Research has shown that both fluorescence and photoprotection generate detectible leaf- and canopy-scale reflectance changes that are highly correlated with LUE at both the leaf and forest stand levels. Current research includes study of how these biophysical changes, with significant leaf- and forest-level reflectance correlations, can be used to quantify the degree of photosynthetic down-regulation in a spatially continuous mode. Future research directions could take the following forms.
Development of a theoretical (physically based) canopy-level model to predict reflectance changes at 531 nm. One of the basic research needs identified in this article is for the development of an improved understanding of the relationship between remotely sensed photochemical reflectance spectra and canopy-level down-regulation of LUE as it varies with sensor view and solar illumination conditions, forest stand geometry, and unstressed leaf optical properties. The ultimate goal of this research is to develop physically based models that are more generally applicable than the empirical studies used to demonstrate the general relationship between LUE and spectral observations.
There is as yet no physically based algorithm to robustly relate forest-level reflectance changes at 531 nm to down-regulation. Such algorithms need to be based on canopy reflectance models that account for the leaf-level reflectance changes as a function of photosynthetic down-regulation and that can scale these variations from the leaf up to the canopy level. Down-regulation in turn depends on incident PAR levels, which themselves vary throughout the canopy, being lowest in shaded portions and highest in directly sunlit areas. For a given level of incident PAR, down-regulation also depends on the availability of other resources required for photosynthesis, as well as species type (Barton and North 2001). In this regard, there are two limitations of existing canopy reflectance models: (1) None permits leaf-level optical properties to depend on leaf-level illumination conditions, and (2) leaf-level reflectance models that can compute reflectance changes as a function of down-regulation have not yet been developed. Until these limitations are addressed, algorithms will remain empirical, with model-forms and their coefficients based on EC tower-derived measures of vegetation LUE.
An intensive modeling effort will also lead to the development of a more comprehensive understanding of changes in resource allocation across the vertical profile of a forest canopy. Modeling will also help assess the impact of differing illumination conditions that can result in significant physiological differences throughout the crown. The results of these efforts can then be compared with the significant work already undertaken on the extent of diurnal, seasonal, and annual variations on the vertical distribution of the leaf physiological attributes. The recent development of easily deployed, robust, automatic, remote spectral sensors (e.g., Garrity et al. 2010) to detect differences in leaf-level reflectance changes throughout a tree crown will provide additional important measurement technology, allowing linkages with existing studies on photosynthetic light saturation curves, chlorophyll fluorescence, and other similar measurements of canopy photosynthesis.
Validation tool for conventional EC approaches. As discussed throughout this article, a critical calibration and validation tool to assess the accuracy of predicted canopy LUE has been the comparison with EC-derived estimates, which have their own intrinsic uncertainties and limitations. One future direction could therefore be the more integrated use of PRI and other reflectance-based approaches with EC techniques to overcome some of the inherent limitations of both approaches. For example, reflectance data might be used to help quantify the spatial variation observed within the EC footprint-a method of removing bias in the CO2 estimates. Secondly, as satellite-based LUE algorithms are developed, they will play a key role in the calibration and validation of models that use EC-based data sets.
Satellite-based applications. In addition to the development of improved theoretically based algorithms, we need satellite-based LUE algorithms to account for the fraction of shadow viewed by the satellite and also for the canopy illumination conditions at the time of observation. Sunlit leaves, which undergo the largest changes in reflectance at 531 nm, dominate the satellite-observed changes in canopy reflectance. However, the bulk canopy LUE is an average of the canopy, including both sunlit and shaded leaves. A sensor at a single view angle rarely measures the same sunlit-to-shaded ratio as that of the illuminated canopy. Thus, at a minimum, satellite approaches must account for the difference between what the sensor sees at a particular view angle and what the canopy sees at a particular illumination angle, and direct-to-diffuse ratio of incident radiance. So far, estimations of the fraction of shadow and sunlit leaves viewed by the sensor are achieved using mixture decomposition algorithms (Hall and Sellers 1995), provided shaded and sunlit end-member values are available by species. The actual fraction of shaded and sunlit leaves seen by the entire canopy is dependent on canopy morphology, illumination geometry, and the ratio of direct to diffuse radiation.
