Satellite Solar-induced Chlorophyll Fluorescence Research Articles

Tracking Gross Primary Productivity Using Satellite Solar Induced Fluorescence: Insights Across Agricultural Ecosystems of India

AbstractAccurately estimating gross primary productivity (GPP) in heterogeneous cropping systems from satellite data poses a challenge. In this study, for the first time, we examined how satellite solar‐induced chlorophyll fluorescence (SIF) tracks GPP in India's diverse cropping system. A significant correlation (r = 0.70–0.90, p ≤ 0.05) at 0.0833° spatial resolution is observed between 8daily and monthly FLUXCOM GPP and Global Ozone Monitoring Experiment‐2 (GOME‐2) SIF. Compared to GOME‐2 SIF the finer resolution, TROPOspheric Monitoring Instrument (TROPOMI) SIF showed greater potential (r = 0.80–0.95, p ≤ 0.05) in estimating GPP and crop productivity, except for Southern and Northeastern parts of India. These regions of India have heterogeneous cropland and low crop area fraction as identified from normalized difference vegetation index (NDVI), near‐infrared reflectance of vegetation and local moran's index map of NDVI. This heterogeneity leads to differences in the timing of growth stages across the annual cycle, such that the SIF signal is characterized by low seasonal variability across the cycle, leading to low correlation between GPP and SIF. Further, in addition to physiological control the intra‐annual variability of GPP‐SIF relationship (GPP/SIF ratio) is influenced by temperature, radiation, and moisture availability during peak/late growing seasons. However, in the early growing season, it primarily relies on soil moisture. Our findings should help improve GPP estimation using SIF across the agricultural ecosystems of India.

Estimation of global ecosystem isohydricity from solar-induced chlorophyll fluorescence and meteorological datasets

Plants exhibit varying strategies for optimizing the trade-off between CO2 uptake and water loss through transpiration in response to increasing air or soil dryness. Anisohydric plants generally keep their stomata open to maintain or enhance carbon uptake, but this exposes them to a greater risk of hydraulic failure. In contrast, isohydric plants tend to maintain hydraulic integrity by enforcing stricter stomatal and xylem regulation, albeit at the expense of reduced carbon assimilation. Information on the degree of ecosystem isohydricity (σ) is important for predicting plant mortality during drought. However, the current σ estimation method lacks a physiological basis by not explicitly accounting for the photosynthesis process. Recent advances in observing solar-induced chlorophyll fluorescence (SIF), an effective proxy for photosynthetic rate, provides new potential to estimate σ at regional and global scales. Based on the revised mechanistic light response (rMLR) model, we developed a mechanistic, SIF-based model of ecosystem-scale isohydricity forced by satellite SIF observations and meteorological datasets. This model was used to estimate daily global ecosystem isohydricity from 2019 to 2020 at a spatial resolution of 0.25°. During the study period, the mean ecosystem isohydricity of global ecosystems was 0.75, suggesting that, on the whole, global terrestrial ecosystems tend to exhibit more anisohydric behavior. Specifically, herbaceous vegetation mostly displaying highly anisohydric behavior, while woody vegetation tended to be more isohydric. Ecosystem isohydricity exhibits clear seasonality, becoming more isohydric in summer. A random-forest analysis revealed that the three most important factors influencing global ecosystem isohydricity are net photosynthesis rate (Anet), canopy height (h), and vapor pressure deficit (VPD), which collectively accounted for 86.1% of the importance. We also found that ecosystem isohydricity reflects a plant-environment interaction, with the contribution of intrinsic hydraulic traits likely exceeding 50%. The proposed model enables us to incorporate plant physiological controls into the estimation of ecosystem-scale σ, thereby enhancing our ability to track plant water-use strategies in response to rising water stress in a changing climate.

