Satellite Solar-induced Chlorophyll Fluorescence Research Articles

Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis

Future projections of climate extremes are expected to become more frequent. Parts of Europe experienced an extensive heatwave and drought during 2018. However, its impacts on terrestrial carbon cycle remain elusive. Here we investigated the vegetation responses to the heatwave and drought during 2018 based on satellite solar-induced chlorophyll fluorescence (SIF) and near-infrared reflectance (NIRv) data, which were used to estimate gross primary productivity (GPP). Results showed that there were no significant (p= 0.60) reductions in GPP across most of Europe during April–August of 2018. The higher temperatures in spring enhanced vegetation GPP, largely alleviated the negative impacts of heatwave and drought on vegetation photosynthesis during the subsequent summer, which resulted in evident compensation effects. Concurrently, warmer spring also had lagged effects by diminishing soil moisture, accompanied by scarce precipitation, leading to water stress on plant growth during summer. This observation-based study highlights the need for more considerations of seasonal compensation and lagged effects on the interactions between climate extreme events and biosphere.

Assessing the response of vegetation photosynthesis to meteorological drought across northern China

AbstractSatellite‐based solar‐induced chlorophyll fluorescence (SIF) has the potential to offer early detection and accurate impact assessment of meteorological drought on vegetation photosynthesis. However, how the response of satellite SIF to meteorological drought varies under different climatic conditions and biome types remains poorly understood. In this study, we determined the drought time‐scale at which the vegetation photosynthesis response was highest based on the standardized precipitation evapotranspiration index (SPEI) and satellite SIF and examined how the sensitivity of SIF signals from different ecosystems to drought varied along an aridity gradient in northern China. The results showed that spatial variability of the annual maximum SIF was constrained by wetness conditions and biome types. Annual maximum SIF was positively correlated with SPEI in 57.9% of vegetated lands (p < .05). About 34.8% of humid ecosystems were characterized by a significant SIF‐SPEI correlation (p < .05). This percentage reached 44, 71.4, and 86.2% for arid, subhumid, and semiarid ecosystems, respectively. The variation of SIF‐SPEI correlations was a Gaussian function of the aridity index (AI), with the highest SIF‐SPEI correlation appearing in the AI bin of 0.4 (0.37–0.46). The drivers for this pattern were vegetation composition and water availability. The variation of SIF time scales in response to SPEI was a linear function of the AI, but the slope varied among biomes. To summarize, with increasing aridity drought‐induced declines in vegetation photosynthesis will be quicker and more significant.

Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity

Solar-induced chlorophyll fluorescence (SIF) provides a new and direct way of monitoring photosynthetic activity. However, current SIF products are limited by low spatial resolution or sparse sampling. In this paper, we present a data-driven method of generating a global, spatially continuous TanSat SIF product. Firstly, the key explanatory variables for modelling canopy SIF were investigated using in-situ and satellite observations. According to theoretical and experimental analysis, the solar radiation intensity was found to be a dominant driving environmental variable for the SIF yield at both the canopy and global scales; this has, however, been neglected in previous research. The cosine value of the solar zenith angle at noon (cos (SZA0)), a proxy for solar radiation intensity, was found to be a dominant abiotic factor for the SIF yield. Next, a Random Forest (RF) approach was employed for SIF prediction based on Moderate Resolution Imaging Spectroradiometer (MODIS) visible-to-NIR reflectance data, the normalized difference vegetation (NDVI), cos (SZA0), and air temperature. The machine learning model performed well at predicting SIF, giving R2 values of 0.73, an RMSE of 0.30 mW m−2 nm−1 sr−1 and a bias of 0.22 mW m−2 nm−1 sr−1 for 2018. If cos (SZA0) was not included, the accuracy of the RF model decreased: the R2 value was then 0.65, the RMSE 0.34 mW m−2 nm−1 sr−1 and an bias of 0.26 mW m−2 nm−1 sr−1, further verifying the importance of cos (SZA0). Finally, the globally continuous TanSat SIF product was developed and compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIF data. The results showed that the globally continuous TanSat SIF product agreed well with the TROPOMI SIF data, with an R2 value of 0.73. Thus, this paper presents an improved approach to modelling satellite SIF that has a better accuracy, and the study also generated a global, spatially continuous TanSat SIF product with a spatial resolution of 0.05°.

Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations.

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF760) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF760 tracked GPP well at both diurnal and seasonal scales, and that SIF760 was more linearly correlated to PAR than GPP was. Therefore, the SIF760–GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R2 value was 0.66 in 2017 and 2018. Based on daily mean observations, the R2 value was 0.82 and 0.76 in 2017 and 2018, respectively. Secondly, it was found that the SIF760–GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF760 decreased noticeably as the CI increased. Finally, the SIF760-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF760-based GPP estimates based on daily mean observations: the value of R2 increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF760-based GPP estimation was improved greatly by including the CI, giving a higher R2 and a lower RMSE. These values improved from R2 = 0.66 and RMSE = 7.02 mw/m2/nm/sr to R2 = 0.76 and RMSE = 6.36 mw/m2/nm/sr for the linear model, and from R2 = 0.71 and RMSE = 4.76 mw/m2/nm/sr to R2 = 0.78 and RMSE = 3.50 mw/m2/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF760 is a reliable proxy for GPP and that SIF760-based GPP estimation can be greatly improved by integrating the CI with SIF760. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.

Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence

Quantifying global photosynthesis remains a challenge due to a lack of accurate remote sensing proxies. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a good indicator of photosynthetic activity across various spatial scales. However, a global and spatially challenging estimate of terrestrial gross primary production (GPP) based on satellite SIF remains unresolved due to the confounding effects of species-specific physical and physiological traits and external factors, such as canopy structure or photosynthetic pathway (C3 or C4). Here we analyze an ensemble of far-red SIF data from OCO-2 satellite and ground observations at multiple sites, using the spectral invariant theory to reduce the effects of canopy structure and to retrieve a structure-corrected total canopy SIF emission (SIFtotal). We find that the relationships between observed canopy-leaving SIF and ecosystem GPP vary significantly among biomes. In contrast, the relationships between SIFtotal and GPP converge around two unique models, one for C3 and one for C4 plants. We show that the two single empirical models can be used to globally scale satellite SIF observations to terrestrial GPP. We obtain an independent estimate of global terrestrial GPP of 129.56 ± 6.54 PgC/year for the 2015–2017 period, which is consistent with the state-of-the-art data- and process-oriented models. The new GPP product shows improved sensitivity to previously undetected ‘hotspots’ of productivity, being able to resolve the double-peak in GPP due to rotational cropping systems. We suggest that the direct scheme to estimate GPP presented here, which is based on satellite SIF, may open up new possibilities to resolve the dynamics of global terrestrial GPP across space and time.

Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing

Growing seasons of vegetation generally start earlier and last longer due to anthropogenic warming. To facilitate the detection and monitoring of these phenological changes, we developed a discrete, hierarchical set of global “phenoregions” using self-organizing maps and three satellite-based vegetation indices representing multiple aspects of vegetation structure and function, including the normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and vegetation optical depth (VOD). Here, we describe the distribution and phenological characteristics of these phenoregions, including their mean temperature and precipitation, differences among the three satellite indices, the number of annual growth cycles within each phenoregion and index, and recent changes in the land area of each phenoregion. We found that the phenoregions “self-organized” along two primary dimensions: degree of seasonality and peak productivity. The three satellite-based indices each appeared to provide unique information on land surface phenology, with SIF and VOD improving the ability to detect distinct annual and subannual growth cycles in some regions. Over the nine-year study period (limited in length by the short satellite SIF record), there was generally a decrease in the spatial extent of the highest productivity phenoregions, though whether due to climate or land use change remains unclear.

