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Red and far red Sun-induced chlorophyll fluorescence as a measure of plant photosynthesis
Abstract
Remote estimation of sun-induced chlorophyll fluorescence emitted by terrestrial vegetation can provide an unparalleled opportunity to track spatio-temporal variations of photosynthetic efficiency. Here we provide the first direct experimental evidence that the two peaks of the chlorophyll fluorescence spectrum can be accurately mapped from high-resolution radiance spectra and that the signal is linked to variations in actual photosynthetic efficiency. Red and far-red fluorescence measured using a novel airborne imaging spectrometer over a grass carpet treated with an herbicide known to inhibit photosynthesis was significantly higher than the corresponding signal from an equivalent untreated grass carpet. The reflectance signal of the two grass carpets was indistinguishable, confirming that the fast dynamic changes in fluorescence emission were related to variations in the functional status of actual photosynthesis induced by herbicide application. Our results from a controlled experiment at the local scale illustrate the potential for the global mapping of terrestrial photosynthesis through space-borne measurements of chlorophyll fluorescence.
... A number of studies have shown that SIF has great potential for detecting environmental vegetation stress, such as drought stress (Daumard et al., 2010;Helm et al., 2020;Liu et al., 2018;Xu et al., 2021), heat stress (Qiu et al., 2020;Shekhar et al., 2020;Song et al., 2018;Wang et al., 2019b), and herbicide stress (Pinto et al., 2020(Pinto et al., , 2016Rossini et al., 2015). Although the different stress types influence the relationship between SIF and photosynthesis differently, the common focus of these previous studies was to investigate whether SIF can respond earlier and whether SIF is more sensitive to stress than traditional VIs. ...
... To the best of our knowledge, there are only a few studies on the use of canopy SIF observation to detect herbicide stress. Rossini et al. (2015) used a novel airborne imaging spectrometer (HyPlant) over an herbicide-treated grass carpet and confirmed that red and far-red SIF can respond quickly and track the photosynthetic variations induced by herbicides. Pinto et al. (2016) reported a clear increase in far-red SIF after spraying herbicides on maize and wheat. ...
... Different from the single-vegetation scenario of the previous studies (Celesti et al., 2018;Pinto et al., 2020Pinto et al., , 2017Rossini et al., 2015), we explored whether SIF and GPP measurements could track the effects of the herbicide treatment in a complex cropland ecosystem where maize and weeds coexist. Our results highlight that SIF and GPP were sensitive and quickly captured herbicide-induced variations in plant photosynthetic efficiency in the complex scenario. ...
Solar-induced chlorophyll fluorescence (SIF) has shown great potential for detecting changes in vegetation function under herbicide stress. However, how physiological (ΦF, canopy SIF emission efficiency) and non-physiological (e.g., structure and illumination) dynamics regulate canopy SIF, and the coupling between SIF and gross primary production (GPP) under herbicide stress remains unclear. Here, we conducted continuous eddy covariance flux and far-red SIF measurements during the early stage of maize in an herbicide-resistant maize field, where herbicide exclusively affects weeds. We investigated the performance of SIF, GPP, and vegetation indices (VIs) in capturing herbicide stress and then explored the sensitivity of SIF to the effects of herbicide treatments by disentangling canopy SIF into the physiological (ΦF) and non-physiological components (NIRvP). We found that SIF rapidly increased in response to the herbicide and that GPP decreased, and that both were more responsive than VIs in capturing the early effects of herbicides. Thus, the opposing responses in SIF and GPP disrupted their otherwise linear relationship during herbicide treatment. More importantly, we found that the increased ΦF dominated the variation of SIF during the early stages of herbicide stress, while the influence of NIRvP was prominent in the variability of SIF in the absence of herbicide. By unraveling its physiological and non-physiological contributions, our findings advance our understanding of how SIF responds to herbicide stress in heterogeneous cropland and will improve our interpretation of SIF as a tool for monitoring photosynthesis.
... For this reason, DCMU makes an efficient weed killer, for instance. The large application amount relative to what might be used for weed control in a natural setting was chosen to illicit an intentional stress response due to photosynthetic system shutdown in order to test the capability to detect and track [51]. In this scenario, CF would be expected to rise dramatically in the beginning stages because the energy supplied by incoming illumination would be blocked from entering PSII and a greater amount of excess energy would be given off as CF. ...
... In this scenario, CF would be expected to rise dramatically in the beginning stages because the energy supplied by incoming illumination would be blocked from entering PSII and a greater amount of excess energy would be given off as CF. After this initial increase, a decline in function results in a decline in CF. shutdown in order to test the capability to detect and track [51]. In this scenario, CF would be expected to rise dramatically in the beginning stages because the energy supplied by incoming illumination would be blocked from entering PSII and a greater amount of excess energy would be given off as CF. ...
... The resulting CF spectra were smoothed (central-moving average, window = 5). In addition to the full CF spectrum, CF values at 685 nm and 740 nm were extracted (F red , F far , respectively) and then the F red /F far ratio calculated [21,[51][52][53]. ...
Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.
... It can be used to evaluate drought-related vegetation conditions [19]. The estimations of potential photosynthesis from VIs are used to effectively understand the response of vegetation growth to droughts [20]. However, an intrinsic limitation is that greenness indices only reflect the changes of pigment contents but not the instantaneous photosynthetic rate [15], [20]. ...
... The estimations of potential photosynthesis from VIs are used to effectively understand the response of vegetation growth to droughts [20]. However, an intrinsic limitation is that greenness indices only reflect the changes of pigment contents but not the instantaneous photosynthetic rate [15], [20]. However, in terms of reflecting the influence of environmental stress on vegetation, VIs usually show delayed responses [20], [21]. ...
... However, an intrinsic limitation is that greenness indices only reflect the changes of pigment contents but not the instantaneous photosynthetic rate [15], [20]. However, in terms of reflecting the influence of environmental stress on vegetation, VIs usually show delayed responses [20], [21]. For instance, NDVI generally lags for ten days to two months [19], [22]. ...
Drought is a frequent global phenomenon that has the most significant influence on agriculture. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. This study aims to monitor and assess monthly agricultural drought using SIF data with 0.05-degree spatial resolution. The scaled SIF index was calculated during the crop-growing season (March-October, 2000-2017) in agricultural areas of North China Plain (NCP), and the monthly agricultural drought spatial distribution and severity were mapped. Results indicated that NCP experienced mild to severe drought during the study period, the severe drought (proportion more than 50%) affected months including March (2000, 2001, 2003, 2005, 2006, 2010, 2011 and 2012), April (2000, 2001, 2003, 2010 and 2011), May (2000, 2001, 2002 and 2004), June (2000 and 2001), September (2002) and October (2001 and 2002). By statistics, the average drought areas decreased from 2000 to 2017 in NCP. For frequency analysis, the frequencies of mild, moderate and severe droughts were less than 0.4 in most areas of NCP, but severe drought frequency exceeded 0.6 in some areas. The monthly correlation analysis showed that the scaled SIF index had a significant positive correlation with precipitation and crop yield (wheat and corn); the maximum correlation coefficients (R) were 0.53 (September), 0.76 (May) and 0.77 (October). These results indicated that the scaled SIF index is suitable for region agricultural drought monitoring.
... Chlorophyll fluorescence (CF) is an especially promising parameter to remotely sense 36 photosynthetic status of plants. CF varies with changes in photochemistry and it is sensitive to 37 environmental conditions, which make it especially suitable for measuring physiological traits and 38 a better predictor of plant health and seasonality than broadband indices, such as the normalized 39 difference vegetation index (NDVI) [1][2][3][4]. CF and heat from non-photochemical quenching are the 40 two processes by which plants regulate excess energy input to the photosynthesis machinery. If 41 combined with information about input illumination, these quantities reveal details about 0.74 μm and corresponds to Photosystem I (PSI) activity. ...
... Further, because PAM is 68 bound by the limits of relative measurements, comparisons between studies, such as remote sensing 69 studies, are difficult to accomplish [7,9,[12][13][14]. SIF, measured through passive measurement 70 techniques, has been demonstrated to be related to plant photosynthetic functioning and has been 71 shown to be proportional to carbon uptake and gross primary production (GPP) [3,[15][16][17][18]. Studies 72 also suggest SIF is an earlier indicator of vegetation stress than other remote sensing vegetative 73 health indices [11,19]. ...
... In this study, the instrument 81 used was an ASD Handheld 2 Pro spectroradiometer (Analytical Spectral Devices/Panalytical, 82 Boulder, CO.). ASD Field spectrometers specifically have been used in various studies for FLD 83 retrievals estimating SIF [3,18,[20][21][22][23]. The ASD Handheld 2 Pro spectroradiometer (HH2), which is 84 used in this study, is small, has a fixed fiber optic, and can be operated untethered from a laptop. ...
In this study, we evaluated chlorophyll fluorescence (CF) under two extreme illumination conditions at plant scale with a passive spectroradiometer. Fluorescence (F) was estimated by reading directly from radiance spectra of a variety of plants illuminated with light-emitting diode (LED) grow lights in the laboratory. Solar-induced fluorescence (SIF) was estimated from spectral measurements of the same plants under sunlight using the Fraunhofer Line Depth (FLD) method. Chlorophyll fluorescence yield (Fyield) and solar-induced fluorescence yield (SIFyield) were calculated by normalizing F and SIF with absorbed photosynthetically active radiation (APAR). Two approaches to estimating APAR were compared: utilizing white reference spectra and reflected spectra versus white reference spectra combined with the fraction of absorbed photosynthetically active radiation (fPAR) derived from literature. Average F and SIF were different by a factor of approximately twenty-four (F = 0.110 ± 0.038 Wm−2μm−1sr−1 versus SIF = 2.60 ± 1.87 Wm−2μm−1sr−1). In contrast, the average normalized values Fyield and SIFyield were within the margin of error of one another (Fyield = 0.022 ± 0.008 μm−1sr−1 and SIFyield = 0.030 ± 0.020 μm−1sr−1). This study highlights the influence of APAR on CF and the importance of properly accounting for it when estimating yield and demonstrates the ability of two simple and portable experimental setups with a passive instrument to obtain fluorescence metrics.
... Furthermore, the fluorescence emission has two peaks: one around the 685 nm wavelength, corresponding to the red region of the visible spectra and called red SIF (SIF R ) in this study; and another peak around 735 nm, corresponding to the far-red region of the visible spectra, called far-red SIF (SIF FR ). While SIF R is at a wavelength that is easily re-absorbed by leaves, SIF FR photons are generally scattered by emitting vegetation according to chemical composition, thickness and shape of leaves, canopy structure, leaf angles and leaf-area density [13][14][15]. Previous studies on red and far-red SIF have found differences on the relationships between these two fluorescence emissions and climate, that depended greatly on vegetation structure and composition [14,[16][17][18]. ...
... While SIF R is at a wavelength that is easily re-absorbed by leaves, SIF FR photons are generally scattered by emitting vegetation according to chemical composition, thickness and shape of leaves, canopy structure, leaf angles and leaf-area density [13][14][15]. Previous studies on red and far-red SIF have found differences on the relationships between these two fluorescence emissions and climate, that depended greatly on vegetation structure and composition [14,[16][17][18]. The authors of these studies suggested that more investigation of SIF responses at both wavelengths, and from heterogeneous vegetation, was necessary to improve our understanding on the relationships between SIF, Gross Primary Productivity (GPP), phenology and vegetation responses to environmental stress. ...
... This is understandable considering the above-mentioned influences of leaf and plant community structure and biochemistry on the radiative transfer of SIF but, the overall effect of such influences is uncertain, particularly when interpreting results from heterogeneous vegetation [15,16,25]. Therefore, we have chosen to test photosynthetic responses of vegetation using SIF data and also through adjustments made to SIF based on work investigating the effects of vegetation structure and biochemistry on chlorophyll fluorescence [14,15,26] as well as on previous observations concerning SIF data characteristics and radiative transfer [27][28][29]. ...
Sun-Induced chlorophyll Fluorescence (SIF) relates directly to photosynthesis yield and stress but there are still uncertainties in its interpretation. Most of these uncertainties concern the influences of the emitting vegetation's structure (e.g., leaf angles, leaf clumping) and biochemistry (e.g., chlorophyll content, other pigments) on the radiative transfer of fluorescent photons. The Caatinga is a large region in northeast Brazil of semiarid climate and heterogeneous vegetation, where such biochemical and structural characteristics can vary greatly even within a single hectare. With this study we aimed to characterize eleven years of SIF seasonal variation from Caatinga vegetation (2007 to 2017) and to study its responses to a major drought in 2012. Orbital SIF data from the instrument GOME-2 was used along with MODIS MAIAC EVI and NDVI. Environmental data included precipitation rate (TRMM), surface temperature (MODIS) and soil moisture (ESA CCI). To support the interpretation of SIF responses we used red and far-red SIF adjusted by the Sun's zenith angle (SIF-SZA) and by daily Photosynthetically Active Radiation (dSIF). Furthermore, we also adjusted SIF through two contrasting formulations using NDVI data as proxy for structure and biochemistry, based on previous leaf-level and landscape level studies: SIF-Yield and SIF-Prod. Data was tested with time-series decomposition, rank correlation, spatial correlation and Linear Mixed Models (LMM). Results show that GOME-2 SIF and adjusted SIF formulations responded consistently to the observed environmental variation and showed a marked decrease in SIF emissions in response to a 2012 drought that was generally larger than the corresponding NDVI and EVI decreases. Drought sensitivity of SIF, as inferred from LMM slopes, was correlated to land cover at different regions of the Caatinga. This is the first study to show correlation between landscape-level SIF and an emergent property of ecosystems (i.e., resilience), showcasing the value of remotely sensed fluorescence for ecological studies.
