Solar-Induced Fluorescence Detects Interannual Variationin Gross Primary Production of Coniferous Forestsin the Western United StatesLauren M. Zuromski1 , David R. Bowling1,2 , Philipp Köhler3 , Christian Frankenberg3 ,Michael L. Goulden4 , Peter D. Blanken5 , and John C. Lin1

1Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USA, 2Department of Biology, University ofUtah, Salt Lake City, UT, USA, 3Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena,CA, USA, 4Department of Earth System Science, University of California, Irvine, CA, USA, 5Department of Geography,University of Colorado Boulder, Boulder, CO, USA

Abstract Quantifying gross primary production (GPP), the largest flux of the terrestrial carbon cycle,remains difficult at the landscape scale. Evergreen needleleaf (coniferous) forests in the western UnitedStates constitute an important carbon reservoir whose annual GPP varies from year-to-year due to drought,mortality, and other ecosystem disturbances. Evergreen forest productivity is challenging to determine viatraditional remote sensing indices (i.e., NDVI and EVI), because detecting environmental stress conditions isdifficult. We investigated the utility of solar-induced chlorophyll fluorescence (SIF) to detect year-to-yearvariation in GPP in four coniferous forests varying in species composition in the western United States (SierraNevada, Cascade, and Rocky Mountains). We show that annually averaged, satellite-based observationsof SIF (retrieved from GOME-2) were significantly correlated with annual GPP observed at eddy covariancetowers over several years. Further, SIF responded quantitatively to drought-induced mortality, suggestingthat SIF may be capable of detecting ecosystem disturbance in coniferous forests.

Plain Language Summary Understanding and quantifying how the productivity ofconiferous forests responds to environmental change (e.g., drought and bark beetle-induced mortality) isimportant for the western United States, as these forests dominate the montane landscape. Often, carbonuptake by plants is tracked by measuring changes in light reflected from leaves, but these methods haveproven problematic for evergreen forests. A new means of studying carbon uptake using solar-inducedchlorophyll fluorescence (light emitted, rather than reflected, from sunlit leaves) is promising to studyphotosynthesis at the regional to global scales. We show that solar-induced fluorescence better tracksinterannual variation of conifer productivity than reflectance-based methods. Further, we demonstrate thatsolar-induced fluorescence captures decreasing productivity associated with drought-inducedforest mortality.

1. Introduction

Global- to regional-scale estimates of the terrestrial carbon sink are highly uncertain due to variation amongecosystems in both space and time, as well as due to uncertainties in both measurements and models.Estimates of gross primary production (GPP), the largest terrestrial carbon flux (Beer, 2010), vary significantlyacross the planet, but also differ between remote sensing products, eddy covariance-based measurements,and ecosystem models (Wu et al., 2017). Interannual variation in GPP derived from observations is smallerthan variation in GPP from ecosystem models (Anav et al., 2015), underscoring the need to match thespatiotemporal scales of differing observation platforms and models (Wood et al., 2017). Observedinterannual variability and decadal trends in carbon fluxes highlight that ecosystems are responding toenvironmental change (Ballantyne et al., 2017; Forkel et al., 2016; Keenan et al., 2013), providing insight intothe contemporary carbon cycle, and potentially future climate conditions such as higher temperature ordrought (Cox et al., 2013; Liu et al., 2017).

Satellite-based remote sensing has the potential to provide insight into GPP over multiple biomes. Remotelysensed GPP is often expressed by the light use efficiency (LUE) approach (Monteith, 1972; Running et al.,2004), where GPP is approximated as the product of LUE, photosynthetically active radiation (PAR), andthe fraction of PAR (fPAR) absorbed by the canopy, where APAR = PAR × fPAR:

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RESEARCH LETTER10.1029/2018GL077906

Key Points:• Solar-induced fluorescence detected

interannual variation in GPP withgreater success than traditionalsatellite-based products

• Solar-induced fluorescence detectedminor forest disturbances, whiletraditional satellite-based productsfailed to discern them

Supporting Information:• Supporting Information S1• Figure S1• Figure S2• Figure S3• Figure S4• Figure S5• Figure S6• Figure S7• Figure S8