A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)

Projected increases in food demand driven by population growth coupled with heightened agricultural vulnerability to climate change jointly pose severe threats to global food security in the coming decades, especially for developing nations. By providing real-time and low-cost observations, satellite remote sensing has been widely employed to estimate crop yield across various scales. Most such efforts are based on statistical approaches that require large amounts of ground measurements for model training/calibration, which may be challenging to obtain on a large scale in developing countries that are most food-insecure and climate-vulnerable. In this paper, we develop a generalizable framework that is mechanism-guided and practically parsimonious for crop yield estimation. We then apply this framework to estimate crop yield for two crops (corn and wheat) in two contrasting regions, the US Corn Belt US-CB, and India’s Indo–Gangetic plain Wheat Belt IGP-WB, respectively. This framework is based on the mechanistic light reactions (MLR) model utilizing remotely sensed solar-induced chlorophyll fluorescence (SIF) as a major input. We compared the performance of MLR to two commonly used machine learning (ML) algorithms: artificial neural network and random forest. We found that MLR-SIF has comparable performance to ML algorithms in US-CB, where abundant and high-quality ground measurements of crop yield are routinely available (for model calibration). In IGP-WB, MLR-SIF significantly outperforms ML algorithms. These results demonstrate the potential advantage of MLR-SIF for yield estimation in developing countries where ground truth data is limited in quantity and quality. In addition, high-resolution and crop-specific satellite SIF is crucial for accurate yield estimation. Therefore, harnessing the mechanism-guided MLR-SIF and rapidly growing satellite SIF measurements (with high resolution and crop-specificity) hold promise to enhance food security in developing countries towards more effective responses to food crises, agricultural policies, and more efficient commodity pricing.

Spatial-temporal heterogeneity of spring phenology in boreal forests as estimated by satellite solar-induced chlorophyll fluorescence and vegetation index

Changes in spring phenology in boreal forests around the subarctic region have a profound impact on the global carbon and water cycles. To date, photosynthesis and greenness dynamics in evergreen coniferous forests have been found to be decoupled on a large scale, but whether the spatial heterogeneity of seasonal dynamics in boreal forests is coupled in various datasets remains unclear. Therefore, based on the solar-induced chlorophyll fluorescence (SIF) retrieved by satellite and the modelled SIF (GOME-2 SIF and GOSIF) during 2007–2018, we calculated the start of the growing season (SOS) of boreal forests (45–70°N) and compared the results with 2-band Enhanced Vegetation Index (EVI2) phenological products (MCD12Q2 C6) and flux data (Gross primary productivity, GPP) phenology. The spatial-temporal variations in the SOS were analyzed by linear regression and piecewise linear regression. The results exhibited that GOME-2 SIF showed an SOS of 11 days earlier than GOSIF, 8 days earlier than EVI2 in North America, and 5 days earlier than GOSIF, 3 days earlier than EVI2 in Eurasia. However, all relationships between SOSs of the three remote sensing datasets were significant in North America and Eurasia, and the significance was higher in Eurasia. With increasing latitude and longitude, almost all SOSs estimated from the three datasets significantly increased. Also, both in North America and Eurasia, the SOSs exhibited a trend of being advanced to being delayed from the western to the eastern parts of the continents over 2007–2018. This indicated the spatial consistency of photosynthesis and greenness dynamics in the spring phenology of boreal forests, especially in Eurasia. In this study, the validity of the spatial-temporal variation in spring phenology of boreal forests based on GOME-2 SIF retrieved from satellites and MCD12Q2 C6 was confirmed, and further exploration is needed to determine their potential advantages.

Contrasting responses of relationship between solar-induced fluorescence and gross primary production to drought across aridity gradients

