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Journal Article

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


Santoro,  Maurizio
External Organizations;

Cartus,  Oliver
External Organizations;

Wegmüller,  Urs
External Organizations;

Besnard,  Simon
External Organizations;

Carvalhais,  Nuno
External Organizations;

Araza,  Arnan
External Organizations;


Herold,  Martin
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Liang,  Jingjing
External Organizations;

Cavlovic,  Jura
External Organizations;

Engdahl,  Marcus E.
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Santoro, M., Cartus, O., Wegmüller, U., Besnard, S., Carvalhais, N., Araza, A., Herold, M., Liang, J., Cavlovic, J., Engdahl, M. E. (2022): Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure. - Remote Sensing of Environment, 279, 113114.

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5012087
The backscattered power recorded by a spaceborne scatterometer operating at C-band is sensitive to land surface parameters and is operationally used by some global remote sensing services, e.g., to estimate soil moisture. The estimation of forest variables, in particular above-ground biomass (AGB), from scatterometer data instead was seldom explored. Given the availability of multi-decadal sets of scatterometer observations from space, it is of interest to address the contribution of C-band scatterometer data to the quantification of carbon stocks stored in forests even if the spatial resolution of spaceborne scatterometers is very coarse. In this paper, we investigated the prospects of AGB estimation using backscatter observations by the MetOp Advanced SCATterometer (ASCAT) with a spatial resolution of 0.25°. For this study, ASCAT observations acquired in 2010 were used to be contemporary with AGB datasets selected to benchmark the performance of the estimation. A Water Cloud Model that integrates two allometric equations derived from spaceborne LiDAR data reproduced the relationship between observations of radar backscatter as a function of AGB. Estimates of AGB from individual observations were then combined with a weighted average to reduce uncertainties. Finally, a correction was introduced to compensate for the offset introduced by sloped terrain and surfaces not covered by woody vegetation on the AGB estimate of a pixel. Uncertainties associated with the scatterometer observations, and the modelling framework were propagated to obtain per-pixel values of the standard deviation of an AGB estimate. The proposed method explains much of the variance in AGB estimates when compared to measurements from inventory data (R2 = 0.72) and generated unbiased estimates globally (bias: −3.3 Mg⋅ha−1). Nonetheless, the discrepancy between estimated and plot-based AGB values tended to increase for decreasing biomass level from 20% to 60% of the reference AGB level. A further assessment related to global stocks indicated that the value estimated from the scatterometer dataset (596 Pg, 95% of which 563 Pg stored in forest land) was in line with two published estimates based on forest inventory data only (571 Pg and 600 Pg, respectively). Despite the coarse spatial resolution, our results indicate that C-band scatterometer observations from space can contribute to the characterization of terrestrial biomass pools. The record of observations starting in the early 1990s may provide an unprecedented way to look at long-term forest dynamics as well as to constrain the strength of carbon-climate cycle feedback simulated by Earth System models.