Potential of Sun‐Induced Chlorophyll Fluorescence for Indicating Mangrove Canopy Photosynthesis

Abstract

Accurate characterization of gross primary productivity (GPP) is critically important in assessing mangrove carbon budgets, but the current knowledge of the temporal variations of GPP in evergreen mangroves is very limited. Remote sensing of sun‐induced chlorophyll fluorescence (SIF) has emerged as a promising approach to approximating GPP across ecosystems, but its capability for tracking GPP in evergreen mangroves has not been assessed. The SIF‐GPP link at a subtropical mangrove and its environmental controls are explored using 1‐year time‐series measurements from tower‐based hyperspectral and eddy covariance systems. Both the relationship between SIF and GPP as well as that between SIFy (SIF yield: the ratio of SIF over absorbed photosynthetically active radiation [APAR]) and LUE (light use efficiency: the ratio of GPP over APAR) at diurnal and seasonal time scales are analyzed. The temporal variations of SIF and GPP shared overall similar changing patterns, but their functional relationship tended to be time scale‐dependent. Midday depressions in SIF were observed when environmental stresses occurred around noon (including excess light and high VPD), and the strength of the SIF‐GPP link was affected by changing environmental conditions. The SIFy‐LUE relationship was temporally more dynamic, tending to match during midday hours but diverge from each other during morning and afternoon hours. These findings confirm SIF can serve as a potential remotely sensed indicator of mangrove canopy photosynthesis. This paper provides the first, high temporal‐resolution, continuous SIF measurements in mangroves, and highlights the importance of the impacts of environmental conditions on the SIF‐GPP relationship.

