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There is an urgent need to address the escalating impacts of climate change, particularly the exacerbation of drought conditions, which pose significant threats to global food security and agricultural sustainability. Innovative solutions are imperative, and one such solution involves integrating advanced technologies like the ''PlantArray'' system to monitor and enhance plant physiological responses to water scarcity. The ''PlantArray'' system enables the precise measurement of critical whole-plant physiological traits such as transpiration rate, canopy stomatal conductance, and growth rate with an exceptional spatiotemporal resolution. Augmenting this system with photosynthesis measurements offers an additional layer of information, facilitating a more focused interpretation of the system parameters. To overcome the limitations of single-leaf photosynthesis measurement techniques, this study employs a remote sensing approach to rapidly scan numerous samples at multiple time points, revealing insights into drought stress responses of S. licopersicum lines. An ultra-spectral spectroradiometer mounted on a mobile cart was positioned above an experimental matrix comprising drought-stressed S. licopersicum obsolete and mutagenic lines. Our findings reveal that the vegetation index Photochemical Reflectance Index (PRI) exhibited greater sensitivity to drought stress compared to other vegetation and photosynthesis remote sensing indices. Photosynthesis indices demonstrated increased sensitivity to daily biomass accumulation and served as predictors of final plant yield. Interestingly, Solar-Induced Fluorescence (SIF) parameters, solely indicative of photosynthesis-emitted fluorescence, exhibited no correlation with stress levels or final biomass production. This study articulates the potential to monitor plant responses to agricultural stressors through real-time physiological tracking across complete diel cycles, thereby enriching our understanding of plant-environment interactions. Ultimately, this integrated system shows promise in screening and developing crop cultivars with ideal physiological and photosynthetic traits, vital to cultivating resilient crops in extreme droughts and weather conditions.

Terrestrial photosynthesis is regulated by plant phenology and environmental conditions, both of which experienced substantial changes in recent decades. Unlike early-season photosynthesis, which is mostly driven by temperature or wet-season onset, late-season photosynthesis can be limited by several factors and the underlying mechanisms are less understood. Here, we analyze the temperature and water limitations on the ending date of photosynthesis (EOP), using data from both remote-sensing and flux tower-based measurements. We find a contrasting spatial pattern of temperature and water limitations on EOP. The threshold separating these is determined by the balance between energy availability and soil water supply. This coordinated temperature and moisture regulation can be explained by "law of minimum," i.e., as temperature limitation diminishes, higher soil water is needed to support increased vegetation activity, especially during the late growing season. Models project future warming and drying, especially during late season, both of which should further expand the water-limited regions, causing large variations and potential decreases in photosynthesis.

Inevitable environmental changes empower the researchers to understand and analyze the plant traits for improving the ecosystem. Solar-induced fluorescence (SIF) is one of the functional traits to analyze the vegetation and assess plant phenotyping. Estimation of SIF through hyperspectral imaging technique gaining its popularity in the recent days than any other estimating techniques due to its contiguous spectrum property, which allows us to obtain more information. Another merit of hyperspectral images is that they can be used to acquire data on different scales. In our review, we have focused on three major areas as follows, a.) Hyperspectral imaging techniques in estimating SIF in different scales varying from Ground Scale to Orbital Scales. b.) Correlation of other functional traits and factors influences the SIF estimation c.) Machine learning techniques used to interpret the SIF traits for Agricultural Monitoring. Moreover, the aforementioned areas are becoming crucial in the recent trend, and we confine our review with the state-of-the-art techniques exclusively from 2010 to 2021. We comprehend the details in the review to provide insights on the breakthrough made in hyperspectral imaging for SIF estimation, allowing the reader to deepen their understanding in the areas of plant phenotyping, which would enable them to explore the field for future research.

