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We assessed the performance of reflectance-based vegetation indices and solar-induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the Gross Primary Production (GPP)-based phenology in a temperate deciduous forest. The reflectance-based indices include the green chromatic coordinate (GCC), field measured and satellite remotely sensed Normalized Difference Vegetation Index (NDVI); and the SIF datasets include ground-based measurement and satellite-based products. We found that, if negative impacts due to coarse spatial and temporal resolutions are effectively reduced, all these data can serve as good indicators of phenological metrics for spring. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the those derived from ground-based GPP estimates (flux sites). This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we found that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates in middle spring and autumn-the two most important metrics-can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics.

Abstract. Plant phenology has a significant impact on the forest ecosystem carbon balance. Detecting plant phenology by capturing the time-series canopy images through digital camera has become popular in recent years. However, the relationship between color indices derived from camera images and plant physiological characters are elusive during the growing season in temperate ecosystems. We collected continuous images of forest canopy, leaf size, leaf area index (LAI) and leaf chlorophyll measured by a soil plant analysis development (SPAD) analyzer in a northern subtropical oak forest in China. Our results show that (1) the spring peak of color indices, Gcc (Green Chromatic Coordinates) and ExG (Excess Green), was 18 days earlier than the 90% maximum SPAD value; (2) the 90% maximum SPAD value coincided with the change point of Gcc and ExG immediately after their spring peak; and (3) the spring curves of Gcc and ExG before their peaks were highly synchronous with the expansion of leaf size and the development of LAI value. We suggest it needs to be adjusted if camera-derived Gcc or ExG is used as a proxy of chlorophyll or gross primary productivity, and images observation should be complemented with field phenological and physiological information to interpret the physiological meaning of leaf seasonality.

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Camera‐based observation of forest canopies allows for low‐cost, continuous, high temporal‐spatial resolutions of plant phenology and seasonality of functional traits. In this study, we extracted canopy color index (green chromatic coordinate, Gcc) from the time‐series canopy images provided by a digital camera in a deciduous forest in Massachusetts, USA. We also measured leaf‐level photosynthetic activities and leaf area index (LAI) development in the field during the growing season, and corresponding leaf chlorophyll concentrations in the laboratory. We used the Bayesian change point (BCP) approach to analyze Gcc. Our results showed that (1) the date of starting decline of LAI (DOY 263), defined as the start of senescence, could be mathematically identified from the autumn Gcc pattern by analyzing change points of the Gcc curve, and Gcc is highly correlated with LAI after the first change point when LAI was decreasing (R2 = 0.88, LAI < 2.5 m2/m2); (2) the second change point of Gcc (DOY 289) started a more rapid decline of Gcc when chlorophyll concentration and photosynthesis rates were relatively low (13.4 ± 10.0% and 23.7 ± 13.4% of their maximum values, respectively) and continuously reducing; and (3) the third change point of Gcc (DOY 295) marked the end of growing season, defined by the termination of photosynthetic activities, two weeks earlier than the end of Gcc curve decline. Our results suggested that with the change point analysis, camera‐based phenology observation can effectively quantify the dynamic pattern of the start of senescence (with declining LAI) and the end of senescence (when photosynthetic activities terminated) in the deciduous forest.

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 inaccessibility 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(Alongi, , 2014. Gross primary production (GPP) is the beginning of vegetation carbon biogeochemical cycle and the key indicator of vegetation carbon fluxes, and thus accurate 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 productivity 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 CO 2 (NEE) using

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Water-use efficiency (WUE) is a critical variable describing the interrelationship between carbon uptake and water loss in land ecosystems. Different WUE formulations (WUEs) including intrinsic water use efficiency (WUE i), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE) have been proposed. Based on continuous measurements of carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) at a temperate forest, we analyze the correlations between SIF emission and the different WUEs at the canopy level by using linear regression (LR) and Gaussian processes regression (GPR) models. Overall, we find that SIF emission has a good potential to estimate IWUE and uWUE, especially when a combination of different SIF bands and a GPR model is used. At an hourly time step, canopy-level SIF emission can explain as high as 65% and 61% of the variances in IWUE and uWUE. Specifically, we find that (1) a daily time step by averaging hourly values during daytime can enhance the SIF-IWUE correlations, (2) the SIF-IWUE correlations decrease when photosynthetically active radiation and air temperature exceed their optimal biological thresholds, (3) a low Leaf Area Index (LAI) has a negative effect on the SIF-IWUE correlations due to large evaporation fluxes, (4) a high LAI in summer also reduces the SIF-IWUE correlations most likely due to increasing scattering and (re)absorption of the SIF signal, and (5) the observation time during the day has a strong impact on the SIF-IWUE correlations and SIF measurements in the early morning have the lowest power to estimate IWUE due to the large evaporation of dew. This study provides a new way to evaluate the stomatal regulation of plant-gas exchange without complex parameterizations.