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Solar-induced chlorophyll fluorescence (SIF) is widely accepted as a proxy for gross primary productivity (GPP). Among the various SIF measurements, tower-based SIF measurements allow for continuous monitoring of SIF variation at a canopy scale with high temporal resolution, making it suitable for monitoring highly variable plant physiological responses to environmental changes. However, because of the strong and close relationship between SIF and absorbed photosynthetically active radiation (aPAR), it may be difficult to detect the influence of environmental drivers other than light conditions. Among the drivers, atmospheric dryness (vapor pressure deficit, VPD) is projected to increase as drought becomes more frequent and severe in the future, negatively impacting plants. In this study, we evaluated the tower-based high-frequency SIF measurement as a tool for detecting plant response to highly variable VPD. The study was performed in a mixed temperate forest in Virginia, USA, where a 40-m-tall flux tower has been measuring gas and energy exchanges and ancillary environmental drivers, and the Fluospec 2 system has been measuring SIF. We show that a proper definition of light availability to vegetation can reproduce SIF response to changing VPD that is comparable to GPP response as estimated from eddy covariance measurement: GPP decreased with rising VPD regardless of how aPAR was defined, whereas SIF decreased only when aPAR was defined as the PAR absorbed by chlorophyll (aPARchl) or simulated by a model (Soil Canopy Observation, Photochemistry and Energy fluxes, SCOPE). We simulated the effect of VPD on SIF with two different simulation modes of fluorescence emission representing contrasting moisture conditions, ‘Moderate’ and ‘Soil Moisture (SM) Stress’ modes. The decreasing SIF to rising VPD was only found in the SM Stress mode, implying that the SIF-VPD relationship depends on soil moisture conditions. Furthermore, we observed a similar response of SIF to VPD at hourly and daily scales, indicating that satellite measurements can be used to study the effects of environmental drivers other than light conditions. Finally, the definition of aPAR emphasizes the importance of canopy structure research to interpret remote sensing observations properly.

Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables.

Objective: The value of solar-induced chlorophyll fluorescence (SIF) is of great significance for characterizing the growth status of crops and early diagnosis of vegetation stress. At present, a large range of SIF values are retrieved by remote sensing technology. However, due to the limitations of low spatial resolution and sparse sampling, it is difficult to meet the application of spatial scales below the provincial level. To solve the above problems, this paper proposes a SIF downscaling model construction method based on random forest (RF) algorithm. Methods: First, multi-source remote sensing data are fused with orbiting carbon observation-2 (OCO-2) SIF, and the nonlinear relationship with characteristic variables is established under the same scale of SIF. Based on the assumption that the spatial scale relationship is constant, 500 mx500 m spatial resolution SIF data are inversely performed by RF algorithm. Results: The coefficients of determination, mean absolute error and root mean squared error of the prediction model reached 0.72, 0.24 mW·m-2 ·nm-1 ·sr-1 and 0.33 mW·m-2 ·nm-1 ·sr-1, respectively, indicating the effectiveness of this method. Then, based on the SIF data after the model downscaling, the spatial correlation analysis is carried out with the enhanced vegetation index, normalized difference vegetation index, and the new OCO-2 SIF data, and verified with the SIF of the Chinese carbon dioxide observation satellite mission and the ground SIF station data. The results show good correlation. In addition, the time series analysis of the downscaled SIF in Henan Province, shows that the downscaled products can better capture vegetation change information. Conclusions: The RF algorithm can perform SIF products with 500 mx500 m spatial resolution well by integrating multi-source data. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm ( S I F T O C _ 760 ) was downscaled to the broadband photosystem-level SIF for photosystem II ( S I F T O T _ F U L L _ P S I I ). Our results show that GPP can be accurately estimated using a multi-linear model with S I F T O T _ F U L L _ P S I I and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method ( G P P E C ) to GPP estimated using S I F T O T _ F U L L _ P S I I and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using S I F T O T _ F U L L _ P S I I and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μ mol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μ mol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using S I F T O T _ F U L L _ P S I I and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions. [ABSTRACT FROM AUTHOR]

Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information. [ABSTRACT FROM AUTHOR]

Effective yield forecasting is a key strategy for adaptation when facing food loss to climate variability. Currently, solar-induced chlorophyll fluorescence (SIF) is an emerging remote-sensing index owing to its high relevance to plant photosynthesis, and sensitivity to drought. Despite many studies have focused on drought monitoring and production assessment by SIF, little puts it into practice for in-season yield prediction. In this study, we combined multi-source satellite and meteorological data, especially coupling with subseasonal-to-seasonal (S2S) dynamic atmospheric prediction climate model (IAP-CAS FGOALS-f2), with an addition of SIF, to predict maize yields in the U.S. Corn Belt, based on the developed machine learning dynamical hybrid model (MHCF). By comparison, we found that SIF performed well in the correlation analysis with yield, with average correlations up to 0.719 in August. Then we utilized different algorithms, different models (S2S data for MHCF, climate data for the Benchmark), and different input combinations to train and predict maize yields. All four algorithms using SIF significantly improved prediction performance. S2S + VIs + SIF combination (FGOALS-f2、NDVI、EVI、SIF) can achieve the best performance, while the XGBoost algorithm reached 0.897 of R2. With the best combination, it can achieve 4 months before maize harvest (with R2 value of 0.85, and RMSE < 13 bu/acre). In 2012, the year had a severe drought, although predictive capability decreased in all the predictions, the models with SIF still maintained robust and improved the prediction (improved R2 by 5.92%, and RMSE decreased by 18.08% of XGBoost). According to the study, it can be expected, the combination of MHCF and SIF will play a greater role in subseasonal yield prediction. We also provide an operational proposition of hybrid yield forecasting method to fully integrating climate prediction and machine learning for early notice of crop production losses. [ABSTRACT FROM AUTHOR]