Remote sensing of solar‐induced chlorophyll fluorescence (SIF) provides a powerful proxy for gross primary productivity (GPP). It is particularly promising in boreal ecosystems where seasonal downregulation of photosynthesis occurs without significant changes in canopy structure or chlorophyll content. The use of SIF as a proxy for GPP is complicated by inherent non‐linearities due to both physical (illumination effects) and ecophysiological (light use efficiencies) controls at fine spatial (tower/leaf) and temporal (half‐hourly) scales. To study the SIF‐GPP relationship, we investigated the diurnal and seasonal dynamics of continuous tower‐based measurements of SIF, GPP, and common vegetation indices at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA over the course of two years. We find that SIF outperforms other vegetation indices as a proxy for GPP at all temporal scales but shows a non‐linear relationship with GPP at a half‐hourly resolution. At small temporal scales, SIF and GPP are predominantly driven by light and non‐linearity between SIF and GPP is due to the light saturation of GPP. Averaged over daily and monthly scales, the relationship between SIF and GPP is linear due to a reduction in the observed PAR range. Seasonal changes in the light responses of SIF and GPP are driven by changes in light use efficiency which co‐vary with changes in temperature, while illumination and canopy structure partially linearize the SIF‐GPP relationship. Additionally, we find that the SIF‐GPP relationship has a seasonal dependency. Our results help clarify the utility of SIF for estimating carbon assimilation in boreal forests. Plain Language Summary: Remote sensing can help us better understand plants' role in the global carbon cycle. In particular, a small light signal emitted by plants during photosynthesis, known as solar‐induced chlorophyll fluorescence (SIF), is a promising remotely sensed proxy for carbon uptake due to its strong relationship with gross primary productivity (GPP, ecosystem carbon assimilated during photosynthesis) when observed by satellite. Recent work at the leaf and tower‐scale has highlighted differences in the relationship between SIF and GPP, thereby raising questions over the utility of SIF for this purpose. We collect tower‐based SIF and GPP measurements from the southern end of the boreal forest to clarify their connection in boreal ecosystems. We find that temporal resolution is critical for understanding the nuances between SIF and GPP and that light and temperature effects complicate their relationship. Additionally, the relationship between SIF and GPP has a seasonal dependency that should be considered when using SIF. Taken together, our results suggest that SIF is a powerful tool for estimating carbon assimilation in boreal forests, but some key considerations need to be addressed for properly interpreting this faint signal. Key Points: Tower‐based solar‐induced chlorophyll fluorescence (SIF) closely tracks gross primary productivity (GPP) over two years in a mixed‐species boreal forestLight saturation of photosynthesis drives non‐linearity between SIF and GPPThe SIF‐GPP relationship is seasonally variant due to dynamics between LUEF and LUEP [ABSTRACT FROM AUTHOR]

In this paper, we introduce a methodology that can improve the estimations of Gross Primary Productivity (GPP) and ecosystem Respiration (R eco ) processes at a regional scale. This method is based on a satellite data-driven approach which is suitable for regions like India where there exists a serious shortage of ground-based observations of biospheric carbon fluxes (e.g., Eddy Covariance (EC) flux measurements). We relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance for capturing vegetation dynamics in the Light-Use Efficiency (LUE)-based vegetation model. Further, we utilised recently available satellite-based Solar-Induced Fluorescence (SIF) and other variables such as Soil Moisture (SM) and Soil Temperature (ST) to refine the predictions of GPP and R eco . The methodology involves establishing a relationship between SIF and GPP for different vegetation classes over India. The SIF-GPP relationship established across the biomes was then used to correct the GPP fluxes simulated by the LUE-based model. Similarly, the ecosystem respiration estimations by the model have undergone refinement by incorporating ST and SM information. This innovative method shows remarkable potential to improve biospheric CO 2 uptake and release, especially for in situ data-constrained regions like India. • SIF-based information is introduced to a light-use efficiency-based vegetation model. • SIF-GPP relationship is established for major biomes across India. • SM and ST information is incorporated into the R eco simulations in the model., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

