Predicting Landscape-Scale CO 2 Flux at a Pasture and Rice Paddy with Long-Term Hyperspectral Canopy Reflectance Measurements

Measurements of hyperspectral canopy reflectance provide a detailed snapshot of information regarding canopy biochemistry, structure and physiology. In this study, we collected 5 years of repeated canopy hyperspectral reflectance measurements for a total of over 100 site visits within the flux footprints of two eddy covariance towers at a pasture and rice paddy in northern California. The vegetation at both sites exhibited dynamic phenology, with significant interannual variability in the timing of seasonal patterns that propagated into interannual variability in measured hyperspectral reflectance. We used partial least-squares regression (PLSR) modeling to leverage the information contained within the entire canopy reflectance spectra (400–900 nm) in order to investigate questions regarding the connection between measured hyperspectral reflectance and landscape-scale fluxes of net ecosystem exchange (NEE) and gross primary productivity (GPP) across multiple timescales, from instantaneous flux to monthly integrated flux

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate system through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. <br><br> Terrestrial biosphere models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we used the Harvard Forest phenology record to investigate and characterize sources of uncertainty in predicting phenology, and the subsequent impacts on model forecasts of carbon and water cycling. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species, with 12 leaf bud-burst models that varied in complexity. <br><br> Akaike's Information Criterion indicated support for spring warming models with photoperiod limitations and, to a lesser extent, models that included chilling requirements. <br><br> We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO<sub>2</sub> emissions vs. low CO<sub>2</sub> emissions scenario). Parameter uncertainty was the smallest (average 95% Confidence Interval – CI: 2.4 days century<sup>−1</sup> for scenario B1 and 4.5 days century<sup>−1</sup> for A1fi), whereas driver uncertainty was the largest (up to 8.4 days century<sup>−1</sup> in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied among models (±7.7 days century<sup>−1</sup> for A1fi, ±3.6 days century<sup>−1</sup> for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 days °C<sup>−1</sup> and 5.2 days °C<sup>−1</sup> depending on model structure. <br><br> We quantified the impact of uncertainties in bud-burst forecasts on simulated photosynthetic CO<sub>2</sub> uptake and evapotranspiration (ET) using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of forest seasonality, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and ET of 9.6% and 2.9%, respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0% for annual GPP and about ±2.0% for ET. <br><br> For phenology models, differences among future climate scenarios (i.e. driver) represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of ecosystem processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenological model parameterization

The importance of calcium and amorphous silica for arctic soil CO₂ production

Future warming of the Arctic not only threatens to destabilize the enormous pool of organic carbon accumulated in permafrost soils but may also mobilize elements such as calcium (Ca) or silicon (Si). While for Greenlandic soils, it was recently shown that both elements may have a strong effect on carbon dioxide (CO2) production with Ca strongly decreasing and Si increasing CO2 production, little is known about the effects of Si and Ca on carbon cycle processes in soils from Siberia, the Canadian Shield, or Alaska. In this study, we incubated five different soils (rich organic soil from the Canadian Shield and from Siberia (one from the top and one from the deeper soil layer) and one acidic and one non-acidic soil from Alaska) for 6 months under both drained and waterlogged conditions and at different Ca and amorphous Si (ASi) concentrations. Our results show a strong decrease in soil CO2 production for all soils under both drained and waterlogged conditions with increasing Ca concentrations. The ASi effect was not clear across the different soils used, with soil CO2 production increasing, decreasing, or not being significantly affected depending on the soil type and if the soils were initially drained or waterlogged. We found no methane production in any of the soils regardless of treatment. Taking into account the predicted change in Si and Ca availability under a future warmer Arctic climate, the associated fertilization effects would imply potentially lower greenhouse gas production from Siberia and slightly increased greenhouse gas emissions from the Canadian Shield. Including Ca as a controlling factor for Arctic soil CO2 production rates may, therefore, reduces uncertainties in modeling future scenarios on how Arctic regions may respond to climate change

Reviews and syntheses: Recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic–boreal regions

