Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure

Santoro, Maurizio; Cartus, Oliver; Wegmüller, U.; Besnard, Simon; Araza, Arnan; Herold, Martin; Liang, Jingjing; Čavlović, Juro; Engdahl, Marcus

doi:10.1016/j.rse.2022.113114

Cited by 14 publications

(11 citation statements)

References 61 publications

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“…Annual AGB estimates were derived from C-band satellite radar signals between 1992 and 2016 with a pixel size of 25 km (Santoro et al, 2022). The very dense time series of observations by the European Remote Sensing (ERS) WindScatterometer, the MetOp Advanced SCATterometer (ASCAT), and the Envisat Advanced Synthetic Aperture Radar (ASAR) were used to maximize the information content of forest structure in the signal, allowing for AGB estimates of higher accuracy compared to values obtained from a single observation (Santoro et al, 2022). The annual estimation of AGB is obtained by synthesizing all daily observations of the radar backscatter at one location in a pixel (0.25º×0.25º), enabling the inference of a continuous time series of AGB estimation.…”

Section: The Multi-decadal Estimates Of Agb Datasetmentioning

confidence: 99%

“…Although we used different streams of data and methods to account for the uncertainties in the estimations of carbon turnover, several factors may limit the results of our study. First, the estimations of AGB by Santoro et al (2022) is derived at relatively coarse spatial resolution at 0.25º which makes it impossible to compare with measurements of AGB at plot level. To overcome the limitation, Santoro et al relied on gridded datasets including a vegetation density map, an independent AGB map, a global land cover map and a model of global elevation to estimate the unknown model parameters that are necessary to infer vegetation biomass.…”

Section: Implications Limitations and Conclusionmentioning

confidence: 99%

“…In this study, we used estimates of annual changes in vegetation carbon derived from a multi-decadal dataset and global estimations of gross primary productivity (GPP) that are driven by meteorological observations Santoro et al, 2022;Jung et al 2020), for estimating and comparing τ estimates that are derived from SSA and non-steady-state assumption (hereafter NSSA), respectively, at local, biome and global scales.…”

Section: Introductionmentioning

confidence: 99%

“…2.1 The multi-decadal estimates of AGB dataset Annual AGB estimates were derived from C-band satellite radar signals between 1992 and 2016 with a pixel size of 25 km (Santoro et al, 2022). The very dense time series of observations by the European Remote Sensing (ERS) WindScatterometer, the MetOp Advanced SCATterometer (ASCAT), and the Envisat Advanced Synthetic Aperture Radar (ASAR) were used to maximize the information content of forest structure in the signal, allowing for AGB estimates of higher accuracy compared to values obtained from a single observation (Santoro et al, 2022). The annual estimation of AGB is obtained by synthesizing all daily observations of the radar backscatter at one location in a pixel (0.25º×0.25º), enabling the inference of a continuous time series of AGB estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Remote Sensing of Environment, 115(2), 490-507. Santoro, M., Cartus, O., Wegmüller, U.,Besnard, S., Carvalhais, N., Araza, A., et al (2022).…”

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confidence: 99%

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Implications of the Steady-state Assumption for the Global Vegetation Carbon Turnover

Fan

Santoro

Besnard

et al. 2022

Preprint

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Vegetation turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found that the forest ecosystem is close to a steady state at global scale, contrasting with the larger differences between τ under steady state and τ under non-steady state at the grid cell level. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.

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Section: The Multi-decadal Estimates Of Agb Datasetmentioning

confidence: 99%

Section: Implications Limitations and Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Remote Sensing of Environment, 115(2), 490-507. Santoro, M., Cartus, O., Wegmüller, U.,Besnard, S., Carvalhais, N., Araza, A., et al (2022).…”

mentioning

confidence: 99%