We analyzed seasonal and spatial variations of evapotranspiration (ET) for five Amazon sub-basins and their response to the 2015/16 El Niño episode using a recently developed water-budget approach. ET varied typically between ~7 and 10 cm/month with exception of the Xingu basin for which it varied between 10 and 15 cm/month. Outstanding features of ET seasonality are (i) generally weak seasonality, (ii) two ET peaks for the two very wet catchments Solimões and Negro, with one occurring during the wet season and one during the drier season, and (iii) a steady increase of ET during the second half of the dry season for the three drier catchments (Madeira, Tapajos, Xingu). Peak ET occurs during the first half of the wet season consistent with leaf flush occurring before the onset of the wet season. With regards to inter-annual variation, we found firstly that for the Solimões and Madeira catchments the period with large positive wet season anomalies (2012–2015) is associated with negative ET anomalies, and negative SIF (solar induced fluorescence) anomalies. Furthermore, we found negative ET of several cm/months and SIF (up to 50%) anomalies for most of the Amazon basin during the 2015/16 El Niño event suggesting down-regulation of productivity as a main factor of positive carbon flux anomalies during anomalously hot and dry conditions. These results are of interest in view of predicted warmer and more erratic future climate conditions.

• Proximal remote sensing tools can be used for estimating gross primary productivity. • The most relevant electromagnetic regions were ∼550 nm and ∼710 nm. • Daily gross primary productivity was related to Plant Senescence Reflectance Index (PSRI) and the Greenness Index (GCC). • Sun Induced Fluorescence (SIF) was not a good predictor for GPP. Salt marshes are highly productive ecosystems relevant for Blue Carbon assessments, but information for estimating gross primary productivity (GPP) from proximal remote sensing (PRS) is limited. Temperate salt marshes have seasonal canopy structure and metabolism changes, defining different canopy phenological phases, GPP rates, and spectral reflectance. We combined multi-annual PRS data (i.e., PhenoCam, discrete hyperspectral measurements, and automated spectral reflectance sensors) with GPP derived from eddy covariance. We tested the performance of empirical models to predict GPP from 12 common vegetation indices (VIs; e.g., NDVI, EVI, PSRI, GCC), Sun-Induced Fluorescence (SIF), and reflectance from different areas of the electromagnetic spectrum (i.e., VIS-IR, RedEdge, IR, and SIF) across the annual cycle and canopy phenological phases (i.e., Greenup, Maturity, Senescence, and Dormancy). Plant Senescence Reflectance Index (PSRI) from hyperspectral data and the Greenness Index (GCC) from PhenoCam, showed the strongest relationship with daily GPP across the annual cycle and within phenological phases (r2=0.30–0.92). Information from the visible-infrared electromagnetic region (VIS-IR) coupled with a partial least square approach (PLSR) showed the highest data-model agreement with GPP, mainly because of its relevance to respond to physiological and structural changes in the canopy, compared with indices (e.g., GCC) that particularly react to changes in the greenness of the canopy. The most relevant electromagnetic regions to model GPP were ∼550 nm and ∼710 nm. Canopy phenological phases impose challenges for modeling GPP with VIs and the PLSR approach, particularly during Maturity, Senescence, and Dormancy. As more eddy covariance sites are established in salt marshes, the application of PRS can be widely tested. Our results highlight the potential to use canopy reflectance from the visible spectrum region for modeling annual GPP in salt marshes as an example of advances within the AmeriFlux network. [ABSTRACT FROM AUTHOR]

