• We present four methods to calculate LAI on a daily basis from PAR. • Each method shows high linear correlation to MODIS and LAI-2000 datasets. • All methods provide a precise indication of start and end of the growing season. • PAR based LAI has broad potential to reveal phenological response to global change. Leaf area index (LAI) is a critical biophysical indicator that describes foliage abundance in ecosystems. An accurate and continuous estimation of LAI is therefore desirable to quantify ecosystem status and function (e.g. carbon and water exchange between the land surface and the atmosphere). However, deriving accurate LAI measurements at regular temporal intervals remains challenging, requiring either destructive sampling or manual collection of canopy gap fraction measurements at discrete time intervals. In this study, we present four methods to obtain continuous LAI data, simply derived from above and below canopy measurements of photosynthetically active radiation (PAR) at the Borden Forest Research Station from 1999 to 2018. We compared LAI derived using the four PAR-based methods to independent measurements of LAI from optical methods and the MODIS satellite LAI product. LAI derived from all four PAR-based methods captured the seasonal changes in observed and remotely sensed LAI and showed a close linear correspondence with one another (R 2 of 0.55 to 0.76 compared to MODIS LAI, and R 2 of 0.78 to 0.84 compared to LAI-2000 measurements). A PAR-based method using Miller's Integral theorem showed the strongest linear relationship with LAI-2000 measurements (R 2 =0.84, p<0.001, SE=0.40). In many years MODIS LAI indicated an earlier start of season and earlier end of season than the daily PAR-based LAI datasets showing systematic biases in the MODIS assessment of growing season. The four PAR-based LAI methods outlined in this study provide an LAI dataset of unprecedented temporal resolution. These methods will allow precise determination of phenological events, improve leaf to canopy scaling in process-based models, and provide valuable insight into dynamic vegetation responses to global climate change.

Remote sensing is a key method for advancing our understanding of global photosynthesis and is thus critical to understanding terrestrial carbon uptake and climate change. Increasingly sophisticated spectral indices including solar-induced florescence (SIF) and the photochemical reflectance index (PRI) are considered good proxies of canopy structure, biochemistry, and physiology. However, the relative influences of illumination intensity and angle on these measures are difficult to unravel, particularly at the scale of whole forest canopies. We exploit the solar dimming during the 2017 North American solar eclipse as well as a clear day before and cloudy day after the day of the eclipse. This novel approach allows us to assess changes in spectral vegetation indices due to illumination intensity independent of changes in illumination angle. Physiologically relevant spectral indices were most affected by dimming, with illumination level explaining 97% of variability in SIF and 99% of variability in PRI during the eclipse. The spectral change in reflectance through the eclipse period revealed changes in PRI were driven by reflectance differences at the 570 nm reference band rather than at the 531 nm signal band associated with xanthophyll pigment interconversions. This study refines our interpretation of vegetation properties from space with implications for our interpretation of signals related to terrestrial photosynthesis derived from sensors spanning a range of illumination conditions and angles.

Abstract This study presents the energy, water, and carbon (C) flux dynamics of a young afforested temperate white pine (Pinus strobus L.) forest in southern Ontario, Canada during the initial fourteen years (2003–2016) of establishment. Energy fluxes, namely, net radiation (Rn), latent heat (LE), and sensible heat (H) flux increased over time, due to canopy development. Annual values of ground heat flux (G) peaked in 2007 and then gradually declined in response to canopy closure. The forest became a consistent C-sink only 5 years after establishment owing in part to low respiratory fluxes from the former agricultural, sandy soils with low residual soil organic matter. Mean annual values of gross ecosystem productivity (GEP), ecosystem respiration (RE), and net ecosystem productivity (NEP) ranged from 494 to 1913, 515 to 1774 and −126 to 216 g C m−2 year−1 respectively, over the study period. Annual evapotranspiration (ET) values ranged from 328 to 429 mm year−1 over the same period. Water use efficiency (WUE) increased with stand age with a mean WUE value of 3.92 g C kg−1 H2O from 2008 to 2016. Multivariable linear regression analysis conducted using observed data suggested that the overall, C and water dynamics of the stand were primarily driven by radiation and temperature, both of which explained 77%, 48%, 28%, and 76% of the variability in GEP, RE, NEP, and ET, respectively. However, late summer droughts, which were prevalent in the region, reduced NEP. The reduction in NEP was enhanced when summer drought events were accompanied by increased heat such as those in 2005, 2012 and 2016. This study contributes to our understanding of the energy, water and C dynamics of afforested temperate conifer plantations and how these forests may respond to changing climate conditions during the crucial initial stage of their life cycle. Our findings also demonstrate the potential of pine plantation stands to sequester atmospheric CO2 in eastern North America.

Author(s): Luo, X; Chen, JM; Liu, J; Black, TA; Croft, H; Staebler, R; He, L; Arain, MA; Chen, B; Mo, G; Gonsamo, A; McCaughey, H | Abstract: Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2n=n0.71, root-mean-square errorn=n0.05nmm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.