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Other Space Activities

Above-Ground Biomass

Mar 20, 2024


Biomass is the sum weight of carbon of all living or recently-living organisms. As the looming threat of anthropogenic climate change grows ever more pressing and realised, so too does our interest in global carbon stocks. Life, particularly plant-life, is an exceptionally integral component of the Earth’s carbon cycle. Anthropogenic (human) activities are causing the breakdown of this cycle from two main contributors, which has instigated the current climate crisis. The first point of failure is input of far too much carbon into the atmosphere, primarily from ancient underground stores (e.g. fossil fuels). The second point of failure is the widespread removal of carbon-storing vegetation (deforestation). This removal contributes to both the over-input of carbon into the cycle - through burning of vegetation for energy or for land clearing - and to a reduced pulling of carbon from the atmosphere. As a result, atmospheric carbon dioxide levels are significantly higher than they should be for a stable climate, and a key part of understanding, preparing for, and tackling this issue is to pay keen attention to the levels of biomass stored on Earth. 1) 2) 3)

Accurate quantification of global biomass is a difficult task, as the diversity of life on Earth is as diverse as it is extensive. To help manage the task, biomass is generally split up into different components. Above-Ground Biomass (AGB), below ground biomass, and oceanic biomass constitute the vast majority of global biomass, with AGB being the most substantial. AGB is also the easiest to measure, and hence most of the effort spent monitoring biomass focuses towards quantifying AGB. However, it is worth remembering always that a significant portion of biomass exists outside of this realm, with up to 25% of global plant biomass persisting underground. Nevertheless, AGB estimates are currently our most important and developed biomass estimations globally. 1) 2) 4) 5) 6) 7)

Figure 1: a diagram demonstrating the distribution of biomass on Earth. Plants by far exceed all other lifeforms as the dominant form of biomass (Image credit: Bar-on et al., 2018).

Monitoring of AGB is achieved in several ways. The most basic - though not to be underestimated in importance - is that of field studies: physically travelling to and collecting data on vegetation from the ground. While highly effective and accurate for quantifying biomass on small scales, and without major theoretical complications, this method is extremely limited in that vegetation is simply too extensive to be monitored in totality from ground surveys. It is often also difficult to traverse through many vegetated regions, due to thick brushland, topographical extremes, and dangerous organisms. 1) 2)

Biomass can be measured from space using passive optical sensors, microwave Synthetic Aperture Radar (SAR), and lidar. Each has its own advantages and limitations, and the use of combined data from the three methods, and using field-based survey data for validation, has proved successful in generating accurate global biomass maps. 1) 2) 8)

Figure 2: a depiction of the different forms of aerial remote sensing of forest biomass that are carried out (Image credit: Tian et al., 2023)

Passive optical remote sensing systems observe radiation that has originated from an external source. For biomass monitoring, this can involve both measuring reflectance data from sunlight incident on vegetation, or radiance emitted from photosynthesising plants - a process called Solar-induced Chlorophyll Fluorescence (SIF).

The variables that are primarily observed by these systems include spectral reflectance, Vegetation Indices (VIs)spatial textureLeaf Area Index (LAI)Forest Canopy Density (FCD), and forest coverage. A range of passive optical instruments are deployed with different specifications, each with their own unique benefits and drawbacks. For example, coarse-resolution sensors can observe larger areas more frequently and at a lower cost, but are unable to distinguish individual trees. While optical sensors are extremely useful for global remote sensing, they are limited by their reliance on daylight hours and their inability to penetrate clouds or smoke. 1)

SAR imagery is a novel and effective approach for biomass monitoring, as its acquisition is unaffected by atmospheric conditions but sensitive to the structure of vegetation. Depending on what waveband is selected, one can probe vegetation at desired depths. High frequencies like X-band (12-8 GHz) will reveal information on upper vegetation canopies, while lower frequencies like L-band (2-1 GHz) will penetrate vegetation cover and detail lower canopy structures. 34)

Figure 3: Global Mosaic SAR data HH + HV polarisation composite from ALOS PALSAR at 25 m pixel spacing (Image credit: JAXA EORC)

Lidar is another novel technology valuable for biomass estimation and monitoring. Spaceborne lidar data can derive metrics like canopy height, stem diameter and density, which together are predictive of biomass. These observed metrics can then be translated to AGB through empirical allometric equations, derived specifically for lidar biomass estimating. This process is applied for various geographical, climatic and ecological regions to facilitate accurate global AGB monitoring with lidar. Common lidar observables linked to biomass include descriptive metrics, height percentile, intensities, and canopy cover. 35) 36)

Figure 4: Diagram of lidar satellite demonstrating how the instrument obtains ground data (Image credit: JAXA)

Example Products

GEDI (Global Ecosystem Dynamics Investigation) Product

The GEDI mission provides publicly available footprint and gridded datasets that describe the 3D features of the Earth. The products are assigned to different levels which indicate the amount of processing that the data has undergone since collection. Also available from GEDI are demonstrative products - outputs created specifically for limited domains. Of these are included prognostic ecosystem model outputs, such as the Ecosystem Demography model (ED). ED provides estimates of carbon stocks and fluxes over large areas at a fine resolution. It is a height structured model derived from lidar data. Also included in the demonstrative products are enhanced height/biomass models using fusion with TanDEM-X and with Landsat, and biodiversity and habitat models. 11)

Figure 5: Biomass estimates for Hubbard Brook experimental forest, New Hampshire, USA, created by fusing GEDI and 25 m TanDEM-X (TDX) data (Image credit: GEDI)

Globbiomass Project

Globbiomass is an ESA-funded project that aims to better characterise and to reduce uncertainties of AGB estimates through the development of an innovative synergistic mapping approach. The project plans to demonstrate how EO data can be integrated with in situ measurements and ecological understanding to provide improved biomass estimates that can be effectively exploited by users. The expected regional outcomes are for reduced uncertainty in modelling, estimating, observing and monitoring; improved national carbon reporting, land management, and capacity building in REDD+; and to improve harvesting methods, forest resource planning, and the auditing of remote areas. The expected global outcomes of Globbiomass are for improved global carbon modelling and estimation of carbon sources and sinks, trans-boundary logging monitoring and estimations of fuel wood for management, and the detection of large, industrial logging disturbances, deforestation, and degradation. 12) 13)

Figure 6: Individual 40° x 40° tiles from the Globbiomass project showing global AGB and GSV (Image credit: ESA)


Another ESA-run project, the CCI BIOMASS Project, aims to provide global maps of AGB for multiple epochs, with these being capable of supporting quantifications of biomass change. Mapping is at 100 m grid spacing with a target relative error of < 20% where AGB exceeds 50 Mg ha-1. This fine resolution will allow more refined information to be inferred that is relevant for climate and has the potential to be exploited by carbon cycle and climate models as they develop. 14)

Figure 7: AGB recovery rates in Brazil based on geographically weighted regression (GWR) models (Image credit: N. Chen et al, 38))

Related Missions

BIOMASS (Biomass Monitoring Mission for Carbon Assessment)

ESA’s BIOMASS mission will use a P-band SAR and Antenna Feed Subsystem to provide quantifiable data on the global carbon cycle. The long P-band wavelength will provide insight into the topography beneath thick forest canopies and of features in deserts and ice sheets. The data will be used to provide scientific support for international treaties and agreements, improve predictions of landscape-scale carbon dynamics, provide observations to initialise and test the land element of Earth system models, reduce uncertainties in carbon flux, and provide key information for forest resources management. 5)

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ALOS (Advanced Land Observing Satellite) / Daichi Programme

The Japanese Aerospace Exploration Agency (JAXA) ALOS programme consists of a series of L-band SAR satellites with the objective to make observations for land and agriculture, disaster monitoring and natural resources. Several iterations of the Phased Array type L-band Synthetic Aperture Radar (PALSAR) instrument have been flown on ALOS, and has the capability to derive in-depth information about forest structure and biomass distribution at different layers. PALSAR-2 onboard ALOS-2 is equipped with an interferometric operating mode which facilitates the production of digital elevation models (DEMs), interferograms, as well as the measurement of a suite of biomass-related variables.




ISS: GEDI (Global Ecosystems Dynamics Investigations lidar)

GEDI is a NASA mission onboard the International Space Station (ISS) that produced elevation profiles of forests and habitat quality on Earth; the instrument operated between December 2018 and March 2023 and is in storage before operations are planned to continue from 2024. The instrument studied a range of climates including canopy structure and tundra environments in northern latitudes, and its data will help scientists understand changes to natural carbon storage within the carbon cycle. GEDI data has been used in the development of biomass models, fusing data with TanDEM-X and Landsat missions, and biodiversity and habitat models.

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Launched 11 February 2013, NASA’s Landsat-8 adds onto the long-running Landsat series to collect and archive thermal and multispectral image data consistent with previous Landsat mission data. Data from Landsat-8 has been successfully used in numerous forest AGB estimation studies. 16) 17) 18) 19)

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NISAR (NASA-ISRO Synthetic Aperture Radar)

A cooperative development between NASA and ISRO (Indian Space Research Organisation), NISAR will carry two SARs. One will operate in L-band and the other in S-band, making this the first satellite to use two different radar frequencies. L-SAR will observe landscape topography and heavily forested areas while the S-SAR will monitor soil moisture, particularly in polar regions as S-band frequencies are less perturbed by the ionosphere, presenting a key player in biomass estimation and monitoring. 9)

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Copernicus: Sentinel-2

Sentinel-2, a constellation of two optical image satellites, is a part of Copernicus, the EU’s EO program, and operated by ESA. Launched 23 June 2015, the two identical satellites provide EO imagery with their Multispectral Instruments (MSIs) that provide data on vegetation, radiation budget, albedo and reflectance, and multi-purpose land imagery.

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TanDEM / TerraSAR

Tandem and TerraSAR are German Aerospace Centre (DLR) missions consisting of interferometric SAR satellite missions, TerraSAR (TSX) and TanDEM-X (TDX) launched in June 2007 and 2010 respectively, with Tandem-L considered for launch in 2028. TDX and TSX are identical, both supporting the production of high accuracy DEMs and DTMs with biomass applications. TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) is an extension of TSX, with the two flying in a close formation to form a bistatic SAR interferometer system. Interferometric imagery from TDX has applications in biomass estimates and monitoring, as well as to produce global forest maps.

TanDEM-X | TerraSAR-X | Tandem-L


ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2)

A follow up mission to ICESat from NASA, ICESat-2 - launched 15 September 2018 - carries the Advanced Topographic Laser Altimeter System (ATLAS), an advanced technology designed to acquire high resolution measurements of Earth’s surface whilst also obtaining atmospheric backscatter from molecules, clouds, and aerosols. While it is largely intended for ice sheet studies, one of its primary objectives is to measure vegetation canopy height as a basis for estimating large-scale biomass and biomass change.

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Other Missions of Interest


1) Tian L., et al. (2023) Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects; Forests, Vol. 14, issue 6, article 1086.

2) “The Earth Observation Handbook - Special Edition for Rio+20 ǀ Updated for 2014,” CEOS, December 2013, URL:

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4) Bar-On Y.M., Phillips R., Milo R. (2018) The Biomass Distribution on Earth; PNAS, Vol. 115, issue 25, pp. 6506-6511.

5) Ma H., et al. (2021) The Global Distribution and Environmental Drivers of Aboveground Versus Belowground Plant Biomass; Nature Ecology & Evolution, Vol. 5, pp. 1110-1122.

6) Gross M. (2021) Life Underground; Current Biology, Vol. 31, issue 9, pp. R415-R417.

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8) “What is Remote Sensing and What is it Used For?,” United States Geological Survey (USGS), URL:,sense%22%20things%20about%20the%20Earth 

9) “A Layman’s Interpretation Guide to L-band and C-band Synthetic Aperture Radar Data,” CEOS & GFOI (Global Forest Observations Initiative), May 2023, URL:

10) “Lidar (Light Detection and Ranging),” EoPortal, ESA, July 2023, URL: 

11) “Products,” GEDI, 2023, URL: 

12) “Project,” Globbiomass, URL: 

13) Maurizio S., et al. (2018) A Detailed Portrait of the Forest Aboveground Biomass Pool for the Year 2010 Obtained from Multiple Remote Sensing Observations; Geophysical Research Abstracts, Vol. 20, pp. EGU 2018-18932.

14) “Biomass,” ESA Climate Office, URL: 

15) “Living Wales,” Living Wales, 2023, URL: 

16) Li Y., et al. (2020) Forest Aboveground Biomass Estimation Using Landsat 8 and Sentinel-1A Data with Machine Learning Algorithms, Scientific Reports, Vol. 10, article 9952.

17) López-Serrano P.M., et al. (2020) Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests; Forests, Vol. 11, issue 1, p. 11.

18) Gizachew B., et al. (2016) Mapping and Estimating the Total Living Biomass and Carbon in Low-Biomass Woodlands Using Landsat 8 CDR Data; Carbon Balance and Management, Vol. 11, article 13.

19) Zhang D., Wang X., Zan M. (2021) Estimation of Vegetation Aboveground Biomass in the Wei-Ku Oasis Based on Landsat 8 OLI Images; Acta Prataculturae Sinica, Vol. 30, issue 11, pp. 1-12.

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23) “Biomass (Biomass Monitoring Mission for Carbon Assessment),” EoPortal, ESA, November 2017, URL:

24) “Terra (EOS/AM-1),” EoPortal, ESA, June 2012, URL:

25) “Aqua (EOS/PM-1),” EoPortal, ESA, May 2012: URL:

26) “Ikonos-2,” EoPortal, ESA, May 2012, URL:

27) “QuickBird-2,” EoPortal, ESA, June 2012, URL:

28) “WorldView-2 Overview,” Earth Online, ESA, URL:

29) “WorldView-2,” EoPortal, ESA, URL: 

30) “NISAR (NASA-ISRO Synthetic Aperture Radar),” EoPortal, ESA, June 2018, URL:

31) “Tandem-L Interferometric Radar Mission,” EoPortal, ESA, September 2016, URL:

32) “ALOS-2 (Advanced Land Observing Satellite-2) / Daichi-2,” EoPortal, ESA, May 2012, URL:

33) “ALOS-4 (Advanced Land Observing Satellite-4),” EoPortal, ESA, December 2022, URL: 

34) Parag M, Lottering R, Peerbhay K, Agjee N, Poona N. “The use of synthetic aperture radar technology for crop biomass monitoring: A systematic review,” Remote Sens Appl Soc Environ. 2024;33:101107. URL:

35) Cindy Chiao, Oriana Chegwidden, Joe Hamman, “Using LiDAR to estimate forest biomass,” (carbon)plan, 16 February 2022, URL:

36) Chan, E.P.Y., Fung, T. & Wong, F.K.K. Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong. Sci Rep 11, 1751 (2021). URL:

37) Planque C, Lucas R, Punalekar S, Chognard S, Hurford C, Owers C, Horton C, Guest P, King S, Williams S, et al. National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm. Remote Sensing. 2021; 13(5):846. URL:

38) Chen N, Tsendbazar NE, Suarez DR, Silva-Junior CHL, Verbesselt J, Herold M. Revealing the spatial variation in biomass uptake rates of Brazil’s secondary forests. ISPRS J Photogramm Remote Sens. 2024;208:233-244. URL: