Interactive comment on “ Satellite-based estimates of light-use efficiency in a subtropical mangrove forest equipped with CO 2 eddy covariance ”

Introduction Conclusions References


Introduction
Mangrove forests have received significant attention recently due to an increased recognition of the role these systems play in global carbon (C) cycles (Donato et al., 2011).However, compared to terrestrial systems, the processes that regulate ecosystem-atmosphere carbon dioxide (CO 2 ) fluxes, including gross primary productivity (GPP) and ecosystem respiration (R E ), are not well understood.Tower-based, eddy covariance (EC) measures of the net (i.e.GPP-R E ) ecosystem-atmosphere CO 2 exchange (or NEE) in conjunction with continuous measurements of environmental variables were started only recently compared to terrestrial systems (see Barr et al., 2010) and remain extremely rare.These observations show that canopy-scale CO 2 fluxes are influenced by stressors that are unique to mangrove forests, including periodic flooding and variable soil pore-water salinity.Using these EC data to calculate canopy-level light use efficiency (LUE, defined as GPP divided by incoming photosynthetic active radiation PAR) will improve our understanding of C cycling in these forests.Modeling canopy-level LUE in relation to PAR and ground-based scalars in turn provides a first step towards using satellite reflectance data to define the larger role these forests play in both regional and global C budgets.However, typical LUE models developed for terrestrial systems do not account for the unique factors that influence C dynamics in tidal forests, and new approaches are needed.
For all plant communities, including mangrove forests, the net ecosystem carbon  (Barr et al., 2012).However, comprehensive in situ measures of mangrove forest C dynamics that simultaneously account for both vertical C fluxes (i.e.NEE) and lateral C fluxes (F ) have not been attempted.Continuous and long-term estimates of F DIC , F DOC , and F POC usually do not exist.Instead, lateral C fluxes are ordinarily determined only during short-term intensive field campaigns (e.g.Romigh et al., 2006;Alongi et al., 2004;Souza et al., 2009;Mayorga et al., 2005).In the absence of these measurements, nighttime, tower-based NEE estimates in many mangrove forests cannot be used as a direct proxy for R E as they are in terrestrial systems, since the actual ecosystem-scale respiratory CO 2 fluxes in tidal systems will also include respiratory fluxes derived from F transported outside of the EC footprint.Nonstandard methods for calculating R E and, therefore, GPP are required in mangrove forests utilizing EC.Monteith (1966Monteith ( , 1972) ) first proposed the concept of relating GPP to PAR through a light use efficiency term, ε, or multiplicative efficiency terms.Light use efficiencies describe the process of solar irradiance transmission through the atmosphere, light absorption by green vegetation, and photosynthetic CO 2 assimilation by foliage.
Light use efficiency terms in ecosystem models (e.g.Xiao et al., 2004;Cook et al., 2008;Potter, 2010) are calculated in a two-step process.First, functional relationships are established between environmental drivers, such as temperature and water stress that regulate physiological functioning and thus GPP.A second step is to determine how much of the incident solar irradiance is absorbed by photosynthetic active green vegetation.Useful proxies for the process of light absorption by vegetation can be determined using remote sensing information (Zhao and Running, 2008).
In have been tested and validated using relationships between remote sensing information and ground-based C flux data (Heinsch et al., 2006;Turner et al., 2006;Zhao et al., 2005).More recently, Chen et al. (2010) applied the enhanced vegetation index (EVI) as input into a vegetation photosynthesis model (VPM, Xiao et al., 2004) to take advantage of the high return frequency (1-2 per day) of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the increased spatial resolution (30m) of LANDSAT.However, the usefulness of satellite reflectance-driven models such as these developed for simulating terrestrial GPP, such as the MODIS GPP product http://modis.gsfc.nasa.gov/data/dataprod/nontech/MOD17.php) has not been determined for mangrove forests.These types of models are needed to better integrate estimates of mangrove forest CO 2 assimilation patterns across tropical and subtropical coastal zones into global-scale C balance calculations.Therefore, the objectives of this study are: (1) to calculate R E and GPP in a tidal mangrove forest using a novel application of EC-based estimates of NEE, (2) to parameterize and test a model of daily canopy GPP and LUE driven by satellite reflectance data, and (3) to compare these GPP estimates to the MODIS GPP product for this location.

Site description and meteorological and eddy covariance measurements
The study site (25.3646• N, 81.0779 • W), located within Everglades National Park, is near the mouth of the Shark River and ∼ 4 km from the Gulf of Mexico (Fig. 1).The During October 2005, the forest experienced a major disturbance caused by Hurricane Wilma.The disturbance caused major defoliation of the forest and tree mortality, with 25 % of stems > 1.5 m in height being destroyed by the hurricane winds (Barr et al., 2012).Following Hurricane Wilma, instruments were deployed on a new 30-m tower with renewed measurements beginning in November 2006 (Barr et al., 2012).Continuous meteorological measurements are recorded as 1-min averages on data loggers (model CR3000, Campbell Scientific Inc. Logan, UT), and stored in files saved at 30-min intervals in a laptop computer located on site.High frequency (10 Hz) EC data are stored directly on the laptop computer for subsequence processing to derive 30-min average fluxes (using Matlab code, The Mathworks, Inc., Natick, MA), following the protocols employed by scientists associated with the AmeriFlux network (http://public.ornl.gov/ameriflux/index.html).Data gap-filling procedures were implemented to produce continuous time series.Additional details for site characteristics and data processing protocols are provided in Barr et al. (2010) and Barr et al. (2012).

Partitioning NEE into R E and GPP
Estimates of ecosystem respiration (R E ; µmol CO 2 m −2 s −1 ) are needed to calculate GPP (µmol CO 2 m −2 s −1 ), which is defined as GPP = −NEE + R E .In tidal mangrove forests equipped with EC, nighttime NEE can be considered as a proxy for nighttime R E only when the sediment surface is exposed to the atmosphere during low tides.When the sediment surface is inundated during a flood tide, a fraction of the CO 2 respired by soil, roots, and detritus is dissolved in the overlying water column and transported into the adjacent estuary as DIC during the subsequent ebb tide.Therefore, towerbased nighttime NEE = R E when the surface is inundated.To correct for this effect in our calculations of GPP, non-linear least squares regression analyses were performed to express nighttime R E as a function of air temperature, T A (after Reichstein et al., 2005)  (Fig. 2).Regression analyses of nighttime NEE as a function of T A during high tides show significantly different relationships than at low tide and are included in Fig. 2 for comparison.High tide data were excluded from our calculations and the function relating low-tide R E to T A was used to gap-fill these periods.Data gaps occurring when the EC system was not operating, or when there was insufficient turbulence and when the flux footprint included large contributions from adjacent rivers (Barr et al., 2010) were also filled using this function.The R E function in (2) includes both an Arrheniustype activation component and a high temperature deactivation response.
The R E20 ( µ mol CO 2 m −2 s −1 ) represents the ecosystem-level respiration rate at the reference air temperature, T REF , which is set as 293.15K.This R E20 value differs from the more common reference temperature of 283.15K (Lloyd and Taylor, 1994) because it is a closer approximation of the minimum temperature range frequently observed in this forest.Also, the R E was related to air temperature rather than the more prevalently used soil temperature (Lloyd and Taylor, 1994).The use of air, rather than soil temperature, was justified by considering the sources contributing to R E .Foliage respiration alone can contribute 73 % of the total R E during low tide periods at night (Barr et al., 2010).Measurements of soil respiration in mangrove forests throughout the Caribbean, Australia, and New Zealand suggest that soils are a relatively minor contributor to R E (Lovelock, 2008).
In (2), the E 0 (K) and E D (K) parameters are temperature-dependent activation energy and deactivation sensitivity, respectively.The T 0 (K) also accounts for changes in activation energy associated with variations in temperature.Its expected values range between 0 K and observed air temperature (Lloyd and Taylor 1994) functional response to high temperature (> 35 • C) as relationships describing foliage carboxylation and dark respiration rates (Campbell and Norman, 1998).The response of R E to temperature is a dynamic process, and consequently the fitted characteristics in (2) are expected to change seasonally.To capture such variability in respiratory responses, values of R E and regression characteristics (e.g.R E20 , E 0 , T 0 , E D , T D ) were determined for a 3-day moving window using nighttime data during low tide periods from a 15-day centered window.Similar to the findings of Reichstein et al. (2005), a window size of 15 days was sufficiently long to provide adequate data and temperature range for performing the non-linear regression of (2) and short enough to minimize the confounding seasonal changes in respiration response.During each 3-day period, the relationship in (2) was used to compute half-hourly daytime R E , and halfhourly GPP values were computed as the difference between R E and daytime NEE (i.e.GPP = −NEE + R E ).Half-hourly values of GPP (µ mol C m −2 s −1 ) were summed as shown in (3) to provide daily GPP and 8-day average values in units of mol C m −2 day −1 .This 8-day time step matches that of the MODIS product and removes noise in the daily data while retaining seasonal trends.The coefficient of 0.0216 in (3) converts units of µ mol CO 2 m −2 s −1 to g C m −2 per each 30-min flux averaging interval.

Albedo and EVI
We investigate seasonal changes in canopy structural properties using two measures of canopy reflectance: albedo and EVI.The surface albedo (Fig. 3a) was estimated as the ratio of reflected to incoming solar irradiance measured above the canopy.The adjusted albedo was estimated as the average of albedo values for the periods when the solar elevation angle ranged between 35 and 50 • .This adjustment was necessary to remove the influence of changing daily solar elevation angles over the course of the study.Introduction

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Full The MODIS EVI product was used to examine seasonal patterns in the mangrove canopy reflectance properties.It is well established (Huete et al., 2002;Jiang et al., 2008) that the EVI data are more reliable compared to NDVI in environments with high biomass content.For this study, the EVI data (Fig. 3b) were obtained from the MOD13A1 product (EOS; http://modis.gsfc.nasa.gov/).The mangrove flux tower site is included in grid h10v06, with a 500-m spatial resolution.Using GIS (Geographic Information System) software (Matlab Mapping Toolbox, The Mathworks Inc., Natick, MA), the 16-day composite average EVI values for the pixel corresponding to the flux tower site and the 8 adjacent pixels were extracted for the period 2000 to 2011.This 9-pixel domain approximates the extent of the EC measurement footprint (see Fig. 1 in Barr et al., 2010).

LUE modeling framework
The mangrove vegetation photosynthesis light-use efficiency model (MVP-LUE) presented here is based on the production efficiency modeling (PEM) framework (Prince and Goward, 1995;Running et al., 1999;Running et al., 2000).It has the basic form of where LUE (mol C (mol photons) −1 ) is calculated as the ratio of 8-day sums of GPP to PAR (mol photons m −2 ).The ε g is a quantum efficiency (mol C (mol photons) −1 ) that describes conversion of incident PAR into gross primary production specific to the irradiance incident on green vegetation.FAPAR (unitless) is the ratio (8-day average) of PAR absorbed by green vegetation to total incident PAR determined above the vegetated landscape.Previous modeling studies suggested that FAPAR linearly increased with EVI (Xiao et al., 2004) or NDVI (Goetz et al., 1999;Schubert et al., 2010).However, in the present study, FAPAR increases in response to increasing EVI as determined Introduction

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Full where m EVI determines the initial slope of FAPAR response to increasing EVI.The rate of increase in 8-day average FAPAR to increasing EVI diminishes and is dependent on the value of m EVI .We found that the observed quantum LUE of the mangrove ecosystem approaches some optimum efficiency, ε (defined as the light conditions when maximum NEE is attained).The ε is not known a priori and must be determined from an optimization procedure.Most of the time, environmental conditions are less than optimal, and therefore ε g is often less than ε.The ε g represents a multiplicative chain of efficiencies (Monteith, 1972) where each f term in the chain accounts for a reduction in quantum LUE below ε 0 .
Several variables contribute to reducing the quantum efficiency in this forest.The first is elevated foliage temperature resulting from air temperatures (T A > 303 K) which elicit sub-optimal carboxylation rates (Barr et al., 2010).Such responses to elevated temperature can be expressed by the relationship shown in (6) formulated by Raich et al. (1991):