Correspondence to:L. M. Zuromski,lauren.zuromski@utah.edu

Citation:Zuromski, L. M., Bowling, D. R., Köhler, P.,Frankenberg, C., Goulden, M. L.,Blanken, P. D., & Lin, J. C. (2018). Solar-induced fluorescence detectsinterannual variation in gross primaryproduction of coniferous forests in thewestern United States. GeophysicalResearch Letters, 45, 7184–7193. https://doi.org/10.1029/2018GL077906

Received 12 MAR 2018Accepted 14 JUN 2018Accepted article online 25 JUN 2018Published online 20 JUL 2018

©2018. American Geophysical Union.All Rights Reserved.

GPP ¼ PAR�fPAR�LUE ¼ APAR�LUE (1)

Ecosystems worldwide endure physiological stress from soil moisture deficit, high evaporative demand, andtemperature extremes, and also from herbivory and pathogens. While remote sensing based on theaforementioned LUE approach has enabled monitoring of mortality and herbivory over landscape scales(e.g., Hicke et al., 2012; Kurz et al., 2008), detection of environmental stress in evergreen forests is moredifficult due to changes in LUE that are not easily quantified (M. He et al., 2016; Yulong Zhang et al., 2015).Thus, methods that can reliably detect GPP changes due to environmental stress would be valuable asindicators of evergreen forest health.

Recent advances in satellite sensors and retrieval techniques have enabled remote sensing of solar-inducedchlorophyll fluorescence (SIF), the natural emission of photons from the light-harvesting structures of sunlitplants, which is a biophysical consequence of light absorption. Previous work has shown that space-based SIFobservations correlate significantly with GPP derived from ground-based eddy covariance, reliably predictingthemonthly or average seasonal pattern of GPP for a range of biomes (Frankenberg et al., 2011; Guanter et al.,2014; Joiner et al., 2014; Sanders et al., 2015). SIF appears promising as a more physiologically meaningfulproxy to photosynthesis (e.g., Yang et al., 2015) than do traditional remote sensing-based vegetation indices,such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI; Myneni &Williams, 1994; Rahman et al., 2005; Shi et al., 2017; Sims et al., 2006; Xiao et al., 2004).

SIF can be used to estimate GPP via the product of APAR and the emitted SIF per absorbed photon, SIFyield(Guanter et al., 2014), implying that the direct correlation of GPP and SIF is related to APAR and also the ratioof their respective yields (Lee et al., 2013). Under stress, these yields covary, which relates SIF not only to APARbut also to LUE (Yang et al., 2015). Coniferous forests are of particular significance in this regard as their fPARexhibits minimal variation, in contrast to their seasonally varying LUE (Porcar-Castell et al., 2014). Recentstudies suggest that SIF provides information on LUE in coniferous forests (Walther et al., 2016; Yao Zhanget al., 2016), underscoring its possible advantage over traditional remote sensing indices (i.e., NDVI andEVI), which do not provide information about LUE. The slope of the GPP-SIF relationship has shown to bebiome-specific (Damm et al., 2015; Guanter et al., 2012, 2014; Parazoo et al., 2014; Sanders et al., 2015), butrecent work with OCO-2 SIF data suggests there may be a universal relationship (Sun et al., 2017).However, due to physiological and phenological differences between plant species and functional types,as well as structural complexities of vegetation canopies across the landscape, a universal relationship mayprove elusive (see reviews by Gamon (2015) and Porcar-Castell et al. (2014)).

Conifers make up a significant fraction of forests in the western United States and comprise an importantportion of the North American carbon sink (Pacala, 2001). Over half of the western United States carbon sinkoccurs at elevations above 750m in amodel-based study (Schimel et al., 2002), with high carbon uptake areasin the Sierra Nevada, Cascade, and Rocky Mountains (Berner & Law, 2015; Desai et al., 2011; Kelly & Goulden,2016; Trujillo et al., 2012). However, the strength of these sinks is expected to weaken over the comingcentury, with increasing temperature, decreased and earlier snowmelt, and associated climate change-intensified disturbances—e.g., wildfires (Westerling et al., 2006), drought (B. He et al., 2014), and barkbeetle-induced forest mortality (Amiro et al., 2010; Kurz et al., 2008).