Satellite solar-induced chlorophyll fluorescence (SIF) can provide valuable insights on terrestrial gross primary production (GPP) and its response to climate from region to global scales. However, the relationship between SIF and GPP under different climate conditions (such as drought) is not yet fully understood and remains a topic of debate in the literature. Here we examined the responses of the SIF–GPP relationship (i.e., the slope k of the linear SIF–GPP relationship, GPP = k × SIF) to drought along the aridity gradient using seven satellite SIF datasets, three GPP products, and 62 flux towers. The results revealed divergent SIF–GPP relationships under different aridity and drought conditions based on different SIF and GPP datasets. Overall, we found that with increasing aridity index (AI) (more humid), k derived from SIF with afternoon overpass (dailySIFPM) decreased, while k from SIF with morning overpass (dailySIFAM) increased. When there is a drought, for arid and semi-arid regions with AI from 0 to 0.4, kPM (the slope of dailySIFPM–GPP) increased, but kAM (the slope of dailySIFAM–GPP) decreased. The dailySIFPM also showed larger decreases than dailySIFAM and GPP during the drought. The contrasting responses among two groups of SIF may result from different SIF overpass time which detected different levels of water stresses on photosynthesis. The dryland ecosystems may exhibit strong midday and afternoon depression in photosynthesis under stress, which led to underestimation of daily SIF that was converted from midday/afternoon-observed SIF by assuming ideal symmetrical diurnal cycle of solar radiation. Our study highlights that in addition to multiple previously reported factors, different observation time of SIF may also affect the SIF–GPP relationship.

Long-term dynamics of peak photosynthesis timing and environmental controls in the Tibetan Plateau monitored by satellite solar-induced chlorophyll fluorescence

ABSTRACT Vegetation phenological metrics have attracted increasing attentions during recent years. However, compared with the peak greenness timing (PGT), the peak photosynthesis timing (PPT) has been overlooked until now. As the world's third pole, the Tibetan Plateau is one of the most sensitive areas to global climatic change. In this study, long-term time-series solar-induced chlorophyll fluorescence (SIF) data were used to identify the PPT patterns, trends, and responses in the Tibetan Plateau over the past two decades. Our findings indicated that the SIF-derived PPT can better capture the characteristics from flux-measured gross primary productivity compared with the MODIS EVI-based PGT, exhibiting great potential to characterize large-scale vegetation PPT dynamics. The multi-year mean PPT mainly appeared in late July and early August with a gradually delayed trend from southeast to northwest. Meanwhile, early PPT occurred in many areas of the Tibetan Plateau (67.36%), with the overall trend advanced by about 0.89 day decade−1. Interestingly, opposite trends of PPT were found with elevation below and above 2500 m asl. Preseason temperature, shortwave radiation, and precipitation jointly explained the strong spatial heterogeneity of PPT trend. All analyses help clarify the role of the ‘third pole’ in responding to environmental change and regional carbon cycles.

Mapping crop phenophases in reproductive growth period by satellite solar-induced chlorophyll fluorescence: A case study in mid-temperate zone in China

Extracting crop phenophases from satellite remote sensing data is crucial for managing agricultural activities and estimating crop yield over large scales. The traditional Vegetation Indices (VIs), such as the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), primarily indicate changes in vegetation greenness, which characterize crop phenophases well in vegetative growth period but may be difficult to associate with phenophases in reproductive growth period that are more related with physiology status. In this study, we investigated the potential of the satellite Solar-Induced chlorophyll Fluorescence (SIF) on extracting the crop phenophases in reproductive growth period, i.e., milk-ripe phase and maturity phase, using single-season cropland in mid-temperate zone in China as a test bed. We found that SIF outperformed EVI and NDVI in extracting milk-ripe phase and maturity phase of maize, rice and wheat using double logistic method. In particular, SIF-derived maturity phase were closer to field-observed phenophases (Mean Bias = 0.73 days) with higher R2 (0.87) than that from EVI and NDVI. At a regional scale, the milk-ripe phase and maturity phase were observed from August to mid-September, and from mid-September to mid-October, respectively, which varied with crop types and in spatial distribution. Out of all crops in mid-temperate zone in China, 65% experienced a delay in the milk-ripe phase, whereas 77% exhibited a delay in the maturity phase from 2001 to 2019. In addition to the adjustment of human managements under climate warming, we further found that crop phenophases in reproductive growth period exhibited the strongest correlation with preseason water-related environmental variables, particularly vapor pressure deficit and total precipitation. This work highlighted the potential of satellite SIF in identifying the crop-specific milk-ripe and maturity phases, and improving our understanding for spatio-temporal variations of crop phenophases in reproductive growth period as well as their responses to preseason environmental variables, which will in turn promote SIF applications in agriculture.