Full text

1. Introduction
As typical tropical and subtropical coastal wetland ecosystems, mangroves are characteristic of rich carbon
storage and strong carbon sequestration potential (Atwood etal.,2017) and thus have been recognized as
important and effective long-term blue carbon sinks in climate change mitigation (Howard etal., 2017;
Nellemann & Corcoran,2009). Mangroves may experience a variety of environmental stresses, including
periodical inundation (Crase etal.,2015), high salinity (Song etal., 2011), wastewater pollution (Jiang
etal.,2018), all of which are temporally varying and/or spatially heterogeneous. Moreover, due to the in-
accessibility to mangroves and notoriously difficult field environments, manual monitoring of mangroves
on a regular basis is impractical. Therefore, it is challenging to accurately assess mangrove carbon fluxes
that vary temporally and spatially (Alongi,2012,2014). Gross primary production (GPP) is the beginning
of vegetation carbon biogeochemical cycle and the key indicator of vegetation carbon fluxes, and thus ac-
curate characterization of photosynthesis is critically important in assessing carbon dynamics and carbon
sequestration potential.
Unfortunately, GPP cannot be directly observed but must be simulated by process-based ecosystem produc-
tivity models (e.g., BEPS; Liu etal.,1997) or empirically estimated from other measurements. One empirical
approach is to estimate GPP from continuous measurements of net ecosystem exchange of CO2 (NEE) using
Abstract Accurate characterization of gross primary productivity (GPP) is critically important in
assessing mangrove carbon budgets, but the current knowledge of the temporal variations of GPP in
evergreen mangroves is very limited. Remote sensing of sun-induced chlorophyll fluorescence (SIF)
has emerged as a promising approach to approximating GPP across ecosystems, but its capability for
tracking GPP in evergreen mangroves has not been assessed. The SIF-GPP link at a subtropical mangrove
and its environmental controls are explored using 1-year time-series measurements from tower-based
hyperspectral and eddy covariance systems. Both the relationship between SIF and GPP as well as that
between SIFy (SIF yield: the ratio of SIF over absorbed photosynthetically active radiation [APAR]) and
LUE (light use efficiency: the ratio of GPP over APAR) at diurnal and seasonal time scales are analyzed.
The temporal variations of SIF and GPP shared overall similar changing patterns, but their functional
relationship tended to be time scale-dependent. Midday depressions in SIF were observed when
environmental stresses occurred around noon (including excess light and high VPD), and the strength
of the SIF-GPP link was affected by changing environmental conditions. The SIFy-LUE relationship
was temporally more dynamic, tending to match during midday hours but diverge from each other
during morning and afternoon hours. These findings confirm SIF can serve as a potential remotely
sensed indicator of mangrove canopy photosynthesis. This paper provides the first, high temporal-
resolution, continuous SIF measurements in mangroves, and highlights the importance of the impacts of
environmental conditions on the SIF-GPP relationship.
ZHU ET AL.
© 2021. American Geophysical Union.
All Rights Reserved.
Potential of Sun-Induced Chlorophyll Fluorescence for
Indicating Mangrove Canopy Photosynthesis
Xudong Zhu1,2 , Yuwen Hou1, Yongguang Zhang3 , Xiaoliang Lu4 , Zhunqiao Liu4, and
Qihao Weng5
1Taiwan Strait Marine Ecosystem National Observation and Research Station, Key Laboratory of the Coastal
and Wetland Ecosystems (Ministry of Education), College of the Environment and Ecology, Coastal and Ocean
Management Institute, Xiamen University, Xiamen, Fujian, China, 2Southern Marine Science and Engineering
Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China, 3International Institute for Earth System Science,
Nanjing University, Nanjing, Jiangsu, China, 4State Key Laboratory of Soil Erosion and Dryland Farming on the Loess
Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi, China, 5Department
of Earth and Environmental Systems, Center for Urban and Environmental Change, Indiana State University, Terre
Haute, IN, USA
Key Points:
• First continuous coordinated
measurements of sun-induced
chlorophyll fluorescence (SIF) and
eddy covariance in mangrove is
presented
• SIF serve as a potential remotely
sensed indicator of tracking diurnal/
seasonal variation in mangrove gross
primary productivity (GPP)
• Environmental stresses tend
to weaken mangrove SIF-GPP
correlation
Supporting Information:
• Supporting Information S1
Correspondence to:
X. Zhu,
xdzhu@xmu.edu.cn
Citation:
Zhu, X., Hou, Y., Zhang, Y., Lu,
X., Liu, Z., & Weng, Q. (2021).
Potential of sun-induced chlorophyll
fluorescence for indicating mangrove
canopy photosynthesis. Journal of
Geophysical Research: Biogeosciences,
126, e2020JG006159. https://doi.
org/10.1029/2020JG006159
Received 6 NOV 2020
Accepted 18 FEB 2021
Author Contributions:
Conceptualization: Xudong Zhu,
Qihao Weng
Formal analysis: Xudong Zhu, Yuwen
Hou, Xiaoliang Lu, Zhunqiao Liu
Funding acquisition: Xudong Zhu
Investigation: Xudong Zhu, Yuwen
Hou
Methodology: Xudong Zhu,
Yongguang Zhang, Xiaoliang Lu,
Zhunqiao Liu
Project Administration: Xudong Zhu
Resources: Xudong Zhu
Software: Yongguang Zhang
Visualization: Xudong Zhu, Yuwen
Hou
Writing – original draft: Xudong Zhu,
Qihao Weng
Writing – review & editing: Xudong
Zhu, Yuwen Hou, Yongguang Zhang,
Xiaoliang Lu, Zhunqiao Liu, Qihao
Weng
10.1029/2020JG006159
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Journal of Geophysical Research: Biogeosciences
the eddy covariance (EC) methodology (Baldocchi etal.,2001), in which GPP can be derived through par-
titioning measured NEE. EC has been used for various ecosystems including mangroves (Barr etal.,2010;
Liu & Lai,2019; Zhu etal.,2019), since it provides a long term, continuous, and near-direct assessment of
ecosystem-atmospheric NEE. Another empirical approach to estimate GPP is based on spectral measure-
ments from remote sensors by linking optical signals with ecosystem photosynthesis, mostly through light
use efficiency (LUE) models (Gamon,2015). The LUE-based GPP models might work better for deciduous
or annual vegetation, since their photosynthesis is mainly regulated by canopy greenness that can be well
tracked with commonly used vegetation indices (VIs) like normalized difference vegetation index (NDVI;
Myneni & Williams,1994) and enhanced vegetation index (EVI; Huete etal.,2002). For evergreen vegeta-
tion like mangroves with relatively stable canopy greenness, the seasonality of GPP is largely decoupled
with these reflectance-based VIs (Barr etal.,2013; Zhu etal.,2019). As a complement to reflectance-based
VIs, sun-induced fluorescence (SIF) has recently emerged as a promising spectral approach to approximat-
ing GPP given that the strong SIF-GPP correlation has been confirmed across spatial scales with spaceborne
(Frankenberg etal.,2011; Guanter etal.,2014; Sun etal.,2018), airborne (Damm etal.,2014; Zarco-Tejada
etal.,2013), and ground-based (Miao etal.,2018; Yang etal.,2015) platforms.
SIF is a chlorophyll fluorescence emitted by photosynthetic pigments after solar light absorption. SIF carries
direct information on the actual electron transport in the light reactions of photosynthesis and is mechan-
ically linked with GPP (Gu etal.,2019; Porcar-Castell etal.,2014). In practice, SIF can be retrieved within
deep and narrow atmospheric absorption bands where a small fluorescence signal is technically discernible
from large background solar radiation (Meroni etal.,2009). Although many previous studies have empiri-
cally shown a close relationship between SIF and GPP across ecosystems and platforms, the knowledge of
the controlling mechanisms of SIF signal and its linkage with GPP is still limited. First, although SIF and
GPP are closely coupled at the physiological level, there is theoretically no universal scaling relationship
between each other because several space- and time-dependent physiological and canopy-structure factors
affect how measured SIF is related to GPP (Gu etal.,2019; He etal.,2017; Zhang etal.,2019). For example,
the absorbed photon energy in the light reactions excites Chl molecules, and then the excitation energy is
consumed by three main pathways including photochemical processes, fluorescence emissions, and heat
dissipation through non-photochemical quenching, all of which are temporally dynamic and under differ-
ent physiological controls (Porcar-Castell etal.,2014). Although there is a close connection among three
pathways, one can only physiologically interpret the link between two processes when one or more of these
three processes are determined. Thus, the SIF-GPP relationship is also a function of environmental stresses
(Ač etal., 2015; Maxwell & Johnson,2000). Second, both linear and nonlinear empirical functions have
been proposed to represent the SIF-GPP relationships, but it is unclear which one works better in estimating
GPP from SIF. Although many previous studies have reported linear SIF-GPP relationships, in particular,
with spaceborne sensors (Frankenberg etal.,2011; Guanter etal.,2014; Sun etal.,2017), this linear relation-
ship on a global scale is likely to be an artifact due to temporal and spatial aggregation of original SIF signals
(Gu etal.,2019; Magney etal.,2019). In fact, nonlinear asymptotic SIF-GPP relationships have also been
reported in both spaceborne (Li etal.,2018) and ground-based (Paul-Limoges etal.,2018) studies. There-
fore, large-scale SIF-GPP patterns might not represent actual SIF-GPP correlations at plant and ecosystem
scales (Damm etal.,2015). Third, spaceborne and airborne remote sensing generally provide snapshots of
SIF under clear sky conditions, and thus there is an under-sampling of SIF under other light conditions,
like diffuse light. Without temporally continuous SIF signals, it is difficult to fully assess the influence of
light conditions on SIF and its correlation with GPP, which is non-trivial given the significant contribution
of diffuse light to GPP (Mercado etal.,2009; Zhang etal.,2011). Fourth, there is limited knowledge of the
relationship between the quantum yield of SIF (SIFy: the ratio of SIF over absorbed photosynthetically
active radiation [APAR]) and LUE (the ratio of GPP over APAR), and how this relationship changes with
APAR (Miao etal.,2018). Although the SIF-GPP ratio is by definition equivalent to the SIFy-LUE ratio at
instantaneous scales, the SIF-GPP relationship at long-term scales is jointly determined by the SIFy-LUE re-
lationship and the temporal variations in APAR. Due to much larger variations in APAR than those in both
SIFy and LUE, APAR has been found to be the dominant factor controlling the linear SIF-GPP relationship
(Yang etal.,2015). The LUE-APAR relationship has been widely reported across ecosystems (Gitelson &
Gamon,2015; Turner et al.,2003), but the SIFy-APAR and SIFy-LUE relationships are rarely investigated
and still unclear (Miao etal.,2018; Verma etal.,2017).
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In the face of the challenges discussed above, ground-based continuous and concurrent time-series meas-
urements of SIF and GPP (e.g., via EC flux towers) are highly needed to capture both short-term and long-
term temporal variations of SIF, GPP and their relationship under different environmental conditions.
Although the SIF-GPP correlation has been confirmed in several tower-based field studies, they were con-
ducted largely for croplands (Goulas etal., 2017; Liu et al., 2017) or temperate/boreal forests (Magney
etal.,2019; Yang etal.,2015). Until now, we are not aware of any SIF study for mangroves with long-term
continuous high-frequency measurements of SIF. Although the empirical linear SIF-GPP relationship has
been established in previous tower-based field studies on temperate/boreal forests (Magney etal., 2019;
Yang etal.,2015), it is unclear whether this linearity is applicable to subtropical/tropical evergreen forests
like mangroves. As low-latitude coastal wetland vegetation, evergreen mangroves are experiencing strong-
er atmospheric interference (e.g., cloudiness and aerosols) and smaller temperature difference (both diur-
nal and seasonal). Thus, the responses of SIF and GPP to varying light and temperature conditions might
not be obvious. Furthermore, evergreen mangroves have low stomatal conductance and intercellular CO2
levels, and thus the photosynthetic rates tend to saturate at relatively lower light intensity (Alongi,2009;
Ball,1996), which could make mangrove's SIF-GPP relationship different from others. Here, based on con-
tinuous and concurrent time-series measurements from tower-based hyperspectral and EC systems, we
derived and analyzed 1-year temporally continuous high-frequency signals of SIF and GPP at a subtropical
mangrove. The main objectives of this study were (1) to explore the diurnal and seasonal variations of man-
grove SIF and GPP and their environmental controls; (2) to investigate the empirical relationship between
SIF and GPP at diurnal and seasonal time scales; and (3) to examine the influence of different environmen-
tal conditions on mangrove SIF-GPP relationships.
2. Materials and Methods
2.1. Field Site
A mangrove forest within an intertidal estuarine wetland of southeastern China was investigated with a
variety of in-situ time-series measurements at a mangrove flux tower (23.9240°N, 117.4147°E; Yunxiao
mangrove site of ChinaFLUX and USCCC; Figure1). With a subtropical monsoon climate, this estuarine
wetland has an annual mean air temperature of 21.2°C, rainfall of 1,714.5mm (mostly during spring and
summer), and relative humidity of 79% (Lin,2001). The mangrove forest around the flux tower has dense
canopy structure with species composition mainly of Kandelia obovate, Avicennia marina, and Aegiceras
corniculatum (Zhu etal.,2019). With irregular semi-diurnal tide (mean tide range of ∼2m), the understory
sediment surface at the flux tower is normally inundated twice a day with varying maximal tidal heights
(up to∼1m) (Zhu etal.,2019). This mangrove forest is administrated by the Zhangjiang Estuary Mangrove
National Nature Reserve, China, and all permits for our research activities are acquired from them.
2.2. Environmental and Eddy Covariance Measurements
Air temperature and relative humidity were measured above the canopy using an HMP155A sensor (Vais-
ala), and vapor pressure deficit (VPD) was derived from air temperature and relative humidity (Mur-
ra y, 1966). Photosynthetically active radiation (PAR) was measured above the canopy using a PQS1 PAR
Quantum sensor (Kipp & Zonen). Rainfall was measured above the canopy using a TE525MM Rain Gage
(Campbell Scientific, Inc.). Soil temperature was measured at a depth of 20cm using a soil thermocouple
probe (model 109; Campbell Scientific, Inc.). Incoming (SWin) and outgoing (SWout) shortwave radiation
were measured above the canopy using a CNR4 Net Radiometer (Kipp & Zonen). Meteorological measure-
ments were recorded using a CR1000 datalogger (Campbell Scientific, Inc.). Tidal surface water level was
calculated based on pressure difference from a pair of pressure sensors: one (HOBO U20L-04 Water Level
Logger; Onset) was deployed just above sediment surface at the flux tower to measure varying pressures
with tidal cycles, and the other (CS106 barometer; Vaisala) was deployed on the flux tower to monitor air
pressure. Raw 10-min time series of meteorological and tidal measurements were consistently converted to
30-min data for further analysis.
Mangrove-atmospheric NEE was measured continuously using the EC technique (Baldocchi etal.,2001).
The EC system consisted of a three-axis sonic anemometer (CSAT-3; Campbell Scientific, Inc.) and an open
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Journal of Geophysical Research: Biogeosciences
path infrared gas analyzer (LI-7500; Li-COR Inc.). Mangrove forests had varying canopy heights averaged
at ∼4m (Zhu etal.,2019), and the EC system was mounted on the tower at a height of ∼6m. Raw EC data
at 10Hz was recorded using a CR3000 datalogger (Campbell Scientific, Inc.) and processed to 30-min NEE
using the EddyPro6.1 software (Li-COR Inc.). Necessary flux correction (including axis rotation, ultrasonic
correction, and frequency response correction) and quality control (including steady-state test, turbulent
conditions test, statistical tests, absolute limits test, and rain test) procedures were implemented in Ed-
dyPro6.1 to ensure the quality of processed EC data. Nighttime poor flux measurements under insufficient
turbulence were filtered out using a friction velocity threshold calculated as in Reichstein etal. (2005).
Storage fluxes of CO2 calculated in EddyPro6.1 using a single-point concentration profile method were
also considered in computing NEE. Co-spectral analysis indicated the EC system was able to capture eddy
fluxes across the whole range of frequencies. The daytime 30-min ecosystem respiration (Re) was first es-
timated from daytime soil temperature based on the fitted nighttime Re-soil temperature response curve,
and then used for partitioning 30-min GPP from daytime 30-min NEE (more details in Zhu etal.[2019]).
Similar to Barr etal.(2013), nighttime NEE values with inundated sediment surface were excluded in fitting
the Re-temperature response curve since measured nighttime NEE under inundated conditions was likely
lower than actual nighttime Re because respired CO2 might dissolve into the overlying tidal water and be
exported. The LUE (mol mol−1) was calculated as the ratio of GPP and APAR (Equation1), which is the
product of PAR and fraction of APAR (fAPAR) (Equation2). The fAPAR was approximately estimated for the
canopy based on concurrent measurements of SWin and SWout (Equation3) (Nichol etal.,2019). The daily
GPP and LUE were calculated as the average of 30-min GPP and LUE, respectively. The monthly GPP and
LUE were averaged from the daily values.
LUE GPP / APAR
(1)
 
APAR
APAR PAR f
(2)
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Figure 1. Wetland landscape of the field site (a) and horizontal view of mangrove flux tower (b) deployed for
meteorological, eddy covariance, and hyperspectral measurements (for visual reference, the forest strip around the
flux tower is ∼200-m wide). Various instruments, including two spectrometers, were integrated as shown in the sketch
diagram (c) to collect continuous hyperspectral measurements for retrieving SIF and vegetation indices.

Journal of Geophysical Research: Biogeosciences
 
APAR out in
1 SW / SWf
(3)
2.3. Hyperspectral Measurements and Calculations
Following the FluoSpec2 system (Miao etal.,2018; Yang etal.,2015), two high-accuracy hyperspectral spec-
trometers were deployed on the mangrove flux tower in December 2017 to acquire long-term continuous
hyperspectral measurements for retrieving SIF and VIs (Figure1). The first hyperspectral spectrometer (QE-
PRO, OceanOptics, Inc.), covering the wavelength from 730 to 786nm with a spectral resolution of 0.1nm
(full width half maximum, FWHM), was used for SIF retrieval at the O2-A band (760nm). The second one
(HR2000+, OceanOptics, Inc.), covering the wavelength from 200 to 1,100nm with a spectral resolution of
1.5nm (FWHM), was used to calculate VIs. Each spectrometer was connected to a pair of upward-facing
(irradiance path; collecting signals from the sun) and downward-facing (radiance path; collecting signals
from the mangrove canopy) fiber optics (mounted at the height of ∼7m above the canopy), and an inline
fiber optic shutter (FOS-2X2-TTL, OceanOptics, Inc.) was embedded between the spectrometer and fiber
optic to switch between irradiance and radiance paths. The upward-facing fiber optic attached with a cosine
corrector (CC-3-UV-S, OceanOptics, Inc.) had a field of view (FOV) of 180°, while the downward-facing
bare fiber optic had a FOV of 25° (footprint of ∼3m in diameter). The spectrometers and inline shutters
were housed in a temperature-controlled enclosure.
The spectrometer-shutter system first collected solar irradiance, then collected canopy radiance with shut-
ter switched, and then collected solar irradiance with shutter switched back. To improve signal-to-noise
ratio, the system was configured to have an adaptive illumination-dependent integrating time during each
collection (<10s). The system triggered an irradiance-radiance-irradiance measurement cycle every 5min
to acquire one canopy radiance measurement and the mean of two solar irradiance measurements. Radi-
ometric calibrations of the system were performed before data collection: irradiance signals were calibrated
using a standard light source (HL-3P-CAL, OceanOptics, Inc.) and radiance signals were calibrated using
a standard reflection board (Spectralon®, Labsphere). All measurements were corrected for dark current,
and raw data collected by the spectrometers were converted to irradiance (mW m−2nm−1) and radiance
(mW m−2nm−1 sr−1) (Perez-Priego etal.,2005). For example irradiance, radiance, and apparent reflectance
spectra over the course of the day were given in FigureS1. To avoid potential large deviation from the cosine
corrector (e.g., self-shading issue from the recessed cavity), we excluded the raw measurements acquired
with solar elevation angle <30° (71% data remaining at this stage). Following Cogliati etal.(2015), addition-
al data quality control procedures were applied to filter out poor-quality spectra measurements: (1) collected
data were checked not to represent spectrometer saturation values (68% data remaining); (2) poor-quality
data were rejected by checking the magnitude and stability of irradiance and radiance within each irradi-
ance-radiance-irradiance measurement cycle, to avoid potential disturbance from cloudy conditions (e.g.,
a quick cloudy/clear shift) (67% data remaining); (3) too low irradiance (vs. dark current) was rejected to
exclude poor-quality measurements under low-illumination conditions (55% data remaining); (4) data ac-
quired with non-optimal integration time (maximum irradiance<half of spectrometer saturation values)
were also rejected (33% data remaining). Thus, the availability of valid data reduced from 71% to 33% with
higher availability in autumn (57%; how much of the data were valid within this season) and winter (42%)
and lower availability in summer (29%) and spring (21%).
SIF at 760nm was retrieved from the hyperspectral measurement (QE-PRO) in the O2-A band (atmospheric
absorption spectrum) using the spectral fitting method (SFM; Meroni etal.,2009). By assuming both canopy
reflectance (r(λ)) and SIF (F(λ)) are linear functions of wavelength (λ; nm) in the O2-A band, SIF can be
retrieved from hyperspectral measurements of incident solar irradiance (E(λ)) and reflected radiance (L(λ)):
    

   


  

, 757.834, 768.763
rE
LF
(4)
where ε(λ) is the model error. In practice, a total of 194 bands within the spectral range between 757.834 and
768.763nm were used to retrieve SIF at 760.351nm.
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Journal of Geophysical Research: Biogeosciences
Since photochemical reflectance index (PRI; Gamon etal.,1992) has been often found to scale well with
GPP across different ecosystems including evergreens (Garbulsky etal.,2011), the PRI-GPP relationship
was also examined here. Specifically, PRI was retrieved from canopy reflectance data at specific wavelengths
(r(λ)) based on incident and reflected spectral signals from HR2000+:
  

531 570 531 570
PRI /
rr rr
(5)
Both SIF and PRI were calculated every 5-min and then averaged to 30-min values for further analyses.
Spectral measurements under rainy conditions were excluded. To remove the effect of APAR variation on
the SIF-GPP relationship, we also compared LUE with the quantum yield of SIF (SIFy; J nm−1 sr−1mmol−1),
which was calculated as the ratio of 30-min SIF and APAR:

/
y
SIF SIF APAR
(6)
Daily values of SIF, PRI, and SIFy were calculated by averaging all 30-min values. The monthly values were
averaged from the daily values.
3. Results
3.1. Temporal Variations of GPP and LUE
The 30-min GPP showed significant diurnal and seasonal variations from December 2017 to October 2018
(Figure2a). The magnitude of GPP typically varied from 0 to 25μmol m−2 s−1 with the daytime mean GPP
of 16.0μmol m−2 s−1. At the diurnal scale, the 30-min GPP showed humped varying patterns with values
peaking around noon. At the seasonal scale, the 30-min GPP had the highest and lowest values in spring
(daytime mean value of 17.0μmol m−2 s−1) and winter (14.8μmol m−2 s−1), respectively. The daily GPP
(average of 30-min values), ranging from 3.52 to 21.8μmol m−2 s−1, also had a strong variation over the year
with the highest and the lowest mean daily values in spring (16.9μmol m−2 s−1) and winter (14.7μmol m−2
s−1), respectively (Figure3a). There were also significant diurnal variations in 30-min LUE values typically
varying from 0.01 to 0.04mol mol−1 with the daily average of 0.019mol mol−1, while there was no obvious
seasonal varying pattern in 30-min LUE (Figure2c). The 30-min LUE tended to show U-shaped diurnal pat-
terns with the values lower around noon and higher at dawn and dusk. The daily LUE varied from 0.010 to
0.054mol mol−1 with an average of 0.020mol mol−1, and daily LUE was higher in winter (0.023mol mol−1)
than in other seasons (0.019mol mol−1) (Figure3b).
3.2. Temporal Variations of SIF and SIFy
The 30-min SIF varied across time at both diurnal and seasonal scales, and the magnitude typically changed
from 0.2 to 1.2mW m−2nm−1 sr−1 with the daytime mean of 0.44mW m−2nm−1 sr−1 (Figure2b). Like GPP,
the 30-min SIF followed humped diurnal patterns with statistically significant (p<0.05) higher mean values
in summer (0.55mW m−2nm−1 sr−1) and autumn (0.54mW m−2nm−1 sr−1) than winter (0.30mW m−2nm−1
sr−1) and spring (0.16mW m−2nm−1 sr−1). The daily SIF, ranging from 0.03 to 1.01mW m−2nm−1 sr−1, varied
over the year with statistically significant (p<0.05) higher mean values in summer (0.44mW m−2nm−1
sr−1) and autumn (0.46mW m−2nm−1 sr−1) than in winter (0.32mW m−2nm−1 sr−1) and spring (0.20mW
m−2nm-1 sr−1) (Figure3a). The 30-min SIFy ranged from 1.0×10−4 to 1.3×10−3J nm−1 sr−1mmol−1 (daily
average of 4.1×10−4J nm−1 sr−1mmol−1) (Figure2d), and the daily SIFy ranged from 4×10−6 to 1.2×10−3J
nm−1 sr−1mmol−1 (average of 3.5 × 10−4J nm−1 sr−1mmol−1) (Figure3b). Same as SIF, the mean values
of both 30-min and daily SIFy were found to be statistically significant (p<0.05) higher in summer and
autumn than in winter and spring.
3.3. Correlation Between SIF and GPP
Although there was not good consistency in temporal variations of daily SIF and GPP, the running mean
of the daily values indicated that SIF tracked with GPP to a certain extent, where the peaks and troughs
of the smoothed curves roughly matched each other (Figure 3a). The strength of the SIF-GPP correla-
tions varied with seasons, with better covariations between 30-min SIF and GPP in summer (correlation
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coefficient between diurnal mean values, r=0.84, p< 0.05) and autumn (r=0.82, p <0.05) (Figure4).
The 30-min GPP followed humped varying patterns across seasons without obvious downregulation around
noon, while the diurnal courses of SIF experienced obvious downregulations around noon, in particular
for summer and autumn. The positive SIF-GPP relationships at diurnal and seasonal time scales were con-
firmed by the statistical analysis using 30-min and daily data, respectively (Figures5a and5c). Moreover,
the positive SIF-GPP relationship was found to be more nonlinear and linear at diurnal and seasonal time
scales, respectively. At diurnal time scale, a nonlinear regression (
  
  GPP 18.7 SIF 2.93 / SIF 0.24
)
explained 14% of the variations (p<0.05), while at seasonal time scale the SIF-GPP relationship was bet-
ter described by a linear regression (
 GPP 14.29 4.01 SIF
; R2=0.17, p<0.05). The SIF-GPP correla-
tion was comparable with the PRI-GPP correlations at both diurnal (Figure5a vs. Figure5b) and seasonal
(Figure5c vs. Figure5d) time scales. Further analysis of environmental influence on the SIF-GPP correla-
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Figure 2. Diurnal and seasonal variations in 30-min measurements of (a) GPP, (b) SIF, (c) LUE, (d) SIFy, and environmental factors (e–h: PAR, air temperature,
VPD, and surface water level) from December 2017 to October 2018. The data with solar elevation angle ≥30° were shown only for the former four variables.
Gaps in the data resulted from instrument failure and quality control. GPP, gross primary productivity; LUE, light use efficiency; PAR, photosynthetically active
radiation; SIF, sun-induced chlorophyll fluorescence; SIFy, quantum yield of SIF; VPD, vapor pressure deficit.

Journal of Geophysical Research: Biogeosciences
tion indicted that the correlation coefficients between 30-min SIF and GPP were not constant across various
environmental gradients (Figure6). Among the five investigated environmental variables (PAR, air temper-
ature, VPD, surface water salinity, and surface water level), the impacts of VPD on the SIF-GPP relationship
were the most obvious with lower SIF-GPP correlations at higher VPD.
3.4. Correlation Between SIFy and LUE
The correlation between SIFy and LUE was found to be weaker than the SIF-GPP correlation. At seasonal
time scale, both of the time series of daily SIFy and LUE fluctuated a lot, but there was no obvious covar-
iation between them (Figure3b). At diurnal time scale, the strength of the SIFy-LUE correlations varied
across seasons, with better covariations in spring (r=0.72, p<0.05) and winter (r=0.64, p<0.05) (Fig-
ure7). During the course of the day, 30-min LUE tended to be higher at morning/afternoon hours and
lower around noon, although this U-shaped diurnal pattern was not obvious due to the dismissal of early
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Figure 3. Seasonal variations in daily GPP, SIF, LUE, SIFy, and environmental factors (PAR, air temperature, VPD,
and surface water level) from December 2017 to October 2018. The daily mean values were shown for the variables
except for surface water level (using daily maximum values). Lines represented 11 days moving average of daily values.
GPP, gross primary productivity; LUE, light use efficiency; PAR, photosynthetically active radiation; SIF, sun-induced
chlorophyll fluorescence; SIFy, quantum yield of SIF; VPD, vapor pressure deficit.

Journal of Geophysical Research: Biogeosciences
morning/late afternoon hours with solar elevation angle <30°. For 30-min SIFy, there was no obvious diur-
nal variation for each season. The comparison between 30-min SIFy and LUE indicated that they tended to
match during midday hours but diverge from each other during morning and afternoon hours.
4. Discussion
4.1. The SIF-GPP Link and Its Environmental Controls
The relationship between SIF and GPP observed from this study conform to previous understanding that
there is a link between photosynthetic CO2 assimilation and accompanying chlorophyll fluorescence emis-
sions. The coupled SIF-GPP relationship demonstrates the potential of SIF to track the temporal dynamics
of GPP in mangroves. Although the SIF-GPP link has been confirmed across ecosystems and platforms,
whether the link is linear or nonlinear has not been well understood (Damm etal.,2015; Lee etal.,2015;
Yang etal.,2017). Many of previous SIF studies reported a linear SIF-GPP relationship (Magney etal.,2019;
Nichol etal.,2019; Yang etal.,2015), but recent studies have also revealed that the SIF-GPP relationship
tended to be nonlinear, especially at shorter time scales (Lee etal.,2015; Paul-Limoges etal.,2018; Zhang
etal.,2016). Based on 1-year time-series measurements from hyperspectral and EC systems, we were able
to better examine the SIF-GPP relationship in mangroves across time scales. The strength of the SIF-GPP
linkage at diurnal and seasonal time scales was comparable, but the positive link tended to shift from a
nonlinear pattern at diurnal time scale to a linear pattern at seasonal time scale (Figures5a and5c). This
finding is consistent with previous empirical/modeling studies (Damm etal.,2015; Li etal.,2018; Zhang
etal.,2016) showing the functional relationship between SIF and GPP was not consistent across time scales
with a trend of being more linear with temporal aggregations. This finding also agrees with recent research
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Figure 4. Diurnal mean GPP and SIF across seasons: (a) winter, (b) spring, (c) summer, and (d) autumn. Markers and
error bars represented mean values and standard deviations, respectively. Pearson's correlation coefficients (r) between
diurnal mean GPP and SIF were calculated for each season (all coefficients were statistically significant at p<0.05).
Note the data with solar elevation angle <30° were excluded via quality control. GPP, gross primary productivity; SIF,
sun-induced chlorophyll fluorescence.

Journal of Geophysical Research: Biogeosciences
review by Gu etal.(2019) demonstrating that nonlinear instantaneous SIF-GPP relationship should in the-
ory move toward more linear when integrated over time.
Midday depression in SIF was observed from our measurements, and this phenomenon was found to be
more obvious in summer and autumn (Figure4). Although few remotely sensed SIF studies have reported
midday depression in SIF likely due to the lack of continuous, high temporal-resolution SIF measurements,
the midday reduction in chlorophyll fluorescence emissions has been widely reported in many studies at
leaf level (Martínez-Ferri etal., 2000; Raschke & Resemann,1986; Špunda et al.,2005), which attributed
this phenomenon to both stomatal and non-stomatal limitations from environmental stresses such as excess
light and high VPD. Excess absorbed solar energy at midday increases energy allocation to non-radiative
heat dissipation, which leads to reduced energy allocation to photochemistry and fluorescence, resulting in
the midday depression in fluorescence (Demmig-Adams,1990; Martínez-Ferri etal.,2000). The depression
also occurs when the plant suffers from moisture stress with VPD exceeding the threshold (Raschke & Rese-
mann,1986). The midday depression in SIF from our measurements in mangroves corresponded to midday
environmental stresses including excess light (Figure2e) and high VPD (Figure2g). Higher midday light/
VPD/temperature around noon (Figures2e–2g) might explain why midday depression in SIF was more
obvious in summer and autumn.
In comparison, midday depression in GPP was not as obvious as that in SIF (Figure4). Same with SIF, the
extent of midday depression in GPP varied across seasons with relatively stronger suppression in summer
and autumn (i.e., change from a humped phase to a stationary phase; Figures4c and 4d). The reasons
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Figure 5. The relationships between GPP and SIF and between GPP and PRI at diurnal (a) and (b) and seasonal (c)
and (d) time scales. Statistically significant (p<0.05) fitting curves and corresponding R2 were also shown. The color
bars in the first row indicated point density of 30-min values. GPP, gross primary productivity; PRI, photochemical
reflectance index; SIF, sun-induced chlorophyll fluorescence.

Journal of Geophysical Research: Biogeosciences
why the extent of midday depression differed between GPP and SIF are manifold. First, the absorbed so-
lar energy may also be consumed by non-radiative heat dissipation in addition to that used for SIF and
photosynthetic activity, and thus there is no universal scaling relationship between SIF and GPP. Several
studies have attributed the midday SIF depression to higher non-radiative heat dissipation (Li etal.,2000;
Peguero-Pina etal.,2008; Paul-Limoges et al., 2018). Higher midday non-radiative heat dissipation was
also supported by our previous study showing lower PRI at midday hours for the same mangrove forests
(Zhu etal.,2019), since lower PRI corresponded to stronger non-radiative heat dissipation via xanthophyll
cycle (Gamon etal.,1992). Second, although SIF is directly coupled to photosynthetic activity during light
reactions, the whole physiological processes involved in SIF (light reactions only) and GPP (both light and
carbon reactions) are not the same (Gu etal.,2019), and thus they are very likely subjected to different envi-
ronmental controls (Porcar-Castell etal.,2014). Frequent cloud formation during the midday in subtropical
coastal region leads to a decrease in PAR but an increase in LUE (due to higher fraction of diffuse radiation)
(Barr etal., 2010; Gu et al., 2003). The combination of these two canceling effects might help to explain
the relatively stationary GPP around noon especially in summer and autumn (Figures4c and4d). Third, it
could also result from the difference in horizontal and vertical footprint sizes between SIF and EC systems.
For horizontal footprint, SIF signal had a much smaller footprint (∼3m in diameter for tower-based SIF
vs.∼100m for tower-based GPP), and thus it is not a surprise that SIF was generally more sensitive to envi-
ronmental stresses than GPP. For vertical footprint, tower-based SIF signals mainly represented the top-can-
opy leaves exposed to heavier environmental stresses around noon (e.g., high light, temperature, and VPD)
than the leaves lower in the canopy, while tower-based GPP from EC measurements represented ecosystem
carbon assimilation integrated over the whole canopy. Therefore, tower-based SIF was more likely affected
by environmental stresses around noon than tower-based GPP, leading to their midday mismatch.
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Figure 6. Influences of environmental factors on the correlation between 30-min GPP and SIF. Pearson's correlation
coefficients were calculated for each single day and then grouped by daily values of environmental factors. The daily
mean values were used for the environmental factors except surface water level (using daily maximum values). The
vertical and horizontal error bars indicated standard deviations of SIF-GPP correlations and environmental factors,
respectively. GPP, gross primary productivity; PAR, photosynthetically active radiation; SIF: sun-induced chlorophyll
fluorescence; VPD, vapor pressure deficit.

Journal of Geophysical Research: Biogeosciences
The analysis on the impact of the environmental factors on the SIF-GPP relationship revealed that the
strengths of the SIF-GPP link varied across environmental gradients (Figure6). Among these environmen-
tal factors, VPD was the only one exerting statistically significant (p<0.05) negative effect on the SIF-GPP
correlation, where lower VPD corresponded to stronger SIF-GPP correlation. The SIF-GPP correlation tend-
ed to be affected by PAR and air temperature, where the correlation peaked at medium PAR and air tem-
perature. There was no indication that the SIF-GPP correlation was regulated by surface water salinity or
surface water level. It is possible that tidal salinity at this field site (0–18 ppt for daily salinity) was not high
enough to cause apparent salinity inhibition of mangrove photosynthesis, which usually occurs with daily
salinity>15 ppt (Barr etal.,2013; Cui etal.,2018). Although the environmental impacts on the SIF-GPP
correlation differed in strength, as a whole the SIF-GPP correlations tended to be lower under atmospheric
stresses (i.e., high PAR, air temperature and VPD), which was consistent with previous studies showing that
the occurrence of environmental stresses decoupled the relationship between SIF and plant physiological
dynamics (Atherton etal.,2016; Lu etal.,2018).
4.2. The SIFy-LUE Link and Its Environmental Controls
In contrast with widely reported positive relationship between SIF and GPP across ecosystems and plat-
forms, there is a lack of consensus regarding the functional relationship between SIFy and LUE (Miao
et al., 2018). Positive correlations (Verma et al., 2017; Yang et al., 2015), negative correlations (Damm
etal.,2010), and even both of them (Miao etal.,2018; Zhang etal.,2016) have been found in previous stud-
ies on the SIFy-LUE link across the time scales. It is not a surprise the SIFy-LUE relationship is more variable
than the SIF-GPP relationships, since the influence of APAR with much larger temporal variation has been
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Figure 7. Diurnal variations in seasonally mean LUE and SIFy across seasons: (a) winter, (b) spring, (c) summer,
and (d) autumn. Markers and error bars represented mean values and standard deviations, respectively. Pearson's
correlation coefficients (r) between diurnal mean LUE and SIFy were calculated for each season (all coefficients were
statistically significant at p<0.05). Note the data with solar elevation angle <30° were excluded via quality control.
LUE, light use efficiency; SIFy, quantum yield of sun-induced chlorophyll fluorescence.

Journal of Geophysical Research: Biogeosciences
excluded in the former. From the physiological perspective, it can be possible to have both positive and
negative SIFy-LUE relationship since the proportion of absorbed energy flowing along each of the energy
partitioning pathways can be dynamic and under various physiological controls. Porcar-Castell etal.(2014)
discussed the physiological processes that control the energy partitioning in details at the photosystem level,
and suggested a clear two-phased inverted “V” relationship between quantum yields of fluorescence and
photochemistry over the course of a day based on leaf-level pulse amplitude-modulated (PAM) fluorescence
measurements: (1) inversely proportional under low light phase; and (2) directly proportional under high
light phase. Under low light, the change in photochemistry yield are dominated by photochemical quench-
ing (PQ) with low and constant non-photochemical quenching (NPQ), while under high light the change
in photochemistry yield are dominated by NPQ with constant PQ. Since decreasing PQ and increasing NPQ
have opposite effects on fluorescence yield, it generates a two-phased “V” relationship between fluores-
cence yield and photochemistry yield (Porcar-Castell etal.,2014).
Although it may not be meaningful to directly compare canopy-level SIF measurements (non-steady SIF)
with leaf-level PAM fluorescence measurement (steady SIF), we did observe a similar two-phased diurnal
relationship between SIFy and LUE (Figure7). The difference in diurnal variations between SIFy and LUE
supports the notion that the functional relationship between SIFy and LUE depends on light conditions
(Miao etal.,2018; Zhang etal.,2016). Another possible reason explaining the divergence between SIFy and
LUE at early morning/late afternoon is that, at lower solar elevations, larger proportion of light is absorbed
by within-canopy leaves rather than by top-canopy leaves, resulting in less detected SIF emissions due to
the within-canopy re-absorption effect (Du etal.,2017; Porcar-Castell etal.,2014). The escape probability
of emitted fluorescence was not considered here, and thus it was possible that the observed diverge between
SIFy and LUE at early morning/late afternoon might have been magnified by ignoring the escape probabili-
ty that can vary substantially over the day (Yang & van der Tol,2018; Zeng etal.,2019).
4.3. Implications and Uncertainties
The asymptotic behavior of the SIF-GPP relationship at diurnal scale (increasing SIF vs. saturating GPP)
indicates that the increase in SIF is faster than the increase in GPP under light-saturating conditions. For
this reason, we expect that the estimation of GPP from sporadic spaceborne and airborne SIF measurements
(usually under light-saturating conditions) would be higher than the actual values if a linear SIF-GPP em-
pirical relationship was assumed. It should be cautious to establish functional relationship between SIF and
GPP since it could be time scale-dependent. The mismatch of midday depression between SIF and GPP un-
der high light observed from our results also suggests that the retrieval of SIF-GPP relationship from those
sporadic SIF measurements could contain biases.
Although the SIF-GPP correlations are not as strong as those reported by previous ground-based SIF stud-
ies, our results demonstrate the potential of SIF in indicating mangrove canopy GPP with comparable per-
formance of PRI. This confirms that SIF can serve an important complement to reflectance-based VIs in
indicating mangrove carbon fluxes. It should be noted that previous ground-based SIF mainly focused on
vegetation experiencing either seasonally distinct canopy structure or temperature variation over the course
of the year, while evergreen mangroves in this study are experiencing indistinct canopy structure and small
temperature variation. This can partially explain why the SIF-GPP correlation in mangrove forests is rela-
tively weaker. Both of the midday depression in SIF and the variation in SIFy at the diurnal scale suggest
that SIF might be highly sensitive to various environmental stresses, which reveals the potential of applying
SIF to examine the influences of various types of stresses on mangrove carbon fluxes.
Although tower-based continuous measurements of both SIF and EC systems enabled us to examine the
SIF-GPP and SIFy-LUE relationships and to disentangle the confounding effects of various environmental
factors, several uncertainties are involved in field experiment setups and data processing/analyses. First, the
direct comparison between tower-based SIF and GPP suffers from the difference in footprint sizes (both in
horizontal and vertical dimensions) between SIF and EC instrumentations. The impacts of the difference
in horizontal footprint sizes were not considered in data analyses of this study, but future studies should
examine how this “horizontal mismatch” affects the SIF-GPP correlations. Tower-based SIF signals pre-
dominately come from top-canopy leaves because of the re-absorption of emitted SIF within the canopy
(Du etal.,2017; Porcar-Castell etal.,2014), while GPP represents carbon assimilations integrated over the
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whole canopy. This “vertical mismatch” may affect the empirical relationships and their sensitivities to
environmental stresses. Second, tower-based SIF measurements are affected by changing fractions of sunlit
and shaded leaves over the course of a day (He etal., 2017; Tol etal.,2009; Zhang et al., 2020), and this
sun-viewing-geometry issue requires further investigations (Liu etal.,2016; Pinto etal.,2017). Third, there
is a growing recognition of the importance of escape probability of emitted fluorescence, which is highly
dependent on canopy structure and affects the amount and temporal variation of fluorescence “seen” by the
spectrometer (Yang & van der Tol,2018; Zeng etal.,2019). The retrieved SIF could be smaller than actual
fluorescence emissions by ignoring the escape probability, which propagates uncertainty to the analyses
of temporal variation of SIF and the SIF-GPP correlations. This limitation needs further investigation in
future studies. Fourth, the choice of various SIF retrieval algorithms could affect our data analyses. To
examine the impact of algorithms on the retrieved SIF, we tried another commonly used algorithm, 3FLD
(Meroni etal.,2009), and the comparison showed little difference between 3FLD-based and SFM-based SIF
(FigureS2), which supported the reliability of retrieved SIF. Fifth, the dismissal of atmospheric absorption
(a 7-m distance between fiber optics and mangrove canopy as in this study) of reflected canopy radiance
could bias the retrieved SIF (Liu etal.,2017). A rough analysis of the influence of atmospheric absorption
on SFM-based SIF retrieval (assuming 1% decrease in upwelling atmospheric transmittance for a 7-m dis-
tance according to Liu etal. [2017]) indicated that the difference was negligible (<2%) between retrieved
SIF with and without considering atmospheric absorption. Sixth, further attentions should be paid to the
uncertainties associated with the partitioning approaches for EC-derived GPP (Reichstein etal.,2005) and
the applicability of the approaches in tide-affected mangrove wetlands (Barr etal.,2013). See Wohlfahrt
and Gu(2015) for a comprehensive discussion on the potential difference between EC-derived and “true”
photosynthesis as well as the issues associated with the partitioning approaches. Last but not least, our data
analyses suffered from the sparseness of the data and resultant sampling biases over the day and the season
due to instrument failure and quality control (e.g., low data availability in winter, and under-sampling in
early mornings and late afternoons), and thus improvements of instrumentations and maintenance for
more data availability are highly need to further confirm these findings.
5. Conclusions
Qualitative and quantitative analyses have been conducted here to explore the temporal dynamics of SIF
and GPP as well as their link under various environmental conditions at a subtropical mangrove forest
of southeastern China, based on 1-year continuous and concurrent time-series measurements from tow-
er-based hyperspectral and EC systems. Both of the SIF-GPP and the SIFy-LUE relationships were examined
to assess the capability of SIF for tracking mangrove photosynthesis at diurnal and seasonal time scales. The
main findings are summarized as follows. (1) The temporal variations of SIF and GPP shared overall similar
changing patterns at diurnal and seasonal time scales. (2) The SIF-GPP correlations was comparable with
the PRI-GPP correlations and thus SIF can serve a potential remotely sensed indicator of mangrove canopy
GPP. (3) The functional relationship between SIF and GPP in mangroves might be time scale-dependent
with more nonlinear and linear at diurnal and seasonal time scales, respectively. (4) Midday depression
in SIF was observed in mangroves when environmental stresses occurred around noon (including excess
light and high VPD), and the strength of the SIF-GPP relationship was affected by changing environmental
conditions. (5) The SIFy-LUE relationship was temporally more dynamic than the SIF-GPP relationship,
and SIF and LUE tended to match during midday hours but diverge from each other during morning and
afternoon hours. This study provides the first, high temporal-resolution, continuous SIF measurements in
mangroves and highlights the importance of characterizing the impacts of environmental conditions on the
SIF-GPP relationship. Future studies should take into account these issues in order to reduce the uncertain-
ty in estimating GPP from remotely sensed SIF measurements.
Conflict of Interest
The authors declare no conflicts of interest relevant to this study.
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Data Availability Statement
The data necessary to reproduce key findings in this study can be accessed at http://doi.org/10.5281/
zenodo.4543631.
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Acknowledgments
The authors thank Yaqing Lu, Kang-
ming Chen, Chenyang Sun, Cheng-
juan Zheng, and Guanmin Huang for
their help in the fieldwork and data
quality control. The authors thank
the Zhangjiang Estuary Mangrove
National Nature Reserve for its long-
term support to our ecological research
program. The authors also thank
ChinaFLUX and the U.S.-China Carbon
Consortium (USCCC) for helpful
discussions and exchange of ideas. This
study was supported by the National
Natural Science Foundation of China
(31600368), the National Key Research
and Development Program of China
(2017YFC0506102), the Natural Science
Foundation of Fujian Province, China
(2017J01069, 2020J01112079), the Youth
InnovationFoundation of Xiamen,
China (3502Z20206038), the Funda-
mental Research Funds for the Central
Universities of China (20720180118,
20720190104), the Key Laboratory of
the Coastal and Wetland Ecosystems
(WELRI201601), and the State Key
Laboratory of Marine Environmental
Science (MELRI1603).

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