Studies to date using SIF to assess GPP have largely focused on seasonality (e.g., Joiner et al., 2014). As we willshow, seasonal changes in GPP and SIF are fairly large compared to the annual variation in GPP and SIF, pre-senting a methodological challenge for SIF to reliably detect small interannual variability in GPP. Promisingly,Smith et al. (2018) examined the interannual SIF-GPP relationship across dryland ecosystems of southwesternNorth America and found that SIF served as a stronger predictor of both seasonal and interannual variability inGPP than other satellite-derived indices. However, both the magnitude and seasonality of GPP in drylandecosystems differ markedly from those of the more mesic coniferous forests of the western United States,which are characterized by higher biomass and potentially larger contributions to regional scale carbon fluxes.

In this paper, we investigate whether variation in annually averaged SIF can be used to quantify year-to-yearvariation in GPP derived from tower-based eddy covariance measurements from four coniferous forests inthe western United States. We compare the performance of space-borne SIF to serve as a predictor ofinterannual variability of tower-based GPP with that of satellite-based vegetation indices (NDVI and EVI), aswell as to satellite-based GPP (GPPMODIS).

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2. Materials and Methods2.1. SIF from GOME-2

SIF data used here were derived frommeasurements of the Global OzoneMonitoring Experiment-2 (GOME-2)instrument aboard the polar-orbiting MetOp-A satellite (Munro et al., 2006). The GOME-2 instrument has afootprint of 40 × 40 km2 (40 × 80 km2 before July 2013) and a normal swath width of 960 km (1,920 km).The instrument has four detection channels, the last of which contains the spectral subchannel, between720 and 758 nm, used to evaluate SIF at 740 nm (Köhler et al., 2015).

The key to retrieving SIF from space-borne instruments lies in disentangling the weak fluorescence signalfrom top-of-atmosphere-reflected solar radiation. Here retrievals were performed using a backward eliminat-ing principal component method to simulate components of planetary reflectance (including atmosphericabsorption; Köhler et al., 2015). The SIF retrievals were screened for cloud cover (<50%) and an additional cor-rection for converting the instantaneous SIF measurement to daily averages using a length of day correction(Frankenberg et al., 2011). Further, SIF retrievals were extracted for our specific eddy covariance sites (Table 1):only a subset of the GOME-2 footprints containing each flux tower was selected. From 5 to 30 SIF measure-ments were retained per month for each site, which were then averaged per month to derive monthlySIF values.

2.2. Flux Tower Study Locations and GPPTower

To quantify seasonal to interannual variation in tower-based GPP (GPPTower), we used multiyear eddy covar-iance observations of net ecosystem exchange (NEE) at middle- to high-elevation coniferous forests in thefollowing U.S. states (Table 1 and Figure S1 in supporting information S1). Oregon (Metolius; “US-Me2”),California (two elevations: Sierrahigh and Sierralow; “US-CZ3” and “US-CZ2”, respectively), and Colorado(Niwot Ridge; “US-NR1”). Sites were selected based on a requirement for consistency between land coverin the footprints of the flux towers and in the GOME-2 pixel.

Eddy covariance-based NEE was partitioned into GPPTower and ecosystem respiration following Reichsteinet al. (2005), by estimating ecosystem respiration as a function of air temperature using nighttime eddy cov-ariance data and extrapolating to daytime. The partitioning was carried out via the Max Planck Instituteonline tool (https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.rhtml). A friction velocity (u*) thresh-old was implemented for each site to remove data from periods with insufficient turbulence. Missing datawere gap filled according to Falge et al. (2001). Daily GPPTower were aggregated to monthly totals and thensummed to determine cumulative annual GPPTower (12 months) or active season GPPTower (a subset of the12 months; see below) for each year.