Assessing the responses of different vegetation types to drought with satellite solar-induced chlorophyll fluorescence over the Yunnan-Guizhou Plateau.

The Yunnan-Guizhou Plateau (YGP) is an important ecological region in southwestern China with frequent and severe droughts affecting its vegetation and ecosystem. Many studies have used vegetation indices to monitor drought effects on vegetation across the entire ecosystem. However, the drought response of different vegetation types in the YGP is unclear. This study used solar-induced chlorophyll fluorescence (SIF) and normalized difference vegetation Index (NDVI) data to monitor different vegetation types. The results showed that cropland was most sensitive and woody savanna was most resistant to drought. SIF had a stronger correlation with drought than NDVI, indicating its potential for vegetation monitoring.

Improving the ability of solar-induced chlorophyll fluorescence to track gross primary production through differentiating sunlit and shaded leaves

Recently, solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP). Photosynthesis gradually saturates with the increasing light, but fluorescence tends to keep increasing, leading to a nonlinear SIF-GPP relationship. This nonlinearity occurs for sunlit leaves but not for shaded leaves for which photosynthesis is light-limited. However, the separation of sunlit and shaded SIF has not been systematically investigated when estimating GPP from SIF. Therefore, it is promising to develop a model for GPP estimation considering such differences. This study proposed an approach to separate the total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) SIF into their sunlit and shaded components (SIFsun and SIFshade). The nonlinearity and linearity in SIF-GPP relationships for sunlit and shaded leaves were incorporated into a two-leaf hybrid model, which was fitted using flux tower data and then evaluated using leave-one-site-out crossing validation. We also elucidated the distinct SIF-GPP relationships between sunlit and shaded leaves using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulation. Compared to previously used linear (R2 = 0.68, RMSE = 2.13 gC·m−2·d−1) or hyperbolic (R2 = 0.72, RMSE = 2.01 gC·m−2·d−1) model based on the big-leaf assumption, our proposed two-leaf hybrid model has the best performance on GPP estimation (R2 = 0.77, RMSE = 1.79 gC·m−2·d−1). We also applied this two-leaf hybrid model to estimate the global GPP during the main growing season in Northern Hemisphere, which were highly correlated with several existing GPP products, with R2 ranging from 0.79 to 0.88. These results will improve our understanding of the relationship between SIF and GPP for sunlit and shaded leaves and will advance application of satellite SIF data to GPP estimation.

Solar-induced chlorophyll fluorescence captures photosynthetic phenology better than traditional vegetation indices

Accurate characterization of plant phenology is of great importance for monitoring global carbon, water, and energy cycling. Remotely sensed satellite observations have been widely used to estimate land surface phenology across multiple spatial scales in the last three decades. Recent development on satellite solar-induced chlorophyll fluorescence (SIF) observations have opened an opportunity to monitor the seasonality of plant growth from the perspective of photosynthesis phenology. The SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) with high spatial resolution (up to 7 km × 3.5 km pixels) and near-daily global coverage provide unprecedented opportunity to observe photosynthetic and land surface phenology from space. However, the performance of TROPOMI SIF-derived phenology has not been systematically evaluated. In this study, we used flux tower gross primary productivity (GPP) and PhenoCam green chromatic coordinate (Gcc) data as the benchmark to verify phenology metrics derived from satellite observations. The phenology metrics including the start (SOS), end (EOS), length of growing season (LOS), and the peak of growing season (POS) were estimated from TROPOMI SIF, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), near‐infrared reflectance of vegetation (NIRv), and Global Vegetation Phenology product (MCD12Q2), and the latter four were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) across six vegetation types over North America during the period of 2018–2020. We found that the overall agreements of SIF (R2 ranging from 0.30 to 0.63) against GPP-estimated phenology were stronger than NDVI (0.17–0.41), EVI (0.19–0.39), NIRv (0.23–0.62), and MCD12Q2 (0.19–0.48) derived phenological events. In reference to GPP and Gcc-estimated phenology, SIF also generally has the least error and bias compared to other satellite remote sensing-derived phenology metrics. No significant differences were found between SIF and GPP-derived phenology (P>0.05, two-tailed t-test). In addition, we found that the spatial distribution of SIF-derived phenology reflected the expected latitudinal patterns in phenology dates. SOS, EOS, LOS, and POS observed by MCD12Q2 appeared to be earlier, later, longer, and earlier than TROPOMI SIF-derived phenology, respectively. SIF-based phenological transition dates more closely tracked GPP-based phenology dates, indicating TROPOMI SIF could be a great measure to track photosynthesis seasonality and land surface phenology.