These illumination issues could be partly resolved through the use of multiangle remote sensing to measure the variation in the photochemical index (PRI) as a function of sensor view angle. Hall and colleagues (2008) showed that when reflectance of the sunlit and shaded leaves in a forest is equal, PRI is not dependent on the shadow fraction (or by inference, the sunlit fraction) viewed by the canopy. Thus, when the canopy is not strongly down-regulated, a multi-angle measurement of PRI when viewing different shadow and sunlit fractions will not vary strongly with view angle; conversely, the variation in PRI with viewed shadow fraction will be related to the degree of down-regulation. However, this approach requires measurement of the canopy PRI at a number, of sensor view angles during a sufficiently brief time interval, when LUE is relatively constant. Such sensors exist, however, and have been flown on both aircraft and spacecraft (e.g., Barnsley et al. 2004).
Satellite-observed reflectance depends upon-in addition to canopy reflectance-the aerosol optical thickness of the atmosphere. Aerosols affect satellite measurements of canopy reflectance through their effects on atmospheric path radiance and aerosol optical depth at the time of the satellite acquisition. These atmospheric effects must be removed or minimized in order to stabilize the satellite PRI signal for spatial and temporal variations in the atmosphere. One challenge with most atmospheric correction algorithms is that they require a priori information, as only one orbit observation is used to solve for aerosol optical thickness (AOT) and surface reflectance. A common solution to this issue is the retrieval of AOT over dark objects (Levy et al. 2007); however, this does not allow for multiangular observations. More recently, a new aerosol-surface reflectance retrieval algorithm was developed (Lyapustin and Wang 2009) that calculates AOT from the differences in path length observed by multiorbit acquisitions. This method has been demonstrated to stabilize the MODIS PRI signal over a growing season for a wide range of atmospheric conditions and view angles (Hilker et al. 2009) and may therefore be a possible way to remove atmospheric effects from PRI observations.
Following MODIS, the acquisition of the critical wavelengths from satellite-based radiometers is less clear. MODIS's operational replacement, the visible infrared imager/radiometer suite, currently is not configured with a 531-nm spectral band (http://jointmission.gsfc.nasa.gov/science/VIIRSScience.html). Other future missions, however, such as HyspIRI, one of NASA's decadal survey priorities set for launch late next decade, offer more promise for space-based estimation of LUE. HyspIRI is an imaging spectrometer that measures at wavelengths ranging from the visible to the shortwave infrared, with a spatial resolution of 60 m at nadir, and a three-week revisit time. HyspIRI has 10-nm spectral bands suitable for the calculation of PRI. Similarly EnMAP, to be launched by the German Aerospace Center in 2013, is also an imaging spectrometer that allows calculation of PRI at 30-m spatial resolution on a four-day repeat cycle and offering significant potential.
With increasing confidence in our ability to estimate LUE at a variety of scales and under a variety of illumination and view angle conditions, we can also consider the development of a more comprehensive integration of spatial and temporal predictions of LUE within existing or even new process-based forest production models. A range of models known as "light-use efficiency models," such as 3-PG (Physiological Principles Predicting Growth; Landsberg and Waring 1997), FOREST-BGC (Running and Coughlan 1988), BIOME-BGC (Heinsch 2006), and BIOMASS (McMurtrie et al. 1990), all use an implementation of the Monteith modeling approach, driven primarily by vegetation light absorption, which in turn determines potential photosynthesis rates. In these approaches, a maximum LUE value is set, either on a species or biome basis, and is then reduced by modifiers that represent the degree to which photosynthesis is inhibited by environmental factors. The modeled modified LUE represents the realized LUE of the forest canopy in response to its environment. Clearly, linkages that incorporate a variable LUE derived from remote sensing into these existing forest production models are a logical and critical next step.