... Furthermore, the previous studies did not directly test if the developed system could reliably retrieve SIF with dedicated experiments that can considerably change the fluorescence yield during relatively short periods of time. Such experiments include treating plants with herbicides such as 3-(3 ′ ,4 ′ -dichlorophenyl)-1, 1-dimethylurea (DCMU) (Pinto et al., 2020;Rossini et al., 2015), or suddenly exposing dark-adapted vegetation to strong light (Grossmann et al., 2018;Zeng et al., 2022), i.e. making use of the Kautsky effect, or controlling the amount of light emitted from the surface with light emitting diode (LED) with a similar spectral shape as SIF (Burkart et al., 2015). Therefore, further experiments should be conducted to confirm the reliability of the retrieved SIF. ...
... A DCMU treatment therefore triggers a breakdown in linear photosynthetic electron transport (Ruban et al., 1992), and the resulting excess energy causes an increase in chlorophyll fluorescence emissions (Maxwell and Johnson, 2000). Binding DCMU to photosystem II does not change the leaf pigment composition or canopy structure in the short-term, so we assumed that the spectral reflectance was not affected by the herbicide treatment during the experiment (Rossini et al., 2015). To confirm the effect of DCMU on chlorophyll fluorescence, we measured steady state fluorescence (F s ) using a portable porometer with a pulse-amplitude modulation fluorometer (LI-600; LI-COR). ...
Observations of sun-induced chlorophyll fluorescence (SIF) by remote sensing have improved our understanding of the structural and physiological dynamics of vegetation. Substantial efforts have been made to measure SIF with ground-based sensing systems, but field observation data for various plant functional types are still sparse. This is partly due to the limited availability of commercial SIF measurement systems, the relatively high cost of hyperspectral spectroradiometers, and the difficulties of sensor calibration and maintenance in the field. We developed a filter-based smart near-surface remote sensing system for SIF (4S-SIF) to overcome the technical challenges of monitoring SIF in the field, which also decreased the sensor cost, thus enabling more comprehensive spatial sampling. To retrieve SIF, we combined ultra-narrow bandpass filters (full width half maximum <1.3 nm) and photodiode detectors to observe electromagnetic radiation at specific wavelengths (757, 761, and 770 nm). We confirmed that the spectral and radiometric performance of the bandpass filters was satisfactory to retrieve SIF by comparing them to a high-spectral-resolution spectroradiometer that served as a reference. In particular, we confirmed that the digital numbers (DNs) from 4S-SIF exhibited linear relationships with the DN from the reference spectroradiometer in each band (R2 > 0.99). In addition, we developed equations to correct for temperature-induced changes in filter transmittance, such that SIF can be reliably extracted in outdoor environments without the need to actively stabilize the temperature. Furthermore, we confirmed that the SIF signal from 4S-SIF had a strong linear relationship with the reference spectroradiometer-based SIF. Importantly, this relationship held even when the physiological mechanisms of the plant were altered by a herbicide treatment that induced substantial changes in the SIF signal (R2 = 0.85, relative RMSE = 0.22), which indicated that 4S-SIF could be used to retrieve SIF. We believe that 4S-SIF will be a useful tool for collecting in-situ SIF data across multiple spatial and temporal scales.
... SIF is therefore a direct indicator of photosynthetic efficiency [17]. Research on SIF for vegetation water stress has been carried out on different experimental platforms [17,[18][19][20][21]. SIF remote sensing requires an extremely high signal to noise ratio (SNR), which in turn limits the spectral or spatial resolution of the sensor. ...
... In addition to this limitation, fluorescence emission peaks appear in two ultra-narrow spectral window regions (e.g., around 670 and 760 nm), which requires nano-scale hyperspectral resolution technology. Despite the many challenges, the potential of SIF remote sensing for vegetation water stress is significant, which can also be inferred from the FLEX (Fluorescence Detector) satellite mission supported by the European Space Agency (ESA) [20]. ...
Research on grassland monitoring based on temperature/emissivity separation based on hyperspectral thermal infrared (HTIR) remote sensing is rare. Based on the longwave TIR instrument (Hyper-CAM), this study designed two experiments to collect HTIR datasets, separate the temperature and emissivity of different vegetation of grassland, and analyze the relationship between the emissivity of vegetation and soil moisture content. First, we collected the HTIR remotely sensed dataset of different kinds of vegetation and used the temperature/emissivity separation algorithm to separate the temperature and emissivity of seven types of vegetation. The temperature and emissivity of these types of vegetation were separated. Then, the absorption characteristics of the emissivity spectral curves of each type of grass were analyzed. The distribution and differences of the temperature and specific emissivity in different parts of these seven grassland vegetation types were quantitatively analyzed, and the relationship between their changes and vegetation leaf moisture and vegetation health status was also analyzed. Second, to monitor the drought of grassland vegetation, a second experiment was designed to measure the changes in the emissivity under different soil water contents. This observation experiment took Artemisia frigida as the research object. From the results of the separation of the temperature and emissivity, we found that the emis-sivity of Artemisia frigida has significantly changed with the increase in the water content, and the emissivity showed an overall increasing trend. We also quantitatively analyzed the differences in the temperature and specific emissivity between Artemisia frigida and Artemisia subulata Nakai, both belonging to the genus Artemisia, under different water content conditions. The overall waveform characteristics and their similarities and differences at 850-1280 cm −1 were compared and analyzed. The experimental results shows that Hyper-CAM can effectively obtain the emissivity of various types of grassland vegetation as the absorption characteristics of grassland vegetation in the thermal infrared spectral region were quite notable, which shows the significant potential ability of identification and discrimination of different types of grassland vegetation.
... Remote sensing data recorded from satellites and aircraft have provided such information for decades. Most of the approaches for monitoring vegetation conditions, however, were based solely on estimates of vegetation greenness derived from vegetation indices (VIs), which only allow observations of changes in potential photosynthesis (Campbell et al., 2019;Rossini et al., 2015). In contrast, solar-induced chlorophyll fluorescence (SIF) is the most direct measure of photosynthetic activity (ESA, 2015), since it is emitted from the core of the photosynthetic machinery (Meroni et al., 2009;Porcar-Castell et al., 2014). ...
... This is in accordance with previous studies presenting diurnal courses of SIF 760 of different plants at canopy (Campbell et al., 2019;Liu et al., 2017) and leaf level (Süß et al., 2016;Amoros-Lopez et al., 2008). In addition, several studies reported data ranges of SIF canopy 760 (Liu et al., 2019;Pinto et al., 2017;Rossini et al., 2015;Wieneke et al., 2016) comparable to those shown here. A validation of derived SIF leaf 760 maps is more complex due to a lack of corresponding results. ...
Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.
... As our experiment was the first to test the possibility of using fluorescence parameters to detect CO 2 leakage, there is no reference with which to compare our results. However, Rossini et al. (2015) reported that the effect of herbicide on chlorophyl fluorescence parameters was earlier than that on NDVI. In summary, we suggest that Pn, Gs, Tr, and Y(II) are early indicators of CO 2 leakage monitoring. ...
... Field applicability of the parameters.Rossini et al. (2015), https://www. walz.com; https://fluorometers.psi.cz; https://www.hansatech-instruments.com ...
Vegetation monitoring can be used to detect CO 2 leakage in carbon capture and storage (CCS) sites because it can monitor a large area at a relatively low cost. However, a rapidly responsive, sensitive, and cost-effective plant parameters must be suggested for vegetation monitoring to be practically utilized as a CCS management strategy. To screen the proper plant parameters for leakage monitoring, a greenhouse experiment was conducted by exposing kale ( Brassica oleracea var. viridis), a sensitive plant, to 10%, 20%, and 40% soil CO 2 concentrations. Water and water with CO 2 stress treatments were also introduced to examine the parameters differentiating CO 2 stress from water stresses. We tested the hypothesis that chlorophyl fluorescence parameters would be early and sensitive indicator to detect CO 2 leakage. The results showed that the fluorescence parameters of effective quantum yield of photosystem II (Y(II)), detected the difference between CO 2 treatments and control earlier than any other parameters, such as chlorophyl content, hyperspectral vegetation indices, and biomass. For systematic comparison among many parameters, we proposed an indicator evaluation score (IES) method based on four categories: CO 2 specificity, early detection, field applicability, and cost. The IES results showed that fluorescence parameters (Y(II)) had the highest IES scores, and the parameters from spectral sensors (380–800 nm wavelength) had the second highest values. We suggest the IES system as a useful tool for evaluating new parameters in vegetation monitoring.
... At both two scales, the values of SIF N were negative and positive. As it was reported that SIF emission in red region near 687 nm strongly overlaps with the maximum absorption region of chlorophyll near 680 nm (Van Wittenberghe et al., 2015;Rossini et al., 2015). Therefore, re-absorption of red SIF peak along the propagation process reduces the SIF 687 and SIF 687 yield. ...
... In this paper, results and discussions were basically limited to healthy leaves. Various studies have demonstrated that SIF is a novel remote sensing method, which can be used as a direct indicator of photosynthesis from leaf to canopy and regional levels (Guanter et al., 2014;Rossini et al., 2015;Pinto et al., 2016). The result in this study and previous studies have showed that SIF is also highly related to area-based LCC and LNC (Tubuxin et al., 2015;Jia et al., 2018). ...
Leaf nitrogen content (LNC), an indicator for the amount of photosynthetic proteins, plays an important role to understand plant function and status. In previous studies, vegetation indices (VIs) have been demonstrated to monitor LNC non-destructively, but which is influenced by backgrounds, and lacks specificity for nitrogen stress. In this study, sun-induced chlorophyll fluorescence (SIF), a novel technique related to plant physiology state, was proposed to estimate area-based and mass-based LNC at both leaf and canopy scales. In addition, SIF indices were evaluated to retrieve photosynthesis nitrogen use efficiency (PNUE), an important trait of leaf economics and physiology, based on the relationships between SIF, photosynthesis, and LNC.
This study was conducted on two field experiments of winter wheat with different nitrogen regimes in Rugao, Jiangsu Province, China during 2016-2017 and 2017-2018 growing seasons. We took measurements of SIF, reflectance, biochemical and growth structural parameters at the leaf and canopy scales. The SIF signal was collected using ASD (Analytical Spectral Devices, Boulder, CO, USA) and QEpro (Ocean Optics, Dunedin, FL, USA) spectrometers at the two observational scales, with a full width at half maximum (FWHM) of 1.4 nm and 0.13 nm, respectively. SIF indices were calculated based on the SIF signal extracted at two oxygen absorption bands. Our results demonstrated that area-based LNC was better related to SIF indices and VIs than mass-based LNC. SIF ratio index (SIFR) and normalized SIF index (SIFN), defined as SIF761/SIF687 and (SIF761-SIF687)/(SIF761+SIF687) separately, performed better in monitoring area-based LNC at the two observation scales than CIred edge, which performed best in VIs group. Compared with CIred edge, the best estimation accuracy of SIF indices for area-based LNC increased by 0.08 and 0.02 at the leaf and canopy scales, separately. And when using SIFR and SIFN to monitor area-based LNC, there is no saturation phenomenon, which occurs using traditional VIs. From the whole range of data, area-based LNC was closely related to several plant traits (leaf: area-based leaf chlorophyll content (LCC) (LCCarea), leaf mass per area (LMA); canopy: area-based canopy LCC (CCCarea), leaf area index (LAI), leaf dry weight (LDW) per unit soil area, and LMA), which was consistent with previous studies. However, in specific group with fixed area-based LCC value, although area-based LNC almost wasn’t significantly correlated with these traits, SIFR and SIFN were instead always highly correlated with area-based LNC in each small datasets on two observation scales (leaf scale: R²>0.50, R²>0.46; canopy scale: R²>0.41, R²>0.42). Thus, the contribution of SIFR and SIFN to estimate area-based LNC wasn’t only the plant traits listed, but also other internal characters, like nitrogen allocation and proportion. Moreover, SIFR and SIFN were proved to be potential detectors to retrieve PNUE. These findings would provide us a new perspective for understanding plant nitrogen status from remote sensing observations, detecting plant function and managing precise agriculture.