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Full (i.e.GPP/GPP Max ) that followed the relationship in (6) but each possessed its own unique characteristics (T Min , T Max , and T Opt ).While this relationship in ( 6) was not previously quantified for the mangrove ecosystem, the shape of the curve is consistent with −NEE response to T A during 2004-2005 (see Fig. 6 in Barr et al., 2010) for conditions when PAR > 1000 µ mol (photons) m −2 s −1 .Barr et al. (2010) showed a linear decline in the 8-day averages of LUE/LUE salinity=0 versus 8-day average soil pore salinity between 10-40 parts per thousand (ppt) of dissolved solutes during both pre (2004)(2005) and post-hurricane (November 2006 to December 2011) periods.This reduction in LUE attributed to changes in salinity (f sal ) is defined in (7).LUE salinity=0 was determined from the intercept of the regression The m sal defines the rate of decrease in f sal in response to increasing salinity.The decline in LUE with increasing salinity may be partially attributed to photosynthetic saturation under high PAR (> 50 mol photons m −2 day −1 ) which coincides with maximal salinity during May and June.A linear function in (8) was included to account for photosynthesis saturation manifested as declining LUE with increasing PAR.
The m PAR defines the rate of decrease in f PAR in response to increasing PAR.Since FAPAR, f TA , f sal , and f PAR have a maximum value of 1, light-use efficiencies approach ε as EVI attains the value of 1, air temperature approaches T Opt , and salinity (ppt) and PAR (mol photons m −2 day −1 ) decrease to zero.The overall resulting quantum efficiency may then be expressed as the multiplicative set of efficiencies to account for the effects of temperature, salinity, and PAR as shown in (9).
To implement the model described in (4) to ( 9 optimization approach.We apply a Bayesian framework to solve for the posterior probability of model parameters and LUE during the periods 2004-2005and November 2006 to 2011 when EC-derived estimates of GPP and LUE are available.The Bayesian analytical framework provides several advantages over more traditional model optimization approaches, including the ability to directly estimate uncertainties in modeled LUE without the use of ad hoc procedures.Outputs from the optimization procedure provide the forcing terms (e.g.m EVI T Opt , etc.) that are described probabilistically, thereby allowing us to assess the applicability of each term.To cast this model within the Bayesian framework, LUE was considered to exhibit a normal distribution as: where µ LUE is the time-varying mean and is equal to the expected 8-day average LUE with variance σ LUE .A quantile-quantile (QQ) plot of LUE data against the standard normal distribution was used to verify the normality assumption.The forcing terms were considered to have a prior probability distribution which, when taken together, follow a multivariate normal distribution.That is, uncertainty about Σ before the data are taken into account.The inverse-Wishart distribution represents the multivariate generalization of the scaled inverse-chi-squared distribution, which is the conjugate prior of the univariate normal distribution with unknown mean and variance.The inverse-Wishart distribution is defined by its own set of parameters, Ω and ν, commonly referred to as hyperparameters that represent the inverse scale matrix and degrees of freedom of the distribution, respectively.
The Ω was initialized with a 6×6 identity matrix, and the degrees of freedom, ν = 6, representing the number or forcing terms.To learn the optimal probability distributions of the forcing terms (ε, m EVI T Min , T Max , T Opt , m sal ), a Markov chain Monte Carlo (MCMC) procedure with Gibbs sampling (Cassella and George, 1992;Gilks et al., 1995) was performed in Matbugs.Matbugs is a Matlab (The Mathworks Inc., Natick, MA) interface to WinBUGS (Spiegelhalter et al., 2003).Gibbs sampling is the simplest of the Markov chain simulation algorithms (Gelman et al., 2004)  the world exhibited a quadratic function response with increasing soil temperature.In the case of the Everglades site, the respiratory response declined above some critical temperature that also changed with seasons and as a result of disturbance.The increase in nighttime NEE in response to increasing T A following the hurricane disturbance was most evident during dry season months when the values increased by ∼ 1 µmol CO 2 m −2 s −1 as T A exceeded 20 • C, and continued on an upward trend until reaching a maximum of ∼ 4 µ mol CO 2 m −2 s −1 at 22 • C. Before the hurricane disturbance, NEE declined with temperatures exceeding 19 • C. Due to the long-term nature of this investigation (spanning several years), the ecosystem respiration response captured the broad temperature ranges and levels of inundation experienced by the mangrove forest.As a result, it was possible to identify and quantify the dynamic character of respiratory responses to temperature and subsequent declines in respiration above T opt .

Physical drivers of mangrove productivity
In South Florida, mangroves receive highly variable amounts of PAR (Fig. 4a) resulting from sinusoidal seasonal patterns and cloud cover from localized convective storms during the May to October wet season.During the extended cold spell of January 2010, temperatures reached nearly the freezing point during several early morning periods, with an 8-day average of approximately 10 • C. Surface water salinity (Fig. 4c) values exceeded 40 ppt at the end of the dry seasons in May and June, but declined throughout the wet seasons with the lowest values reaching 15-20 ppt during October and November when freshwater discharge through Shark River reached peak levels.has adapted a physiological strategy for maintaining high GPP rates throughout most of the year.Elevated GPP values during cloudy days may be caused by higher fractions of diffuse compared to direct solar irradiance penetrating to lower canopy layers and raising the whole canopy LUE (Barr et al., 2010).Other forests experience enhanced C assimilation when subject to elevated diffuse irradiance (Gu et al., 2003).

