The Western US accounts for a significant amount of the forested biomass and carbon uptake within the conterminous United States. Warming and drying climate trends combined with a legacy of fire suppression have left Western forests particularly vulnerable to disturbance from insects, fire and drought mortality. These challenging conditions may significantly weaken this region’s ability to uptake carbon from the atmosphere and warrant continued monitoring. Traditional methods of carbon monitoring are limited by the complex terrain of the Rocky Mountains that lead to complex atmospheric flows coupled with heterogeneous climate and soil conditions. Recently, solar induced fluorescence (SIF) has been found to be a strong indicator of GPP, and with the increased availability of remotely-sensed SIF, provides an opportunity to estimate GPP and ecosystem function across the Western US. Although the SIF-GPP empirical linkage is strong, the mechanistic understanding between SIF and GPP is lacking, and ultimately depends upon changes in leaf chemistry that convert absorbed radiation into photochemistry, heat (via non-photochemical quenching (NPQ)), leaf damage or SIF. Understanding of the mechanistic detail is necessary to fully leverage observed SIF to constrain model estimates of GPP and improve representation of ecosystem processes. Here, we include an improved fluorescence model within CLM 4.5 to simulate seasonal changes in SIF at a sub-alpine forest in Colorado. We find that when the model includes a representation of sustained NPQ the simulated fluorescence is much closer to the seasonal pattern of SIF observed from the GOME-2 satellite platform and a custom tower mounted spectrometer system. We also find that average air temperature may be used as a predictor of sustained NPQ when observations are not available. This relationship to air temperature is promising because it may allow for efficient spatial upscaling of SIF simulations, given widespread availability of temperature data, but not NPQ observations. Further improvements to the fluorescence model should focus upon distinguishing between the impacts of NPQ versus the de-activation of photosystems brought on by high-stress environmental conditions.

The boreal forest plays an important role in the global carbon cycle but has remained a significant source of uncertainty. Remote sensing can help us better understand the boreal forest’s role in the global carbon cycle. A faint light signal emitted by plant’s photosynthetic machinery, known as solar-induced chlorophyll fluorescence (SIF), is a promising remotely sensed proxy for carbon uptake, also known as gross primary productivity (GPP), due to its connection to photosynthesis and its strong relationship with GPP when observed by satellite. However, SIF and GPP are fundamentally different quantities that describe distinct, but related, physiological processes. The relationship between SIF and GPP is therefore complicated by both physical and ecophysiological controls. In particular, the dynamics of the SIF/GPP relationship are poorly understood under varying viewing directions and light conditions. This is further complicated in evergreen systems where canopy clumping and the presence of needles create a unique radiative environment. We use a combination of tower-based SIF and GPP measurements from a boreal forest field site compared with a coupled biochemical-radiative transfer model to understand illumination effects on the SIF/GPP relationship. We find that GPP is amplified under cloudy sky conditions in both measurements and model results. SIF on the other hand, shows no significant difference between sunny or cloudy sky conditions in modeled results, but does show a difference in measurements. We suggest that these differences may be due to viewing geometry effects that are important for SIF under sunny sky conditions or the presence of clumping. Accounting for the differences in the SIF/GPP relationship therefore is critical for the utility of SIF as a proxy for GPP. In summation, our results provide insight into how we can use remote sensing as a tool to understand photosynthesis in the boreal forest.

Solar-Induced Chlorophyll Fluorescence (SIF) is a powerful proxy for gross primary productivity (GPP) in Boreal ecosystems. However, SIF and GPP are fundamentally different quantities that describe distinct, but related, physiological processes. Recent work has highlighted non-linearities between SIF and GPP at finer spatial (leaf- to canopy- level) and temporal (half-hourly) scales. Therefore, questions have arisen about when, where, and why SIF is a good proxy for GPP and what the potential sources for divergence between the two are. The goal of this study is to answer two specific questions: 1) At what temporal scale is SIF a good proxy for GPP and 2) What are the predominant physical and ecophysiological drivers of nonlinearity between SIF and GPP in boreal ecosystems? We collected tower-based measurements of SIF (and other common vegetation indices) with PhotoSpec (a custom spectrometer system) and eddy-covariance GPP data at a 30-minute resolution at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA. We applied a combination of statistical and machine learning approaches to disentangle the influence of structural/illumination effects and ecophysiological variations on the SIF signal. Our results show that at a high temporal resolution (half-hourly), SIF and GPP are predominantly dependent on photosynthetically active radiation (PAR). Therefore, the non-linear light response of GPP drives non-linearity between SIF and GPP. Additionally, canopy structure and illumination effects become important to the SIF signal at high temporal resolutions. At the seasonal timescale, SIF and GPP exhibit co-varying responses to PAR, even when accounting for changes in canopy structure. We attribute changes in the light responses of SIF and GPP to sustained photoprotection over winter which co-varies with changes in temperature. Finally, we show that the relationship between SIF and GPP has a seasonal dependence caused by small differences between the light use efficiencies of fluorescence and photosynthesis. Accounting for this seasonally variable relationship will improve the use of SIF as a proxy for GPP.