Remotely sensed solar-induced chlorophyll fluorescence "SIF" is applicable as an indicator of changing photosynthetic activity in terrestrial ecosystems. The vegetation of Borneo has previously been affected by drought and fire during El Niño events. Changes in satellite-based SIF data during an El Niño event in Borneo in 2015 were examined using three satellites-GOSAT, GOME-2, and OCO-2-covering the whole island and its southern and northern areas, respectively. Relationships between environmental factors and vegetation damage, precipitation, fire incidence, vegetation indices, and gross primary production "GPP", which were determined using machine-learning methods, were also examined for the period 2007-2018. SIF tended to be low in dry seasons, even in normal years, possibly because of increased drought stress and / or a higher incidence of fires with less precipitation. During the dry season of 2015, there were significant reductions in SIF in southern Borneo where fires were frequent. Other vegetation indices and GPP were also lower. Serious drought conditions with frequent fires during the El Niño event might have caused ecological degradation throughout Borneo, with a significant decrease in SIF. [ABSTRACT FROM AUTHOR]

Photosynthesis in vegetation is one of the key processes in maintaining regional ecological balance and climate stability, and it is of significant importance for understanding the health of regional ecosystems and addressing climate change. Based on 2001–2021 Global OCO-2 Solar-Induced Fluorescence (GOSIF) dataset, this study analyzed spatiotemporal variations in Asian vegetation photosynthesis and its response to climate and human activities. Results show the following: (1) From 2001 to 2021, the overall photosynthetic activity of vegetation in the Asian region has shown an upward trend, exhibiting a stable distribution pattern with higher values in the eastern and southern regions and lower values in the central, western, and northern regions. In specific regions such as the Turgen Plateau in northwestern Kazakhstan, Cambodia, Laos, and northeastern Syria, photosynthesis significantly declined. (2) Meteorological factors influencing photosynthesis exhibit differences based on latitude and vertical zones. In low-latitude regions, temperature is the primary driver, while in mid-latitude areas, solar radiation and precipitation are crucial. High-latitude regions are primarily influenced by temperature, and high-altitude areas depend on precipitation and solar radiation. (3) Human activities (56.44%) have a slightly greater impact on the dynamics of Asian vegetation photosynthesis compared to climate change (43.56%). This research deepens our comprehension of the mechanisms behind the fluctuations in Asian vegetation photosynthesis, offering valuable perspectives for initiatives in environmental conservation, sustainability, and climate research. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging of solar-induced chlorophyll fluorescence (SIF) is required for plant phenotyping and stress detection. However, the most accurate instruments for SIF quantification, such as sub-nanometer (≤1-nm full-width at half-maximum, FWHM) airborne hyperspectral imagers, are expensive and uncommon. Previous studies have demonstrated that standard narrow-band hyperspectral imagers (i.e., 4–6-nm FWHM) are more cost-effective and can provide far-red SIF quantified at 760 nm (SIF760), which correlates strongly with precise sub-nanometer resolution measurements. Nevertheless, narrow-band SIF760 quantifications are subject to systematic overestimation owing to the influence of the spectral resolution (SR). In this study, we propose a modelling approach based on the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with the objective of enhancing the accuracy of absolute SIF760 levels derived from standard airborne hyperspectral imagers in practical settings. The performance of the proposed method was evaluated using airborne imagery acquired from two airborne hyperspectral imagers (FWHM ≤ 0.2-nm and 5.8-nm) flown in tandem on board an aircraft that collected data from two different wheat and maize phenotyping trials. Leaf biophysical and biochemical traits were first estimated from airborne narrow-band reflectance imagery and subsequently used as SCOPE model inputs to simulate a range of top-of-canopy (TOC) radiance and SIF spectra at 1-nm FWHM. The SCOPE simulated radiance spectra were then convolved to match the spectral configuration of the narrow-band imager to compute the 5.8-nm FWHM SIF760. A site-specific model was constructed by employing the convolved 5.8-nm SR SIF760 as the independent variable and the 1-nm SR SIF760 directly simulated by SCOPE as the dependent variable. When applied to the airborne dataset, the estimated SIF760 at 1-nm SR from the standard narrow-band hyperspectral imager matched the reference sub-nanometer quantified SIF760 with root mean square error (RMSE) less than 0.5 mW/m2/nm/sr, yielding R2 = 0.93–0.95 from the two experiments. These results suggest that the proposed modelling approach enables the interpretation of SIF760 quantified using standard hyperspectral imagers of 4–6 nm FWHM for stress detection and plant physiological condition assessment.