Light use efficiency
Seasonal LUE patterns (Fig. 5d) were different compared to GPP and exhibited seasonal maxima (13-20 mmol C (mol PAR) −1 ) during the months of September to December while PAR and salinity levels were declining or at their seasonal minima.LUE values generally declined with the progression of the dry seasons, reaching annual minima of 7-10 mmol C (mol PAR) −1 during the months of April to June, with some inter-annual variability.For example, in January 2010 LUE declined from an 8-day average of 19 to 6 mmol C (mol PAR) −1 from 19 December 2009 to 4 January 2010.This period coincided with several weeks of nighttime temperatures that approached 0 • C.These cold air masses induced foliage senescence and extensive litterfall.Salinity represents an important control on mangrove forest LUE, and therefore on ecosystem productivity.
The regression analyses that relate the 8-day average LUE (i.e.LUE/LUE salinity=0 ) to salinity (Fig. 6) provided a 1.5 % reduction in this ratio for every 1 ppt salinity increase.
The trend formed the basis of the linear forcing of salinity on LUE included in relationship (7) which was required in the MVP-LUE model.The linear forcing was also consistent with previous results (Barr et al., 2010) and showed that midday LUE declined linearly with increasing salinity resulting in a 48 % reduction in LUE from the lowest (16.7 ppt) to highest (34., 1995;Sobrado, 1999;Krauss and Allen, 2003;Parida et al., 2004) that indicated declines in leaf-level C assimilation in response to increasing soil water salinity By extrapolating the observed linear decline in LUE, the productivity of the mangrove ecosystem ceases at surface water salinity approaching 70 ppt according to the model expressed in relationship ( 7).Whereas such high salinity levels do not occur at the Everglades study site, this estimate is in close agreement with average salinity tolerances of 60-90 ppt reported for red, white, and black mangroves (Odum et al., 1982).

MVP-LUE model results
The cross-validated LUE model (Sect.2.4) was capable of reproducing the observed responses of LUE and GPP to seasonal changes in environmental variables and recovery from a major hurricane disturbance.The modeled LUE median and 95 % uncertainty bounds (Fig. 7) provided posterior predictions from 10 000 MCMC iterations in each of 3 independent chains determined from 5-fold cross validation salinity (Fig. 6).This apparent sharper decline in LUE with increasing salinity masked the effect of increasing PAR on LUE since seasonal PAR peaks in nearly the same season (May-June) as salinity.The Bayesian model results suggest that LUE significantly declined with increasing PAR with a slope, m PAR of 0.0101 ± 0.0004 (Table 1).Photosynthetic saturation with increasing PAR is currently not included in many light-use efficiency based models of productivity using satellite data (e.g.Xiao et al., 2004;Cook, et al., 2008;Chen et al., 2010) An increase from the lowest (15 ppt) to highest (39 ppt) salinity values observed during the study period was predicted to result in an 11 % reduction in LUE.Also, an increase in PAR from the lowest (17 mol photons m −2 day −1 ) to highest (67 mol photons m 2 day −1 ) 8-day average during 2004-2011 resulted in a 51 % reduction in LUE.Air temperature, salinity, PAR, and EVI were all determined as significant predictors of LUE (Fig. 7).Low temperatures (∼ 10 • C) during passages of cold fronts can last from a few days to weeks during December to February resulting in large reductions in LUE and therefore GPP.For instance, the passage of cold fronts during January 2010 resulted in estimated and modeled LUE of ∼ 6 mmol C (mol photons) −1 .While other controls on LUE remained constant, a change in air temperature from 28 • C (optimum temperature) to 10 • C was predicted to result in a 65 % reduction in LUE.These results confirm that mangrove forests become severely stressed when daily average temperatures drop below ∼ 5 • C. Ross et al. (2009) measured high mangrove mortality following freeze events and the inability of mangroves to survive in climates where temperatures near the freezing point are frequent.

MVP-LUE and MODIS GPP compared to EC GPP estimates
The calibrated MVP-LUE model provided an improved mechanistic understanding of mangrove forest productivity compared to the standard MODIS GPP product.
Specifically, least squares linear regressions of 8-day MVP-LUE modeled GPP values to EC-estimated GPP (Fig. 8a grove forests to temperature has not been calibrated in the MODIS product and may partially explain the lack of correlation between MODIS GPP and EC-estimates.Also, the increased variance in MODIS GPP compared to EC-estimates (Fig. 8b) may be attributed to the MODIS model structure, which considers GPP as linearly increasing with PAR.The dampened GPP response to PAR identified in the MVP-LUE model resulted in seasonal variability in GPP better matching observations.Also, the MVP-LUE model captured the sustained plateau in EC-estimated GPP into November.This resulted from lowered salinity stress during September to November represented in the model (Eq.7) and a modulated response of GPP to daily PAR integrals represented by the decline in LUE with increasing PAR (Eq.8).

