Forest above-ground biomass estimates across three decades from spaceborne scatterometer observations

Abstract

<p>The large uncertainty characterizing the terrestrial carbon (C) cycle is a consequence of the sparse and irregular observations on the ground. In terms of observations, spaceborne remote sensing has been achieving global, repeated coverages of the Earth since the late 1970s, with a continuous increase in terms of density of observations in time and spatial resolution, thus potentially qualifying as data source to fill such gap in knowledge. Above-ground biomass is a baseline for quantifying the terrestrial C pool; however, remote sensing observations do not measure the organic mass of vegetation. Above-ground biomass (AGB) of forests can only be inferred by inverting numerical models relating and combining multiple remote sensing observations. One of the longest time record of observations from space is represented by the backscattered intensity from the European Remote Sensing Wind Scatterometer (ERS WindScat) and the MetOp Advanced Scatterometer (ASCAT), both operating at C-band (wavelength of 6 cm). An almost unbroken time series of backscatter observations at 0.25° spatial resolution exists since 1991 and data continuity is guaranteed in the next decades. In spite of the weak sensitivity of C-band backscatter to AGB, wall-to-wall estimates of AGB have been derived from high-resolution SAR observations by exploiting multiple observations acquired in a relatively short time period  (Santoro et al., Rem. Sens. Env., 2011; Santoro et al., Rem. Sens. Env., 2015). We have now applied this approach to generate a global time series of AGB estimates for each year between 1992 and 2018 from the C-band scatterometer data at 0.25° spatial resolution. The spatial patterns of AGB match known patterns from in situ records and other remote sensing datasets. The uncertainty of our AGB estimates is between 30% and 40% of the estimated value at the pixel level, providing strong confidence in multi-decadal AGB trends. We identify a constant increase of biomass across most boreal and temperate forests of the northern hemisphere. In contrast, we detect severe loss of biomass throughout the wet tropics during the 1990s and the beginning of the 2000 decade in consequence of massive deforestation. This loss in biomass is followed by a steady increase during the 2000s and the beginning of the most recent decade, coming more recently into saturation. Overall, we find that the global AGB density at 0.25° steadily increased by 9% from 71.8 Mg ha<sup>-1</sup> Pg in the 1990s to 78.1 Mg ha<sup>-1</sup> in the 2010s. Combining our AGB density estimates with the annual maps of the Climate Change Initiative (CCI) Land Cover dataset, we show that total AGB in forests decreased slightly from 566 Pg in the 1990s to 560 Pg in the 2000s, then increased to 593 Pg in the 2010s, resulting in an almost 5% net increase during the last three decades.</p>

Global sensitivities of forest carbon changes to environmental conditions

“…The covariance terms took into account the temporal correlation between the individual AGB estimates and their errors. The assessment of the AGB estimates for the year 2010 was strongly correlated with spaceborne LiDAR‐based metrics of vegetation structure (canopy density and height) and with AGB averages from forest field inventory plots collected around the same epoch (Santoro et al, 2020). For this study, the AGB estimates, expressed as dry organic mass, were converted to carbon density using the Intergovernmental Panel on Climate Change (IPCC) default carbon fraction of 0.47 (McGroddy et al, 2004).…”

“…An almost unbroken time series of up to daily observations by the European Remote Sensing (ERS) WindScatterometer and the MetOp Advanced SCATterometer (ASCAT) was used in an AGB retrieval approach that maximizes the information content on forest structural parameters in the data while reducing external contributions to the backscatter due to moisture conditions, precipitation and snow cover. For each year, all daily observations of the radar backscatter at one location (pixel size of 0.25°) were synthesized to generate a single estimate of AGB, allowing for a smooth time series of AGB estimates (Santoro et al, 2020). The uncertainties of the annual AGB estimates consisted of a variance term expressed as the weighted average of the individual, daily uncertainties of AGB and a covariance term.…”

“…C‐band backscatter measurements were used for monitoring AGB (Santoro et al, 2021). Recently, multiple C‐band sensors were merged to capture multi‐decadal information on the signature of the aboveground vegetation both in space and time (Santoro et al, 2020).…”

Global sensitivities of forest carbon changes to environmental conditions 

“…The covariance terms took into account the temporal correlation between the individual AGB estimates and their errors. The assessment of the AGB estimates for the year 2010 was strongly correlated with spaceborne LiDARbased metrics of vegetation structure (canopy density and height) and with AGB averages from forest field inventory plots collected around the same epoch (Santoro et al, 2020). For this study, the AGB estimates, expressed as dry organic mass, were converted to carbon density using the Intergovernmental Panel on Climate Change (IPCC) default carbon fraction of 0.47 (McGroddy et al, 2004).…”

“…C-band backscatter measurements were used for monitoring AGB (Santoro et al, 2021). Recently, multiple C-band sensors were merged to capture multi-decadal information on the signature of the aboveground vegetation both in space and time (Santoro et al, 2020).…”