... During drought, increased temperature within the plant growth range leads to an increase in photosynthesis [49]. However, the stress caused by particularly high temperatures reduces the SIF values by a reduction in red and far-red fluorescent [50,51]. ...
In the context of the continuous change in global climate, the frequency and intensity of drought and heatwaves are increasing. This study took the extreme drought event in southwest China in 2009/2010 as a case study. Based on the sunlight-induced chlorophyll fluorescence (SIF), we explored the effects of high-temperature weather on the photosynthetic efficiency, the vegetation responses to drought in two ecosystems, and the differences in influencing factors. The results showed a disproportionate change between the vegetation productivity represented by SIF and the greenness symbolized by the leaf area index (LAI). The response of photosynthetic efficiency to drought was significantly inequitable between the grassland and cropland. The geodetector showed that grassland ecosystems with more superficial canopy structures were more susceptible to high temperature. The correlation between the Photosynthesis efficiency index (PEI) and temperature (T) and vapor pressure deficit (VPD) of the grassland ecosystem was above 0.6. This study suggests that drought exacerbates the disproportionate change between vegetation productivity and greenness, and grasslands are more vulnerable to drought. The result is helpful for ecosystem management.
... Traditional vegetation indices (VIs), such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), have been used by many researchers in the past few decades to assess vegetation conditions under thermal and water stress. However, traditional VIs are associated with underlying photosynthesis and only reflect changes in pigment content, not changes in instantaneous photosynthetic rate [8][9][10]. Therefore, traditional VIs may be weak in monitoring rapid changes in photosynthesis west area is prone to drought events in the fall and winter. ...
With the increasing trend of global warming, drought events frequently occur, which have an impact on human life and the environment. In this study, an extreme drought event in Southwest China in 2009/2010 was used as an example to explore the potential of using satellite observations of sun-induced chlorophyll fluorescence (SIF) for drought monitoring. The results indicated that the SIF observations show more proper responses to drought than EVI, which underestimated the losses by approximately 50%. The SIF reduction in this drought event (19% in March 2010 and 11% in May 2010) was more obvious than that of the enhanced vegetation index (EVI) (4% and 5%). The drought severity index (DSI) overestimates the drought during most dry months. SIF can be a reliable tool for monitoring drought in a timely and accurate manner. In addition, the significant correlation coefficient with SIF and ET (reaching 0.8 at the beginning and end of the drought stage), indicates the ability of SIF to reveal the interaction of carbon and water during drought, which provides us with ideas for future research on the terrestrial carbon–water cycle.
... Hyperspectral images were acquired over both sites using the HyPlant airborne imaging spectrometer developed by Forschungszentrum Jülich in cooperation with Specim Ltd. (Oulu, Finland) [33,34]. Airborne imagery was collected on 26 June 2018 and on 30 July 2018 in Selhausen and Braccagni, respectively. ...
The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.
... Although many studies have focused on the SIF retrieval and applications at the far-red or NIR band, several studies have suggested that red band SIF retrieval provides vital supplementary information for various applications [43][44][45][46][47]. Red band SIF contains more information from photosystem II (PSII) that is more sensitive to photosynthesis. ...
The importance of solar-induced chlorophyll fluorescence (SIF) to monitoring vegetation photosynthesis has attracted much attention from the ecological and remote sensing research communities. Space-borne SIF products have been obtained owing to the rapid development of atmospheric satellites in recent years. The SIF Imaging Spectrometer (SIFIS) is a payload onboard the upcoming Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) that is specifically designed for SIF monitoring. We conducted an in situ experiment to evaluate the performance of SIFIS on spectral measurement and SIF retrieval through comparison to the commercial spectrometer QE Pro. Disregarding the spatiotemporal mismatch between the collected measurements of the two spectrometers, the radiance spectra obtained synchronously by SIFIS and QE Pro showed a high level of consistency. The SIF retrieval, normalized difference vegetation index (NDVI), and near-infrared radiance of vegetation (NIRvR) results for a push-broom image shows consistent spatial distributions over both vegetated and nonvegetated surfaces. A quantitative comparison was conducted by strictly filtering matching pixels. For the far-red band, a high correlation was obtained between the SIF retrieval performances of SIFIS and QE Pro with R 2 = 0.70 and RMSE = 0.30 mW m − 2 s r − − 1 n m − 1 . However, a relatively poor correlation was observed for the red band with an R 2 value of 0.23 and an RMSE of 0.26 mWm ⁻² sr ⁻⁻¹ nm ⁻¹ . Despite the large uncertainties associated with this experiment, the results indicate that TECIS-1 should offer a reliable SIF monitoring performance after its launch.
... GPP is determined by both absorbed photosynthetic solar radiation linked with canopy structural changes and light use efficiency (LUE) related to physiological changes (Bradford et al., 2005;Coops et al., 2010;Running et al., 2000;Turner et al., 2003). The reflectance-based GPP estimates are primarily sensitive to canopy structural changes in ecosystems, but are less sensitive to physiological changes induced by environmental changes, such as variations in air temperature, soil moisture, vapor pressure deficit, and atmospheric rising CO 2 (Dobrowski et al., 2005;Gamon et al., 1995;He, Wood et al., 2020;Huete et al., 2002;Rossini et al., 2015;Song et al., 2018;Frankenberg et al., 2021). This could partially explain the weaker trends seen in forest ecosystems. ...
An increase in the seasonal cycle amplitude (SCA) of atmospheric CO2 since the 1960s has been observed in the Northern Hemisphere (NH). However, the underlying dominant drivers are still debated. The peak season CO2 uptake by vegetation is critical in shaping the CO2 seasonality. Using satellite‐upscaled gross primary production (GPP) from FLUXCOM and near‐infrared reflectance of vegetation (NIRV), we demonstrate that peak GPP has increased across the NH over the last two decades. We relate this productivity increase to changes in the CO2 SCA using an atmospheric transport model. The increased photosynthesis has strongly contributed to CO2 SCA trends, but with substantial latitudinal and longitudinal variations. Despite a general increase in the CO2 SCA, there are distinct regional differences. These differences are mainly controlled by regional biosphere carbon fluxes, with the remainder explained by non‐biome factors, including large‐scale atmospheric transport, changes in fossil fuel combustion, biomass burning and oceanic fluxes. Using the global flask and in situ CO2 measurement sites, we find that SCA trends at high latitude are mainly driven by increasingly productive natural ecosystems, whereas mid latitude sites around the Midwest United States are mainly impacted by intensified agriculture and atmospheric transport. Averaging across the 15 long‐term surface sites, forests contribute 26% (7%) to the SCA trends, while crops contribute 17% (24%) and the combined shrubland, grassland and wetland regions contribute 23% (37%) for simulations driven by FLUXCOM (NIRv) ecosystem fluxes. Our findings demonstrate that satellite inferred trends of ecosystem fluxes can capture the observed CO2 SCA trend.
... A significant difference was only found at 11 a.m. in lower leaves of the paprika plant, and it was 1.5 times higher in S1-treated plants than in S2. Researchers noted that the photosynthetic efficiency of a plant is reflected by the activities of its chlorophyll fluorescence [38,39]. In addition, the photosynthetic production of leaves depends on received light irradiance, and is increased by the increasing light exposure of leaves [40]. ...
The aim of this study was to investigate the effect of shade screens on the physiological activity, growth parameters and fruit characteristics of the paprika (Capsicum annuum L.) plant. Plants were grown in a protected greenhouse and treated under two different shade screens, S1 (single screen) and S2 (double screens; 10% low light intensity compared to S1), during summer at a particular time of the day. The results revealed that the plant height was significantly enlarged by the S2 treatment. However, the number of leaves, leaf fresh weight and leaf dry weight were significantly decreased under S2-treated plants compared to those grown in the S1 treatment. The stem diameter and shoot fresh weight were not significantly different between the treatments. The sap flow and normalized difference vegetation index (NDVI) were higher in S1-treated plants than in those grown in the S2 treatment. The chlorophyll fluorescence fluctuated in both treatments. The fruit fresh weight, number of fruits, fruit pericarp thickness, fruit firmness, fruit volume, sugar content and acidity were significantly higher in S1-treated plants than in S2. Hunter values a and b were significantly higher in S2-treated plants. Moreover, the fruit length and width were not significantly different between the two treatments. The sugar content and acidity of paprika showed a positive correlation. These results suggest that, compared to a double screen for shade in the greenhouse, a single screen is suitable for the growth of paprika plants and enhanced their fruit production.
... Solar quanta absorbed by plant chlorophyll are distributed in three competitive pathways, which consist of photosynthesis (photochemical quenching, PQ), heat dissipation (non-photochemical quenching, NPQ), and chlorophyll fluorescence (ChF). This unique functional connection to photosynthesis makes ChF a powerful indicator of vegetation photosynthetic dynamics Rossini et al., 2015). Intensive studies at the leaf level have contributed significantly to our understanding of plant PQ and NPQ regulation mechanisms at the subcellular to leaf scales using active fluorometers (Cendrero-Mateo et al., 2016;Porcar-Castell et al., 2014;Zarco-Tejada et al., 2016, 2000. ...
Sun-induced chlorophyll fluorescence (SIF) is a promising proxy of the dynamic photosynthetic process. Unmanned Aerial Vehicles (UAVs) are flexible and cost-effective for acquiring SIF data at high temporal and spatial resolution. The UAV-based point spectrometer FluorSpec was designed to measure SIF within agricultural fields. To correctly understand SIF values and further photosynthetic research, the ability of the UAV-based FluorSpec to provide reliable SIF information within agricultural fields needs evaluation. In this paper, the UAV-based FluorSpec was compared with the high-performance airborne imaging spectrometer HyPlant using diurnal far-red SIF measurements over different crop types (i.e. two varieties of winter wheat, two varieties of spring barley, bean, and maize), which were acquired by almost simultaneous airborne and UAV flights during a clear sky day in 2019. After improving the footprint geolocation of FluorSpec measurements using concurrent red-green-blue (RGB) images, we compared the FluorSpec and HyPlant SIF measurements, their diurnal developments, and spatial distributions for different crop types. The results from both systems show consistent, clear diurnal patterns that are positively correlated with photosynthetically active radiation (PAR) over most crop types. Similar SIF spatial patterns were shown within crop fields as well. UAV-based FluorSpec SIF showed a good linear correlation with HyPlant SIF with an R² up to 0.76. The good agreement confirms that the UAV-based FluorSpec system is able to measure meaningful SIF values at the field scale and thus stimulates SIF applications in agriculture. The systematic errors up to 0.3 mW m⁻² sr⁻¹ nm⁻¹ from the linear regression between the two systems indicate that the UAV-based FluorSpec system should be improved by considering the main sources of uncertainty discussed in this paper. Future studies with dedicated experiments are recommended to assess the systematic uncertainties of UAV-based FluorSpec derived SIF information.
... Satellite-based Sun-induced chlorophyll fluorescence provides a new opportunity to monitor GPP for terrestrial ecosystems. As part of the vegetation photosynthesis process, the variation in SIF emitted by plants can be used to infer the actual functional state of the photosynthetic apparatus, since photosynthetic efficiency affects the efficiency of the fluorescence emission (Rossini et al., 2015). In this study, the latest version (v28) of the monthly SIF product from the Global Ozone Monitoring Experiment-2 (GOME-2) aboard the MetOp-A satellite (Guanter et al., 2014;Joiner et al., 2013), from the period of February 2007 to February 2019, is used to investigate its correlation with simulated GPP. ...
The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area
index (LAI) and soil moisture observations to improve the representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric
processes for this region, but instead it degrades the simulation of the interannual variation in evapotranspiration (ET) and carbon fluxes, mainly due to model
weaknesses in representing prognostic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the prognostic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.
... The Earth Observation (EO) dataset is represented by two hyperspectral images acquired by the HyPlant-DUAL instrument [40][41][42] on 7 and 30 July. HyPlant is a novel airborne imaging spectrometer, developed by the Jülich Forschungszentrum in cooperation with SPECIM Spectral Imaging Ltd (Oulu, Finland). ...
In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This missions will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the “agriculture and food security” domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R2 = 0.79, RMSE = 0.38 g m−2 for CCC and R2 = 0.84, RMSE = 1.10 g m−2 for CNC) but failed at leaf level. The HAL approach improved retrieval accuracy at canopy level (best metric: R2 = 0.88 and RMSE = 0.21 g m−2 for CCC; R2 = 0.93 and RMSE = 0.71 g m−2 for CNC), providing good results also at leaf level (best metrics: R2 = 0.72 and RMSE = 3.31 μg cm−2 for LCC; R2 = 0.56 and RMSE = 0.02 mg cm−2 for LNC). The promising results obtained through the hybrid approach support the feasibility of an operational retrieval of chlorophyll and nitrogen content, e.g., in the framework of the future CHIME mission. However, further efforts are required to investigate the approach across different years, sites and crop types in order to improve its transferability to other contexts.