Spaceborne microwave remote sensing (300 MHz–100 GHz) provides a valuable method for characterizing environmental changes, especially in Arctic–boreal regions (ABRs) where ground observations are generally spatially and temporally scarce. Although direct measurements of carbon fluxes are not feasible, spaceborne microwave radiometers and radar can monitor various important surface and near-surface variables that affect terrestrial carbon cycle processes such as respiratory carbon dioxide (CO2) fluxes; photosynthetic CO2 uptake; and processes related to net methane (CH4) exchange including CH4 production, transport and consumption. Examples of such controls include soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties and land cover. Microwave remote sensing also provides a means for independent aboveground biomass estimates that can be used to estimate aboveground carbon stocks. The microwave data record spans multiple decades going back to the 1970s with frequent (daily to weekly) global coverage independent of atmospheric conditions and solar illumination. Collectively, these advantages hold substantial untapped potential to monitor and better understand carbon cycle processes across ABRs. Given rapid climate warming across ABRs and the associated carbon cycle feedbacks to the global climate system, this review argues for the importance of rapid integration of microwave information into ABR terrestrial carbon cycle science.</p

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of autumn, can be derived from sensor-based time series data at the near-surface and remote scales, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, as well as to perform digital image analysis for time series-based estimates of phenology dates. We then compare these near-surface results to remote sensing metrics of phenology at the landscape scale, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit, using a generalized sigmoid, to estimate phenology dates. We quantify the statistical uncertainty of phenophase transition dates estimated using this method and show that the generalized sigmoid results in less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than remote sensing metrics of phenology, and that dates derived from the remotely-sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time series estimates for the start of spring are found to closely match visual assessment of leaf out, as well as remote sensing-derived estimates of the start of spring. However late spring and autumn phenology exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.Organismic and Evolutionary Biolog

Dielectric characterization of vegetation at L band using an open-ended coaxial probe

Decoupling the integrated microwave signal originating from soil and vegetation remains a challenge for all microwave remote sensing applications. To improve satellite and airborne microwave data products in forest environments, a precise and reliable estimation of the relative permittivity (ε = ε′ − iε′′) of trees is required. We developed an open-ended coaxial probe suitable for in situ permittivity measurements of tree trunks at L-band frequencies (1–2&thinsp;GHz). The probe is characterized by uncertainty ratios under 3.3&thinsp;% for a broad range of relative permittivities (unitless), [2–40] for ε′ and [0.1–20] for ε′′. We quantified the complex number describing the permittivity of seven different tree species in both frozen and thawed states: black spruce, larch, red spruce, balsam fir, red pine, aspen and black cherry. Permittivity variability is substantial and can range up to 300&thinsp;% for certain species. Our results show that the permittivity of wood is linked to the freeze–thaw state of vegetation and that even short winter thaw events can lead to an increase in vegetation permittivity. The open-ended coaxial probe proved to be precise enough to capture the diurnal cycle of water storage inside the trunk for the length of the growing season.</p

Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment

Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.Organismic and Evolutionary Biolog

Pan-Arctic soil element availability estimations

Arctic soils store large amounts of organic carbon and other elements, such as amorphous silicon, silicon, calcium, iron, aluminum, and phosphorous. Global warming is projected to be most pronounced in the Arctic, leading to thawing permafrost which, in turn, changes the soil element availability. To project how biogeochemical cycling in Arctic ecosystems will be affected by climate change, there is a need for data on element availability. Here, we analyzed the amorphous silicon (ASi) content as a solid fraction of the soils as well as Mehlich III extractions for the bioavailability of silicon (Si), calcium (Ca), iron (Fe), phosphorus (P), and aluminum (Al) from 574 soil samples from the circumpolar Arctic region. We show large differences in the ASi fraction and in Si, Ca, Fe, Al, and P availability among different lithologies and Arctic regions. We summarize these data in pan-Arctic maps of the ASi fraction and available Si, Ca, Fe, P, and Al concentrations, focusing on the top 100 cm of Arctic soil. Furthermore, we provide element availability values for the organic and mineral layers of the seasonally thawing active layer as well as for the uppermost permafrost layer. Our spatially explicit data on differences in the availability of elements between the different lithological classes and regions now and in the future will improve Arctic Earth system models for estimating current and future carbon and nutrient feedbacks under climate change (https://doi.org/10.17617/3.8KGQUN, Schaller and Goeckede, 2022).</p