Summary and conclusions
This research represents a first attempt to design and verify a light use efficiency model for mangroves through the integration of remotely sensed information, and meteorological and hydrologic data.This study is the first one to quantify the respiratory responses of mangrove forests over temporal scales of several growing seasons.Ecosystem respiration was successfully modeled using an atypical response function that includes a high temperature (∼ 33 Full temperature-dependent response of ecosystem respiration to air temperature provided a critical first step in modeling mangrove GPP.Observed seasonal patterns in 8-day LUE were controlled by variability in daily PAR and air temperature, and to a lesser extent salinity and EVI fluctuations.LUE was lower when seasonal PAR was highest during April and May as a result of photosynthetic saturation.Also, salinity maxima of 35 to 40 ppt contributed to canopy-scale reductions in LUE during April to early June amounting to a 5 % reduction in LUE per each 10 ppt increase in salinity.Lowered LUE values during December and January were the result of lower air temperatures and lowered physiological activity.As temperatures approach 3 • C, our model predicts that CO 2 uptake in these forests approaches zero.Significantly reduced EVI values after Hurricane Wilma in 2005 also resulted in significantly lowered model estimates of CO 2 uptake during the period when the EC tower was not operating.These results suggest that mangrove forest LUE can be quite variable in subtropical environments that experience seasonal variations in solar irradiance and air temperature, and disturbance from tropical storms. The model and functional relationships determined in this study provide an important first step for understanding the larger role mangrove forests play in both regional and global C budgets.Remote sensing applications building on these results provide a means to estimate CO 2 fluxes in areas outside the flux tower footprint and in other mangrove forests around the tropics and subtropics.To do this, spatiotemporal patterns in salinity are required as model input, which may be resolved as in the Everglades from networks of hydrologic monitoring stations.PAR and air temperature data fields are also required.However, validating this model in locations not equipped with EC will require novel approaches that link predicted GPP values to other measurable parameters, such as biomass accumulation, or NECB at appropriate time scales.As more EC towers are deployed in other types of mangrove forests, LUE models such as this one may be used to identify patterns in quantum efficiencies across species, across forest structural characteristics (e.g.scattered or dwarf forests), and latitudinal Introduction