Improving plant photosynthesis provides the best possibility for increasing crop yield potential, which is considered a crucial effort for global food security. Chlorophyll fluorescence is an important indicator for the study of plant photosynthesis. Previous studies have intensively examined the use of spectrometer, airborne, and spaceborne spectral data to retrieve solar induced fluorescence (SIF) for estimating gross primary productivity and carbon fixation. None of the methods, however, had a spatial resolution and a scanning throughput suitable for applications at the canopy and sub-canopy levels, thereby limiting photosynthesis analysis for breeding programs and genetics/genomics studies. The goal of this study was to develop a hyperspectral imaging approach to characterize plant photosynthesis at the canopy level. An experimental field was planted with two cotton cultivars that received two different treatments (control and herbicide treated), with each cultivar-treatment combination having eight replicate 10 m plots. A ground mobile sensing system (GPhenoVision) was configured with a hyperspectral module consisting of a spectrometer and a hyperspectral camera that covered the spectral range from 400 to 1000 nm with a spectral sampling resolution of 2 nm. The system acquired downwelling irradiance spectra from the spectrometer and reflected radiance spectral images from the hyperspectral camera. On the day after 24 h of the DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) application, the system was used to conduct six data collection trials in the experiment field from 08:00 to 18:00 with an interval of two hours. A data processing pipeline was developed to measure SIF using the irradiance and radiance spectral data. Diurnal SIF measurements were used to estimate the effective quantum yield and electron transport rate, deriving rapid light curves (RLCs) to characterize photosynthetic efficiency at the group and plot levels. Experimental results showed that the effective quantum yields estimated by the developed method highly correlated with those measured by a pulse amplitude modulation (PAM) fluorometer. In addition, RLC characteristics calculated using the developed method showed similar statistical trends with those derived using the PAM data. Both the RLC and PAM data agreed with destructive growth analyses. This suggests that the developed method can be used as an effective tool for future breeding programs and genetics/genomics studies to characterize plant photosynthesis at the canopy level.

The utilization of remotely sensed observations for light use efficiency (LUE) and tower-based gross primary production (GPP) estimates was studied in a USDA cornfield. Nadir hyperspectral reflectance measurements were acquired at canopy level during a collaborative field campaign conducted in four growing seasons. The Photochemical Reflectance Index (PRI) and solar induced chlorophyll fluorescence (SIF), were derived. SIF retrievals were accomplished in the two telluric atmospheric oxygen absorption features centered at 688 nm (O2-B) and 760 nm (O2-A). The PRI and SIF were examined in conjunction with GPP and LUE determined by flux tower-based measurements. All of these fluxes, environmental variables, and the PRI and SIF exhibited diurnal as well as day-to-day dynamics across the four growing seasons. Consistent with previous studies, the PRI was shown to be related to LUE (r2 = 0.54 with a logarithm fit), but the relationship varied each year. By combining the PRI and SIF in a linear regression model, stronger performances for GPP estimation were obtained. The strongest relationship (r2 = 0.80, RMSE = 0.186 mg CO2/m2/s) was achieved when using the PRI and SIF retrievals at 688 nm. Cross-validation approaches were utilized to demonstrate the robustness and consistency of the performance. This study highlights a GPP retrieval method based entirely on hyperspectral remote sensing observations.

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. In this study, we combined airborne SIF data with simultaneous 'ground truth' data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O 2 A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation. • We compared airborne and ground SIF measurements across time and space. • Better agreements between airborne and ground SIF were obtained at O 2 A than O 2 B band. • Matching the irradiance and radiance is critical for airborne SIF retrievals. • Airborne SIF spectrum retrieval was sensitive to the 715–740 nm water bands. [ABSTRACT FROM AUTHOR]