... RF is a tree-based model first proposed by [63] and has been widely used in remote sensing application. It has been demonstrated that RF has outstanding performance in regression tasks for large and multi-dimensional datasets [64,65]. For generating spatially contiguous high-resolution SIF product, RF has also been tested and found to be efficient in previous studies [30,33]. ...
Satellite-derived solar-induced chlorophyll fluorescence (SIF) has been proven to be a valuable tool for monitoring vegetation’s photosynthetic activity at regional or global scales. However, the coarse spatiotemporal resolution or discrete space coverage of most satellite SIF datasets hinders their full potential for studying carbon cycle and ecological processes at finer scales. Although the recent TROPOspheric Monitoring Instrument (TROPOMI) partially addresses this issue, the SIF still has drawbacks in spatial insufficiency and spatiotemporal discontinuities when gridded at high spatiotemporal resolutions (e.g., 0.05°, 1-day or 2-day) due to its nonuniform sampling sizes, swath gaps, and clouds contaminations. Here, we generated a new global SIF product with Seamless spatiotemporal coverage at Daily and 0.05° resolutions (SDSIF) during 2018–2020, using the random forest (RF) approach together with TROPOMI SIF, MODIS reflectance and meteorological datasets. We investigated how the model accuracy was affected by selection of explanatory variables and model constraints. Eventually, models were trained and applied for specific continents and months given the similar response of SIF to environmental variables within closer space and time. This strategy achieved better accuracy (R2 = 0.928, RMSE = 0.0597 mW/m2/nm/sr) than one universal model (R2 = 0.913, RMSE = 0.0653 mW/m2/nm/sr) for testing samples. The SDSIF product can well preserve the temporal and spatial characteristics in original TROPOMI SIF with high temporal correlations (mean R2 around 0.750) and low spatial residuals (less than ±0.081 mW/m2/nm/sr) between them two at most regions (80% of global pixels). Compared with the original SIF at five flux sites, SDSIF filled the temporal gaps and was better consistent with tower-based SIF at the daily scale (the mean R2 increased from 0.467 to 0.744. Consequently, it provided more reliable 4-day SIF averages than the original ones from sparse daily observations (e.g., the R2 at Daman site was raised from 0.614 to 0.837), which resulted in a better correlation with 4-day tower-based GPP. Additionally, the global coverage ratio and local spatial details had also been improved by the reconstructed seamless SIF. Our product has advantages in spatiotemporal continuities and details over the original TROPOMI SIF, which will benefit the application of satellite SIF for understanding carbon cycle and ecological processes at finer spatial and temporal scales.
... Particularly the fact that SIF provides the most direct observation of plant photosynthesis at ecosystem scale from RS data, determines its increasing importance to studies of ecosystem functioning (Mohammed et al., 2019;Ryu et al., 2019). SIF was used to quantify photosynthetic activity at ecosystem scale Rossini et al., 2015) and constrain associated gas exchange processes including gross primary productivity (Damm et al., 2015a) and transpiration (Qiu et al., 2018;Shan et al., 2019;Shan et al., 2021). The assessment of drought effects in crops ) and on plant ecosystems or agricultural productivity Pagán et al., 2019;Sun et al., 2015) was also found to be possible with empirical relationships to SIF. ...
The upcoming Fluorescence Explorer (FLEX) satellite mission aims to provide high quality radiometric measurements for subsequent retrieval of sun-induced chlorophyll fluorescence (SIF). The combination of SIF with other observations stemming from the FLEX/Sentinel-3 tandem mission holds the potential to assess complex ecosystem processes. The calibration and validation (cal/val) of these radiometric measurements and derived products are central but challenging components of the mission. This contribution outlines strategies for the assessment of in situ radiometric measurements and retrieved SIF. We demonstrate how in situ spectrometer measurements can be analysed in terms of radiometric, spectral and spatial uncertainties. The analysis of more than 200 k spectra yields an average bias between two radiometric measurements by two individual spectrometers of 8%, with a larger variability in measurements of downwelling radiance (25%) compared to upwelling radiance (6%). Spectral shifts in the spectrometer relevant for SIF retrievals are consistently below 1 spectral pixel (up to 0.75). Found spectral shifts appear to be mostly dependent on temperature (as measured by a temperature probe in the instrument). Retrieved SIF shows a low variability of 1.8% compared with a noise reduced SIF estimate based on APAR. A combination of airborne imaging and in situ non-imaging fluorescence spectroscopy highlights the importance of a homogenous sampling surface and holds the potential to further uncover SIF retrieval issues as here shown for early evening acquisitions. Our experiments clearly indicate the need for careful site selection, measurement protocols, as well as the need for harmonized processing. This work thus contributes to guiding cal/val activities for the upcoming FLEX mission.
... However, most of these parameters still cannot overcome the problem of time latency. When precipitation shortages and soil moisture deficits take place, they show no significant change within a temporal range of ten days to two months [127][128][129][130] because they are reflections of accumulated vegetation growth rather than instant indicators. Solar-induced chlorophyll fluorescence (SIF) is a stimulated emission which occurs after the chlorophyll absorbs light [131]. ...
By effectively observing the land surface and obtaining farmland conditions, satellite remote sensing has played an essential role in agricultural drought monitoring over past decades. Among all remote sensing techniques, optical and thermal remote sensing have the most extended history of being utilized in drought monitoring. The primary goal of this paper is to illustrate how optical and thermal remote sensing have been and will be applied in the monitoring, assessment, and prediction of agricultural drought. We group the methods into four categories: optical, thermal, optical and thermal, and multi-source. For each category, a concise explanation is given to show the inherent mechanisms. We pay special attention to solar-induced chlorophyll fluorescence, which has great potential in early drought detection. Finally, we look at the future directions of agricultural drought monitoring, including (1) early detection; (2) spatio-temporal resolution; (3) organic combination of multi-source data; and (4) smart prediction and assessment based on deep learning and cloud computing.
... The passive method of chlorophyll fluorescence measurement can also be used to measure through UAVs (multi-copter), aircraft, and even satellites. Recently, droneassociated sun-induced fluorescence sensors and the airborne sensors were introduced to quantify sun-induced fluorescence from a research aircraft [118,139]. Similarly, HyPlant, an airborne sensor, is proven to be useful in delivering sufficient information in large field experiments [118]. ...
With the advancement of sequencing technology, rapid progress in genomic studies has been achieved; however, the lack of large-scale accurate estimation of phenotypic data is one of the major bottlenecks slowing down the progress in functional genomics and crop breeding studies. With the advancement of recent high-throughput phenotyping technologies, researchers can explore complex traits that are impossible to measure with conventional phenotyping methods. In this chapter, the major phenotyping tools and techniques used in field conditions and controlled environments have been discussed, along with their advantages and limitations. Some conceptual challenges have been proposed, and our perception to bridge the gap between phenotype and genotype are also discussed. There is no doubt that the recent developments in high-throughput plant phenotyping will accelerate crop genetic improvement and promote next-generation green revolution.
... However, the improvement of remote sensing (RS) optical sensors and techniques for retrieving the SIF signal (Meroni et al., 2009;Mohammed et al., 2019) has opened new avenues for monitoring the functional state of vegetation. SIF can be used to track actual photosynthetic efficiency (Rossini et al., 2015;Campbell et al., 2019;Yang et al., 2021), to improve assessment of plant gross primary production (Guanter et al., 2014;Liu et al., 2019aLiu et al., , 2019bLiu et al., , 2019c, and to detect vegetation stress . This diverse potential of SIF for vegetation monitoring spurred the development of methods for space-borne measurements and new satellite missions, such as the FLuorescence EXplorer (FLEX) selected by the European Space Agency (ESA) as its 8th Earth explorer scientific mission (Drusch et al., 2017). ...
Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly used as a valuable source of information about vegetation photosynthetic activity, the RS SIF observations are significantly influenced by canopy-specific structural features (i.e., canopy architecture including leaf area index and presence of woody components), atmospheric conditions during their acquisition (e.g., proportion of direct and diffuse irradiance) and observational geometric configurations (e.g., sun and viewing directions). Radiative transfer (RT) models have the potential to provide a better understanding of the canopy structural effects on the SIF emission and RS signals. Here, we used the DART model to assess the daily influence, from morning to evening, of forest 3D architecture on SIF nadir radiance, emission, escape factor and nadir yield of eight 100 m × 100 m forest study plots established in a temperate deciduous forest of the Smithsonian Environmental Research Center (Edgewater, MD, USA). The 3D architecture of each plot was derived from airborne LiDAR. DART simulations of these 3D forest plots and their 1D (i.e., vertical profile of sun-adapted and shade-adapted leaves) and 0D (i.e., homogeneous layer of sun-adapted leaves above an homogeneous layer of shade-adapted leaves) abstractions were compared to assess the relative errors (ε1D−3D and ε0D−3D) associated with horizontal and vertical structural heterogeneity, respectively. Forest 3D structure, especially horizontal heterogeneity, had a great influence on forest nadir SIF radiance, resulting in ε1D−3D up to 55% at 8:00 and 18:00 (i.e., for oblique sun directions). The key indicators of this impact, in the descending order of importance, were the SIF escape factor (ε1D−3D up to 40%), the attenuation of incident photosynthetically active radiation (ε1D−3D less than 5%), and the SIF emission yield (ε1D−3D less than 2%). The influence of forest architecture on the nadir SIF escape factor and SIF yield (ε1D−3D up to 40%) varied over time, with differences in forest stand structure, and per spectral domain, being always larger between 640 and 700 nm than between 700 and 850 nm. In addition, woody elements demonstrated a large influence on forest SIF radiance due to their “shading” effect (ε up to 17%) and their “blocking” effect (ε ≈ 10%), both of them higher for far-red than for red SIF. These results underline the importance of 3D forest canopy architecture, especially 2D heterogeneity, and inclusion of woody elements in RT modeling used for interpretation of the RS SIF signal, and subsequently for the estimation of gross primary production and detection of vegetation stress.
... It is critical to accurately simulate the absorbed PAR for vegetation photosynthesis and GPP in the terrestrial ecosystem models and land surface models because errors in these simulations propagate through the models to introduce additional errors in simulated biomass and other fluxes. The sunlight absorbed by the canopy chlorophyll (APAR chl ) is associated with the photochemistry process (primary pathway), the fluorescence from PSII and PSI, the non-photochemical quenching (NPQ) from PSII, and metabolic heat dissipation (sensible heat) van der Tol et al., 2014van der Tol et al., , 2016Yang et al., 2017;Rascher et al., 2009;Rossini et al., 2015). The sunlight absorbed by the entire canopy (APAR canopy ) is also partially allocated to non-chlorophyll components (APAR non-chl ) for other processes (Rossini et al., 2010;Yang et al., 2020a). ...
It remains challenging to interpret seasonal profile of vegetation dynamics from empirical indices NDVI and EVI for boreal forests due to confounding impacts of snow, soil and snowmelt in winter and spring. This work aims to characterize the seasonally snow-covered Howland boreal forest ecosystem in Maine, USA with the Moderate Resolution Imaging Spectrometer (MODIS) images. Vegetation cover fraction (VGCF), fractional absorption of photosynthetically active radiation (fAPAR) by all canopy components (fAPARcanopy), fAPAR by canopy chlorophyll (fAPARchl) and fAPAR by canopy non-chlorophyll components (fAPARnon-chl) were extracted from MODIS images in multiple years (2001 - 2014). Snow exposed during December to April. Top of canopy viewable snow cover fraction in April of multiple years varied between 0.02 and 0.16 (0.06 ± 0.04). Seasonal VGCF and fAPARcanopy showed a summer plateau (VGCF: 0.97 ± 0.01; fAPARcanopy: 0.90 ± 0.01). Both seasonal fAPARchl and fAPARnon-chl changed with time, and seasonal fAPARnon-chl had a bimodal shape. Spring VGCF varied between 0.54 and 0.69 (0.61 ± 0.04). Spring fAPARchl and fAPARnon-chl were 0.22 ± 0.03 and 0.21 ± 0.02, respectively. Peak summer fAPARchl was 0.58 ± 0.02. The lowest summer fAPARnon-chl was 0.32 ± 0.02. Replacing fAPARcanopy with fAPARchl to simulate boreal forest ecosystem gross primary production (GPP) could reduce uncertainties in GPP simulations.
... In recent years, solar-induced chlorophyll fluorescence (SIF), which is more directly related to photosynthesis than reflectance-based parameters, has been found to be an ideal remote-sensing indicator of GPP (Guanter et al., 2014;Gu et al., 2019). Observations from ground-based (e.g., Rossini et al., 2015;Yang et al., 2015;Zhang et al., 2016), airborne (e.g., Zarco-Tejada et al., 2013), and satellite (e.g., Frankenberg et al., 2011;Guanter et al., 2014) platforms have shown a good correlation between SIF and GPP, and the use of these data has significant advantages over traditional remote sensing models for estimating GPP. ...
