Sun-induced Chlorophyll Fluorescence Research Articles

The Respective Effects of Vapor Pressure Deficit and Soil Moisture on Ecosystem Productivity in Southwest China

This study aims to examine the individual and combined effects of soil moisture (SM) and vapor pressure deficit (VPD) on ecosystem productivity in Southwest China. Utilizing the community land model (CLM) to simulate the regional soil moisture and vapor pressure deficit, we analyzed their impacts on ecosystem productivity through a data binning approach and employed sun-induced chlorophyll fluorescence yield (SIFyield) as a productivity indicator. Our findings highlight a significant coupling effect between SM and VPD, which diminishes with finer temporal data resolution. The data binning analysis indicates that VPD has a predominant influence on SIFyield across 70% of the study area, whereas SM is more influential in the remaining 30%. Notably, the correlation between SIFyield and SM, modulated by VPD, is stronger in forest and shrubland ecosystems, whereas in grasslands, the influence pattern is reversed, with VPD having a more significant impact. The study concludes that in Southwest China, ecosystem productivity is more significantly affected by VPD than by SM.

Data-based investigation of the effects of canopy structure and shadows on chlorophyll fluorescence in a deciduous oak forest

Abstract. Data from satellite, aircraft, drone, and ground-based measurements have already shown that canopy-scale sun-induced chlorophyll fluorescence (SIF) is tightly related to photosynthesis, which is linked to vegetation carbon assimilation. However, our ability to effectively use those findings are hindered by confounding factors, including canopy structure, fluctuations in solar radiation, and sun–canopy geometry that highly affect the SIF signal. Thus, disentangling these factors has become paramount in order to use SIF for monitoring vegetation functioning at the canopy scale and beyond. Active chlorophyll fluorescence measurements (FyieldLIF), which directly measures the apparent fluorescence yield, have been widely used to detect physiological variation of the vegetation at the leaf scale. Recently, the measurement of FyieldLIF has become feasible at the canopy scale, opening up new opportunities to decouple structural, biophysical, and physiological components of SIF at the canopy scale. In this study, based on top-of-canopy measurements above a mature deciduous forest, reflectance (R), SIF, SIF normalized by incoming photosynthetically active radiation (SIFy), FyieldLIF, and the ratio between SIFy and FyieldLIF (named Φk) were used to investigate the effects of canopy structure and shadows on the diurnal and seasonal dynamics of SIF. Further, random forest (RF) models were also used to not only predict FyieldLIF and Φk, but also provide an interpretation framework by considering additional variables, including the R in the blue, red, green, red-edge, and near-infrared bands; SIF; SIFy; and solar zenith angle (SZA) and solar azimuth angle (SAA). Results revealed that the SIF signal is highly affected by the canopy structure and sun–canopy geometry effects compared to FyieldLIF. This was evidenced by the weak correlations obtained between SIFy and FyieldLIF at the diurnal timescale. Furthermore, the daily mean SIF‾y captured the seasonal dynamics of daily mean F‾yieldLIF and explained 58 % of its variability. The findings also revealed that reflectance in the near-infrared (R-NIR) and the NIRv (the product of R-NIR and normalized difference vegetation index (NDVI)) are good proxies of Φk at the diurnal timescale, while their correlations with Φk decrease at the seasonal timescale. With FyieldLIF and Φk as outputs and the abovementioned variables as predictors, this study also showed that the RF models can explain between 86 % and 90 % of FyieldLIF, as well as 60 % and 70 % of Φk variations under clear-sky conditions. In addition, the predictor importance estimates for FyieldLIF RF models revealed that R at 410, 665, 740, and 830 nm; SIF; SIFy; SZA; and SAA emerged as the most useful and influential factors for predicting FyieldLIF, while R at 410, 665, 705, and 740 nm; SZA; and SAA are crucial for predicting Φk. This study highlighted the complexity of interpreting diurnal and seasonal dynamics of SIF in forest canopies. These dynamics are highly dependent on the complex interactions between the structure of the canopy, the vegetation biochemical properties, the illumination angles (SZA and SAA), and the light conditions (ratio of diffuse to direct solar radiation). However, such measurements are necessary to better separate the variability in SIF attributable to radiation and measurement conditions from the subtler variability attributable to plant physiological processes.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects

Accurately estimating leaf chlorophyll content (Chl) at large spatial scales is crucial for monitoring agricultural production and plant photosynthesis. Sentinel-2 and Landsat-7/8 offer the potential to assess Chl with high spatial resolutions using various physically-based, empirical, and hybrid methods, but they still present challenges due to the confounding effects of canopy structure and soil background. The near-infrared reflectance of vegetation (NIRV) has been proposed to characterize canopy structural and soil background effects in sun-induced chlorophyll fluorescence. Therefore, in this study, we developed a novel strategy that incorporates NIRV to minimize canopy structural and soil background effects on Chl retrieval, using bidirectional reflectance factor (BRF) from Sentinel-2 and Landsat-7/8 imagery. We compared Chl estimation using empirical random forest (RF), the physically-based PROSAIL-5B inversion, and a hybrid method using RF to train simulated datasets from PROSAIL-5B. We validated Chl retrieval performance with measured datasets from the National Ecological Observatory Network, covering seven vegetation types. We also compared spatiotemporal Chl variations from Sentinel-2, Landsat-7/8, and MODIS. The results showed that NIRV effectively characterized the canopy structural effect on Chl estimation, associated with leaf area index, average leaf angle, and canopy fraction of senescent leaves. Compared to BRF, correcting BRF by NIRV improved Chl retrieval with RMSE values reduced by 12.2%–27.0% for Sentinel-2, 7.4%–16.4% for Landsat-7, and 6.9%–13.7% for Landsat-8, respectively. This also resulted in a significant increase in R2 values for Sentinel-2 (0.13–0.46), Landsat-7 (0.08–0.19), and Landsat-8 (0.12–0.2), respectively. Sentinel-2 outperformed Landsat-7/8 in Chl estimation, due to the contribution of red edge bands and a higher spatial resolution. Furthermore, the hybrid method performed best for Chl estimation from Sentinel-2 and Landsat-7/8 with relative RMSE values of 17.33%, 20.22%, and 19.23%, respectively. Our hybrid method demonstrated lower model uncertainty with smaller variations of RMSE values across varying biochemical and structural traits and solar and viewing angles, and exhibited more consistent spatiotemporal variations of Chl from Sentinel-2 with phenology compared to Landsat-7/8 and the published MODIS Chl data. Our hybrid method demonstrates significant potential in mitigating canopy structural effects, enhancing Chl retrieval, and facilitating the development of Chl data from various satellite imagery sources.

Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence

Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R2 of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R2 increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R2 = 0.9612, RMSE = 66.27 kg·ha−1, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R2 = 0.8898) and Ada Boost algorithm (R2 = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield.

The Difference between the Responses of Gross Primary Production and Sun-Induced Chlorophyll Fluorescence to the Environment Based on Tower-Based and TROPOMI SIF Data

The strong correlation between gross primary production (GPP) and sun-induced chlorophyll fluorescence (SIF) has been reported in many studies and is the basis of the SIF-based GPP estimation. However, GPP and SIF are not fully synchronous under various environmental conditions, which may destroy a stable GPP–SIF relationship. Therefore, exploring the difference between responses of GPP and SIF to the environment is essential to correctly understand the GPP–SIF relationship. As the common driver of GPP and SIF, the incident radiation could cause GPP and SIF to have similar responses to the environment, which may obscure the discrepancies in the responses of GPP and SIF to the other environmental variables, and further result in the ambiguity of the GPP–SIF relationship and uncertainties in the application of SIF. Therefore, we tried to exclude the dominant role of radiation in the responses of GPP and SIF to the environment based on the binning method, in which continuous tower-based SIF, satellite SIF, and eddy covariance GPP data from two growing seasons were used to investigate the differences in the responses of GPP and SIF to radiation, air temperature (Ta), and evaporation fraction (EF). We found that the following: (1) At both the site and satellite scales, there were divergences in the light response speeds between GPP and SIF which were affected by Ta and EF. (2) SIF and its light response curves were insensitive to EF and Ta compared to GPP, and the consistency in GPP and SIF light responses was gradually improved with the improvement of Ta and EF. (3) The dynamic slope values of the GPP–SIF relationship were mostly caused by the different sensitivities of GPP and SIF to EF and Ta. Our results highlighted that GPP and SIF were not highly consistent, having differences in environmental responses that further confused the GPP–SIF relationship, leading to complex SIF application.

Knowledge mapping on the sun-induced chlorophyll fluorescence technology research: a scientometric and visualization analysis.

The sun-induced chlorophyll fluorescence (SIF) has received increasing attention over the past few years. This scientometric study analyzed the SIF research field, based on publications available in Web of Science Core Collection visualization, and cluster analysis enabled us to map the knowledge domain and intellectual landscape of this field and identify thematic trends, landmark articles, and emerging research themes. In this study, the software VOSviewer and CiteSpace were used to identify the intellectual base and research front using visualization and analysis. Then, we analyzed synthesized networks of co-authorship (author, institution, and country), co-citation (author, document, and journal), and co-occurring keywords. SIF has increased its publication output steadily since 2002. This study provided a visual knowledge map of the major research domains of SIF. Also, explanations and implications of the findings were explored, and the emerging trends were identified. This report provides a thorough overview of the current state and growth trends of SIF research, and it helps researchers and practitioners understand the major areas of attention in this area and provides insights into prospective areas for further study.

Exploring Physiological and Nonphysiological Responses of Sun-Induced Chlorophyll Fluorescence to Different Levels of Water Stress in Winter Wheat

Exploring Physiological and Nonphysiological Responses of Sun-Induced Chlorophyll Fluorescence to Different Levels of Water Stress in Winter Wheat

Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure

Transpiration (T) represents plant water use, while sun-induced chlorophyll fluorescence (SIF) emitted during photosynthesis, relates well to gross primary production. SIF can be influenced by vegetation structure, while uncertainties remain on how this might impact the relationship between SIF and T, especially for open and sparse woodlands. In this study, a method was developed to map T in riverine floodplain open woodland environments using satellite data coupled with a radiative transfer model (RTM). Specifically, we used FluorFLiES, a three-dimensional SIF RTM, to simulate the full spectrum of SIF for three open woodland sites with varying fractional vegetation cover. Five specific SIF bands were selected to quantify their correlation with field measured T derived from sap flow sensors. The coefficient of determination of the simulated far-red SIF and field measured T at a monthly scale was 0.93. However, when comparing red SIF from leaf scale to canopy scale to predict T, performance declined by 24%. In addition, varying soil reflectance and understory leaf area index had little effect on the correlation between SIF and T. The method developed can be applied regionally to predict tree water use using remotely sensed SIF datasets in areas of low data availability or accessibility.

Alternative method for determining leaf CO2 assimilation without gas exchange measurements: Performance, comparison and sensitivity analysis.

We present an alternative method to determine leaf CO2 assimilation rate (An ), eliminating the need for gas exchange measurements in proximal and remote sensing. This method combines the Farquhar-von Caemmerer-Berry photosynthesis model with mechanistic light reaction (MLR) theory and leaf energy balance (EB) analysis. The MLR theory estimates the actual electron transport rate (J) by leveraging chlorophyll fluorescence via pulse amplitude-modulated fluorometry for proximal sensing or sun-induced chlorophyll fluorescence measurements for remote sensing, along with spectral reflectance. The EB equation is used to directly estimate stomatal conductance from leaf temperature. In wheat and soybean, the MLR-EB model successfully estimated An variations, including midday depression, under various environmental and phenological conditions. Sensitivity analysis revealed that the leaf boundary layer conductance (gb ) played an equal, if not more, crucial role compared to the variables for J. This was primarily caused by the indirect influence of gb through the EB equation rather than its direct impact on convective CO2 exchange on the leaf. Although the MLR-EB model requires an accurate estimation of gb , it can potentially reduce uncertainties and enhance applicability in photosynthesis assessment when gas exchange measurements are unavailable.

Water availability and atmospheric dryness controls on spaceborne sun-induced chlorophyll fluorescence yield

Climate change is amplifying the duration, frequency, and intensity of droughts, harming global ecosystems. During droughts, plants can close their stomata to save water, at the expense of a reduced carboxylation rate. When in a carboxylation-limited regime, plants benefit from an increase in water availability, as it increases their photosynthetic rate. The sun-induced chlorophyll fluorescence (SIF) signal, measurable from satellites, is mechanistically linked to this rate. Like canopy photosynthesis, SIF carries an imprint from the available irradiation (PAR) as well as the canopy structure and the efficiency of the photosynthesis at the photosystem level. Normalizing the global TROPOMI SIF observations with TROPOMI reflectance and MODIS Normalised Difference Vegetation Index (NDVI) data, we extracted the fluorescence quantum yield (ϕF), which lab-scale experiments have found to be linked to the photosynthetic electron transport. Plant physiologists have long proved the photosynthetic electron transport to be sensitive to plant water status. Here, the plant water status is controlled by the soil moisture (SM) and the vapour pressure deficit (VPD). Combining data from the TROPOMI, AIRS and SMAP satellite sensors, this study describes how SM and VPD control the ϕF at the global scale. We identify a VPD range (VPD<1.5 kPa) in which the ϕF is mainly controlled by VPD, and another (VPD>1.5 kPa) in which the ϕF is co-regulated by SM and VPD. The precise values of this range, as well as the magnitude of ϕF values, are modulated by the plant isohydricity. To gain a deeper understanding of the link between ϕF and photosynthetic efficiency at large scale, we used the link between ϕF and data on the canopy conductance (Gs), which were calculated using remote sensing data-driven models. A comparison found that the ϕF-Gs relationship at large scale is in line with the ϕF-Gs relationship described in plant-level studies.

Phenology of Vegetation in Arid Northwest China Based on Sun-Induced Chlorophyll Fluorescence

Phenology of Vegetation in Arid Northwest China Based on Sun-Induced Chlorophyll Fluorescence

Early diagnosis and mechanistic understanding of citrus Huanglongbing via sun-induced chlorophyll fluorescence

Citrus Huanglongbing (HLB) is a serious disease that is devastating the citrus industry worldwide. The infection of HLB causes a significant impact on the photosynthesis and growth of citrus but the early diagnosis of HLB is challenging. This study aims to propose an effective method for early HLB diagnosis and mechanistic understanding via sun-induced chlorophyll fluorescence (SIF) during the whole growth period of citrus. SIF signals and net photosynthetic rate (A) of healthy, asymptomatic HLB (aHLB) and symptomatic HLB (sHLB) citrus leaf samples were acquired throughout the whole citrus growth period. Seasonal variations of the peak SIF yield at 687 nm (SIFY687) and 741 nm (SIFY741) and A were investigated. Leaf chlorophyll a content (Ca), chlorophyll b content (Cb), and carotenoids content (Cxc) were also measured by spectrophotometry in the laboratory. It was found that the A of the aHLB was close to that of sHLB, which was significantly different from the healthy leaves at the physiological fruit dropping (PFD) period. The ratio of chlorophyll content (Chl) to Cxc (Chl/Cxc) was evidently different between aHLB and healthy leaves at the late PFD and early fruit expanding (FE) stage, which could be a potential biomarker to identify HLB in the incubation period. The classification result showed PFD was the optimal stage for HLB diagnosis and the index based on SIFY741 was recommended (overall accuracy > 90 %). Moreover, HLB had a great influence on the relationship between A and SIF at the PFD stage, and the coefficients of determination (R2) of the univariate linear regression model between A and SIFY687 and SIFY741 decreased from 0.81 and 0.72 to 0.21 and 0.17, respectively. These results demonstrate the HLB effect on the relationship between A and SIF, and that the SIF could be a proxy to citrus photosynthesis and further used to diagnose early HLB in situ.

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 &gt; 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence.

Optical closure in highly absorptive coastal waters: significance of inelastic scattering processes.

In hydrological optics, "optical closure" means consistency between the apparent optical properties (AOPs) determined from radiometric measurements and those derived from radiative transfer modelling based on concurrently measured inherent optical properties (IOPs) and boundary conditions (sea and sky states). Good optical closure not only provides confidence in the data quality but also informs on the adequacy of the radiative transfer parameterization. Achieving optical closure in highly absorptive coastal waters is challenging due to the low signal-to-noise ratio of radiometric measurements and uncertainties in the measurements of IOPs, namely the spectral absorption and backscattering coefficients. Here, we present an optical closure assessment using a comprehensive set of in situ IOPs acquired in highly absorptive coastal waters optically dominated by chromophoric dissolved organic matter (CDOM). The spectral remote sensing reflectance, Rrs(λ), was modeled using the software HydroLight (HL) with measured IOPs and observed boundary conditions. Corresponding in-water in situRrs(λ) was derived from radiometric measurements made with a Compact Optical Profiling System (C-OPS; Biospherical). The assessment revealed that the inclusion of inelastic scattering processes in the model, specifically sun-induced CDOM fluorescence (fDOM) and sun-induced chlorophyll fluorescence (SICF) from Chlorophyll-a ([chl]), significantly improved the optical closure and led to good agreement between measured and modeled Rrs (i.e., for 440 ≤ λ ≤ 710 nm with no inelastic processes: R2=0.90, slope=0.64; with inelastic processes: R2=0.96, slope=0.90). The analysis also indicated that fDOM and SICF contributed a substantial fraction of the green-red wavelength Rrs in these waters. Specifically, fDOM contributed ∼18% of the modeled Rrs in the green region and SICF accounted for ∼20% of the modeled Rrs in the red region. Overall, this study points out the importance of accounting for fDOM in remote sensing applications in CDOM-dominated waters.

The roles of radiative, structural and physiological information of sun-induced chlorophyll fluorescence in predicting gross primary production of a corn crop at various temporal scales

Extensive research suggests that sun-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) have a near-linear relationship, providing a promising avenue for estimating the carbon uptake of ecosystems. However, the factors influencing the relationship are not yet clear. This study examines the roles of SIF's radiative, structural, and physiological information in predicting GPP, based on four years of field observations of a corn canopy at various temporal scales. We quantified SIF's radiative component by measuring the intensity of incident photosynthetically active radiation (iPAR), and separated the structural and physiological components from SIF observations using the fluorescence correction vegetation index (FCVI). Our results show that the R2 values between SIF and GPP, as estimated by linear models, increased from 0.66 at a half-hour resolution to 0.86 at a one-month resolution. In comparison, the product of FCVI and iPAR, representing the non-physiological information of SIF, performed consistently well in predicting GPP with R2>0.84 at various temporal scales, suggesting a limited contribution of the physiological information of SIF for GPP estimation.. The results further reveal that SIF's radiative and structural components positively impacted the SIF-GPP linearity, while the physiological component had a negative impact on the linearity for most cases, changing from 0.6 % to -27.5 %. As for the temporal dependency, the controls of the SIF-GPP relationship moved from radiation at diurnal scales to structure at the seasonal scales. The structural contribution changed from 14.8 % at a half-hour resolution to 92.4 % at a one-month resolution, while the radiative contribution decreased from 118.0 % at a half-hour resolution to 11.7 % at a one-month resolution. This study contributes to enhancing our understanding of the physiological information conveyed by SIF and the factors influencing the temporal dependency of the SIF-GPP relationship.

Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau

Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.

Seasonal and Diurnal Variations in XCO2 Characteristics in China as Observed by OCO‐2/3 Satellites: Effects of Land Cover and Local Meteorology

AbstractMonitoring the dynamics of atmospheric CO2 is crucial for enhancing comprehension of the carbon cycle. Using column‐averaged dry‐air mole fraction of CO2 (XCO2) data collected by the Orbiting Carbon Observatory (OCO)‐2 and OCO‐3 satellites during 2020–2021, this study explored seasonal and diurnal variations in XCO2 characteristics in typical land cover biomes in China, and investigated their relationships with meteorological drivers. Results showed that XCO2 products retrieved by OCO‐2 and OCO‐3 have good agreement with Total Carbon Column Observing Network measurements, with average deviations of 0.8 and 1.2 ppm, respectively. The satellite observations revealed XCO2 hotpots located mainly in central and eastern China, and areas of low XCO2 values in western China, with a seasonal curve that was highest (lowest) in spring (summer). The largest seasonal cycle amplitude (∼9 ppm) of XCO2 was observed in forest areas, highlighting its key role in carbon exchange. Additionally, XCO2 was found to have a near‐sinusoidal diurnal pattern, characterized by rapid decrease in the early morning as photosynthesis resumed after sunrise, as indicated by the sun‐induced chlorophyll fluorescence (SIF), a peak at around midday, and subsequent decrease as SIF increased after mid‐afternoon. Urban regions had the highest diurnal cycle amplitude (∼6 ppm) among biomes. Statistical analyses revealed seasonal shift and nonlinear variation in the relationships between XCO2 and meteorological variables, suggesting that CO2 uptake is influenced by favorable humidity conditions. These relationships also provide insight into the sensitivity and adaptability of XCO2 to meteorological factors in diverse ecosystems such as savanna and grassland.

The relationship between wheat yield and sun-induced chlorophyll fluorescence from continuous measurements over the growing season

Rapid and accurate estimation of crop yield using remote sensing technology could be an important tool for improved global food security. As an effective probe measuring photosynthesis, sun-induced chlorophyll fluorescence (SIF) has potential for predicting crop yield, particularly when SIF measurements are integrated over an extended time period. However, few studies have investigated how temporal scale, vegetation structure, physiology and environmental factors affect crop yield prediction using SIF. Therefore, in this study we evaluate uncertainties in the relationship between SIF and wheat yield, associated with changes in leaf area index (LAI), chlorophyll a and b content (Cab), photosynthetic active radiation (PAR), and the timing of measurements over a range of temporal scales. Wheat field experiments were carried out over two years. LAI, Cab, PAR and canopy SIF were measured at several temporal scales. We systematically compared the performance of SIF parameters [near-infrared canopy SIF normalized by PAR (SIFyNIR), total near-infrared at photosystem level normalized by PAR (SIFyNIR_tot), and normalized difference fluorescence index (NDFI)] and vegetation indices (VIs) [normalized difference vegetation index (NDVI), and NIR reflectance of vegetation (NIRv)] as predictors of yield estimation. Among the SIF parameters, NDFI appeared to be the most sensitive to LAI and Cab. SIFyNIR_tot at the anthesis stage was the best predictor of wheat yield. SIF outperformed VIs for wheat yield estimation during the late growth period. Moreover, as the temporal scale increased (i.e., as the data values were accumulated over longer intervals of time), the relationship between SIFyNIR and wheat yield tended to be more linear. Overall, the uncertainty in the relationship between SIF and yield was affected more by LAI than Cab, and higher PAR produced a stronger and more stable relationship between SIF and wheat yield. Our findings provide empirical support and an example of an approach for using SIF to predict crop yield, as well as elucidation of the mechanisms underlying the relationship between SIF and production.

Empirical insights on the use of sun-induced chlorophyll fluorescence to estimate short-term changes in crop transpiration under controlled water limitation

Knowledge of actual crop transpiration (T) is important for advanced crop management but challenging to obtain due to the large spatial and temporal variation of T. Remote sensing offers various possibilities to assess T dynamics, while particularly sun-induced chlorophyll fluorescence (SIF) has been demonstrated as a sensitive empirical proxy for T. Despite this success, the advancement of the mechanistic understanding of how SIF relates to T dynamics is key for the future development and implementation of robust and reliable SIF-based T products. This study aims to contribute insights by experimentally assessing the sensitivity of several SIF-based T estimation strategies for evolving soil water limitation. We investigated extensive in situ and airborne data acquired during a water limitation experiment in a maize canopy in northern Italy. We evaluated five empirical strategies to integrate SIF in a T modelling framework based on the Penman-Monteith (PM) and the Ball-Berry-Leuning (BBL) concepts. Our results indicate that replacing model parameters sensitive to canopy conductance with SIF results in the best agreement between modelled and measured T under evolving water limitation. Our study contributes expanding existing knowledge with empirical insights on the sensitivity of SIF based T approaches under increasing soil water limitation at short time scales.