Active GPPTower season (GPPTower > 0, similar to growing season concept for deciduous systems) timing dis-crepancies between datasets can translate into seasonal carbon budget errors (Garrity et al., 2011; Waltheret al., 2016). In the paper we focus on annually summed GPPTower and relate them to annually averagedSIF, NDVI, and EVI, as well as annually summed GPPMODIS (described below). However, we also conducted

Table 1Information on the Eddy Covariance Flux Towers Used in This Analysis

Site name Latitude (°) Longitude (°) Elevation (m) Years Dominant vegetation Reference

Niwot (US-NR1) 40.03 �105.55 3,050 2007–2014 Subalpine firEngelmann spruceLodgepole pine

Blanken et al. (2009)

Metolius (US-Me2) 44.45 �121.56 1,253 2007–2014 Ponderosa pine Kwon et al. (2018)Sierralow (US-CZ2) 37.03 �119.26 1,160 2011–2015 Ponderosa pine

OakGoulden et al. (2012)

Sierrahigh (US-CZ3) 37.07 �119.20 2,015 2009–2015 White firPineCedar

Goulden et al. (2012)

Note. Sites are listed with AmeriFlux site codes (www.ameriflux.lbl.gov). The years analyzed for each site represent the time periods where data for both GPPTowerand GOME-2 SIF were available.

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sensitivity analyses with other active GPPTower season (GPPTower > 0) definitions based on when the bulk ofthe GPPTower occurred for each site (supporting information S1).

2.3. MODIS Data2.3.1. Vegetation IndicesThe NDVI and the EVI detect leaf chlorophyll content by making use of surface reflectance in red and near-infrared spectral bands (Huete et al., 2002). EVI employs an additional blue spectral band that corrects foraerosol influences in the red band, which, along with a better separation of canopy and background signalsand reduced susceptibility to light saturation in dense vegetation, improve EVI’s performance (Huete et al.,2002). These additional features enable EVI to be more sensitive to viewing illumination geometry thanNDVI (Hilker et al., 2015), making EVI better correlated with GPP than NDVI in coniferous forests (X. Xiaoet al., 2004). However, GPP-EVI relationships are weaker in western U.S. coniferous forests compared withother biomes (Biederman et al., 2017; Sims et al., 2006). Contemporary use of EVI to determine GPP requiresconsiderably more data streams combined with sophisticated regression modeling (J. Xiao et al., 2014).

NDVI and EVI were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations onboard the satellite Terra (MOD13C2, v006). These data were projected on a 0.05° geographic climatemodeling grid (CMG) and were provided as a cloud-filtered, level 3 product on a monthly basis. BecauseSIF incorporates information regarding PAR, we calculate solar radiation-weighted NDVI and EVI for compar-ison to SIF. Downward shortwave radiation flux data were obtained from National Centers for EnvironmentalPrediction (NCEP) North America Regional Reanalysis (NARR) on a 3-hourly basis for the four flux towers(Table 1), which were averaged to monthly values that were then multiplied by monthly NDVI and EVI; finally,the product was divided by the total solar radiation for each site’s selected record.2.3.2. GPP Derived From MODIS (GPPMODIS)The MODIS GPP algorithm employs radiation-use conversion efficiency logic to provide 8-day GPP compo-sites on a 1-km2, global basis. The version of MODIS-derived GPP (GPPMODIS) used in this work, MOD17A2v055, is a revised version of MOD17A2 v005, where cloud-contaminated variables have been reprocessed.Cumulative daily GPP was calculated, at the pixel level, by adopting the framework in equation (1)(Heinsch et al., 2006), where LUE depends on a set of biome-specific physiological parameters (Heinschet al., 2006). These parameters were populated into a biome property look-up table, using MODIS land coverclassifications to distinguish biomes (Heinsch et al., 2003), and then extracted at the pixel level. The MODISGPP algorithm tends to overestimate GPP at low-productivity sites, owning to both artificially high fPARvalues (Turner et al., 2006) and to linear constraining assumptions of vapor pressure deficit and temperaturewhen reducing the maximum conversion efficiency (Heinsch et al., 2006).

2.4. Interannual Variability

The magnitude of interannual variation for GPPTower and its predictive variables were examined as standar-dized anomalies (i.e., Z scores). The GPPTower standardized anomalies were calculated, per site, by subtractingthe multiyear average of the annual cumulative GPPTower from each individual year’s annual cumulativeGPPTower, and then dividing by the standard deviation of the annual cumulative GPPTower. Anomalies revealvariability around the multiyear mean, while the standardization allows for the anomalies to be comparedbetween sites with variabilities of different magnitudes. This method was also used to calculate standardizedanomalies of SIF, NDVI, EVI, and GPPMODIS.

