Local-scale heterogeneity of photosynthetically active radiation (PAR), absorbed PAR and net radiation as a function of topography, sky conditions and leaf area index
Introduction
Radiation fluxes at the Earth's surface play important roles in many ecological, climatological, and hydrological systems. Biological activity is strongly dependent on radiative transfer both directly, through the interaction between phytoelements and radiant energy emitted by the sun, and indirectly through micrometeorological controls. Understanding the spatial distribution of photosynthetically active radiation (PAR) is important for predicting patterns of ecosystem functioning within a forest (Vierling & Wessman, 2000) and gross ecological production (GEP) has been directly linked with PAR in numerous studies (e.g. Goulden et al., 1997, Gu et al., 1999, Monteith, 1972, Oliphant et al., 2002, Schmid et al., 2000). Improvements in remote sensing technology and techniques to estimate photosynthetic activity using hyperspectral sensors can be used to assess small scale spatial variability of ecosystem dynamics (Blackburn, 1999, Gamon et al., 1993, Rahman et al., 2001, Rahman et al., 2003). Net all-wave radiation provides the fundamental input to the surface energy balance and spatial variability has been linked with a variety of biometeorological controls including surface climates (Kalthoff et al., 1999) and rates of evapotranspiration (Famiglietti & Wood, 1995) as well as boundary layer processes and local thermal circulations (Kossmann et al., 2002). Since seasonality plays a critical role in temporal variability of ecosystem functioning, it is also important to observe the role of seasons on spatial variability of radiation due to changing geometric relations, optical transmissivity and cloud conditions.
Current theoretical models and satellite derived data can provide valuable insight into spatial variability of surface radiation fluxes (Diak et al., 2004, Dubayah et al., 1990, Dubayah and Loechel, 1997, McKenny et al., 1999, Oliphant et al., 2003). Here we explore the use of theoretical models of surface radiation fluxes in complex topography, IKONOS imagery (visible and near-infrared) and empirical relations determined from data collected from a single flux tower to estimate components of spatial variability of (PAR), absorbed PAR (APAR) and net all-wave radiation (Q⁎) for a forested hilly region of mid-western USA. The primary objective is to estimate local-scale spatial heterogeneity of radiative fluxes at the top of the forest canopy across a topographically complex area. In particular we examine the role of topography, forest heterogeneity, optical transmissivity, cloud cover and seasonality on spatial variability of radiation.
Combining ecosystem modeling, satellite image interpretation and point observations has greatly enhanced the spatial understanding of ecosystem–atmosphere interaction at the continental to global scale (Running et al., 1999), although there remains a need to improve understanding of small-scale variability to inform downscaling of regional or larger models. Furthermore, characterization of ecosystem–atmosphere interaction is often based on data collected at individual towers using the eddy covariance approach (e.g. Schmid et al., 2000) as well as optical sampling techniques (Gamon et al. this issue). Assessing the spatial distribution of radiation components is therefore an important step toward scaling up of point measurements to regional-scale ecosystem estimates and variability within the source areas of turbulent flux measurements.
Section snippets
Site description and observational data
The location for this research was the Morgan–Monroe State Forest (MMSF) in south-central Indiana, mid-western USA (39°19′N, 86°25′W). MMSF is an extensive managed forest with a total area of 95.3 km2 (Schmid et al., 2000). The area used for model and image analysis in this study is a 3.5 × 3.5 km area of near-contiguous forest cover. At the center of this area on a ridge top with unobstructed sky view, is a 46-m instrumented AmeriFlux tower. Fig. 1 shows the shaded relief within the study area
Spatial model description
Surface radiation modeling in complex terrain has received growing attention in recent decades, although many studies have tended to focus on individual components, primarily incident shortwave radiation (Dubayah et al., 1990, Dubayah and Rich, 1995, Dubayah and van Katwijk, 1992, Kumar et al., 1997, Olseth and Skartveit, 2001, Whiteman, 1990, Whiteman and Allwine, 1986) or net shortwave radiation (Dubayah & Loechel, 1997), but including longwave radiation (Marks & Dozier, 1979), and PAR (
Results
This section reports on spatial variability of radiation fluxes found as a function of topography, cloudiness, seasonality and surface heterogeneity. For this analysis, simulations included empirical inputs from data collected from 1998 to 2001.
Conclusions
Potential spatial variability of PAR and Q⁎ were investigated for a hilly mid-latitude forested region in southern Indiana, in order to identify the relative controls of local scale topography, forest heterogeneity and atmospheric conditions. Simulations were run for observed cloudiness as well as the equivalent clear-sky conditions. Monthly mean model output compared well with observed point data with differences typically less than 10% and clear-sky days were simulated more accurately than
Acknowledgements
Funding for this research was provided by NIGEC, Dept. of Energy (Co-operative Agreement No. DE-FC03-90ER61010). The authors would like to thank the large number of people involved in data collection at the MMSF tower site, particularly Steve Scott and Brian Offerle.
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2016, Remote Sensing of EnvironmentCitation Excerpt :LUE is defined a posteriori over a certain time interval as the ratio of gross primary production (GPP) to photosynthetically active radiation absorbed by green canopy elements (APAR) (Gitelson & Gamon, 2015; Monteith, 1977) and is widely utilized in modeling contexts. Both PAR and APAR can vary significantly within a forest canopy and are proportional to, for example, canopy structural parameters (e.g., the distribution of leaf area density) and the angle of illumination (e.g., Gamon et al., 2001; Hall, Hilker & Coops, 2012; Oliphant, Susan, Grimmond, Schmid, & Wayson, 2006). Gamon et al. (2001) demonstrated that APAR alone can explain a significant part of the photosynthetic variation within plant canopies.
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