Solar-induced chlorophyll fluorescence (SIF) has been shown to be an ideal indicator of vegetation gross primary productivity (GPP), but the variation in the ratio of the photosynthetic light use efficiency (LUE = GPP/APAR) to the total SIF quantum yield (SIFyield = SIFtotal/APAR) is an important source of uncertainty in SIF‒GPP models. Incident radiation is one of the key factors influencing LUE and SIFyield. In this study, to investigate the influence of PAR on LUE and SIFyield, pulse-amplitude-modulated (PAM) fluorometry was carried out at the leaf level along with tower-based continuous SIF‒GPP measurements at the canopy level for maize. LUE was found to decrease as PAR increased, following a hyperbolic function, at both the leaf level (R² = 0.978) and canopy level (R² = 0.460 for half-hourly averaged dataset; R² = 0.341 for daily averaged dataset). However, the variation of SIFyield with PAR was found to be very small. By integrating the influence of PAR on LUE, the GPP estimation model based on the red band and near-infrared (NIR) band SIF for maize became more linear. For both the half-hourly and daily datasets, the values of R² for the SIF-GPP model increased (e.g. from 0.573 to 0.718 for the half-hourly NIR band SIF), and the RMSE for the estimated GPP reduced (e.g. from 8.30 to 6.75 μmol CO2 m⁻² s⁻¹ for the half-hourly NIR band SIF). These results highlight that the ratio of LUE to SIFyield is an important source of uncertainty in SIF‒GPP models and should be carefully corrected. The results also show that PAR is a key factor influencing this ratio. This PAR-based LUE model can be integrated not only in SIF‒GPP models but also in other LUE-related GPP estimation models for unstressed maize.
... These measurements by high-resolution spectrometers are essential to better observe and interpret SIF at a relevant spatial scale to be integrated with remote sensing observations, but they have some limitations such as insufficient characterization of the sensor, inadequate measurement protocols, low field-ofview (FOV), low spatial coverage, high prices [2,43]. Similarly, airborne measurements of SIF are campaign-based, periodic and therefore data availability is limited [44,45]. Additionally, low coverage, high operating and data-processing costs (including time) indicate some limitations of the airborne SIF measurements [43,46]. ...
In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIFfuzzy and SIFfuzzy-APAR. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The HyPlant derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIFfuzzy. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIFfuzzy was multiplied by APAR (SIFfuzzy-APAR). The agreement between the modelled SIFfuzzy and actual SIF airborne retrievals expressed by R 2 ranged from 0.38 to 0.69 for SIF760 and from 0.85 to 0.92 for SIF687. The inclusion of APAR improved the R 2 value between SIFfuzzy-APAR and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behavior at an ecosystem-relevant scale.
... To detect plant stresses at an early stage, it is necessary to use an index directly linked to plant photosynthetic function. Recently, solar-induced chlorophyll fluorescence (SIF) has been reported as a stress index, as it is correlated to gross primary production (GPP) and responds to photosynthesis inhibitors such as 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) (Pinto et al. 2016;Rossini et al. 2015). SIF can be monitored using the Fraunhofer line spectra; however, the quantification of SIF requires a high-resolution spectroradiometer with full-width at half maximum (FWHM) < 0.3 nm. ...
High-throughput detection of plant environmental stresses is required for minimizing the reduction in crop yield. Environmental stresses in plants have primarily been validated by the measurements of photosynthesis with gas exchange and chlorophyll fluorescence, which involve complicated procedures. Remote sensing technologies that monitor leaf reflectance in intact plants enable real-time visualization of plant responses to environmental fluctuations. The photochemical reflectance index (PRI), one of the vegetation indices of spectral leaf reflectance, is related to changes in xanthophyll pigment composition. Xanthophyll dynamics are strongly correlated with plant stress because they contribute to the thermal dissipation of excess energy. However, an accurate assessment of plant stress based on PRI requires correction by baseline PRI (PRIo) in the dark, which is difficult to obtain in the field. In this study, we propose a method to correct the PRI using NPQT, which can be measured under light. By this method, we evaluated responses of excess light energy stress under drought in wild watermelon (Citrullus lanatus L.), a xerophyte. Demonstration on the farm, the stress behaviors were observed in maize (Zea mays L.). Furthermore, the stress status of plants and their recovery following re-watering were captured as visual information. These results suggest that the PRI is an excellent indicator of environmental stress and recovery in plants and could be used as a high-throughput stress detection tool in agriculture.
... Additionally, GPP MTE does not consider C4 photosynthesis explicitly which might lead to an underestimation of the global photosynthesis (Ryu et al., 2019). Studies pointed out that incorporating sun-induced chlorophyll fluorescence (SIF) into a machine-learning algorithm would reduce uncertainties in global photosynthesis maps (Alemohammad et al., 2017;Ryu et al., 2019) as it shows superior performance over conventional vegetation indices in capturing the seasonal variations of photosynthesis and the response of canopy stresses across diverse biomes Joiner et al., 2014;Rascher et al., 2015;Rossini et al., 2015;Sun et al., 2017). As our understanding of relationships between SIF and canopy photosynthesis deepens and the length of the SIF data increases, further investigations on ecosystem drought recovery based on these higher quality GPP data sets are needed for a better understanding and prediction of carbon dynamics under future climate change. ...
Drought, as an intermittent disturbance of the water cycle, profoundly impacts the terrestrial ecosystem. Recovery time (RT) of an ecosystem from drought is an important indicator of assessing drought impacts and ecosystem resilience, yet the spatiotemporal pattern of ecosystem post-drought RT remains controversial in existing studies. Here we investigate the spatiotemporal pattern of post-drought RT across global terrestrial ecosystems using two observation-based gross primary productivity (GPP) datasets: direct flux-site observations and gridded estimates by upscaling flux-site observations using machine learning approach. For droughts that occur on average once every 5.2 years, the RT typically ranges between 2 ~ 8 months, with a global mean RT of ~6 months. Spatially, both GPP datasets show a significant bilinear relationship between RT and moisture gradient, and that ecosystems in arid and humid regions tend to recover from drought more rapidly than semi-arid/sub-humid ecosystems. Additionally, forests show an overall longer RT than shrublands and grasslands. Temporally, global ecosystem RT shows a slight yet significant increasing trend (0.032 month yr-1) during 1982-2010, which is partly caused by the increasing drought for the same period. However, the observed patterns of RT across global bio-climatic zones are not captured by the state-of-the-art land surface models, which exhibit a shorter RT in semi-arid/sub-humid ecosystems but longer RT in arid/humid regions, and a larger increasing trend of RT over time (0.069 month yr-1). Our findings provide crucial insights into ecosystem vulnerability to sub-decadal stress events and ecosystem recovery trajectories among diverse bio-climatic regions and highlight potential model deficiencies that should be accounted for in future model developments.
... The spatial referencing is assisted with an inertial measurement unit and global navigation satellite systems. And light-weight platforms, like UAVs, have the advantage of lower flight altitudes but do not provide such high-quality correction signals (Aasen et al., 2015;Rossini et al., 2015). Therefore, researchers adopted in-situ or ground-based spectroscopy measurements to achieve high spatial resolution plus the advantage to minimize the complex atmospheric correction for the images (Katkovsky et al., 2018). ...
Urban trees exhibit a wide range of ecosystem services that have long been unveiled and increasingly reported. The ability to map tree species and analyze tree health conditions would become vividly essential. Remote sensing techniques, especially hyperspectral imaging, are being evolved for species identification and vegetation monitoring from spectral reponse patterns. In this study, a hyperspectral library for urban tree species in Hong Kong was established comprising 75 urban trees belonging to 19 species. 450 bi-monthly images were acquired by a terrestrial hyperspectral camera (SPECIM-IQ) from November 2018 to October 2019. A Deep Neural Network classification model was developed to identify tree species from the hyperspectral imagery with an overall accuracy ranging from 85% to 96% among different seasons. Representative spectral reflectance curves of healthy and unhealthy conditions for each species were extracted and analyzed. The hyperspectral phenology models were developed to achieve high accuracy and optimization of data acquisition. The bi-monthly canopy signatures and vegetation indices revealed different seasonality patterns of evergreen and deciduous species in Hong Kong. We explored the utility of terrestrial hyperspectral remote sensing and Deep Neural Network for urban tree species identification and characterizing. This provides a unique baseline to understand hyperspectral characteristics and seasonality of urban tree species in Hong Kong that can also contribute to hyperspectral imaging and database development elsewhere in the world.
... Demmig et al. 1987;Hong and Xu 1999). Such an increase in chl fluorescence is also detectable in the field; SIF increased in plants treated with 3-(3 0 ,4 0 -dichlorophenyl)-1,1-dimethylurea (DCMU), which is an artificial herbicide that binds to the Q B site of PSII and inhibits electron transport (Rossini et al. 2015;Pinto et al. 2016). Therefore, under the same conditions, it is expected that photosynthesis is negatively related to chl fluorescence across healthy and photoinhibited leaves. ...
Solar-induced chlorophyll (chl) fluorescence (SIF) has been shown to be positively correlated with vegetation photosynthesis, suggesting that it is a useful signal for understanding of environmental responses and spatial heterogeneity of photosynthetic activity at various scales from leaf to the globe. Photosynthesis is often inhibited in stressful environments (photoinhibition), but how photoinhibition influences the relationship between photosynthesis and chl fluorescence remains unclear. Here, I studied light energy allocation among photosynthesis, chl fluorescence and heat dissipation in photoinhibited leaves and tested whether photosynthesis in photoinhibited leaves can be evaluated from chl fluorescence and reflectance spectra in remote sensing. Chl fluorescence and reflection spectra were examined with the pulse amplified modulation (PAM) system and spectroradiometer, respectively. Photoinhibited leaves had lower photosynthetic rates and quantum yields of photochemistry (ΦP) and higher chl fluorescence yields. Consequently, photosynthesis was negatively correlated with chl fluorescence, which contrasts the positive relationships between photosynthesis and SIF observed in past remote sensing studies. This suggests that vegetation photosynthesis evaluated solely from chl fluorescence may be overestimated if the vegetation is dominated by severely photoinhibited leaves. When a model of energy allocation was applied, ΦP estimated from chl fluorescence and photochemical reflectance index (PRI) significantly correlated with the observed ΦP, suggesting that the model is useful to evaluate photosynthetic activities of photoinhibited leaves by remote sensing.
... A new RS approach based on an emitted light signal by plants, suninduced chlorophyll fluorescence (SIF), has recently been proposed to advance estimates of T (Jonard et al., 2020;Lu et al., 2018;Maes et al., 2020;Qiu et al., 2018;Shan et al., 2021). SIF is the most direct measure of photosynthetic activity (ESA, 2015;Rossini et al., 2015) and its relation to plant carbon assimilation (i.e. gross primary productivity, GPP) has been successfully demonstrated (Damm et al., 2015;Frankenberg et al., 2011;Migliavacca et al., 2017). ...
Novel strategies are required to reduce uncertainties in the assessment of ecosystem transpiration (T). A major problem in modelling T is related to the complexity of constraining canopy stomatal resistance (rsc), accounting for the main biological controls on T besides non biological controls such as aerodynamic resistances or energy constraints. The novel Earth observation signal sun-induced chlorophyll fluorescence (SIF) is the most direct measure of plant photosynthesis and offers new pathways to advance estimates of T. The potential of using SIF to study ecosystem T either empirically or in combination with complex mechanistic models has already been demonstrated in recent studies. The diversity of environmental drivers determining diurnal and seasonal dynamics in T and SIF independently requires additional investigation to guide further developments towards robust SIF-informed T retrievals. This study consequently aims to identify relevant biotic and abiotic environmental drivers affecting the capability of SIF to inform estimates of ecosystem T. We used observational data from a temperate mixed forest during the leaf-on period and a Penman-Monteith (PM) based modelling framework, and observed varying sensitivities of SIF-informed approaches for diurnal and seasonal T dynamics (i.e. r² from 0.52 to 0.58 and rRMSD from 17 to 19%). We used the PM based modelling framework to investigate systematically the sensitivity of SIF to diurnal and seasonal variations in rsc when empirically and mechanistically embedded in the models. We used observations and the Soil-Vegetation-Atmosphere-Transfer model SCOPE to study the dependence of SIF and T on abiotic and biotic environmental drivers including net radiation, air temperature, relative humidity, wind speed, and leaf area index. We conclude on the potential of SIF to advance estimates of T and suggest preferring more sophisticated modelling frameworks constrained with SIF and other Earth observation data over the single use of SIF to assess reliably ecosystem T across scales.
... Finer spatial and temporal resolution datasets exist (Houborg and McCabe, 2016) but are normally available at a considerable cost. NDVI is only competent in manifesting delayed reactions to alterations in greenery but is insufficient in identifying early droughts because of its inability in recording early photosynthetic differences (Rossini et al., 2015;Sun et al., 2016;Liu et al., 2018). Despite the shortcomings of satellite-based monitoring, like the need for inter-scene and inter-sensor calibration and big data processing, the NDVI still provides near-real-time data at sufficient frequency which is seamless, consistent, and easy to use (Norman et al., 2016;Zhang et al., 2017). ...