Imaging spectroscopy is a remote-sensing technique that retrieves reflectances across visible to shortwave infrared wavelengths at high spectral resolution (<10 nm). Spectroscopic reflectance data provide novel information on the properties of the Earth’s terrestrial and aquatic surfaces. Until recently, imaging spectroscopy missions were limited spatially and temporally using airborne instruments, such as the Next Generation Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG), providing the main source of observations. Here, we present a land-surface modeling framework to help support end-to-end traceability of emerging imaging spectroscopy spaceborne missions. The LPJ-wsl dynamic global vegetation model is coupled with the canopy radiative transfer model, PROSAIL, to generate global, gridded, daily visible to shortwave infrared (VSWIR) spectra. LPJ-wsl variables are cross-walked to meet required PROSAIL parameters, which include leaf structure, Chlorophyll a+b, brown pigment, equivalent water thickness, and dry matter content. Simulated spectra are compared to a boreal forest site, a temperate forest, managed grassland, and a tropical forest site using reflectance data from canopy imagers mounted on towers and from air and spaceborne platforms. We find that canopy nitrogen and leaf-area index are the most uncertain variables in translating LPJ-wsl to PROSAIL parameters but at first order, LPJ-PROSAIL successfully simulates surface reflectance dynamics. Future work will optimize functional relationships required for improving PROSAIL parameters and include the development of the LPJ-model to represent improvements in leaf water content and canopy nitrogen. The LPJ-PROSAIL model can support missions such as NASA’s Surface Biology and Geology (SBG) and higher-level modeled products.

Reactive chlorine and bromine species emitted from snow and aerosols can significantly alter the oxidative capacity of the polar boundary layer. However, halogen production mechanisms from snow remain highly uncertain, making it difficult for most models to include descriptions of halogen snow emissions and to understand the impact on atmospheric chemistry. We investigate the influence of Arctic halogen emissions from snow on boundary layer oxidation processes using a one-dimensional atmospheric chemistry and transport model (PACT-1D). To understand the combined impact of snow emissions and boundary layer dynamics on atmospheric chemistry, we model \ch{Cl2} and \ch{Br2} primary emissions from snow and include heterogeneous recycling of halogens on both snow and aerosols. We focus on a two-day case study from the 2009 Ocean-Atmosphere-Sea Ice-Snowpack (OASIS) campaign at Utqia\.gvik, Alaska. The model reproduces both the diurnal cycle and high quantity of \ch{Cl2} observed, along with the measured concentrations of \ch{Br2}, \ch{BrO}, and \ch{HOBr}. Due to the combined effects of emissions, recycling, vertical mixing, and atmospheric chemistry, reactive chlorine is confined to the lowest 15 m of the atmosphere, while bromine impacts chemistry up to the boundary layer height. Upon including halogen emissions and recycling, the concentration of \ch{HO_x} (\ch{HO_x} = \ch{OH}+\ch{HO2}) at the surface increases by as much as a factor of 30 at mid-day. The change in \ch{HO_x} due to halogen chemistry, as well as chlorine atoms derived from snow emissions, significantly reduce volatile organic compound (VOC) lifetimes within a shallow layer near the surface.

Solar-Induced chlorophyll Fluorescence (SIF) provides a powerful proxy for determining forest gross primary production (GPP), particularly in evergreen ecosystems where traditional measures of greenness fail. The dynamics of the SIF/GPP relationship, however, are poorly understood under varying viewing directions and light conditions. This is, in large part, due to challenges in measuring SIF at the spatiotemporal scale that is necessary to understand these effects. Therefore, the aim of this work is to utilize high-temporal and spatial resolution SIF measurements to better constrain the response of SIF to ambient canopy illumination and viewing geometry. We use a PhotoSpec instrument and eddy covariance measurements to explore the SIF/GPP relationship under various viewing directions and light conditions during the 2019 and 2020 growing seasons at the Old Black Spruce site in Saskatchewan, Canada. PhotoSpec is a tower-based 2-D scanning spectrometer system capable of taking Fraunhofer-line based SIF retrievals in the red and far-red wavelength ranges with a 0.7 degree field of view at a ~30 second time resolution. Measured SIF and GPP are combined with SCOPE modelling results to provide a mechanistic understanding of the physical and ecophysiological drivers for the SIF/GPP relationship in the Boreal Forest. Our results show that viewing direction and solar zenith/azimuth angles are important for the SIF signal under direct light conditions, but not under diffuse. Furthermore, the SIF/GPP relationship changes under direct and diffuse light conditions at a 30 minute, daily, and monthly resolution. Our ability to use SIF as a proxy for GPP depends on a quantitative understanding of radiative transfer within the canopy and how scanning geometry impacts SIF measurements. These results provide an important insight into these relationships in the Boreal forest, a region where GPP has been traditionally difficult to track using remote sensing.