Downscaled Satellite Solar-Induced Chlorophyll Fluorescence Detects the Early Response of Sugarcane to Drought Stress in a Major Sugarcane-Planting Region of China

Under the background of global warming, seasonal drought has become frequent and intensified in many parts of the world in recent years. Drought is one of the most widespread and severe natural disasters, and poses a serious threat to normal sugarcane growth and yield. However, a deep understanding of sugarcane responses to drought stress remains limited, especially at a large spatial scale. In this work, we used the traditional vegetation index (enhanced vegetation index, EVI) and newly downscaled satellite solar-induced chlorophyll fluorescence (SIF) to investigate the impacts of drought on sugarcane in a major sugarcane-planting region of China (Chongzuo City, Southwest China). The results showed that Chongzuo City experienced an extremely severe drought event during the critical growth periods of sugarcane from August to November 2009. During the early stage of the 2009 drought, sugarcane SIF exhibited a quick negative response with a reduction of approximately 2.5% from the multiyear mean in late August 2009, while EVI was not able to capture the drought stress until late September 2009. Compared with EVI, sugarcane SIF shows more pronounced responses to drought stress during the later stage of drought, especially after late September 2009. SIF anomalies can closely capture the spatial and temporal dynamics of drought stress on sugarcane during this drought event. We also found that sugarcane SIF can provide earlier and much more pronounced physiological responses (as indicated by fluorescence yield) than structural responses (as indicated by the fraction of photosynthetically active radiation) to drought stress. Our results suggest that the satellite SIF has a great potential for sugarcane drought monitoring in a timely manner at a large spatial scale. These results are important for developing early warning models for sugarcane drought monitoring, and provide reliable information for developing measures to relieve the negative impacts of drought on sugarcane yield and regional economics.

Global enhanced vegetation photosynthesis in urban environment and its drivers revealed by satellite solar-induced chlorophyll fluorescence data

Investigation on the future impacts of climatic and environmental change on vegetation photosynthesis has been largely restricted to controlled field experiments, which can hardly be extended to global scale due to limited spatial, species and ecosystem coverages. However, in urban areas plants experience altered environments that mimic potential future conditions, with higher air temperature, atmospheric carbon dioxide (CO2) concentration and pollution levels. Cities can therefore be used as global, unplanned experiments for assessing the photosynthetic response to multiple climatic and environmental drivers. Following this logic, here we investigate the urbanization impact on vegetation primary productivity and its drivers at global 160 mega-cities, using high-spatial resolution satellite solar-induced chlorophyll fluorescence (SIF) data as the proxy of photosynthesis. SIF enhancements were observed across most of the urban-rural gradients, accounting for more than 85% of the investigated land pixels. More importantly, SIF enhancements due to indirect urbanization impact (i.e., the impacts of climatic and environmental factors on vegetation growth) offset approximately 47% of the SIF reductions due to land cover change, a value significantly higher than that observed for a greenness spectral index (Enhanced Vegetation Index, EVI) (30%). Atmospheric CO2, air temperature, radiation and atmospheric nitrogen dioxide (NO2) were found to be the main drivers accounting for the enhanced SIF in urban areas. These results prove a dominant and global enhancement of vegetation photosynthesis in urban conditions, and reveal the specific contribution of climatic and environmental factors. Our findings can help to forecast the impacts of future environmental conditions on vegetation photosynthesis, and enhance our knowledge on the overall response of terrestrial biomes to climate change.

