Vegetation acts as a critical link between the geosphere, biosphere, and atmosphere, regulating the flux of water to the atmosphere via transpiration (E) and the input of carbon from the atmosphere to plants and soil via photosynthetic carbon assimilation (A). The rate of A is known to be seasonally dynamic, however, few studies have investigated how the ratio between E and A, known as the water use efficiency (WUE), changes with phenology. WUE directly impacts regional to global carbon and water cycles and lack of knowledge regarding the dynamics of WUE remains among the largest uncertainties in current earth system model (ESM) projections of carbon and water exchange in temperate forests. Here we attempt to reduce this knowledge gap by studying these dynamics across a range of eight deciduous tree species common to temperate forests of North America. Using gas exchange and spectroscopic measurements, we investigated seasonal patterns in leaf level physiological, biochemical, and anatomical properties, including the seasonal progress of WUE and foliar capacity for carbon assimilation, which corollate with seasonal leaf phenology. We incorporate these findings into a modeling framework that contains the same representation of A, E, and canopy scaling found in ESMs to explore the impact of parameterization, which tracks phenological status, on model forecasts. Our results indicate that both photosynthetic capacity and WUE are seasonally dynamic processes which are not synchronized. WUE increased from a minimum at leaf out toward a more conservative behavior at the mid-summer growth peak. This pattern was explained by a decreased stomatal aperture and a decrease in cuticular leakage with leaf aging. We also observed a seasonal increase in maximum carboxylation capacity, with maximum rates of A and modeled tree net primary productivity (NPP) occurring later toward the end of the summer. This change was primarily driven by an increase in foliar nitrogen content, and a shift in the ratio of Vcmax to Jmax between expanding and mature leaves. By applying our revised parameterization, which captures seasonal dynamics of gas exchange, into our model framework we aim to improve the process representation of leaf function in a temperate forest, and more faithfully represent dynamics of NPP and E in the early and late growth season.

Over the last nearly five decades, optical remote sensing has played a key role in monitoring and quantifying global change, plant diversity, and vegetation functioning across Earth’s terrestrial biomes. As a key tool for researchers, land managers, and policy makers, optical remote sensing facilitates scaling, mapping, and characterizing surface properties over large areas and through time. In addition, steady technological improvements have led to transformational changes in our ability to understand ecosystem state and change, particularly through the expansion of high spectral resolution (i.e. spectroscopic) remote sensing platforms. Point and imaging spectroscopy systems have been used across a range of scales, vegetation types, and biomes to infer plant diversity, leaf traits, and ecosystem functioning. However, despite the acknowledged utility of spectroscopic systems, data availability has been limited to smaller geographic regions given a number of technical challenges, including issues related to data volume and limited spatial coverage by previous Earth Observing (EO) missions (i.e. Hyperion). The NASA Surface Biology and Geology (SBG) mission is designed to fill this gap in ecosystem monitoring. As part of the Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) and Modeling end-to-end traceability (MEET) SBG efforts, we used field, unoccupied aerial system (UAS), and airborne imagery (from NASA’s AVIRIS-NG plafrom) to evaluate the impacts of proposed and theoretical sensor instrument properties on the retrieval of vegetation reflectance across tundra, shrub, and treeline ecosystems in Alaska. Existing observations and open-source tools are used for the simulation of surface reflectance under a range of atmospheric conditions, vegetation types, and different sensor properties. We find that retrieval uncertainty is reduced across all surface types with increasing detector signal-to-noise (SNR) but also key differences across different plant types. Results were also strongly tied to sun-sensor geometry and atmospheric state. Through this exercise we highlight key outcomes to consider for the SBG mission to optimize surface reflectance retrieval in high latitudes that will help to minimize errors in down-stream algorithms, such as functional trait retrievals.

Observations of Planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multi-spectral thermal infrared imager to meet a range of needs. First, and perhaps, foremost, it will be the premier integrated observatory for observing the emerging impacts of climate change . It will characterize the diversity of plant life by resolving chemical and physiological signatures. It will address wildfire, observing pre-fire risk, fire behavior and post-fire recovery. It will inform responses to hazards and disasters guiding responses to a wide range of events, including oil spills, toxic minerals in minelands, harmful algal blooms, landslides and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal and spectral resolution, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. The Research and Applications team examined available algorithms, calibration and validation and societal applications and used end-to-end modeling to assess uncertainty. The team also identified valuable opportunities for international collaboration to increase the frequency of revisit through data sharing, adding value for all partners. Analysis of the science, applications, architecture and partnerships led to a clear measurement strategy and a well-defined observing system architecture.

A primary source of uncertainty in terrestrial biosphere model (TBM) projection of carbon uptake and water cycling from ecosystems is the relationship between CO2 assimilation (A) and water loss via stomatal conductance (gs). A common mathematical framework for modeling this relationship is the “Unified Stomatal model”, which relates A to gs over environmental conditions and is governed by two terms, the stomatal slope (g1) and intercept (g0). Given their importance in determining the relationship between forest productivity and climate, an accurate and mechanistic understanding of the g1 and g0 parameters is crucial, particularly in wet tropical broadleaf forests where changes in water cycling could impact global weather patterns. These stomatal parameters are estimated using leaf-level gas exchange by two alternative methods: (1) a response curve where the environmental conditions are modified for a single leaf, or (2) a survey approach, where repeated measurements are made on multiple leaves over a diurnal range of environmental conditions. We compare the curve and survey approaches by conducting a comprehensive measurement campaign in which we paired diurnal gas exchange surveys with leaf level response curves for the estimation of g1 and g0 on six tropical species across a full range of leaf phenological stages. We examine how these different estimates impact model projection of gs, and how the consideration of a diurnal effect on g1 and g0 can improve predictions relative to a model using parameter estimates which are fixed over the photoperiod. Our results showed that age is an important factor to consider in estimates of g0, however there was no effect of leaf age on estimates of g1. The survey approach identified a diurnal trend associated with g1 and g0, which when accounted for improved model projections of diurnal trends in gs. We found that while both approaches yield equally statistically valid estimates of g1 and g0 at a fixed point in time, they are not directly comparable across diurnal timescales, where shifting water supply and carbon demand lead to dynamic canopy scale water use efficiency (WUE). These results suggest that to improve the accuracy of modelled gs in tropical forests, TBMs should recognize and implement diurnal variation in stomatal parameters which are associated with diurnal shifts in WUE.

The retrival algorithms used for optical remote sensing satellite data to estimate Earth’s geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal to noise ratio (SNR) performance to meet science objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at >10 nm spectral resolution, >30 m spatial resolution, and >8 day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.

Observing the environment in the vast inaccessible regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including NASA’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low-stature plants and very fine-scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long-term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi-scale spectroscopy, from lab studies to satellites that enable frequent and continuous long term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

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.