Responses of Australian Dryland Vegetation to the 2019 Heat Wave at a Subdaily Scale

AbstractSatellite solar‐induced chlorophyll fluorescence (SIF) products from the Global Ozone Monitoring Experiment 2 (GOME‐2) and Orbiting Carbon Observatory 2 (OCO‐2) are used to investigate the responses of vegetation growth to the 2019 heat wave in Australia. Both satellite SIF data sets are more sensitive to water and heat stress than is the greenness‐based vegetation index (enhanced vegetation index). Moreover, the OCO‐2 SIF observations show a more significant reduction and earlier response to the heat stress than does GOME‐2 SIF, indicating that the two satellite SIF data sets differ in how they monitor the drought and heat wave event due to the different timing of observations. Eddy covariance measurements confirm the different responses of dryland vegetation to the 2019 heat wave at a subdaily time scale. The differences in the timing of the satellite SIF products can be used to assess different elements of the impact of heat and water stress on Australian dryland ecosystems.

Extraction of sub-pixel C3/C4 emissions of solar-induced chlorophyll fluorescence (SIF) using artificial neural network

Solar-induced chlorophyll fluorescence (SIF) is a signal directly and functionally related to photosynthetic activity and thus holds great promise for large-scale agricultural monitoring. However, the coarse spatial resolution of existing satellite SIF observations usually consist of mixed SIF signals contributed by different crop types with distinct phenology (modulated by management practices) and varying SIF emission capacities, which impedes effective utilization of existing SIF records for large-scale agricultural applications. This study makes the first effort to overcome this challenge by developing a sub-pixel SIF extraction framework for corn and soybean in the US Corn Belt as a case study. Here we developed a machine learning (ML) based sub-pixel SIF extraction framework using Orbiting Carbon Observatory 2 (OCO-2), whose high-resolution SIF acquired along orbits at nadir enables the identification of relatively pure pixels dominated by single corn or soybean crops, facilitating validation of the developed framework. To achieve this, we first generated artificially mixed SIF pixels from pure pixels randomly weighted by fractional area coverage. We then employed a standard feed forward artificial neural network (ANN) to estimate sub-pixel SIF for corn and soybean respectively, using the following predictors: total mixed SIF, spectral reflectance of corn/soybean (from Moderate Resolution Imaging Spectroradiometer MODIS), and the fractional area coverage of corn/soybean (derived from CropScape-Cropland Data Layer). Our results demonstrated that the estimated sub-pixel SIF could successfully reproduce the original pure SIF values constituting the mixed pixel, with a normalized root mean squared error (NRMSE) of <10% during the peak growing season. We further demonstrated that this ANN-based framework substantially outperforms the parsimonious linear extraction methods. This developed sub-pixel SIF extraction framework was then applied to generate regional-scale SIF maps for corn and soybean at 0.05° in the US Midwest. Although tested for corn and soybean only, the developed framework has the potential to resolve sub-pixel SIF of more endmembers from coarse SIF observations.

On the zero-level offset in the GOSAT TANSO-FTS O&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; A band and the quality of solar-induced chlorophyll fluorescence (SIF): comparison of SIF between GOSAT and OCO-2

Abstract. Satellite remote sensing of solar-induced chlorophyll fluorescence (SIF) has attracted attention as a method for improving the estimation accuracy of the photosynthetic production of terrestrial vegetation in recent years. The Greenhouse gases Observing Satellite (GOSAT) has the ability to observe both SIF and the concentrations of CO2 and CH4 and thus is expected to contribute to the understanding of the global carbon budget. Evaluating artefact signals (e.g. zero-level offset caused by non-linearity in the analogue circuit in the case of GOSAT) is effective for inferring the instrument status and important for retrieving SIF from satellite measurements. Here we investigate the characteristics of the zero-level offset and the consistency of satellite-derived SIFs by comparing the derived SIF with the Orbiting Carbon Observatory-2 (OCO-2) SIF at multiple spatial scales (footprint to global). The zero-level offset was evaluated using filling-in signals over bare soil while investigating the criteria for identifying barren areas. An analysis of the temporal variation of the zero-level offset over a period of 9 years suggests that the radiometric sensitivity of the GOSAT spectrometer changed after switching the optics path selector in January 2015. The GOSAT SIF was highly consistent with the OCO-2 SIF, with a bias within 0.1 mW m−2 nm−1 sr−1 for most months and an inter-region bias of about 0.2 mW m−2 nm−1 sr−1. Our results agree with the previous comparisons and support the consistency among the present satellite SIF data, which is important for the utilization of those data.

Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2

Accurately quantifying gross primary production (GPP) globally is critical for assessing plant productivity, carbon balance, and carbon-climate feedbacks, while current GPP estimates exhibit substantial uncertainty. Solar-induced chlorophyll fluorescence (SIF) observed by the Orbiting Carbon Observatory-2 (OCO-2) has offered unprecedented opportunities for monitoring land photosynthesis, while its sparse coverage remains a bottleneck for mapping finer-resolution GPP globally. Here, we used the global, OCO-2-based SIF product (GOSIF) and linear relationships between SIF and GPP to map GPP globally at a 0.05° spatial resolution and 8-day time step for the period from 2000 to 2017. To account for the uncertainty of GPP estimates resulting from the SIF-GPP relationship, we used a total of eight SIF-GPP relationships with different forms (universal and biome-specific, with and without intercept) at both site and grid cell levels to estimate GPP. Our results showed that all of the eight SIF-GPP relationships performed well in estimating GPP globally. The ensemble mean 8-day GPP was generally highly correlated with flux tower GPP for 91 eddy covariance flux sites across the globe (R2 = 0.74, Root Mean Square Error = 1.92 g C m−2 d−1). Our fine-resolution GPP estimates showed reasonable spatial and seasonal variations across the globe and fully captured both seasonal cycles and spatial patterns present in our coarse-resolution (1°) GPP estimates based on coarse-resolution SIF data directly aggregated from discrete OCO-2 soundings. SIF-GPP relationships with different forms could lead to significant differences in annual GPP particularly in the tropics. Our ensemble global annual GPP estimate (135.5 ± 8.8 Pg C yr−1) is between the median estimate of non-process based methods and the median estimate of process-based models. Our GPP estimates showed interannual variability in many regions and exhibited increasing trends in many parts of the globe particularly in the Northern Hemisphere. With the availability of high-quality, gridded SIF observations from space (e.g., TROPOMI, FLEX), our novel approach does not rely on any other input data (e.g., climate data, soil properties) and therefore can map GPP solely based on satellite SIF observations and potentially lead to more accurate GPP estimates at regional to global scales. The use of a universal SIF-GPP relationship versus biome-specific relationships can also avoid the uncertainty associated with land cover maps. Our novel, independent GPP product (GOSIF GPP), freely available at our data repository, will be valuable for studying photosynthesis, carbon cycle, agricultural production, and ecosystem responses to climate change and disturbances, informing ecosystem management, and benchmarking terrestrial biosphere and Earth system models.

Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model

Abstract. This paper presents the assimilation of solar-induced chlorophyll fluorescence (SIF) into a terrestrial biosphere model to estimate the gross uptake of carbon through photosynthesis (GPP). We use the BETHY-SCOPE model to simulate both GPP and SIF using a process-based formulation, going beyond a simple linear scaling between the two. We then use satellite SIF data from the Orbiting Carbon Observatory-2 (OCO-2) for 2015 in the data assimilation system to constrain model biophysical parameters and GPP. The assimilation results in considerable improvement in the fit between model and observed SIF, despite a limited capability to fit regions with large seasonal variability in SIF. The SIF assimilation increases global GPP by 31 % to 167±5 Pg C yr−1 and shows an improvement in the global distribution of productivity relative to independent estimates, but a large difference in magnitude. This change in global GPP is driven by an overall increase in photosynthetic light-use efficiency across almost all biomes and more minor, regionally distinct changes in APAR. This process-based data assimilation opens up new pathways to the effective utilization of satellite SIF data to improve our understanding of the global carbon cycle.

Impacts of drought and heatwave on the terrestrial ecosystem in China as revealed by satellite solar-induced chlorophyll fluorescence

Droughts and heatwaves have been and will continue to bring large risks to terrestrial ecosystems. However, the understanding of how plants respond to drought and heatwave over broad spatial scales is still limited. In this paper, we use the 2009/2010 drought in Yunnan and the 2013 heatwave over southern China as case studies to investigate the potential of using satellite-observed solar-induced chlorophyll fluorescence (SIF) to monitor vegetation responses to drought and heatwave over broad spatial scales. The 2009/2010 drought onset follows a strong soil moisture deficit due to the yearlong below-normal precipitation in Yunnan from the autumn of 2009 to the spring of 2010. In the summer of 2013, southern China experienced the strongest heatwave due to the sudden temperature increase and rainfall deficit. The results show that SIF can reasonably capture the spatial and temporal dynamics of drought and heatwave development, as indicated by the large reduction in fluorescence yield (SIFyield). Moreover, SIFyield demonstrates a significant reduction and earlier response than traditional vegetation indices (enhanced vegetation index, EVI) during the early stages of drought and heatwave events. For both study areas, the spatial and temporal correlation analysis demonstrates that the SIFyield anomalies are more sensitive to a high vapor pressure deficit (VPD) than low soil moisture. This study implies that satellite observations of SIF have great potential for accurate and timely monitoring of drought and heatwave developments.

SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis.

Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.

Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a suitable remote sensing proxy of photosynthesis at multiple scales. However, the relationship between fluorescence and photosynthesis observed at the leaf level cannot be directly applied to the interpretation of retrieved SIF due to the impact of canopy structure. We carried out a SIF modelling study for a heterogeneous forest canopy considering the effect of canopy structure in the Discrete Anisotropic Radiative Transfer (DART) model. A 3D forest simulation scene consisting of realistic trees and understory, including multi-scale clumping at branch and canopy level, was constructed from terrestrial laser scanning data using the combined model TreeQSM and FaNNI for woody structure and leaf insertion, respectively. Next, using empirical data and a realistic range of leaf-level biochemical and physiological parameters, we conducted a local sensitivity analysis to demonstrate the potential of the approach for assessing the impact of structural, biochemical and physiological factors on top of canopy (TOC) SIF. The analysis gave insight into the factors that drive the intensity and spectral properties of TOC SIF in heterogeneous boreal forest canopies. DART simulated red TOC fluorescence was found to be less affected by biochemical factors such as chlorophyll and dry matter contents or the senescent factor than far-red fluorescence. In contrast, canopy structural factors such as overstory leaf area index (LAI), leaf angle distribution and fractional cover had a substantial and comparable impact across all SIF wavelengths, with the exception of understory LAI that affected predominantly far-red fluorescence. Finally, variations in the fluorescence quantum efficiency (Fqe) of photosystem II affected all TOC SIF wavelengths. Our results also revealed that not only canopy structural factors but also understory fluorescence should be considered in the interpretation of tower, airborne and satellite SIF datasets, especially when acquired in the (near-) nadir viewing direction and for forests with open canopies. We suggest that the modelling strategy introduced in this study, coupled with the increasing availability of TLS and other 3D data sources, can be applied to resolve the interplay between physiological, biochemical and structural factors affecting SIF across ecosystems and independently of canopy complexity, paving the way for future SIF-based 3D photosynthesis models.

Drought detection and assessment with solar-induced chlorophyll fluorescence in summer maize growth period over North China Plain

• Severe drought occurred in summer maize growth period of 2014 in North China Plain. • Solar-induced chlorophyll fluorescence (SIF) has close linear relationship with GPP. • SIF is more sensitive to drought than reflectance-based vegetation indices. • Assessed by SIF, crop GPP decreased by 10.6% in counties with severe drought. Drought is one of the most damaging and complicated disasters in the world. Timely monitoring of droughts over agro-ecosystem is important for reducing yield loss. The potential of satellite-borne solar-induced chlorophyll fluorescence (SIF) for drought detection and assessment were explored in this study. As denoted by the significant negative anomalies of standardized precipitation evapotranspiration index (SPEI), precipitation (PPT), soil moisture (SM) and positive anomalies of land surface temperature (LST) and air temperature difference (DT), a severe drought event was detected at summer maize growth period in 2014 over the North China Plain. In the duration of this drought, the anomalies of SIF and SIF yield (SIF normalized by absorbed photosynthetically active radiation) were negative. The regional averaged SIF and SIF yield declined 9.0% and 9.3% in July, and 11.9% and 12.5% in August, respectively, higher than those of normalized difference vegetation index (NDVI) (4.8% in July and 3.5% in August) and land surface water index (LSWI) (8.2% in July and 4.9% in August). In addition, the linear relationship between gross primary productivity (GPP) and SIF (R 2 = 0.85) was more significant than that between GPP and NDVI (R 2 = 0.60). The GPP estimated by SIF decreased by 10.6% in counties with severe drought in 2014, which was consistent with the yield reduction (9.1%) estimated by county statistical yield data. It is concluded that satellite SIF is more reliable and effective for regional drought monitoring and assessment, which can help policy-makers or farmers implement appropriate measures to mitigate the drought disasters.

Widespread Decline in Vegetation Photosynthesis in Southeast Asia Due to the Prolonged Drought During the 2015/2016 El Niño

El Niño events are known to be associated with climate extremes and have substantial impacts on the global carbon cycle. The drought induced by strong El Niño event occurred in the tropics during 2015 and 2016. However, it is still unclear to what extent the drought could affect photosynthetic activities of crop and forest in Southeast Asia. Here, we used the satellite solar-induced chlorophyll fluorescence (SIF), which is a proxy of actual photosynthesis, along with traditional vegetation indices (Enhanced Vegetation Index, EVI) and total water storage to investigate the impacts of El Niño–induced droughts on vegetation productivity of the forest and crop in the Southeast Asia. We found that SIF was more sensitive to the water stress than traditional vegetation indices (EVI) to monitor drought for both evergreen broadleaf forest and croplands in Southeast Asia. The higher solar radiation partly offset the negative effects of droughts on the vegetation productivity, leading to a larger decrease of SIF yield (SIFyield) than SIF. Therefore, SIFyield had a larger reduction and was more sensitive to precipitation deficit than SIF during the drought. The comparisons of retrieved column-average dry-air mole fraction of atmospheric carbon dioxide with SIF demonstrated the reduction of CO2 uptake by vegetation in Southeast Asia during the drought. This study highlights that SIF is more beneficial than EVI to be an indicator to characterize and monitor the dynamics of drought in tropical vegetated regions.

Monitoring Drought Effects on Vegetation Productivity Using Satellite Solar-Induced Chlorophyll Fluorescence

Around the world, the increasing drought, which is exacerbated by climate change, has significant impacts on vegetation carbon assimilation. Identifying how short-term climate anomalies influence vegetation productivity in a timely and accurate manner at the satellite scale is crucial to monitoring drought. Satellite solar-induced chlorophyll fluorescence (SIF) has recently been reported as a direct proxy of actual vegetation photosynthesis and has more advantages than traditional vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI and the Enhanced Vegetation Index, EVI) in monitoring vegetation vitality. This study aims to evaluate the feasibility of SIF in interpreting drought effects on vegetation productivity in Victoria, Australia, where heat stress and drought are often reported. Drought-induced variations in SIF and absorbed photosynthetically active radiation (APAR) estimations based on NDVI and EVI were investigated and validated against results indicated by gross primary production (GPP). We first compared drought responses of GPP and vegetation proxies (SIF and APAR) during the 2009 drought event, considering potential biome-dependency. Results showed that SIF exhibited more consistent declines with GPP losses induced by drought than did APAR estimations during the 2009 drought period in space and time, where APAR had obvious lagged responses compared with SIF, especially in evergreen broadleaf forest land. We then estimated the sensitivities of the aforementioned variables to meteorology anomalies using the ARx model, where memory effects were considered, and compared the correlations of GPP anomaly with the anomalies of vegetation proxies during a relatively long period (2007–2013). Compared with APAR, GPP and SIF are more sensitive to temperature anomalies for the general Victoria region. For crop land, GPP and vegetation proxies showed similar sensitivities to temperature and water availability. For evergreen broadleaf forest land, SIF anomaly was explained better by meteorology anomalies than APAR anomalies. GPP anomaly showed a stronger linear relationship with SIF anomaly than with APAR anomalies, especially for evergreen broadleaf forest land. We showed that SIF might be a promising tool for effectively evaluating short-term drought impacts on vegetation productivity, especially in drought-vulnerable areas, such as Victoria.

Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard

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.

OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence

Quantifying gross primary production (GPP) remains a major challenge in global carbon cycle research. Spaceborne monitoring of solar-induced chlorophyll fluorescence (SIF), an integrative photosynthetic signal of molecular origin, can assist in terrestrial GPP monitoring. However, the extent to which SIF tracks spatiotemporal variations in GPP remains unresolved. Orbiting Carbon Observatory-2 (OCO-2)'s SIF data acquisition and fine spatial resolution permit direct validation against ground and airborne observations. Empirical orthogonal function analysis shows consistent spatiotemporal correspondence between OCO-2 SIF and GPP globally. A linear SIF-GPP relationship is also obtained at eddy-flux sites covering diverse biomes, setting the stage for future investigations of the robustness of such a relationship across more biomes. Our findings support the central importance of high-quality satellite SIF for studying terrestrial carbon cycle dynamics.

Global Analysis of Bioclimatic Controls on Ecosystem Productivity Using Satellite Observations of Solar-Induced Chlorophyll Fluorescence

Ecosystem productivity models rely on regional climatic information to estimate temperature and moisture constraints influencing plant growth. However, the productivity response to these environmental factors is uncertain at the global scale and has largely been defined using limited observations from sparse monitoring sites, including carbon flux towers. Recent studies have shown that satellite observations of Solar-Induced chlorophyll Fluorescence (SIF) are highly correlated with ecosystem Gross Primary Productivity (GPP). Here, we use a relatively long-term global SIF observational record from the Global Ozone Monitoring Experiment-2 (GOME-2) sensors to investigate the relationships between SIF, used as a proxy for GPP, and selected bio-climatic factors constraining plant growth at the global scale. We compared the satellite SIF retrievals with collocated GPP observations from a global network of tower carbon flux monitoring sites and surface meteorological data from model reanalysis, including soil moisture, Vapor Pressure Deficit (VPD), and minimum daily air temperature (Tmin). We found strong correspondence (R2 &gt; 80%) between SIF and GPP monthly climatologies for tower sites characterized by mixed, deciduous broadleaf, evergreen needleleaf forests, and croplands. For other land cover types including savanna, shrubland, and evergreen broadleaf forest, SIF showed significant but higher variability in correlations between sites. In order to analyze temperature and moisture related effects on ecosystem productivity, we divided SIF by photosynthetically active radiation (SIFp) and examined partial correlations between SIFp and the climatic factors across a global range of flux tower sites, and over broader regional and global extents. We found that productivity in arid ecosystems is more strongly controlled by soil water content to an extent that soil moisture explains a higher proportion of the seasonal cycle in productivity than VPD. At the global scale, ecosystem productivity is affected by joint climatic constraint factors so that VPD, Tmin, and soil moisture were significant (p &lt; 0.05) controls over 60, 59, and 35 percent of the global domain, respectively. Our study identifies and confirms dominant climate control factors influencing productivity at the global scale indicated from satellite SIF observations. The results are generally consistent with climate response characteristics indicated from sparse global tower observations, while providing more extensive coverage for verifying and refining global carbon and climate model assumptions and predictions.