 4910–4919 (2011).

    Article  ADS  Google Scholar 

  202. Hansen, J. et al. Earth’s energy imbalance: confirmation and implications. Science 308, 1431–1435 (2005).

    Article  CAS  PubMed  ADS  Google Scholar 

  203. Atmospheric Aerosols: Life Cycles and Effects on Air Quality and Climate (Wiley, 2017).

  204. Hoff, R. M., Palm, S. P., Engel-Cox, J. A. & Spinhirne, J. GLAS long-range transport observation of the 2003 California forest fire plumes to the northeastern US. Geophys. Res. Lett. 32, n/a–n/a (2005).

    Article  Google Scholar 

  205. Wyngaard, J. C. Structure of the planetary boundary layer and implications for its modeling. J. Appl. Meteorol. Climatol. 24, 1131–1142 (1985).

    Article  CAS  ADS  Google Scholar 

  206. McGrath-Spangler, E. L. & Denning, A. S. Global seasonal variations of midday planetary boundary layer depth from CALIPSO space-borne LIDAR: variations of PBL depth from CALIPSO. J. Geophys. Res. Atmos. 118, 1226–1233 (2013).

    Article  ADS  Google Scholar 

  207. Teixeira, J. et al. Toward a Global Planetary Boundary Layer Observing System: The NASA PBL Incubation Study Team Report (NASA, 2021).

  208. Hancock, S., McGrath, C., Lowe, C., Davenport, I. & Woodhouse, I. Requirements for a global lidar system: spaceborne lidar with wall-to-wall coverage. R. Soc. Open Sci. 8, 211166 (2021).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  209. Berthier, E. et al. Measuring glacier mass changes from space — a review. Rep. Prog. Phys. https://doi.org/10.1088/1361-6633/acaf8e (2023).

    Article  PubMed  Google Scholar 

  210. Ricker, R. et al. Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC. The Cryosphere 17, 1411–1429 (2022).

    Article  ADS  Google Scholar 

  211. Roy, D. P., Kashongwe, H. B. & Armston, J. The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring. Sci. Remote Sens. 4, 100024 (2021).

    Article  Google Scholar 

  212. Tang, H. et al. Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI. Remote Sens. Environ. 291, 113571 (2023).

    Article  Google Scholar 

  213. Kern, M. et al. The copernicus polar ice and snow topography altimeter (CRISTAL) high-priority candidate mission. Cryosphere 14, 2235–2251 (2020).

    Article  ADS  Google Scholar 

  214. Shean, D. et al. SlideRule: enabling rapid, scalable, open science for the NASA ICESat-2 mission and beyond. J. Open Source Softw. 8, 4982 (2023).

    Article  ADS  Google Scholar 

  215. Donnellan, A. et al. Observing Earth’s Changing Surface Topography and Vegetation Structure: A Framework for the Decade NASA Surface Topography and Vegetation Incubation Study (NASA, 2021)

  216. Herzfeld, U. C., Lawson, M., Trantow, T. & Nylen, T. Airborne validation of ICESat-2 ATLAS data over crevassed surfaces and other complex glacial environments: results from experiments of laser altimeter and kinematic GPS data collection from a helicopter over a surging arctic glacier (Negribreen, Svalbard). Remote Sens. 14, 1185 (2022).

    Article  ADS  Google Scholar 

  217. Thomas, N. et al. A purely spaceborne open source approach for regional bathymetry mapping. IEEE Trans. Geosci. Remote Sens. https://doi.org/10.1109/TGRS.2022.3192825 (2022).

    Article  Google Scholar 

  218. Palm, S. P., Benedetti, A. & Spinhirne, J. Validation of ECMWF global forecast model parameters using GLAS atmospheric channel measurements. Geophys. Res. Lett.https://doi.org/10.1029/2005GL023535 (2005).

    Article  Google Scholar 

  219. Luthcke, S. B. Reduction of ICESat systematic geolocation errors and the impact on ice sheet elevation change detection. Geophys. Res. Lett. 32, L21S05 (2005).

    Article  Google Scholar 

  220. Luthcke, S. B. et al. ICESat‐2 pointing calibration and geolocation performance. Earth Space Sci. https://doi.org/10.1029/2020EA001494 (2021).

Download references

Acknowledgements

We thank the ICESat, ICESat-2 and GEDI project science offices, and all of the scientists and engineers who have worked on understanding laser altimetry data to push the technology and science results forward. We express special thanks to the following for their contributions to the figures: C. Smith (Fig. 1), V. Leitold (Fig. 3) and N. Thomas (Fig. 4b) (all Univ. Maryland); I. Parmuzin (Univ. Buffalo; Fig. 2); U. Herzfeld (Univ. Colorado Boulder; Fig. 2c); and H. J. Zwally (Fig. 2b) and S. Palm (Fig. 5) (both NASA Goddard Space Flight Center). All authors received funding from NASA through the ICESat-2 mission as members of the Science Team. L.D. and A.N. are also funded through NASA’s ABoVE science team. which focuses on mapping boreal biomass estimates. L.D. is also funded by the GEDI Science Team.

Author information

Authors and Affiliations

  1. Department of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, Austin, TX, USA

    Lori A. Magruder

  2. Center for Space Research, University of Texas at Austin, Austin, TX, USA

    Lori A. Magruder & Amy Neuenschwander

  3. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    Sinead L. Farrell & Laura Duncanson

  4. Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA

    Sinead L. Farrell

  5. Department of Geological Sciences, University at Buffalo, Buffalo, NY, USA

    Beata Csatho

  6. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Sahra Kacimi

  7. Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Helen A. Fricker

Authors
  1. Lori A. Magruder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sinead L. Farrell
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amy Neuenschwander
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laura Duncanson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Beata Csatho
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sahra Kacimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Helen A. Fricker
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The original concept for the manuscript included L.A.M., S.L.F., H.A.F. and A.N., which resulted in the submission of the synopsis for initial review. All authors contributed to the analysis, writing and editing of the full manuscript.

Corresponding author

Correspondence to Lori A. Magruder.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Earth & Environment thanks Yuekui Yang, Hao Tang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Magruder, L.A., Farrell, S.L., Neuenschwander, A. et al. Monitoring Earth’s climate variables with satellite laser altimetry. Nat Rev Earth Environ 5, 120–136 (2024). https://doi.org/10.1038/s43017-023-00508-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43017-023-00508-8

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene