TY - JOUR

T1 - Design and performance of the Climate Change Initiative Biomass global retrieval algorithm

AU - Santoro, Maurizio

AU - Cartus, Oliver

AU - Quegan, Shaun

AU - Kay, Heather

AU - Lucas, Richard M.

AU - Araza, Arnan

AU - Herold, Martin

AU - Labriere, Nicolas

AU - Chave, Jérôme

AU - Rosenqvist, Åke

AU - Tadono, Takeo

AU - Kobayashi, Kazufumi

AU - Kellndorfer, Josef

AU - Avitabile, Valerio

AU - Brown, Hugh

AU - Carreiras, Joao

AU - Campbell, Michael

AU - Cavlovic, Jura

AU - da Conceição Bispo, Polyanna

AU - Gilani, Hammad

AU - Latif Khan, Mohammed

AU - Kumar, Amit

AU - Lewis, Simon L.

AU - Liang, Jingjing

AU - Mitchard, Edward T.A.

AU - Pascagaza, Ana Maria Pacheco

AU - Phillips, Oliver L.

AU - Ryan, Casey M.

AU - Saikia, Purabi

AU - Schepaschenko, Dmitry

AU - Sukhdeo, Hansrajie

AU - Verbeeck, Hans

AU - Vieilledent, Ghislain

AU - Wijaya, Arief

AU - Willcock, Simon

AU - Martin Seifert, Frank

PY - 2024/9/30

Y1 - 2024/9/30

N2 - The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band synthetic aperture radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (< 50 Mg ha-1) and under-predictions in the high AGB range (> 300 Mg ha-1). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.

AB - The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band synthetic aperture radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (< 50 Mg ha-1) and under-predictions in the high AGB range (> 300 Mg ha-1). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.

KW - above-ground biomass

KW - carbon

KW - forest

KW - synthetic aperture radar

KW - backscatter

KW - Sentinel-1

KW - ALOS-2 PALSAR-2

KW - LiDAR

KW - ICESat GLAS

KW - ICESat-2 ATLAS

KW - retrieval

U2 - 10.1016/j.srs.2024.100169

DO - 10.1016/j.srs.2024.100169

M3 - Article

VL - 10

SP - 100169

JO - Science of Remote Sensing

JF - Science of Remote Sensing

M1 - 100169

ER -