Due to varying data availability, the time period examined for each site differed (Table 1). Although GPPTowerrecords for Niwot and Metolius are much longer than used here, only the latter periods were analyzed tooverlap with the shorter length of the GOME-2 SIF record.

3. Results and Discussion

The SIF derived from GOME-2 served as an effective predictor for the seasonal cycle of GPPTower in coniferousforests (Figures 1a, 1c, 1e, and 1g; r2 for GPPTower-SIF varied from 0.42 to 0.90), a finding consistent with pre-vious studies (Joiner et al., 2014; Walther et al., 2016). For individual sites, SIF more closely corresponded withGPPTower than did NDVI, EVI, or GPPMODIS for Sierralow and Sierrahigh (Figures 1e–1h). However, both SIF andGPPMODIS predicted 70% of the seasonal GPPTower variation for US-Me2 (Figure 1c), while SIF and NDVI bothpredicted 90% of the seasonal GPPTower variation for Niwot (Figures 1a and 1b). GPPTower at Sierralow, and to a

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Figure 1. Multiyear time series of monthly cumulative GPPTower from observations at four flux towers (black, circle), SIF (purple, diamond), NDVI (blue, triangle),EVI (green, square), and GPPMODIS (red, circle) are shown. NDVI and EVI were weighted by solar radiation. The annual average value for each variable is shown as alarger symbol (of each variable’s respective color and shape), plotted at June of each year. The r2 values correspond to the linear regressions between GPPTowerand SIF (purple), GPPTower and NDVI (blue), GPPTower and EVI (green), and GPPTower and GPPMODIS (red) on a monthly basis. Regression equations are listed inTable S3. GPP = gross primary production; SIF = solar-induced chlorophyll fluorescence; NDVI = normalized difference vegetation index; EVI = enhanced vegetationindex.

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lesser extent Sierrahigh, exhibited decreasing multiyear trends over the years. When the time series atSierralow and Sierrahigh were detrended (Figures S2e–S2h), the seasonal patterns were still effectivelypredicted by SIF, though the correlations for NDVI, EVI, and GPPMODIS all generally increased.

Seasonal (monthly) variation in GPPTower was much larger than year-to-year variation, with much largercoefficients of variation for monthly GPP (0.72–1.02) than for annual GPPTower (0.05–0.30, Table S1). Hence,detection of interannual variation in GPPTower using any metric is potentially more difficult than seasonal var-iation due to the small interannual variation. Despite this, SIF was a strong predictor of interannual variationin GPPTower. When all sites were combined, SIF was significantly correlated with GPPTower (Figure 2a, r

2 = 0.46,p < 0.001). The relationships varied, however, for individual sites, as seen in the results of linear regressionsbetween SIF and GPPTower (Figure 2a). Regressions for Sierralow and Sierrahigh were statistically significant(p < 0.05), but were not for Niwot and Metolius. Regressions between GPPTower and NDVI, EVI, andGPPMODIS were generally weak for individual sites, and when combined (Figures 2b–2d). When sites werecombined, SIF explained 46% of the year-to-year variance in GPPTower (Figure 2a), whereas EVI, NDVI, andGPPMODIS were unrelated to GPPTower on interannual timescales (r2 = 0.00, r2 = 0.00, and r2 = 0.05, respec-tively, and the slopes of these regressions were not statistically distinguishable from zero; Figures 2b–2d).

Figure 2. Scatter plots of GPPTower annual standardized anomalies compared to those for SIF (a), NDVI (weighted by solarradiation) (b), EVI (weighted by solar radiation) (c), and GPPMODIS (d). The symbols are coded in both color and shape,with years indicated on the symbol. Site level r2 values and p values are indicated next to the site name. Themultisite r2 andp values are also specified, with a corresponding linear regression, indicated by a dashed line. A 1:1 dotted line isshown for reference. Scatter plots for nonstandardized variables are shown for comparison in Figure S5. GPP = grossprimary production; SIF = solar-induced chlorophyll fluorescence; NDVI = normalized difference vegetationindex; EVI = enhanced vegetation index.

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The fairly robust multisite GPPTower-SIF linear relationship (see supporting information S1 for analysesconducted with different active GPPTower periods and Table S2) provides evidence that SIF was a usefulmetric for GPPTower across years, even with the coarse grid spacing of GOME-2 (but see Limitations below).

Disturbance is a major factor controlling carbon fluxes (Amiro et al., 2010); thus, if SIF is a reliable GPP proxy,then SIF should also be influenced by disturbance. The mortality at Sierralow and Sierrahigh during the studyperiod, along with the absence of such disturbance at Niwot and Metolius, are likely explanations for thediffering GPPTower-SIF relationships between sites (Figure 2). The robust GPPTower-SIF linear regressions atSierralow and Sierrahigh (Figures 1e, 1g, and 2a) highlight the utility of SIF to track disturbance impacts of dif-fering severities on the carbon cycle. The southern Sierra Nevada mountains experienced severe droughtfrom 2012 to 2016, with related forest dieback that killed millions of trees (Bales et al., 2018). The severityof the drought and associated mortality were elevation dependent: the most dieback occurred below1,600 m while minimal dieback occurred above 2,000 m (Bales et al., 2018). Therefore, Sierralow, located at1,160 m, experiencedmore drought-related disturbance than did Sierrahigh, located at 2,015 m. This mortalityled to a decrease in GPPTower during the study years at both sites (Figures 1e and 1g). The statistically signifi-cant site level interannual correlations between GPPTower and SIF indicate that it was influenced by mortalityat Sierralow (r2 = 0.87, p = 0.02, Figure 2a), and Sierrahigh (r2 = 0.62, p = 0.04, Figure 2a). Although NDVIaccounted for 73% of variance in GPP at Sierrahigh, this relationship was negatively correlated.

Our results corroborate previous findings that SIF is a better metric for GPP than are NDVI or EVI (Damm et al.,2015; Duveiller & Cescatti, 2016; Joiner et al., 2014; Smith et al., 2018; Yang et al., 2015). However, an importantquestion remains for SIF in coniferous forests—what happens to the SIF-GPP connection during winterdormancy associated with cold stress? Indices that are dependent on APAR are problematic in evergreensbecause sunlight continues to be absorbed during dormancy (when there is no GPP), while LUE is signifi-cantly or entirely reduced. Among our study sites, Niwot and Metolius are dormant during some portion ofthe winter (Figure S4), while GPP continues year-round at both Sierra sites (Bowling et al., 2018; Goulden &Bales, 2014; Thomas et al., 2009). Note, however, that only at the Niwot site did the SIF signal reach zero inwinter (Figure 1a). Leaf-level studies indicate that chlorophyll fluorescence during the daytime continues inmidwinter when photosynthesis has ceased (e.g., Ottander et al., 1995; Verhoeven, 2014). Different SIFproducts provide inconsistent information about the winter SIF signal (Joiner et al., 2014; Walther et al.,2016). For example, Sun et al. (2018) showed that SIF retrieved from OCO-2 was near zero in winter for theEurasian boreal forest, while SIF from GOSAT and GOME-2 continued in winter at 15%–20% of summermaximum values. This issue needs to be resolved for SIF to be a reliable metric of photosynthetic activityof evergreens.

3.1. Limitations

These results highlight the potential for SIF to serve as a metric for interannual variation in GPP, but severallimitations still exist. The respective footprint sizes of GPPTower and SIF are <1 km2 and 40 × 40 km2 (40 × 80km2 before July 2013), suggesting that a spatial mismatch can occur as most landscapes are heterogeneousat spatial scales greater than 5–10 km (Duveiller & Cescatti, 2016). While the predominant vegetation typeswithin the GOME-2 SIF footprints in the vicinity of the selected eddy covariance towers were coniferousforests, the remaining portions were composed mostly of grasslands (see Figure S6). As a result, the SIF datareflect some combination of evergreen trees that are consistent with the tower footprint and also grasslandsthat are not. These subgrid spatial heterogeneities could have skewed the linear relationships between SIFand GPPTower, especially because coniferous forests and grasslands have different SIF responses to GPP(Frankenberg et al., 2011; Guanter et al., 2012; Parazoo et al., 2014; see supporting information S1 andFigures S7 and S8 for analyses using spatially degraded NDVI and EVI). Despite the coarse resolution ofGOME-2 SIF, it still better predicted GPPTower interannual variation than NDVI, EVI, and GPPMODIS whosespatial resolutions (0.05°, 0.05°, and 1 km2, respectively) are much finer than that of SIF.

The OCO-2 satellite (launched in July 2014) improves upon the spatial limitations of GOME-2 SIF, as OCO-2provides SIF on a 1.30 × 2.25 km2 basis (Frankenberg et al., 2014). OCO-2 SIF has recently been shown to cor-relate well with tower-based GPP for several biomes (Li et al., 2018; Sun et al., 2017; Verma et al., 2017; Woodet al., 2017), although coniferous forests have not yet been examined (but see Sun et al., 2018). Further, thenarrow OCO-2 swaths do not intersect with the four eddy covariance flux towers we included in our analysis,

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and the resolution of a gridded OCO-2 product would likely be spatially coarse (e.g., 1°) because of its narrowswath coverage (Li et al., 2018). The launch of the TROPOspheric Monitoring Instrument (TROPOMI) on boardthe Sentinel-5 Precursor satellite in late 2017, will provide yet another opportunity for finely resolved, bothspatially and temporally, SIF (Guanter et al., 2015).

Sensor degradation has a known impact on the GOME-2 SIF time series (Smith et al., 2018; Yao Zhanget al., 2018). However, our main findings were unchanged after detrending the SIF data (supportinginformation S1), suggesting that SIF may be used to detect actual variability in GPP despite sensor-relatedartifacts. Finally, changes in the satellite overpass time could pose issues when relating instantaneous SIFfrom satellites to daily SIF, due to diurnal variations in SIF yield (Parazoo et al., 2018; Smith et al., 2018).Thus, more field campaigns and studies examining these diurnal GPP-SIF relationships would beof importance.

4. Conclusions

We conclude that SIF is a useful proxy for year-to-year variation in GPP in coniferous forests. SIF wassignificantly correlated with GPPTower in four forests with different species composition and served as a moreeffective metric for GPPTower than conventional greenness indices like NDVI and EVI, as well as satellite-basedGPPMODIS. These annual GPPTower-SIF linear relationships were robust when considering full calendar years orsubsets representing 90% of the GPPTower-active season. For individual flux towers, SIF and GPPTower weremore strongly correlated for sites that underwent disturbances (Sierralow and Sierrahigh) than for those siteswithout major disturbances (Niwot andMetolius) during our study period. While SIF is a promising GPP proxy,spatial limitations remain in relating flux tower-based GPP to satellite-based SIF, a discrepancy that will beimproved upon with future SIF measuring platforms. These results add to the mounting evidence thatsatellite-based SIF is a powerful tool to examine the spatiotemporal variability of GPP across the landscape,even including coniferous forests. Further, these results show that SIF responds to variation in conifer stressand mortality, processes that have been difficult to detect in evergreen forests.

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AcknowledgmentsThe MODIS MOD13C2 v006 andMOD17A2 v055 data products wereretrieved from the online Reverb tool(https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table) courtesy of the NASA EOSDISLand Processes Distributed ActiveArchive Center (LP DAAC), USGS/EarthResources Observation and Science(EROS) Center, Sioux Falls, South Dakota.NARR data were provided by theNOAA/OAR/ESRL PSD, Boulder,Colorado, USA (https://www.esrl.noaa.gov/psd/). This study was supported byNASA’s Carbon Monitoring SystemProgram, under grant NNX16AP33G andthe U.S. Department of Energy’s Officeof Biological and EnvironmentalScience, Terrestrial Ecosystem ScienceProgram, under awards DE-SC0010624and DE-SC0010625. The Niwot site (US-NR1) was supported by the U.S. DOE,Office of Science through the AmeriFluxManagement Project (AMP) at LawrenceBerkeley National Laboratory, award7094866. We are grateful to Bev Lawand Sean Burns for sharing site leveleddy covariance observations. Wethank Simon Brewer, Brett Raczka, andHenrique Duarte for helpful discussions.

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