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Full  Remote, 44, 1908Remote, 44, -1925Remote, 44, , 2006.Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195-213, 2002. Xiao, X., Hollinger, D., Aber, J. D., Goltz, M., Davidson, E., Zhang, Q., and  Full  Full balance (NECB, or the C accumulating in plants and soils, Chapin et al., 2006) can be estimated using the following expression: NECB = −NEE + F DIC + F DOC + F POC (1) where F DIC , F DOC , and F POC are the net lateral exchanges of dissolved inorganic C (DIC), organic C (DOC), and particulate organic C (POC).All terms in Eq. (1) are expressed in g C m −2 t −1 .Negative NEE values represent a loss of C from the atmosphere, and negative F values represent C loss from the ecosystem.In terrestrial systems with BGD Discussion Paper | Discussion Paper | Discussion Paper | minimal F , positive nighttime NEE values are considered a proxy for R E .Compared to terrestrial systems, mangrove forests are characterized by low nighttime NEE, large daytime −NEE values and large −F one of the first attempts to incorporate remote sensing information into ecosystem models, Tucker et al. (1983) estimated the productivity of grasslands using the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) aboard polar orbiting platforms.Several other models Discussion Paper | Discussion Paper | Discussion Paper | onsite 30-m eddy covariance tower is co-located with long-term monitoring sites operated by the Florida Coastal Everglades Long Term Ecological Research (FCE LTER, site SRS6) program and the US Geological Survey (site SH3).Around the tower site the dominant mangrove species include Rhizhophora mangle, Avicennia germinans, and Laguncularia racemosa, and their maximum heights reach about 19 m. Discussion Paper | Discussion Paper | Discussion Paper | measurements and EC observations to determine NEE have been made since 2003 at a height of 27 m.
using only valid NEE values determined when the sediment surface was exposed Discussion Paper | Discussion Paper | Discussion Paper | . The T D (K) term is the temperature at which deactivation occurs, and represents a unique feature in this study that explicitly accounts for a reduction in respiration above a threshold temperature.The deactivation term, represented by the denominator in (2) has the same Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | from the 500-m spatial resolution data according to: FAPAR = 1 − e −m EVI ×EVI(5) Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), the individual forcing terms (i.e.ε, m EVI , T Min , T Max , T Opt , m sal , m PAR ) must be derived from the data through the use of an Discussion Paper | Discussion Paper | Discussion Paper | 11) with mean values, µ, and covariance matrix, Σ. Off diagonal terms in Σ explicitly quantify the inter-dependence of model forcing terms, if such relationships exist.The inverse-Wishart distribution (O'Hagan and Forster, 2004) was used to describe the prior probability distribution of Σ because it represents the conjugate probability distribution of the multivariate normal distribution (Gelman et al., 2004), and expresses the Discussion Paper | Discussion Paper | Discussion Paper | and is used to directly sample from each conditional posterior distribution in a model.The resulting distribution of the forcing terms maximizes the likelihood that the LUE during the study period would be observed given the modeled LUE values.The Gibbs sampling procedure within Win-BUGS requires initial values (i.e.best guesses) for all the forcing terms.Here, initial values were determined using a constrained optimization technique (Matlab Optimization Toolbox) in minimizing the sum of squared errors (SSE) between modeled and EC estimated 8-day LUE values obtained during the entire study period of 2004-2005 and November 2006 to December 2011.The constrained optimization is useful for obtaining a single point estimate of forcing terms, but does not provide a robust fit that includes the probability distribution of both parameters and modeled LUE values.R E and GPP from NEE Temperature, level of inundation, and foliage physiology drive respiration in this forest.Nighttime NEE increased with increasing T A below ∼ 25 • C during both high and low tide periods during 2004-2005 (Fig. 2a).NEE was ∼ 1 µmol CO 2 m −2 s −1 higher during low tides, and NEE rates converged at temperatures > 25 • C for both low and high tide periods during 2004-2005 and 2006-2011 (Fig. 2a and b, respectively).The exponential function with deactivation in (2) generally fit the NEE data during 2004-2005 and 2006-2011 periods (Fig. 2a-d).During 2004-2005, there was some evidence of bimodality in R E response to temperature with maxima occurring at T A values ranging between 15 • C and 20 • C and 25 • C to 28 • C (Fig. 2a).The NEE was ∼ 1 µ mol CO 2 m −2 s −1 higher during 2006-2011 compared to 2004-2005 for temperatures above 25 • C, possibly due to an increased respiratory contribution from decomposing coarse woody debris (CWD) generated by the hurricane.The hurricane disturbance also resulted in warmer soils during 2006-2011 as more solar irradiance reached the soil surface beneath the damaged canopy (Barr et al., 2012).Warmer soils in this system are expected to lead to increased belowground respiration and increased turbulent mixing near the surface, both of which would have led to larger nighttime NEE measurements derived from tower-based EC.The substantial seasonal changes in the respiratory response of the mangrove ecosystem (Fig. 2c and d) required the use of moving windows to fit (2) to these data.By partitioning the data by time, particularly into dry and wet season periods, the apparent bi-modality of nighttime NEE versus T A response (Fig. 2a) was no longer apparent in the fitted model.The deactivation term in (2) that is needed to account for the observed decline in nighttime NEE at elevated T A represents a unique characteristic of NEE patterns at this site compared to terrestrial forests.Lovelock (2008) also observed that direct measurements of soil respiration in mangrove forests around Discussion Paper | Discussion Paper | Discussion Paper | -scale CO 2 fluxes in mangrove forests vary seasonally as a result of changes in leaf area index and physiological responses to stressors.Such changes in the amount and function of foliage were inferred from temporal patterns in locally-measured surface albedo the satellite-based greenness index, EVI.Albedo (Fig. 3a) varied seasonally with minimum values of 0.10 to 0.11 during May and June and maximum values of 0.13 to 0.15 during December to January.Albedo was about 0.12 during January 2007 and represented a decline of > 0.01 compared to values observed before Hurricane Wilma.Raw and adjusted albedo variability resulted in response to the recovery of foliage from the 2005 disturbance, with apparent full recovery observed by 2011.Structural damage and defoliation of the mangrove forest was evident in the 16-day EVI time series (Fig. 3b) in the days following hurricane disturbance on October 24, 2005.The EVI declined from 0.4-0.5 to 0.22 following the 2005 disturbance.When tower measurements resumed in November 2006, refoliation of surviving branches in upper canopy and new shoots in the understory had already occurred, with rapid regrowth occurring Discussion Paper | Discussion Paper | Discussion Paper | during June to October 2006.Yet, recovery was incomplete.EVI exhibited a decline of ∼ 12 % when 2007-2008 values were compared with those obtained in 2004-2005.These patterns were consistent with the 30 % lower annual -NEE obtained for 2007.Though noisy, EVI exhibited similar sinusoidal seasonal patterns with maxima and minima values coinciding with the winter and summer solstices, respectively.EVI values represent a much larger area than do the albedo measurements (∼ 250 000 m 2 pixel −1 , versus ∼ 3000 m 2 , respectively).Such seasonal patterns were consistent with coherent patterns in monthly litter fall rates (Castaneda, 2010) within the flux footprint of the tower site.
-scale CO 2 fluxes During the year-round growing season, this forest exhibited pronounced seasonal NEE patterns.Seasonal maxima in daily CO 2 uptake by the forest were observed during March to May in 2004-2005 (Fig. 5a).Secondary peaks were observed during the month of November both before and after the hurricane.R E values (Fig. 5b) were seasonally highest during June to August with 8-day averages of 0.30-0.40 and 0.30-0.55mol C m −2 day −1 during 2004-2005 and 2007-2011, respectively.R E values were lowest between December and April.GPP (Fig. 5c) values were lowest during January Discussion Paper | Discussion Paper | Discussion Paper | and February coincident with seasonal minima in PAR and T A and exhibited broad seasonal maxima between April and October with values of 0.50-0.63 and 0.50-0.75mol C m −2 day −1 during 2004-2005 and 2007-2011, respectively.LUE and PAR exhibited a strong negative correlation (R 2 = −0.70),suggesting that this ecosystem 7 ppt) salinity recorded during the 2004-2005 study period.The observed reductions in LUE were also consistent with previous studies (Ball Discussion Paper | Discussion Paper | Discussion Paper | and Pidsley . The largest discrepancies between estimated and modeled LUE occurred during March to May of 2007 and 2008 when estimated LUE were seasonally lowest.Posterior means calculated over the full 2004-2011 period of record were evaluated by the Pearson's correlation coefficient (R), the coefficient of efficiency (CoE), and the normalized bias (NB).The model performed nearly as well during validation (R 2 = 0.646, CoE = 0.645; NB = −0.015)compared to training (R 2 = 0.651, CoE = 0.651; NB = −0.015).Posterior distributions of model forcings (Table 1) allowed estimates of mangrove productivity in response to key forcings, including EVI, air temperature, and salinity.The positive m EVI (4.03 ± 0.52; mean ± 1 s.d.) confirmed that FAPAR, and therefore LUE increased with EVI values.Air temperatures of 27.8 • C ±0.3 • C (mean ± 1 s.d.) favored optimal mangrove LUE.These temperatures (Fig. 4b) occurred most frequently during March to May and October to November.Both modeled and estimated LUE values declined as air temperatures approached the T Max value of 33.5 • C ±0.6 • C. The slope, m sal (0.0047 ± 0.0022) of the salinity forcing, f sal , was a factor of three lower compared to the slope (0.0146) determined from the response of LUE/LUE salinity=0 to Discussion Paper | Discussion Paper | Discussion Paper | ) indicated improved performance during 2004-2005 (slope = 0.720, intercept = 0.144, R 2 = 0.56) compared to the 2006-2011 period Discussion Paper | Discussion Paper | Discussion Paper | following hurricane disturbance (slope = 0.483, intercept = 0.249, R 2 = 0.45).The regression of MODIS GPP to EC-estimated GPP (Fig. 8b) suggested that the uncalibrated MODIS model only weakly captured productivity trends during 2004-2005 (slope = 0.477, intercept = 0.238, R 2 = 0.050) and failed to capture any trends in GPP during 2006-2011 (slope = −0.372,intercept = 0.597, R 2 = 0.056).The MVP-LUE model captured the broad seasonal maxima (0.5-0.7 mol C m −2 day −1 ) in ECestimated GPP (Fig. 9) as a result of the strong dependence of mangrove forest productivity on air temperature.During December to February temperatures below ∼ 20 • C, and to a lesser extent, shorter day length and daily PAR resulted in shortlived minima in GPP of 0.2-0.35mol C m −2 day −1 .The productivity response of man- Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | position.The integrated datasets in turn will enable more precise approximations of the role mangrove forests play in global C dynamics.Discussion Paper | Discussion Paper | Discussion Paper |Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations, IEEE T. Geosci.
Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .Fig. 2 .Fig. 9 .
Fig. 1.Map of Everglades National Park showing mangrove zones along the coast and the eddy covariance tower site.The thick green line defines the boundaries of Everglades National Park.

Table 1 .
MVP-LUE model forcing terms and associated uncertainty bounds.