To bring to fruition the capability of nature-based solutions (NBS) in mitigating hydro-meteorological risks (HMRs) and facilitate their widespread uptake require a consolidated knowledge-base related to their monitoring methods, efficiency, functioning and the ecosystem services they provide. We attempt to fill this knowledge gap by reviewing and compiling the existing scientific literature on methods, including ground-based measurements (e.g. gauging stations, wireless sensor network) and remote sensing observations (e.g. from topographic LiDAR, multispectral and radar sensors) that have been used and/or can be relevant to monitor the performance of NBS against five HMRs: floods, droughts, heatwaves, landslides, and storm surges and coastal erosion. These can allow the mapping of the risks and impacts of the specific hydro-meteorological events. We found that the selection and application of monitoring methods mostly rely on the particular NBS being monitored, resource availability (e.g. time, budget, space) and type of HMRs. No standalone method currently exists that can allow monitoring the performance of NBS in its broadest view. However, equipments, tools and technologies developed for other purposes, such as for ground-based measurements and atmospheric observations, can be applied to accurately monitor the performance of NBS to mitigate HMRs. We also focused on the capabilities of passive and active remote sensing, pointing out their associated opportunities and difficulties for NBS monitoring application. We conclude that the advancement in airborne and satellite-based remote sensing technology has signified a leap in the systematic monitoring of NBS performance, as well as provided a robust way for the spatial and temporal comparison of NBS intervention versus its absence. This improved performance measurement can support the evaluation of existing uncertainty and scepticism in selecting NBS over the artificially built concrete structures or grey approaches by addressing the questions of performance precariousness. Remote sensing technical developments, however, take time to shift toward a state of operational readiness for monitoring the progress of NBS in place (e.g. green NBS growth rate, their changes and effectiveness through time). More research is required to develop a holistic approach, which could routinely and continually monitor the performance of NBS over a large scale of intervention. This performance evaluation could increase the ecological and socio-economic benefits of NBS, and also create high levels of their acceptance and confidence by overcoming potential scepticism of NBS implementations.
... At the annual scale, and for the whole study region, average P STD , ET STD , and BIO STD values were −0.3, −0.7, and −0.6, respectively, with those for ET STD and BIO STD presenting strong correlation, while their correlations with P STD were weak. According to , vegetation indexes have often been reported to show delayed responses in terms of reflecting impacts of environmental stress (Lloret et al. 2007;Rossini et al. 2015). Increases on ET STD and BIO STD occurring after increases on P STD are in accordance with Seddon et al. (2016), Bento et al. (2018), and , who reported that water availability to vegetation is regarded as an important meteorological-forcing factor for plant activity. ...
Algorithms were used for biophysical characterization and monitoring of agrometeorological standardized indices, in the agricultural growing areas of the semi‐arid region of northeast Brazil. Remote sensing parameters and agrometeorological stations were coupled, involving Moderate Resolution Imaging Spectroradiometer (MODIS) images and weather data from 2003 to 2017. Anomalies on precipitation (P), actual evapotranspiration (ET), and biomass production (BIO) were noticed, when comparing the year 2017 and the long‐term period, but they occurred differently in natural vegetation and irrigated crops, being negative and positive, respectively. Modeling by using the MOD13Q1 reflectance product and weather data was confirmed as a promising tool for operational agroecosystem monitoring at a 16‐day and 250 m time and spatial scale, respectively. These tools were proven to allow detection of anomalies during different years, being of great potential to subsidize natural resources management, under the climate instability of semi‐arid environments.
... To address this gap, the ESA's Earth Explorer Mission of the 'Fluorescence Explorer' (FLEX) (Kraft et al., 2012), the first mission designed to observe the photosynthetic activity of the vegetation layer has been recently approved, with 2022 as the tentative launch date. This mission will make possible, for the first time, the assessment of the dynamics of photosynthesis on forest canopies through SIF at 300 m spatial resolution, and with potential to distinguish different fluorescence signals from PSI and PSII (Rossini et al., 2015). This offers a great advantage over current techniques used for photosynthesis monitoring based on structural indices (e.g. the Normalized Difference Vegetation Index (NDVI)) acquired from conventional Earth-resource satellites. ...
A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space as a proxy for the photosynthetic activity of terrestrial vegetation. However, the effect of spatial heterogeneity on the SIF retrievals from canopy radiance derived from images with medium and low spatial resolution remains uncharacterised. In images from forest and agricultural landscapes, the background comprises a mixture of soil and understory and can generate confounding effects that limit the interpretation of the SIF at the canopy level. This paper aims to improve the understanding of SIF from coarse spatial resolutions in heterogeneous canopies by considering the separated contribution of tree crowns, understory and background components, using a modified version of the FluorFLIGHT radiative transfer model (RTM). The new model is compared with others through the RAMI model intercomparison framework and is validated with airborne data. The airborne campaign includes high-resolution data collected over a tree-grass ecosystem with the HyPlant imaging spectrometer within the FLuorescence EXplorer (FLEX) preparatory missions. Field data measurements were collected from plots with a varying fraction of tree and understory vegetation cover. The relationship between airborne SIF calculated from pure tree crowns and aggregated pixels shows the effect of the understory at different resolutions. For a pixel size smaller than the mean crown size, the impact of the background was low (R² > 0.99; NRMSE < 0.01). By contrast, for a pixel size larger than the crown size, the goodness of fit decreased (R² < 0.6; NRMSE > 0.2). This study demonstrates that using a 3D RTM model improves the calculation of SIF significantly (R² = 0.83, RMSE = 0.03 mW m⁻² sr⁻¹ nm⁻¹) when the specific contribution of the soil and understory layers are accounted for, in comparison with the SIF calculated from mixed pixels that considers only one layer as background (R² = 0.4, RMSE = 0.28 mW m⁻² sr⁻¹ nm⁻¹). These results demonstrate the need to account for the contribution of SIF emitted by the understory in the quantification of SIF within tree crowns and within the canopy from aggregated pixels in heterogeneous forest canopies.
Vegetation indices (VIs) related to plant greenness have been studied extensively for the remote detection of foliar nitrogen content. Yet, the potential of chlorophyll fluorescence (ChlF) and photoprotection-based indices such as the photochemical reflectance index (PRI) or the chlorophyll/carotenoid index (CCI) for the detection of a wide range of nutrients remains elusive. We measured the dynamics of foliar macro- and micronutrient contents in potato plants as affected by fertilization and water stress, along with leaf and canopy level observations of spectral reflectance and ChlF (or solar-induced fluorescence). ChlF and photoprotection-related indices were more strongly related to a wide range of foliar nutrient contents compared to greenness-based indices. At the leaf level, relationships were largely mediated by foliar chlorophyll contents (Cab) and leaf morphology, which resulted in two contrasting groupings: a group dominated by macronutrients N, P, K, and Mg that decreased during canopy development and was positively correlated with Cab, and a group including Cu, Mn, Zn, and S that increased and was negatively related to Cab. At the canopy-level, spectral indices were additionally influenced by canopy structure, and so their capacity to detect foliar nutrient contents depends on the spatiotemporal covariation between foliar Cab, morphology, and canopy structure within the observations.
Remote sensing employs solar-induced chlorophyll fluorescence (SIF) as a proxy for photosynthesis from field to airborne and satellite sensors. The investigation of SIF offers a unique way of studying vegetation functioning from the local to the global scale. However, the passive, optical retrieval of the SIF signal is still challenging. Common retrieval approaches extract the SIF infilling directly from atmospheric oxygen bands in down-welling and up-welling radiance. They often involve a complex signal correction to compensate for atmospheric reabsorption and require long computing time. In contrast, the exploitation of solar Fraunhofer lines is devoid of atmospheric disturbances. We propose a new retrieval method for red and far-red SIF directly from up-welling radiance spectra in the spectral range between 650 nm and 810 nm by applying Partial Least Squares (PLS) regression machine learning. Solar Fraunhofer lines are exploited for SIF retrieval with the PLS approach by excluding telluric absorption features. The PLS models are trained and tested on synthetic reflectance and SIF data modeled with SCOPE. We identified a logarithmic relationship of the retrieval error with respect to signal-to-noise ratio of the instrument. The approach has been tested with real-world data measured by the Fluorescence Box (FloX), and evaluated against two well-established retrieval methods: the spectral fitting method (SFM) and the singular value decomposition (SVD). PLS models exploiting solar Fraunhofer lines retrieved meaningful SIF values with high precision and demonstrated robustness against atmospheric reabsorption, including from a 100m tall tower. In addition, PLS retrieval requires no complex correction for atmospheric reabsorption and computes 37 times faster than SFM. Hence, PLS retrieval allows fast and robust exploitation of SIF from solar Fraunhofer lines with high precision under conditions in which other retrieval approaches require complex atmospheric correction.
Cell cycle studies in plants and algae are highly dependent on reliable methods for detecting cellular DNA replication. With its short growth cycle and ease of genetic transformation, Phaeodactylum tricornutum is an important model organism for the study of pennate diatoms. Here we explored two different methods to detect the cell cycle of P. tricornutum, one using SYBR‐green I to via flow cytometry, and the other using EdU labeling to observe cell cycle changes under fluorescence microscopy. Both EdU labeling fluorescence microscopy and SYBR‐green I staining flow cytometry accurately indicated that the cells of P. tricornutum enter the G2/M phase after 12 h of light exposure. The results indicate that SYBR Green I was an adequate detection method for nuclear DNA quantitation in cells of P. tricornutum using a flow cytometer and without RNase A treatment. In addition, EdU can be applied to P. tricornutum to reliably detect cell proliferation. Besides, Mg‐ProtoIX was able to reverse the cell cycle division inhibition of P. tricornutum and allow the nuclear DNA replication to proceed normally. Taken together, the photoperiodic division time point was clearly identified, which sheds light on the regulation of cell division mechanism in P. tricornutum.
Terrestrial evapotranspiration (ET) is a critical component of the land surface and its accurate estimation is crucial in understanding the global water and energy budgets. Current large-scale ET estimation models depend on remotely sensed data products of meteorological conditions and vegetation phenology, but high uncertainty remains to correctly represent ecological factors, which are regulated by biochemistry. In recent years, solar-induced fluorescence (SIF), a proxy for photosynthesis, has been extensively used in different ecological research and is conceived to be of great potential for ET estimation to constrain the transpiration flux, which is the significant component of ET. So far, most SIF-based ET estimation methods are achieved via empirical methods, which lack a solid physical foundation. In this work, we estimated ET by combining SIF and meteorological variables with Fick's law and an optimal stomatal behavior model and validated the model across different eddy-covariance flux stations around the globe. The model showed overall good performance across different ecosystems and when compared with other remote sensing-based ET models, such as the Priestley Taylor-JPL (PT-JPL) model, the Moderate Resolution Imaging Spectroradiometer (MODIS)-ET products, and a simple empirical linear SIF-ET model, the RMSE of estimated ET of the model proposed in this paper demonstrated better performance. The model we proposed here can be potentially extended for a reliable ET estimation at a global scale.
Forest vegetation is an essential source of negative air ion (NAI) and influencing factor of the process. A number of studies have focused on the temporal and spatial changes of NAI in different forest communities and their relationship with meteorological factors; however, there is no literature on the relationship between solar-induced chlorophyll fluorescence intensity (SIF) and NAI, and quantitative research on relationship between NAI and photosynthesis of forest vegetation is comparatively limited. In this study, we investigated the dynamic changes NAI, SIF, and meteorological parameters in canopy during the growing season by using a flux tower at the sites Xiaolangdi and Minquan in a warm-temperate monsoon climate zone from June to September of 2019 and 2020. Results showed that the canopy NAI, SIF, and photosynthetically active radiation (PAR) showed significant diurnal and seasonal dynamic change characteristics, and that the changes in the observed indicators at different times were significantly different. The seasonal change characteristics of canopy NAI and SIF were relatively similar, and canopy SIF could accurately capture the dynamic changes of NAI. Additionally, the NAI–SIF relationship was increasingly significant at higher PAR levels, and the coefficient of determination (R²) of the two sites reached 0.652 and 0.570. It showed that when vegetation photosynthesis was fully activated, the dynamic change process of the canopy NAI provided a more robust correlation and more accurate estimation in the vegetation's main growth stage, thus demonstrating the reliability of SIF as an indicator for tracking NAI seasonal changes. In the future, automatic, continuous SIF observations could provide reliable understanding of the photosynthetic characteristics of terrestrial ecosystems, thereby quantifying the contribution potential of forest to NAI application in ground-based optical measurement and airborne and satellite remote sensing. This provides a working foundation for direct assessment of spatial and temporal distribution patterns of global seasonal NAI.
Airborne measurements of sun-induced chlorophyll fluorescence (SIF) are a promising tool for monitoring plant functioning on different scales. However, currently operational airborne imaging spectrometers for SIF measurements still have limited spatial resolution and pointing accuracy. This is challenging in terms of the practical use of SIF maps for crop breeding and plant phenotyping. We developed and tested two spatial aggregation approaches to make airborne SIF data usable in experimental settings with a high number of small experimental plots. The two aggregation approaches generating representative SIF values for experimental plots demonstrated the potential to be used in crop phenotyping. The first aggregation approach (Approach A) aggregates pixel values directly on SIF maps, whereas the second approach (Approach B) aggregates at-sensor radiance before SIF retrieval. The statistical analysis showed that Approaches A and B led to significantly different SIF products for single experimental plots (p < 0.001). To evaluate the usability of the two approaches, aggregated SIF products were fitted against ground-based reference measurements. We found that Approach B provided a better representation of ground truth SIF760 (R² = 0.61, p < 0.001) than Approach A (R² = 0.55, p < 0.001) when combined with weighted averaging and robust outlier detection. Furthermore, our results suggest that a slight decrease in the spatial resolution of the image data improves accuracy of aggregation.
High temporal resolution measurements of solar‐induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time‐dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology‐driven) and slow components (controlled by structural and biochemical properties) from PRI, far‐red F (F760), and far‐red apparent fluorescence yield (Fy∗760). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA‐decomposed components of PRI and Fy∗760 showed high correlations with the reference variables (R² = 0.97 and 0.96, respectively). Fast SSA‐decomposed components of PRI and Fy∗760 were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m² m⁻². The method was also successfully applied to predict light‐use efficiency (LUE) from the fast SSA‐decomposed components of PRI (R² = 0.70) and Fy∗760 (R² = 0.68) when discarding data modeled with LAI < 1 m² m⁻² and short‐wave radiation Rin < 250 W m⁻². The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA‐decomposed component of apparent LUE∗ showed a stronger correlation with the fast SSA‐decomposed component of Fy∗760 (R² = 0.42) than with original Fy∗760 (R² = 0.01). SSA‐based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems.
Solar-induced chlorophyll fluorescence at 760 nm (SIF) is a promising proxy of photosynthesis and can help improving plant stress monitoring. The Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model combines radiative transfer and enzyme kinetics of photosynthesis and is widely used to interpret SIF at different temporal and spatial scales. In this study, growing season canopy hyperspectral reflectance between 400 nm and 900 nm was used to retrieve chlorophyll content (Cab) and leaf inclination (LIDFa) using radiative transfer models (RTMs) combined with the shuffled complex evolution-University of Arizona (SCE-UA) method. These parameters were then used to simulate diurnal and seasonal trends of SIF for paddy rice. The results showed that the accuracy of Cab retrieval was improved when the variation in senescent material (Cs) was considered, especially in the later growth stages. The SCOPE model was able to reliably interpret the diurnal cycle and seasonal trend of SIF with a correlation coefficient of 0.92 and RMSE of 0.12 w m⁻² sr⁻¹ um⁻¹. Our results revealed that the SCOPE model provides a promising method for interpreting SIF variations but its accuracy should be evaluated in different growth stages. This will serve as a significant reference for detecting plant photosynthetic activity and physiological traits at different growth stages.
Photosynthesis, or gross primary productivity (GPP), plays a critical role in the global carbon cycle, since it is the sole pathway for carbon fixation by the biosphere. Quantifying GPP across multiple spatial scales is needed to improve our understanding of current and future behavior of biosphere-atmosphere carbon exchange and subsequent feedbacks on the climate system. Remote sensing represents one method to observe vegetation properties and processes, and solar-induced chlorophyll fluorescence (SIF), a light signal originating from leaves, has been shown to be proportional to GPP on diurnal and seasonal timescales. Recently, new techniques to retrieve SIF from satellite observations have provided an unprecedented opportunity to study GPP on a global scale. The relationship between SIF and GPP, however, is subject to significant uncertainty as it is influenced by a number of ecosystem traits (e.g. plant species, canopy structure, leaf age). In this dissertation, I evaluate SIF signals and their relation to GPP over Northern Hemisphere forest ecosystems. First, I compare climate-driven variations in satellite-based SIF to both longstanding satellite vegetation indices derived from reflected sunlight and tower-based estimates of GPP. Even when aggregated regionally, interannual variability (IAV) of SIF is found to be subject to low signal-to-noise performance, particularly during summer. However, through a statistical analysis, I show that increases in springtime temperature driven by warmer temperatures are offset by drier, less productive conditions later in the growing season. Summer productivity, however, is more strongly correlated with moisture than with temperature, suggesting that moisture exerts a greater influence on growing season-integrated signals. While these results demonstrate that satellite observations can be used to reveal meaningful carbon-climate interactions, they also show that currently available satellite observations of SIF do not allow for robust studies of IAV at scales comparable to surface-based observations. To investigate how SIF signals are related to ecosystem function at a local scale, I built and deployed a PhotoSpec spectrometer system to the AmeriFlux tower at the University of Michigan Biological Station (US-UMB) above a temperate deciduous forest. These observations show a strong correlation between SIF and GPP at diurnal and seasonal timescales, but SIF is more closely tied to solar radiation and exhibits a delayed response to water stress-induced losses in summer GPP. This decoupling during drought highlights the challenges in using SIF to detect changes in summertime productivity. However, an increased ratio between red and far-red SIF during drought indicates that the combination of SIF at multiple wavelengths may improve the detection of water stress. Lastly, I explore diurnal and directional aspects of the SIF signal. Observations of SIF are sensitive to sun-sensor geometry, with smaller incident angles (between solar and viewing angles) leading to stronger signals. However, afternoon SIF is typically lower than morning values at equivalent light levels due to ecosystem downregulation, which obfuscates angular dependencies in the afternoon. While satellite observations typically rely on a clear-sky sunlight proxy to scale instantaneous observations of SIF to daily values, these results demonstrate the need to account for sounding geometry and diurnal hysteresis in SIF signals in order to advance the interpretation of satellite observations. Overall, my results provide a multiscale assessment of SIF over Northern Hemisphere forests and emphasize that careful attention must be given to the spatial and temporal scales at which SIF can be used to make inferences about GPP.
The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model aims at linking satellite observations in the visible, infrared and thermal domains with land surface processes in a physically based manner, and quantifying the micro-climate in the canopy. It simulates radiative transfer in the soil, leaves and vegetation canopies, as well as photosynthesis and non-radiative heat dissipation through convection and mechanical turbulence. Since the first publication 11 years ago, SCOPE has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary productivity (GPP) and directional thermal signals. Here we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements: (1) It enables the definition of layers consisting of leaves with different properties, thus enabling the simulation of vegetation with an understory or with a vertical gradient in leaf chlorophyll concentration; (2) It enables the simulation of soil reflectance; (3) It includes the simulation of leaf and canopy reflectance changes induced by the xanthophyll cycle; and (4) The computation speed has been reduced by 90 % compared to earlier versions due to a fundamental optimization of the model. These new features improve the capability of the model to represent complex canopies and to explore the response of remote sensing signals to vegetation physiology. The improvements in the computational efficiency make it possible to use SCOPE 2.0 routinely for the simulation of satellite data and land surface fluxes. It also strengthens the operability for the numerical retrieval of land surface products from satellite or airborne data.
Solar‐induced fluorescence (SIF) is highly relevant in mapping photosynthesis from remote‐sensing platforms. This requires linking SIF to photosynthesis and understanding the role of non‐photochemical quenching (NPQ) mechanisms under field conditions. Hence, active and passive fluorescence were measured in Arabidopsis thaliana with altered NPQ in outdoor conditions.
Plants had mutations in either violaxanthin de‐epoxidase (npq1) or PsbS protein (npq4), resulting in reduced NPQ capacity. Parallel measurements of NPQ, photosystem II efficiency, SIF and spectral reflectance (ρ) were conducted diurnally on one sunny summer day and two consecutive days during a simulated cold spell.
Results showed that both npq mutants had higher levels of SIF compared to wild type. Changes in reflectance were related to changes in the violaxanthin‐antheraxanthin‐zeaxanthin cycle and not to PsbS‐mediated conformational changes. When plants were exposed to cold temperatures, rapid onset of photoinhibition strongly quenched SIF in all lines.
Using well‐characterized npq mutants of Arabidopsis, we could show for the first time the quantitative link between SIF, photosynthetic efficiency, NPQ components and leaf reflectance. We discuss the functional potential and limitations of SIF and reflectance measurements for estimating photosynthetic efficiency and NPQ in the field.
Effective use of solar‐induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global‐scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold‐and‐wet climates to hot‐and‐dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modelled from climate variables using a machine learning (random forest) framework, which can improve the modelling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modelling of the relationship between SIF and GPP over broad scales.
Sun-Induced chlorophyll Fluorescence (SIF) relates directly to photosynthesis yield and stress but there are still uncertainties in its interpretation. Most of these uncertainties concern the influences of the emitting vegetation’s structure (e.g., leaf angles, leaf clumping) and biochemistry (e.g., chlorophyll content, other pigments) on the radiative transfer of fluorescent photons. The Caatinga is a large region at northeast Brazil of semiarid climate and heterogeneous vegetation, where such biochemical and structural characteristics can vary greatly even within a single hectare. With this study we aimed to characterize eleven years of SIF seasonal variation from Caatinga vegetation (2007 to 2017) and to study its responses to a major drought in 2012. Orbital SIF data from the instrument GOME-2 was used along with MODIS MAIAC EVI and NDVI. Environmental data included precipitation rate (TRMM), surface temperature (MODIS) and soil moisture (ESA CCI). To support the interpretation of SIF responses we have used red and far-red SIF adjusted by the Sun’s zenith angle (SIF-SZA) and by daily Photosynthetically Active Radiation (dSIF). Furthermore, we have also adjusted SIF through two contrasting formulations using NDVI data as proxy for structure and biochemistry, based on previous leaf-level and landscape level studies: SIF-Yield and SIF-Prod. Data was tested with time-series decomposition, rank correlation, spatial correlation and Linear Mixed Models (LMM). Results show that GOME-2 SIF and adjusted SIF formulations responded consistently to the observed environmental variation and showed a marked decrease in SIF emissions in response to a 2012 drought, that was generally larger than the corresponding NDVI and EVI decreases. Drought sensitivity of SIF, as inferred from LMM slopes, was correlated to land cover at different regions of the Caatinga. This is the first study to show correlation between landscape-level SIF and an emergent property of ecosystems (i.e., resilience), showcasing the value of remotely sensed fluorescence for ecological studies.
Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5° × 0.5°. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.
Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. In addition, fluorescence can contaminate photon path estimates from the O2 A-band that has become an integral part of missions to accurately measure greenhouse gas concentrations. Global mapping of far-red (~ 755–770 nm) terrestrial vegetation solar-induced fluorescence from space has been accomplished using the high spectral resolution (ν/Δ ν > 35 000) interferometer on the Japanese Greenhouse gases Observing SATellite (GOSAT). These satellite retrievals of fluorescence rely solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data to disentangle the spectral signatures of three basic components in and surrounding the O2 A-band: atmospheric absorption, surface reflectance, and fluorescence radiance. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate spectral resolution measurements with a relatively high signal-to-noise ratio within and outside the O2 A-band can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with GOSAT. GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. It should be noted that both GOME-2 and GOSAT were designed to make atmospheric trace gas measurements and were not optimized for fluorescence measurements. Our approach can be applied to other existing and future space-based instruments that provide moderate spectral resolution observations in the near-infrared region.
Chlorophyll a fluorescence (ChlF) has been used for decades to study the organization, functioning, and physiology of photosynthesis at the leaf and subcellular levels. ChlF is now measurable from remote sensing platforms. This provides a new optical means to track photosynthesis and gross primary productivity of terrestrial ecosystems. Importantly, the spatiotemporal and methodological context of the new applications is dramatically different compared with most of the available ChlF literature, which raises a number of important considerations. Although we have a good mechanistic understanding of the processes that control the ChlF signal over the short term, the seasonal link between ChlF and photosynthesis remains obscure. Additionally, while the current understanding of in vivo ChlF is based on pulse amplitude-modulated (PAM) measurements, remote sensing applications are based on the measurement of the passive solar-induced chlorophyll fluorescence (SIF), which entails important differences and new challenges that remain to be solved. In this review we introduce and revisit the physical, physiological, and methodological factors that control the leaf-level ChlF signal in the context of the new remote sensing applications. Specifically, we present the basis of photosynthetic acclimation and its optical signals, we introduce the physical and physiological basis of ChlF from the molecular to the leaf level and beyond, and we introduce and compare PAM and SIF methodology. Finally, we evaluate and identify the challenges that still remain to be answered in order to consolidate our mechanistic understanding of the remotely sensed SIF signal.