• There were fundamental differences in the light response of SIF and GPP • SIF responded to light earlier in the year than the spring onset of GPP • SIF did not exhibit a parallel response to temperature and moisture controls on GPP • In summer SIF was not correlated with canopy conductance The phenology of montane conifer forests is likely to shift in response to climate change and altered seasonal dynamics of light, temperature, and moisture. Solar-induced fluorescence (SIF) is expected to provide substantial improvement for mapping temporal changes in evergreen gross primary production (GPP) over greenness-based remote sensing indices. The utility of SIF to monitor seasonal changes in the phenology of conifer photosynthesis depends on the degree to which GPP and SIF respond in synchrony to key environmental drivers. However, to what extent SIF and GPP become decoupled by responding differently to the combined effects of light and other environmental conditions remains unknown. The goal of this study was to characterize the responses of GPP and SIF red to a suite of environmental drivers at the half-hour time scale and determine how these relationships change across seasons. We analyzed one year of tower-based SIF red and eddy covariance-derived GPP data from a conifer forest at Niwot Ridge, Colorado. We compared the light responses of GPP and SIF red across the year, finding that SIF red increased in response to light earlier in the year than did GPP. The light response of GPP had a positive temperature dependence in spring, and this dependency reversed in summer due to increased evaporative demand, while the light response of SIF red was less temperature dependent. Using artificial neural network ensemble analysis, we found that from spring to summer, SIF red did not exhibit a parallel response to the seasonally dynamic temperature and moisture controls on GPP. In summer SIF red was not correlated with canopy conductance, suggesting that SIF is less sensitive to stomatal control than GPP. Our results suggest that, in conifers, photosystems begin to activate in spring prior to when water becomes available for photosynthesis, presenting a challenge for the use of SIF as a phenological indicator in conifer forests. [ABSTRACT FROM AUTHOR]

[Display omitted] • The recent trends in SIF estimation at multiscale and multilevel are discussed in correspondence to the end applications. • The relationship of traits and the factors influencing the impact of SIF-GPP Correlation is described. • Multiple machine learning models used to identify the correlation for developing complex applications are explained. • We have provided future directions and challenges associated with SIF estimation. Inevitable environmental changes empower the researchers to understand and analyze the plant traits for improving the ecosystem. Solar-induced fluorescence (SIF) is one of the functional traits to analyze the vegetation and assess plant phenotyping. Estimation of SIF through hyperspectral imaging technique gaining its popularity in the recent days than any other estimating techniques due to its contiguous spectrum property, which allows us to obtain more information. Another merit of hyperspectral images is that they can be used to acquire data on different scales. In our review, we have focused on three major areas as follows, a.) Hyperspectral imaging techniques in estimating SIF in different scales varying from Ground Scale to Orbital Scales. b.) Correlation of other functional traits and factors influences the SIF estimation c.) Machine learning techniques used to interpret the SIF traits for Agricultural Monitoring. Moreover, the aforementioned areas are becoming crucial in the recent trend, and we confine our review with the state-of-the-art techniques exclusively from 2010 to 2021. We comprehend the details in the review to provide insights on the breakthrough made in hyperspectral imaging for SIF estimation, allowing the reader to deepen their understanding in the areas of plant phenotyping, which would enable them to explore the field for future research. [ABSTRACT FROM AUTHOR]

Attempts to estimate photosynthetic rate or gross primary productivity from remotely sensed absorbed solar radiation depend on knowledge of the light use efficiency (LUE). Early models assumed LUE to be constant, but now most researchers try to adjust it for variations in temperature and moisture stress. However, more exact methods are now required. Hyperspectral remote sensing offers the possibility of sensing the changes in the xanthophyll cycle, which is closely coupled to photosynthesis. Several studies have shown that an index (the photochemical reflectance index) based on the reflectance at 531 nm is strongly correlated with the LUE over hours, days and months. A second hyperspectral approach relies on the remote detection of fluorescence, which is a directly related to the efficiency of photosynthesis. We discuss the state of the art of the two approaches. Both have been demonstrated to be effective, but we specify seven conditions required before the methods can become operational. [ABSTRACT FROM AUTHOR]

Improving plant photosynthesis provides the best possibility for increasing crop yield potential, which is considered a crucial effort for global food security. Chlorophyll fluorescence is an important indicator for the study of plant photosynthesis. Previous studies have intensively examined the use of spectrometer, airborne, and spaceborne spectral data to retrieve solar induced fluorescence (SIF) for estimating gross primary productivity and carbon fixation. None of the methods, however, had a spatial resolution and a scanning throughput suitable for applications at the canopy and sub-canopy levels, thereby limiting photosynthesis analysis for breeding programs and genetics/genomics studies. The goal of this study was to develop a hyperspectral imaging approach to characterize plant photosynthesis at the canopy level. An experimental field was planted with two cotton cultivars that received two different treatments (control and herbicide treated), with each cultivar-treatment combination having eight replicate 10 m plots. A ground mobile sensing system (GPhenoVision) was configured with a hyperspectral module consisting of a spectrometer and a hyperspectral camera that covered the spectral range from 400 to 1000 nm with a spectral sampling resolution of 2 nm. The system acquired downwelling irradiance spectra from the spectrometer and reflected radiance spectral images from the hyperspectral camera. On the day after 24 h of the DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) application, the system was used to conduct six data collection trials in the experiment field from 08:00 to 18:00 with an interval of two hours. A data processing pipeline was developed to measure SIF using the irradiance and radiance spectral data. Diurnal SIF measurements were used to estimate the effective quantum yield and electron transport rate, deriving rapid light curves (RLCs) to characterize photosynthetic efficiency at the group and plot levels. Experimental results showed that the effective quantum yields estimated by the developed method highly correlated with those measured by a pulse amplitude modulation (PAM) fluorometer. In addition, RLC characteristics calculated using the developed method showed similar statistical trends with those derived using the PAM data. Both the RLC and PAM data agreed with destructive growth analyses. This suggests that the developed method can be used as an effective tool for future breeding programs and genetics/genomics studies to characterize plant photosynthesis at the canopy level. [ABSTRACT FROM AUTHOR]

• Multi-bands SIF has better performance in estimating GPP. • The relationships between SIF and GPP appear non-linear under stressful conditions. • Observation time has no clear impact on the SIF-GPP relationships. • Temporal aggregation to daily scales can further enhance the SIF-GPP correlations. When solar-induced chlorophyll fluorescence (SIF) is developed as a novel approach to quantify gross primary production (GPP), narrow SIF emission spectrum around the atmospheric oxygen absorption windows (˜ 761 nm and ˜687 nm) has been widely used to represent SIF and to derive GPP. As SIF is a continuous spectrum, deriving the full broadband SIF emission spectrum over 640–850 nm provides an opportunity to fully explore the potential of SIF in estimating GPP. Using high-frequency measurements of canopy carbon flux and SIF emissions at the atmospheric absorption bands, we reconstruct the full SIF spectrum from SIF signals at the absorption bands, and then analyze correlations between the GPP and the selected single SIF bands and their combinations using both linear regression (LR) and Gaussian processes regression (GPR). Our results indicate that (1) the red SIF bands (640–700 nm) shows low correlation with GPP due to the strong (re)absorption of red SIF emissions by leaf chlorophyll; (2) the individual bands in near-infrared area (at 720 nm, 740 nm, and 761 nm) can determine about 60% and 62% of the variance in GPP with hourly scale by LR and GPR, respectively, and the combination of those SIF bands provide increased predictive power, explaining 66% and 76% variance in GPP with hourly scale by LR and GPR, respectively; (3) the solar radiation saturation, fraction of direct solar radiation, air temperature and leaf area index may have negative impacts on the SIF-GPP correlations when they are beyond optimal thresholds; and (4) the temporal aggregation of SIF-GPP (daily scale) enhances the correlations as compared with the hourly scale: the daily combination of SIF bands (at 687 nm, 720 nm, and 761 nm) can account for 80% and 93% of variance in daily daytime GPP by LR and GPR, respectively, suggesting that the combination of these three bands of SIF on the daily scale is the best proxy for GPP. Our results provide a new approach to analyze the SIF-GPP correlations using the ground-based full broadband SIF emission, suggesting that multi-bands SIF has a stronger capacity in predicting plant GPP than traditionally used signal band SIF data. [ABSTRACT FROM AUTHOR]