Analysing far-red SIF directional anisotropy of three structurally contrasting forest canopies towards improved GPP estimation

Solar-induced chlorophyll fluorescence (SIF) is used to estimate terrestrial gross primary production (GPP) due to its physiological link to photosynthesis. However, strong angular variations in satellite-observed SIF (SIFobs), especially for forest canopies with a high structural heterogeneity, hampers robust estimation of GPP. Here, we use directional SIFobs datasets from OCO-2 and TROPOMI satellite sensors and the 3D discrete anisotropic radiative transfer model to investigate the directional properties of far-red SIFobs and their relationships with two GPP products obtained from MODIS and FluxCom datasets for three European forest types with contrasting canopy structures, i.e., boreal, temperate and Mediterranean forests. We found bowl-like angular distribution patterns of SIFobs in both observed and simulated boreal and temperate forests, but no generic distribution pattern of SIFobs was found for the Mediterranean forest. In our GPP estimation of boreal forest, oblique SIFobs performed better than the widely used nadir SIFobs. However, we did not find an optimal view angle suitable for all three forest types. Further, the angular dependencies and forest structure impacts present in SIFobs were found to still propagate into the estimation of total emitted SIF (SIFtotal), which generally regarded as free of these confounding effects. Finally, our results demonstrate that two commonly used variables, i.e., SIFobs with a constant viewing angle and the approximated SIFtotal, both fail to produce a robust relationship with GPP for the Mediterranean forest. These findings highlight the importance of forest canopy structural heterogeneity, including forest floor, on interpretation of directional satellite SIF observations, even when corrected and expressed as a total canopy SIF emission.

Spatial variations in the response of spring onset of photosynthesis of evergreen vegetation to climate factors across the Tibetan Plateau: The roles of interactions between temperature, precipitation, and solar radiation

Phenological changes of evergreen vegetation affect ecosystem functions and land-atmosphere exchanges. Although recent studies have documented changes in the spring onset of photosynthesis derived from satellite solar-induced chlorophyll fluorescence (SOSSIF) of evergreen vegetation in the Northern Hemisphere, spatial variations in the response of the onset of spring photosynthesis to climate change remain poorly characterized. Using a continuous solar-induced chlorophyll fluorescence dataset, we found that SOSSIF advanced in more than 70.0% of surveyed areas with evergreen vegetation, represented as pixels and spread widely across the Tibetan Plateau. Warming temperatures contributed to advanced SOSSIF in more than 42.0% of surveyed areas, mainly in the southeast and areas between 90 and 97 °E. Increasing precipitation contributed to advanced SOSSIF in about 15.0% of surveyed areas, mainly along the southeastern edge of the plateau. A negative partial correlation between SOSSIF and temperature (RP(SOSSIF,T)) was observed in more than 65.0% of surveyed areas, and stronger negative RP(SOSSIF,T) was found in areas with a larger positive interannual correlation between preseason temperature and precipitation, likely due to the better match between favorable temperature and water conditions in these areas. The positive RP(SOSSIF,T) was likely associated with low water availability. The partial correlation between SOSSIF and precipitation (RP(SOSSIF,P)) was spatially diverse, with negative RP(SOSSIF,P) in about half of surveyed areas. Stronger negative RP(SOSSIF,P) was observed in areas with a stronger negative interannual correlation between preseason precipitation and solar radiation, which was probably caused by the trade-off between water and light availability to maximize the benefits from precipitation. The positive RP(SOSSIF,P) was likely due to the lack of solar radiation. This study provides new explanations for spatial variations in the response of SOSSIF to temperature and precipitation, contributing to assessments of vegetation phenology and global carbon cycle modeling.

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

AbstractThe parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar‐induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP‐SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP‐SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM‐simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML‐based GPP‐SIF relationship. The ELM model when fed with the ML GPP‐SIF models also can well predict the spatial‐temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball‐Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote‐sensing SIF, which can be further improved in the future with more ground‐ and satellite‐based observations.