Significance
Global food and biofuel production and their vulnerability in a changing climate are of paramount societal importance. However, current global model predictions of crop photosynthesis are highly uncertain. Here we demonstrate that new space-based observations of chlorophyll fluorescence, an emission intrinsically linked to plant biochemistry, enable an accurate, global, and time-resolved measurement of crop photosynthesis, which is not possible from any other remote vegetation measurement. Our results show that chlorophyll fluorescence data can be used as a unique benchmark to improve our global models, thus providing more reliable projections of agricultural productivity and climate impact on crop yields. The enormous increase of the observational capabilities for fluorescence in the very near future strengthens the relevance of this study.
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 (O-2-B) and 760 nm (O-2-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 (r(2) = 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 (r(2) = 0.80, RMSE = 0.186 mg CO2/m(2)/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.
Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5° × 0.5°. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.
Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate depends critically on knowing where, when and how carbon dioxide is exchanged between the land and atmosphere. Terrestrial gross primary production (GPP) constitutes the largest flux component in the global carbon budget, however significant uncertainties remain in GPP estimates and its seasonality. Empirically, we show that global spaceborne observations of solar induced chlorophyll fluorescence-occurring during photosynthesis-exhibit a strong linear correlation with GPP. We found that the fluorescence emission even without any additional climatic or model information has the same or better predictive skill in estimating GPP as those derived from traditional remotely-sensed vegetation indices using ancillary data and model assumptions. In boreal summer the generally strong linear correlation between fluorescence and GPP models weakens, attributable to discrepancies in savannas/croplands (18-48% higher fluorescence-based GPP derived by simple linear scaling), and high-latitude needleleaf forests (28-32% lower fluorescence). Our results demonstrate that retrievals of chlorophyll fluorescence provide direct global observational constraints for GPP and open an entirely new viewpoint on the global carbon cycle. We anticipate that global fluorescence data in combination with consolidated plant physiological fluorescence models will be a step-change in carbon cycle research and enable an unprecedented robustness in the understanding of the current and future carbon cycle.
The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO2 fluxes. It was combined with extensive ground-based quantification of leaf- and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Explorer" (FLEX). The aim of this campaign was to test if fluorescence signal detected from an airborne platform can be used to improve estimates of plant mediated exchange on the mesoscale. Canopy fluorescence was quantified from four airborne platforms using a combination of novel sensors: (i) the prototype airborne sensor AirFLEX quantified fluorescence in the oxygen A and B bands, (ii) a hyperspectral spectrometer (ASD) measured reflectance along transects during 12 day courses, (iii) spatially high resolution georeferenced hyperspectral data cubes containing the whole optical spectrum and the thermal region were gathered with an AHS sensor, and (iv) the first employment of the high performance imaging spectrometer HYPER delivered spatially explicit and multi-temporal transects across the whole region. During three measurement periods in April, June and September 2007 structural, functional and radiometric characteristics of more than 20 different vegetation types in the Les Landes region, Southwest France, were extensively characterized on the ground. The campaign concept focussed especially on quantifying plant mediated exchange processes (photosynthetic electron transport, CO2 uptake, evapotranspiration) and fluorescence emission. The comparison between passive sun-induced fluorescence and active laser-induced fluorescence was performed on a corn canopy in the daily cycle and under desiccation stress. Both techniques show good agreement in detecting stress induced fluorescence change at the 760 nm band. On the large scale, airborne and ground-level measurements of fluorescence were compared on several vegetation types supporting the scaling of this novel remote sensing signal. The multi-scale design of the four airborne radiometric measurements along with extensive ground activities fosters a nested approach to quantify photosynthetic efficiency and gross primary productivity (GPP) from passive fluorescence.
This paper presents a field platform for continuous measurement of fluorescence at the canopy level. It consists of a 21-m-high crane equipped for fluorescence measurements. The crane is installed in the middle of the fields dedicated to agricultural research. Thanks to a jib of 24 m and a railway of 100 m distance, fluorescence measurements can be performed at nadir viewing over various field crops. The platform is dedicated to the development and test of future passive or active airborne and space-borne vegetation sensors. A new fully automatic instrument, called TriFLEX, has been installed at the end of the jib. TriFLEX is designed for passive measurement of fluorescence in the oxygen A and B absorption bands. It is based on three spectrometers and allows for continuous measurements with a repetition rate of about 1 Hz. The data products are the radiances of the target, the fluorescence flux at 687 and 760 nm, and several vegetation indexes, including the photochemical reflectance index and the normalized difference vegetation index. A new algorithm for fluorescence retrieval from spectral bands measurement is described. It improves upon the well-known Fraunhofer line discriminator method applied to passive fluorescence measurement by taking into account the spectral shape of fluorescence and the reflectance of vegetation. A measurement campaign of 38 days has been carried out in summer 2008 over a sorghum field. The evolution of the signals showed that the crop was suffering from stress due to lack of water. After several rainy days, a reversion of the water stress was observed.
A comparison has been made between the changes in absorption spectra and chlorophyll fluorescence emission occurring upon the induction of non-photochemical dissipation of excitation energy (qE) in isolated thylakoids and those accompanying the aggregation of detergent-solubilised spinach light-harvesting complex (LHCII). In support of a recent hypothesis for the mechanism of qE (Horton et al. (1991) FEBS Lett. 292, 1–4), it was found that absorbence changes at 530 nm were associated with qE and LHCII aggregation. Antimycin A inhibited these changes and prevented LHCII aggregation, as indicated by the electrophoretic mobility of the complex and its low-temperature fluorescence spectrum. An antimycin-insensitive partial aggregation of LHCII was associated with an absorbance change at 505 nm. Low concentration of detergent caused disaggregation of LHCII and the reversal of qE. These data are discussed in terms of the relationship between structural change in LHCII and the mechanism of non-photochemical quenching of chlorophyll fluorescence in thylakoids.
Reliable time series of vegetation optical properties are needed to improve the modeling of the terrestrial carbon budget with remote sensing data. This paper describes the development of an automatic spectral system able to collect continuous long-term in-field spectral measurements of spectral down-welling and surface reflected irradiance. The paper addresses the development of the system, named hyperspectral irradiometer (HSI), describes its optical design, the acquisition, and processing operations. Measurements gathered on a vegetated surface by the HSI are shown, discussed and compared with experimental outcomes with independent instruments.
Under conditions of excess sunlight the efficient light-harvesting antenna found in the chloroplast membranes of plants is rapidly and reversibly switched into a photoprotected quenched state in which potentially harmful absorbed energy is dissipated as heat, a process measured as the non-photochemical quenching of chlorophyll fluorescence or qE. Although the biological significance of qE is established, the molecular mechanisms involved are not. LHCII, the main light-harvesting complex, has an inbuilt capability to undergo transformation into a dissipative state by conformational change and it was suggested that this provides a molecular basis for qE, but it is not known if such events occur in vivo or how energy is dissipated in this state. The transition into the dissipative state is associated with a twist in the configuration of the LHCII-bound carotenoid neoxanthin, identified using resonance Raman spectroscopy. Applying this technique to study isolated chloroplasts and whole leaves, we show here that the same change in neoxanthin configuration occurs in vivo, to an extent consistent with the magnitude of energy dissipation. Femtosecond transient absorption spectroscopy, performed on purified LHCII in the dissipative state, shows that energy is transferred from chlorophyll a to a low-lying carotenoid excited state, identified as one of the two luteins (lutein 1) in LHCII. Hence, it is experimentally demonstrated that a change in conformation of LHCII occurs in vivo, which opens a channel for energy dissipation by transfer to a bound carotenoid. We suggest that this is the principal mechanism of photoprotection.
Understanding how photosynthesis responds to the environment is crucial for improving plant production and maintaining biodiversity in the context of global change. Covering all aspects of photosynthesis, from basic concepts to methodologies, from the organelle to whole ecosystem levels, this is an integrated guide to photosynthesis in an environmentally dynamic context. Focusing on the ecophysiology of photosynthesis – how photosynthesis varies in time and space, responds and adapts to environmental conditions and differs among species within an evolutionary context – the book features contributions from leaders in the field. The approach is interdisciplinary and the topics covered have applications for ecology, environmental sciences, agronomy, forestry and meteorology. It also addresses applied fields such as climate change, biomass and biofuel production and genetic engineering, making a valuable contribution to our understanding of the impacts of climate change on the primary productivity of the globe and on ecosystem stability.
The remote sensing community puts major efforts into calibration and validation of sensors, measurements, and derived products to quantify and reduce uncertainties. Given recent advances in instrument design, radiometric calibration, atmospheric correction, algorithm development, product development, validation, and delivery, the lack of standardization of reflectance terminology and products becomes a considerable source of error. This article provides full access to the basic concept and definitions of reflectance quantities, as given by Nicodemus et al. [Nicodemus, F.E., Richmond, J.C., Hsia, J.J., Ginsberg, I.W., and Limperis, T. (1977). Geometrical Considerations and Nomenclature for Reflectance. In: National Bureau of Standards, US Department of Commerce, Washington, D.C. URL: http://physics.nist.gov/Divisions/Div844/facilities/specphoto/pdf/ geoConsid.pdf.] and Martonchik et al. [Martonchik, J.V., Bruegge, C.J., and Strahler, A. (2000). A review of reflectance nomenclature used in remote sensing. Remote Sensing Reviews, 19, 9–20.]. Reflectance terms such as BRDF, HDRF, BRF, BHR, DHR, black-sky albedo, white-sky albedo, and blue-sky albedo are defined, explained, and exemplified, while separating conceptual from measurable quantities. We use selected examples from the peer-reviewed literature to demonstrate that very often the current use of reflectance terminology does not fulfill physical standards and can lead to systematic errors. Secondly, the paper highlights the importance of a proper usage of definitions through quantitative comparison of different reflectance products with special emphasis on wavelength dependent effects. Reflectance quantities acquired under hemispherical illumination conditions (i.e., all outdoor measurements) depend not only on the scattering properties of the observed surface, but as well on atmospheric conditions, the object's surroundings, and the topography, with distinct expression of these effects in different wavelengths. We exemplify differences between the hemispherical and directional illumination quantities, based on observations (i.e., MISR), and on reflectance simulations of natural surfaces (i.e., vegetation canopy and snow cover). In order to improve the current situation of frequent ambiguous usage of reflectance terms and quantities, we suggest standardizing the terminology in reflectance product descriptions and that the community carefully utilizes the proposed reflectance terminology in scientific publications.
Understanding how photosynthesis responds to the environment is crucial for improving plant production and maintaining biodiversity in the context of global change. Covering all aspects of photosynthesis, from basic concepts to methodologies, from the organelle to whole ecosystem levels, this is an integrated guide to photosynthesis in an environmentally dynamic context. Focusing on the ecophysiology of photosynthesis how photosynthesis varies in time and space, responds and adapts to environmental conditions and differs among species within an evolutionary context the book features contributions from leaders in the field. The approach is interdisciplinary and the topics covered have applications for ecology, environmental sciences, agronomy, forestry and meteorology. It also addresses applied fields such as climate change, biomass and biofuel production and genetic engineering, making a valuable contribution to our understanding of the impacts of climate change on the primary productivity of the globe and on ecosystem stability.
The UV-laser ( λ 355 nm) induced fluorescence emission spectra of green leaves comprise the blue(F440) and green (F520) fluorescence bands as well as the red (F690) and far-red (F740) chlorophyll fluorescence emission bands. Based on the four UV-laser induced fluorescence bands blue, green, red and farred a high resolution fluorescence imaging system was established, which allows a fast and large scale screening of fluorescence gradients and local disturbances in fluorescence emission over the whole leaf surface. The new imaging method not only permits to screen leaves by means of images in four fluorescence bands (LIF images) but, in addition, via images of the fluorescence ratios blue/red (F440/F690), blue/farred (F440/F740), the chlorophyll fluorescence ratio red/far-red (F690/F740) and the ratio blue/green (F440/F520) (LIF ratio images). By fluorescence imaging we could prove that in aurea tobacco the major part of the leaves' blue and green fluorescence is emitted from the main and side leave veins, whereas the major part of the leaves' red and far-red chlorophyll fluorescence is emitted from the vein-free leaf regions, which also have the highest chlorophyll content. A smaller proportion of the aurea tobacco leaves' blue-green fluorescence emission is derived from the cell walls of epidermis cells. The fluorescence ratios blue/red and blue/far-red are very sensitive to environmental changes, and thus permit early stress and strain detection in plants, and the evaluation of damage to the photosynthetic apparatus. Via monitoring the increase in chlorophyll fluorescence LIF images allow to detect differences in the time-dependent uptake of diuron and the progressing inhibition of photosynthetic electron transport in the treated leaf part. The novel fluorescence imaging technique sets a new dimension for early stress detection in the photosynthetic apparatus and in plants. It has many advantages over the previously applied point-data measurements of selected leaf points using conventional spectrofluorometers. The new fluorescence imaging system proved to be very suitable for remote sensing of plants in the near distance, and can be further developed for far distance remote sensing of the state of health of terrestrial vegetation. Some examples in the many possible ways of computer-aided fluorescence data processing (formation of different fluorescence ratios, screening of fluorescence profiles, histogramme plotting) are indicated.