Regional‐Scale Wilting Point Estimation Using Satellite SIF, Radiative‐Transfer Inversion, and Soil‐Vegetation‐Atmosphere Transfer Simulation: A Grassland Study

AbstractAlthough water availability strongly controls gross primary production (GPP), the impact of soil moisture content (SMC) (wilting point) is poorly quantified on regional and global scales. In this study, we used 10 years of observations of solar‐induced chlorophyll fluorescence (SIF) from the Greenhouse gases Observing Satellite (GOSAT) satellite to estimate the wilting point of a semiarid grassland on the Mongolian Plateau. Radiative‐transfer model inversion and soil‐vegetation‐atmosphere transfer simulation were sequentially conducted to distinguish the drought impacts on plant physiology from the changes in the leaf‐canopy optical properties. We modified an existing inversion algorithm and the widely used Soil‐Canopy Observation of Photosynthesis and Energy fluxes model to adequately evaluate dryland features, for example, sparse canopy and strong convection. The modified model, with retrieved parameters and calibration to GOSAT SIF, predicted realistic GPP values. We found that (a) the SIF yield estimated from GOSAT showed a clear sigmoidal pattern in relation to drought, and the estimated wilting point matched ground‐based observations in the literature within ∼0.01 m3 m−3 for the SMC, (b) tuning the maximum carboxylation rate improved the SIF prediction after considering the changes in the leaf‐canopy optical properties, implying that GOSAT detected drought stress in leaf‐level photosynthesis, and (c) the surface energy balance significantly impacted the grassland's SIF; the modified model reproduced observed SIF well (mean bias = 0.004 mW m−2 nm−1 sr−1 in summer), whereas the original model predicted substantially low values under weak horizontal wind conditions. Some model‐observation mismatches in the SIF suggest that more research is needed for fluorescence parametrization (e.g., photoinhibition) and for additional observation constraints.

Satellite solar-induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought.

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.

Monitoring of Drought Stress in Chinese Forests Based on Satellite Solar-Induced Chlorophyll Fluorescence and Multi-Source Remote Sensing Indices

Greenhouse gas emissions have largely changed the global climate, leading to an increase in the frequency and extent of droughts. Forests are essential natural resources, and they play an important role in maintaining ecological security. Effectively monitoring drought stress in forests can help promote sustainable forestry development. Solar-induced chlorophyll fluorescence is a spectral signal released by vegetation photosynthesis after light absorption. In this study, we used solar-induced chlorophyll fluorescence data (SIF), canopy fluorescence yield (SIFyield) data, vegetation indices (NDVI, EVI), leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (fPAR) to study forest drought stress in the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces in China, respectively. The temporal and spatial ranges of drought stress indicated by the Standardized Precipitation-Evapotranspiration Index (SPEI) values were used as a reference (SPEI ≤ −0.5 indicates the occurrence of drought). Firstly, the standardized anomalous values of SIF, SIFyield, NDVI, EVI, LAI, and fPAR were calculated. The temporal and spatial response abilities of each variable to drought stress were analyzed. Secondly, the correlation between each variable and the drought indicator SPEI was quantified. Finally, the validity and variability of SIF and other variables for drought monitoring were analyzed and verified with a random forest classification model. The results showed that on a temporal scale, SIFyield showed an earlier response to drought stress than other variables and the abnormal change of SIFyield was higher than other variables by 10% or more. Spatially, the range of drought areas indicated by SIFyield and SPEI had more coincident areas than other variables. The overall correlation between SIFyield and SPEI was also higher during the drought period, especially during late drought periods when other variables showed negative correlations. For SIFyield, the correlation coefficients of the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces were 0.57, 0.43, 0.32, and 0.49, respectively. Additionally, the variable importance assessment using a random forest model also indicated that SIFyield is more sensitive to forest droughts. We concluded that SIFyield is an effective tool for monitoring forest drought stress in various regions of China and that it can provide a scientific basis for forest drought monitoring.

Simulations of solar-induced chlorophyll fluorescence over crop canopies using the integrated APSIM model

Agricultural production models predict crop yield by accounting for a variety of species, cultivar, farming management, and environmental impacts on crop photosynthesis. Without suitable constraints, however, large uncertainties may exist in simulations of crop photosynthesis. Recent advances in retrieving solar-induced chlorophyll fluorescence (SIF) at the top-of-canopy (TOC) have provided a promising measurement for crop photosynthesis. Within the framework of the APSIM (Agricultural Production Systems sIMulator) model, a SIF module was developed to connect crop photosynthesis to TOC SIF emission (SIFtoc) which can be measured by remote sensing platforms. The new model (APSIM-SIF) first estimates the leaf-level chlorophyll fluorescence emitted over the full SIF spectrum (SIFtot_full) according to CO2 assimilation in crops. The model then mechanistically decomposes the conversion from SIFtot_full to SIFtoc into two factors: the SIF band conversion factor (ɛ) and the fluorescence escape ratio (fesc) that represent the impact of leaf physiological status and plant structure properties, respectively. ɛ can be estimated using leaf structural and biochemical parameters as inputs; fesc for near-infrared SIF can be expressed as a function of directional reflectance in the near-infrared region (RNIR), Normalized Difference Vegetation Index (NDVI), and the fraction of PAR absorbed by crops (fAPAR). The APSIM-SIF model determined more than 90% of the variation in gross primary productivity (GPP), aboveground biomass and leaf area index (LAI) measurements for maize (Zea mays L.) at two AmeriFlux sites in the U.S. Midwest and it also captured the seasonality of SIF (R2 = 0.84) and GPP (R2 = 0.81) well at an irrigated maize site in China. The APSIM-SIF model was also applied to the simulation of TOC SIF emission of maize and soybean (Glycine max L.) in the U.S. Midwest during the 2018 growing season. The simulated SIFtoc accounted for more than 75% of the variability of daily satellite SIF observations for grid squares with more than 70% crop area. The main contribution of this study lies in two aspects: (1) a physically-based framework is proposed to incorporate the SIF module to the APSIM-DCaPST model, and (2) the two important factors used in this framework (ɛ and fesc) remains largely constant during the peak growing season. These findings provide a theoretically robust and operational basis for linking SIF observations with crop growth.

Temporally corrected long-term satellite solar-induced fluorescence leads to improved estimation of global trends in vegetation photosynthesis during 1995–2018

Terrestrial gross primary production (GPP) is one of the largest flux of carbon cycle and also introduces the largest uncertainties of the estimated global carbon budget. During the past decades, the emerging remotely-sensed solar-induced chlorophyll fluorescence (SIF) data have been rapidly developed as the novel proxy of GPP. Though satellite SIF datasets have been widely used for plenty of climatic and ecological studies, a lack of consistent long-term satellite SIF data has hindered its use for long-term assessments due to their temporal inconsistency induced by sensor degradation. Here, based on the time series of SIF data at the Sahara Desert as the benchmark, we present a practical approach to correct the temporal inconsistency of different satellites (GOME, SCIAMACHY and GOME-2) far-red SIF data. The temporally corrected SIF datasets were then spatially downscaled to a finer resolution of 0.05° with the light-use-efficiency (LUE) method. Then, the individual time series of SIF data from the three satellite sensors were fused together to generate a continuously long-term consistent global SIF dataset from July 1995 to December 2018 (termed as LT_SIFc*). We further validated and compared the spatial and temporal characteristics of LT_SIFc* with the ground-based long-term GPP observations from 15 flux sites, modelled global long-term GPP time series, and high-spatial resolution SIF from TROPOMI and OCO-2, respectively. Our results show that the LT_SIFc* can capture the temporal trends and inter-annual variations for both site- and global-GPP, and also the global spatial distributions of SIF at the spatial resolution of 0.05° validated with TROPOMI SIF (R2 = 0.76, RMSE = 0.11 mW m−2 nm−1 sr-1). Our temporally corrected and spatially downscaled global long-term SIF product has the potential for the better estimations of global spatial–temporal variations in GPP, and also could be helpful for the improvement in the predictions of future land carbon storage and climate change.