Remote sensing observations and artificial intelligence algorithms have emerged as key components for crop yield estimation at various scales during the past decades. However, the utilization of multi-source satellite data and machine learning for estimating aggregated crop yield at the regional level in Europe has been only scarcely explored. Our study aims to bridge this research gap by focusing on the district-level spring barley yield estimation in southern Sweden from 2017 to 2022. We developed an estimation method with the random forest (RF) approach using four satellite-derived products along with two climate variables. These variables were used individually and in combinations as inputs for the RF approach. The results showed that vegetation indices (VIs) outperformed solar-induced chlorophyll fluorescence (SIF) in barley yield estimation, while combining VIs and SIF variables achieved the highest model performance (R2 = 0.77, RMSE = 488 kg/ha). The inclusion of climate variables generally had little added contributions to the model performance. Importantly, barley yield prediction could be achieved two months prior to harvest, using monthly VIs and SIF data from April and May. Our study demonstrated the feasibility of using freely accessible satellite data and the machine learning approach for estimating crop yield at the pan-European regional level. We expect that our proposed methodology can be extended to different crop types and regional-scale crop yield estimation in Europe, benefiting national and local authorities in making agricultural productivity decisions.

Solar-induced chlorophyll fluorescence (SIF) is the release of plant energy during photosynthesis, which is significantly superior to the vegetation index in the characterization of vegetation growth. However, the existing satellite retrieved SIF data have the problems of low spatial resolution and spatial discontinuity. To solve these problems, this paper proposes multiple parameters downscaling method that considers the structural and physiological characteristics of SIF. Multiple linear regression (MLR), random forest (RF), and convolutional neural network (CNN) models were used to construct a downscaling model for the TROPOspheric Monitoring Instrument (TROPOMI) Enhanced SIF (eSIF) data. The theory of spatial scale invariance was applied to invert the 500 m spatial resolution SIF data products for Henan Province from 2012 to 2021 using Moderate-resolution Imaging Spectroradiometer (MODIS) data. The evaluation metrics for assessing downscaling accuracy include the determination coefficient (R²), mean absolute error (MAE), and root mean squared error (RMSE). The experimental results demonstrate that the RF model outperforms others, achieving R², MAE, and RMSE values of 0.935, 0.041mW/m²/nm²/sr, and 0.061mW/m²/nm²/sr, respectively. These results successfully meet the downscaling requirements. The downscaling data products have better fitting effect with eSIF and new Global 'OCO-20 SIF (GOSIF) data both in time and space. The correlation between downscaling SIF data and winter wheat yield is significantly better than that of GOSIF data products and shows strong correlation with Gross Primary Productivity (GPP). By considering the structural and physiological characteristics of SIF, the RF algorithm can effectively retrieve reliable 500 m spatial resolution SIF data, this provides methodological support for the application of SIF data at higher spatial scales. [ABSTRACT FROM AUTHOR]

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI's capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes. [ABSTRACT FROM AUTHOR]

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μ m o l   C O 2 · m − 2 · s − 1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μ m o l   C O 2 · m − 2 · s − 1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI. [ABSTRACT FROM AUTHOR]

Increased frequency and severity of chilling damage events pose potential risks to crop performance and productivity due to climate change. Accurate and real‐time access to chilling damage is important for crop growth and yield stability based on field's actual environment. To precisely identify regional chilling events and evaluate the impacts on crops, this study presents a model to estimate field air temperature in view of field crop situations. Land surface temperature, enhanced vegetation index, solar‐induced chlorophyll fluorescence and solar declination were involved in the model. With field simultaneous continuous monitoring and multisource fused remote sensing data, the model was calibrated and validated in Jiefangzha Irrigation Area (JIA) and Changchun City (CC) in North China, accompanied by the determination coefficient ≥0.756, root mean square error ≤0.782°C, relative error ≤0.041 and consistency index ≥0.902. Meanwhile, sensitivities of the model factors were determined through path analysis, where the factors performed according to the order solar‐induced chlorophyll fluorescence >solar declination >land surface temperature > enhanced vegetation index. Using the validated model, chilling damage to maize was further detected in JIA and CC from 2010 to 2020. Results showed that the severity of chilling damage was greater in CC than in JIA, along with the sterile‐type occurring three events in JIA and seven in CC, while the delayed‐type only twice in JIA in 2012 and 2016, but five times in CC in 2013, 2014, 2016, 2017 and 2019, respectively, being consistent with local statistics. In response to chilling damage, enhanced vegetation index and solar‐induced chlorophyll fluorescence demonstrated the negative chilling effects on greenness and light use efficiency for fluorescence. Serious yield losses were caused, with yield‐reducing by 5.00% (Dehui, 2013), 19.00% (Jiutai, 2014), 21.65% (Suburban district, 2016), 8.83% (Shuangyang, 2017) and 2.19% (Jiutai, 2019) in CC. The linear relationship between yield and growing degree days was a bit weakened by chilling damage, with the determination coefficient varying from 0.614 to 0.531. The increasing rate of yield with growing degree days decreased from 20.365 kg/(°C·d) in non‐chilling damage years to 9.670 kg/(°C·d) in chilling damage years. These findings indicate that the presented model is especially adaptive for agricultural field environments, enabling rapid precision detection of chilling damage on crops at regional scales. It will provide references for gauging the impact of chilling damage on crops, finding efficient solutions to the stress and ensuring sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is strongly correlated with gross primary productivity (GPP). Satellite-observed canopy SIF (SIFobs) captures only a part of the total leaf-emitted SIF (SIFtotal); therefore, SIFobs may hinder the interpretation of the physiological mechanism for GPP estimation. Furthermore, there are still significant discrepancies in the estimated SIFobs escape ratio (fesc) from the canopy to the leaf level with current methods. Here, we selected several vegetation canopy variables and downscaled SIFobs based on the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model from the canopy to the leaf level using machine learning (ML) algorithms and then applied our method to the TROPOspheric Monitoring Instrument (TROPOMI) near-infrared (NIR) SIFobs. The results showed that simulating the fesc with SIFobs, TROPOMI NIR reflectance, and the fraction of photosynthetically active radiation (FPAR) avoided the effects of different sun-sensor geometry conditions introduced by different sensors and was more suitable for satellite-observed SIFobs downscaling. Our downscaled SIFtotal also correlated well with the flux site GPP in areas with sparse vegetation types. SIFtotal better reflected the photosynthetic differences among vegetation types and showed an enhanced relationship with absorbed photosynthetically active radiation (APAR) compared with SIFobs. We provide an efficient canopy-to-leaf SIFobs downscaling method improved SIFtotal and GPP estimation, and our results also demonstrated the potential for using SIFobs as vegetation information in sparse coverage areas.

Evaluation and quantification of vegetation photosynthetic activity (VPA) can provide crucial information for regional ecological environment management, especially in fragile ecosystems, such as the Yellow River Basin (YRB). In this research, MODIS products, solar-induced chlorophyll fluorescence (GOSIF), ChinaFLUX GPP and meteorological datasets were employed to explore the spatiotemporal dynamics of land use types, GOSIF and climate factors in the YRB during 2001-2021. Moreover, the partial derivative method was adopted to quantify the contributions of climate change (CC) and human activities (HA) to GOSIF trends. Results shown that: GOSIF data is applicable in China and can be used to evaluate the VPA in the YRB. The area of cropland, forest, built-up land and water bodies exhibited significant increasing trends, with that of grassland and other lands on the opposite. During 2001-2021, GOSIF and precipitation (Pre) exhibited significant increasing trends at a rate of 0.0014 W $\cdot $ m $^{-2}\cdot \mu $ m $^{-1}\cdot $ sr $^{-1}\cdot $ a−1 and 4.85 mm $\cdot $ a−1, respectively. Temperature (Tem) showed a fluctuating and insignificant upward trend at a rate of 0.0232°C $\cdot $ a−1, while solar radiation (Rad) exhibited an insignificant decreasing trend at a rate of 7.13 MJ $\cdot $ m $^{-2}\cdot $ d $^{-1}\cdot $ a−1. The proportion of climate factors with a significant partial correlation coefficient (PCC) with GOSIF follows the order: Pre (43.92%) > Rad (22.10%) > Tem (17.93%). For climatic factors, the area proportion of GOSIF trends dominated by Tem, Pre, and Rad were 24.36%, 29.89%, and 45.75%, respectively, with the dominance areas of Rad and Pre mainly distributed in most upper and middle YRB. The dominant climatic factor in GOSIF trends was generally consistent with the significance of climatic factors in spatial. For CC and HA, HA was the leading factor in GOSIF trends, accounting for 72.02% areas of the YRB. Combined effects of CC and HA promoted vegetation growth significantly in 85.92% areas of the YRB. The decreasing GOSIF in southwestern upper YRB and parts of northern upper YRB and southern middle YRB were attributed to the single effect of HA (1.83%) and combined effects of HA and CC (1.41%). Results of this study will provide an important reference for promoting ecological protection and high-quality development in the YRB.

Drought is a frequent global phenomenon. Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal, has been proven to be an efficient tool for monitoring and assessing gross primary productivity (GPP) and drought. To address the issue of the sparse resolution of satellite-based SIF, researchers have developed different downscaling algorithms. Recently, the most frequently used SIF products had a spatial resolution of 0.05 degrees. However, these spatial resolution SIF data are not conducive to regional agricultural drought monitoring. In this study, we utilized the global 'OCO-2' solar-induced fluorescence (GOSIF) products along with normalized difference vegetation index (NDVI) and land surface temperature (LST) products. With the powerful advantages offered by Google Earth Engine (GEE), we could conveniently acquire the necessary data. Additionally, employing the random forest (RF) method, we successfully acquired downscaled SIF data at an enhanced spatial resolution of 1 km. Using those downscaled SIF results with 1 km resolution, an SIF anomaly index was established and calculated to monitor drought. Results showed that the RF-based downscaled SIF result followed the same trend as the GOSIF value. Subsequently, correlation coefficients between SIF and GPP were calculated. The downscaled SIF demonstrated a higher correlation with GPP from MODIS compared to 0.05-degree GOSIF, with coefficients of 0.74 and 0.68 in May 2018, respectively. Moreover, the SIF anomaly index showed positive correlations with crop yield; the correlation coefficients were 0.93 for wheat and 0.89 for maize. The drought index had a negative correlation with areas affected by drought, with a correlation coefficient of −0.58. Finally, the SIF anomaly index was used to monitor drought from 2001 to 2020 in Henan Province. The 1 km SIF results obtained through the RF-based downscaled method were deemed reliable, thereby establishing the suitability of the SIF anomaly index for drought monitoring at a regional scale. [ABSTRACT FROM AUTHOR]

Grasslands cover approximately one-fourth of the land in the world and play a crucial role in the carbon cycle. Therefore, quantifying the gross primary productivity (GPP) of grasslands is crucial to assess the sustainable development of terrestrial ecosystems. Drought is a widespread and damaging natural disaster worldwide, which introduces uncertainties in estimating GPP. Solar-induced chlorophyll fluorescence (SIF) is considered as an effective indicator of vegetation photosynthesis and provides new opportunities for monitoring vegetation growth under drought conditions. In this study, using downscaled GOME-2 SIF satellite products and focusing on the drought event in the Xilingol grasslands in 2009, the ability of SIF to evaluate the variations in GPP due to drought was explored. The results showed that the anomalies of SIF in July–August exhibited spatiotemporal characteristics similar to drought indicators, indicating the capability of SIF in monitoring drought. Moreover, the determination coefficient (R2) between SIF and GPP reached 0.95, indicating that SIF is a good indicator for estimating GPP. Particularly under drought conditions, the relationship between SIF and GPP (R2 = 0.90) was significantly higher than NDVI and GPP (R2 = 0.62), demonstrating the superior capability of SIF in tracking changes in grassland photosynthesis caused by drought compared to NDVI. Drought reduces the ability of NDVI to monitor GPP but does not affect that of SIF to monitor GPP. Our study provides a new approach for accurately estimating changes in GPP under drought conditions and is of significant importance for assessing the carbon dynamics of ecosystems. [ABSTRACT FROM AUTHOR]

The characteristics of terrestrial droughts are closely linked to simultaneous fluctuations in climatic factors, notably influenced by sea surface temperature (SST). This study explores the response of vegetation photosynthesis, indicated by solar-induced chlorophyll fluorescence (SIF), in India during the summer monsoon period (JJAS) under drought conditions. Notably, statistically significant associations between SST variations in the tropical Indian Ocean and land-based drought responses (precipitation, temperature, soil moisture, and SIF) were observed, which were attributed to atmospheric teleconnections. The positive phases of El Niño and the Indian Ocean Dipole (IOD) significantly impacted SST, triggering severe droughts in India in 2009 and 2015. The results revealed that positive SST anomalies weaken monsoon flow during the onset period, reducing moisture transmission to the Indian subcontinent. In 2009, the precipitation anomaly showed severe drought conditions (<−1.5) primarily in the northwest, central northeast, and west-central subregions, respectively, with soil moisture deficit and reduced photosynthetic activity (indicated by negative SIF anomalies) mirroring precipitation anomalies. In 2015, moderate to severe drought conditions affected regions primarily in the west-central and peninsular areas, with corresponding consistency in SIF anomalies and soil moisture deficits. These conditions led to decreased photosynthetic rates and negative SIF anomalies observed across India. The findings provide insights for predicting droughts and understanding ecosystem impacts across India amidst rapidly changing climate conditions in the Indian Ocean region. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is strongly physiologically associated with vegetation photosynthesis, and has been broadly employed to monitor gross primary productivity (GPP). However, for some understudied ecosystems, such as the alpine grasslands of the Tibetan Plateau (TP), it is highly uncertain about how SIF performs in tracking GPP. Here, by collecting eddy covariance-derived GPP and climatic data at four grassland sites in the TP, the performances of remotely sensed SIF products (CSIF and GOSIF) in estimating GPP at different time scales (8-day, monthly and growing season) were evaluated and compared against reflectance-based greenness vegetation indices (GVIs) and photochemical reflectance indices (PRIs). Moreover, the SIF-GPP relationship was further examined under different water conditions. The results showed that SIF performed best in tracking GPP variations at all temporal scales compared with GVIs and PRIs. With temporal aggregation, the SIF-GPP relationship was markedly enhanced. We further found that both determination coefficient (R2) values and slopes of the SIF-GPP relationship tended to be lower with decreasing soil water content or increasing vapor pressure deficit. This indicates SIF and GPP will be decoupled with aggravating water stress and their relationship is not universal under different water conditions. Morever, integrating soil water content with SIF improved GPP estimation, underscoring the vital role of water availability in influencing the SIF-GPP relationship. This study can boost our understanding on the variant SIF-GPP relationship and suggests that the regulation effect of water availability need be considered to improve the SIF-derived GPP estimation.

Tropical forest degradation (e.g., anthropogenic disturbances such as selective logging and fires) alters forest structure and function and influences the forest's carbon sink. In this study, we explored structure-function relationships across a variety of degradation levels in the southern Brazilian Amazon by 1) investigating how forest structural properties vary as a function of degradation history using airborne lidar data; 2) assessing the effects of degradation on solar-induced chlorophyll fluorescence (SIF) seasonality using TROPOMI data; and 3) quantifying the contribution of structural variables to SIF using multiple regression models with stepwise selection of lidar metrics. Forest degradation history was obtained through Landsat time-series classification. We found that fire, logging, and time since disturbance were major determinants of forest structure, and that forests affected by fires experienced larger variability in leaf area index (LAI), canopy height and vertical structure relative to logged and intact forests. Moreover, only recently burned forests showed significantly depressed SIF during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Unexpectedly, we found that wet-season SIF was higher in active regenerating forests (~ 4 years after fires or logging) compared with intact forests, despite lower LAI. Our findings help to elucidate the mechanisms of carbon accumulation in anthropogenically disturbed tropical forests and indicate that they can capture large amounts of carbon while recovering.

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy-level is usually not well-represented. Here, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf-level fluorescence yield was simulated by a parametric simplification of the Soil Canopy-Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R 2 > 0.91, root-mean-square error  0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite-observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information.

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (SIFTOC_760) was downscaled to the broadband photosystem-level SIF for photosystem II (SIFTOT_FULL_PSII). Our results show that GPP can be accurately estimated using a multi-linear model with SIFTOT_FULL_PSII and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (GPPEC) to GPP estimated using SIFTOT_FULL_PSII and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using SIFTOT_FULL_PSII and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μmol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μmol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using SIFTOT_FULL_PSII and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions.

A shifting phenology in deciduous broadleaf forests (DBFs) can indicate forest health, resilience, and changes in the face of a rapidly changing climate. The availability of satellite-based solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) promises to add to the understanding of the regional-level DBF phenology that has been developed, for instance, using proxies of gross primary productivity (GPP) from the Moderate Imaging Spectroradiometer (MODIS). It is unclear how OCO-2 and MODIS metrics compare in terms of capturing intra-annual variations and benchmarking DBF seasonality, thus necessitating a comparison. In this study, spatiotemporally matched OCO-2 SIF metrics (at footprint level) and corresponding MODIS GPP, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) products within a temperate DBF were used to compare the phenology captured by the productivity metrics. Additionally, an estimate of the SIF yield (SIFy), derived from OCO-2 SIF measurements, and a MODIS fraction of photosynthetically active radiation (fPAR) were tested. An examination of the trends and correlations showed relatively few qualitative differences among productivity metrics and environmental variables, but it highlighted a lack of seasonal signal in the calculation of SIFy. However, a seasonality analysis quantitatively showed similar seasonal timings and levels of seasonal production in and out of the growing season between SIF and GPP. In contrast, NDVI seasonality was least comparable to that of SIF and GPP, with senescence occurring approximately one month apart. Taken together, we conclude that satellite-based SIF and GPP (and EVI to a smaller degree) provide the most similar measurements of forest function, while NDVI is not sensitive to the same changes. In this regard, phenological metrics calculated with satellite-based SIF, along with those calculated with GPP and EVI from MODIS, can enhance our current understanding of deciduous forest structures and functions and provide additional information over NDVI. We recommend that future studies consider metrics other than NDVI for phenology analyses. [ABSTRACT FROM AUTHOR]

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

Satellite-based solar-induced chlorophyll fluorescence (SIF) provides a direct way of monitoring the photosynthesis 10 of vegetation globally. Global Ozone Monitoring Experiment-2A (GOME-2A) SIF product has become the most popular SIF dataset given its capacity for global coverage since 2007. However, serious temporal degradation of the GOME-2A instrument is a problem, and no temporally consistent GOME-2A SIF products are yet available. In this paper, the GOME-2A instrument's temporal degradation was first calibrated using a pseudo-invariant method, which revealed 16.21 % degradation of the GOME- 2A radiance at the near-infrared (NIR) band from 2007 to 2021. Based on the calibration results, the temporal degradation of 15 the GOME-2A radiance spectra was successfully corrected by using a fitted quadratic polynomial function whose determination coefficient (R2) is 0.851. Next, a data-driven algorithm was applied for SIF retrieval at the 735-758 nm window. Besides, a photosynthetically active radiation (PAR)-based upscaling model was employed to upscale the instantaneous clear-sky observations to monthly average values to compensate for the changes in weather conditions. Accordingly, a global GOME-2A SIF dataset (TCSIF) with correction of temporal degradation was successfully generated from 2007 to 2021, and 20 the spatiotemporal pattern of global SIF was then investigated. Corresponding trend maps of the global temporally consistent GOME-2A SIF showed that 62.91 % of vegetated regions underwent an increase in SIF, and the global annual averaged SIF exhibited a trend of increasing by 0.70 % yr-1 during the 2007-2021 period. The TCSIF dataset is available at https://doi.org/10.5281/zenodo.8242928 (Zou et al., 2023). [ABSTRACT FROM AUTHOR]

Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate. However, the effects of different intensities of extreme climatic events on vegetation phenology remain poorly understood. Using a long-term solar-induced chlorophyll fluorescence dataset, we investigated the response of vegetation phenology to extreme temperatures and precipitation events of different intensities across the Tibetan Plateau (TP) from 2000 to 2018. We found that the effect of maximum temperature exposure days (TxED) and minimum temperature exposure days (TnED) on the start of the growing season (SOS) was initially delayed and shifted to advance along the increasing temperature gradients. However, the response of the end of the growing season (EOS) to TxED and TnED shifted from an advance to a delay with increasing temperature gradients until the temperature thresholds were reached, above which thresholds produced an unfavorable response to vegetation growth and brought the EOS to an early end. The corresponding maximum and minimum temperature thresholds were 10.12 and 2.54°C, respectively. In contrast, cumulative precipitation (CP) was more likely to advance SOS and delay EOS as the precipitation gradient increased, but the advance of SOS is gradually weakening. Four vegetation types (i.e., forest, shrubland, meadow, and steppe) showed similar trends in response to different climates, but the optimal climatic conditions varied between the vegetation types. Generally, meadow and steppe had lower optimal temperatures and precipitation than forest and shrubland. These findings revealed the divergent responses of vegetation phenology to extreme climate events of different intensities, implying that the SOS will continue to advance with warming, whereas the EOS may undergo a partial transformation from delayed areas to advanced areas with continued warming. [ABSTRACT FROM AUTHOR]

The ecology of the Three River Headwaters Region (TRHR) is related to the long-term sustainable development of Qinghai Province and the whole of China. The change in chlorophyll fluorescence is an important index to measure the ecological environment. Therefore, it is of great significance to study the spatial and temporal distributions of Solar-induced chlorophyll fluorescence (SIF)and the related influencing factors in the TRHR. In this study, a high-resolution SIF dataset (2001–2020) was selected to be averaged on a time scale of years and months to investigate the annual and seasonal SIF characteristics, and the influencing climate factors were analyzed in combination with meteorological data by statistical method. The results showed that the SIF values ranged from 0.05 to 0.073 during 2001–2020, with a peak value of 0.073 in 2005 and 2009 and a minimum value of 0.05 in 2002. The averages of SIF values were higher in the source regions of the Yellow River source region (YR) and Langcang (Mekong) River source region (LCR) than in the source region of the Yangtze River source region (YZR). The SIF values of the TRHR in July, August and September were significantly higher than those in other months. The maximum value occurred in August at 0.11, and the minimum value was 0.008 in December. The precipitation had greater effect on the inter-annual variations in SIF. The monthly variation of SIF is influenced by precipitation, temperature and relative humidity. In addition, the influence of human activities and altitude on SIF should not be ignored. The results have certain reference value for protecting vegetation in the TRHR, and provide a reference for other regions to analyze the spatiotemporal changes and influencing factors by using SIF data. [ABSTRACT FROM AUTHOR]

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI’s capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes.

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μmol CO2·m−2·s−1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μmol CO2·m−2·s−1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI.

Solar-induced fluorescence (SIF) provides an exciting opportunity for quantifying terrestrial gross primary production (GPP); however, little attention has been paid to subtropical evergreen forests. Previous research has predominantly concentrated on the linear relationship between SIF and GPP, while more recent studies have gradually reported non-linear relationships and aimed to uncover the underlying mechanism. A year-long synergistic observation of tower-based fluorescence and carbon flux was conducted in the subtropical forest of Southwest China. The SIF and GPP generally exhibited comparable dynamic patterns in subtropical evergreen forests at different scales. However, SIF peaked earlier than GPP in 30-min, daily, and monthly scales. Strongly non-linear relationships existed between SIF and GPP on both 30-min (R2 = 0.42) and daily (R2 = 0.51) scales, which could also extend to different seasons and weather conditions. In particular, the SIF-GPP relationship was stronger on cloudy days than on sunny days, whereas the relationship gradually decoupled as the vapor pressure deficit (VPD) rose. These findings can help to better understand the relationship between canopy-level SIF and GPP in subtropical evergreen forests and emphasize how environmental controls affect the SIF-GPP relationship.

Satellite data and algorithms directly affect the accuracy of phenological estimation; therefore, it is necessary to compare and verify existing phenological models to identify the optimal combination of data and algorithms across the Mongolian Plateau (MP). This study used five phenology fitting algorithms—double logistic (DL) and polynomial fitting (Poly) combined with the dynamic threshold method at thresholds of 35% and 50% (DL-G35, DL-G50, Poly-G35, and Poly-G50) and DL combined with the cumulative curvature extreme value method (DL-CUM)—and two data types—the enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF)—to identify the start (SOS), peak (POS), and end (EOS) of the growing season in alpine meadow (ALM), desert steppe (DRS), forest vegetation (FV), meadow grassland (MEG), and typical grassland (TYG) of the MP. The optimal methods for identifying the SOS, POS, and EOS of typical grassland areas were Poly-G50 (NSE = 0.12, Pbias = 0.22%), DL-G35/50 (NSE = −0.01, Pbias = −0.06%), and Poly-G35 (NSE = 0.02, Pbias = 0.08%), respectively, based on SIF data. The best methods for identifying the SOS, POS, and EOS of desert steppe areas were Poly-G35 (NSE = −0.27, Pbias = −1.49%), Poly-G35/50 (NSE = −0.58, Pbias = −1.39%), and Poly-G35 (NSE = 0.29, Pbias = −0.61%), respectively, based on EVI data. The data source explained most of the differences in phenological estimates. The accuracy of polynomial fitting was significantly greater than that of the DL method, while all methods were better at identifying SOS and POS than they were at identifying EOS. Our findings can help to facilitate the establishment of a phenological estimation system suitable for the Mongolian Plateau and improve the observation methods of vegetation phenology. [ABSTRACT FROM AUTHOR]

A method was put forward to identify the combined heat and drought (CHD) events that occurred in summer and affected spring maize in Liaoning province. The spatial and temporal characteristics of CHD and its effects on maize were evaluated based on daily meteorological data at 52 meteorological stations in Liaoning from 1961 to 2020, as well as agricultural data including details of the maize development periods. The effects of CHD on the photosynthetic capacity of maize were evaluated using SIF remote sensing data from 2001 to 2020. The differences in maize photosynthetic capacity in the summers of 2009 and 2018 were compared in detail. The results show that from 1961 to 2020, the occurrence range, frequency, and severity of summer CHD events increased in Liaoning. CHD events were more frequent in June/July, and higher-intensity CHD events were more frequent in July/August. From 1961 to 2020, CHD events occurred in 69% of the years of reduced meteorological yield, and reduced meteorological yield occurred in 41% of the years with CHD events. Maize solar-induced chlorophyll fluorescence (SIF), an index of photosynthesis, was sensitive to temperature (negatively correlated) and precipitation (positively correlated). The CHD events slowed the increasing SIF from the three-leaf stage to the jointing stage, and they stopped the increasing SIF or decreased it at the tasseling–flowering to silking stages. Therefore, maize photosynthesis may be most sensitive to CHD during the flowering to silking stages, and CHD during the silking to milk stages may have the greatest impact on maize yield. Understanding the effects of CHD on maize growth/yield provides a scientific basis for reducing its negative impacts on maize production. [ABSTRACT FROM AUTHOR]

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

Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three‐year (2018–2021) spaceborne high‐resolution measurements of solar‐induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water‐related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy‐related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy‐limited regime in the north and water‐limited regime in the south. The correlations of GPP with climate variables are supported by other observation‐based products, such as Orbiting Carbon Observatory‐2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra‐annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence, or SIF, is a part of the natural process of photosynthesis. SIF can be measured from space by instruments such as the Orbiting Carbon Observatory-2 (OCO-2), making it a useful proxy for monitoring gross primary production (GPP), which is a critical component of Earth's carbon cycle. The complex physical relationship between SIF and GPP is frequently studied using OCO-2 observations of SIF since they offer the finest spatial resolution available. However, measurement error (noise) and large gaps in spatial coverage limit the use of OCO-2 SIF to highly aggregated scales. To study the relationship between SIF and GPP across varying spatial scales, de-noised and gap-filled (i.e., Level 3) SIF data products are needed. Using a geostatistical methodology called cokriging, which includes kriging as a special case, we develop coSIF: a Level 3 SIF data product at a 0.05-degree resolution. As a natural secondary variable for cokriging, OCO-2 observes column-averaged atmospheric carbon dioxide concentrations (XCO 2 ) simultaneously with SIF. There is a suggested lagged spatio-temporal dependence between SIF and XCO 2 , which we characterize through spatial covariance and cross-covariance functions. Our approach is highly parallelizable and accounts for non-stationary measurement errors in the observations. Importantly, each datum in the resulting coSIF data product is accompanied by a measure of uncertainty. Extant approaches do not provide formal uncertainty quantification, nor do they leverage the cross-dependence with XCO 2 . [ABSTRACT FROM AUTHOR]

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

Shifts in plant phenology regulate ecosystem structure and function, which feeds back to the climate system. However, drivers for the peak of growing season (POS) in seasonal dynamics of terrestrial ecosystems remain unclear. Here, spatial-temporal patterns of POS dynamics were analyzed by solar-induced chlorophyll fluorescence (SIF) and vegetation index in the Northern Hemisphere over the past two decades from 2001 to 2020. Overall, a slow advanced POS was observed in the Northern Hemisphere, while a delayed POS distributed mainly in northeastern North America. Trends of POS were driven by the start of growing season (SOS) rather than pre-POS climate both at hemisphere and biome scale. The effect of SOS on the trends in POS was the strongest in shrublands while the weakest in evergreen broad-leaved forest. These findings highlight the crucial role of biological rhythms rather than climatic factors in exploring seasonal carbon dynamics and global carbon balance. [ABSTRACT FROM AUTHOR]

The Greenhouse gases Observing SATellite 2 (GOSAT-2) was launched in January 2018 as a successor to GOSAT (launched in 2009), the first satellite to specialize in greenhouse gas observations. Compared to the GOSAT sensors, the sensors of GOSAT-2 offer higher performance in most respects. The quality and quantity of data from observations are expected to be improved accordingly. The signal-to-noise ratio (SNR) is better in both the SWIR and TIR bands of TANSO-FTS-2, which is the main sensor of GOSAT-2. This improvement ultimately enhances the accuracy of greenhouse gas concentration analysis. Furthermore, because of the improved SNR in the SWIR band, the northern limit at which data are obtainable in high-latitude regions of the Northern Hemisphere in winter, where observation data have remained unavailable because of weak signal strength, has moved to higher latitudes. As better data are obtained in greater quantities, progress in carbon cycle research for high-latitude regions is anticipated. Moreover, the improvement of SNR in the TIR band is expected to be considerable. Particularly, the resolutions of the vertical concentration distributions of CO2 and CH4 have been improved drastically. The first function introduced for GOSAT-2 that is not in GOSAT is an intelligent pointing mechanism: a cloud area avoidance function using the in-field camera of TANSO-FTS-2. This function can increase the amounts of observation data globally and can improve the accuracy of CO2 emissions estimation and measurements of uptake intensity. The effects are expected to be strong, especially for the tropics because cumulus clouds are the most common cloud type. The intelligent pointing system can avoid the clouds effectively. Another important benefit of TANSO-FTS-2 is that the wavelength range of Band 3 of SWIR has been expanded for measuring carbon monoxide (CO). Because CO originates from combustion, it is used to evaluate some effects of human activities in urban areas and biomass burning in fields. Particularly, black carbon-type aerosols can be measured by the sub-sensor, TANSO-CAI-2, to assess biomass burning along with CO2 and CO by TANSO-FTS-2. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been widely used as a prospective proxy of plant photosynthesis to accurately detect the response of vegetation to climate change and study terrestrial ecosystem carbon cycle. However, the challenge of estimating vegetation gross primary product (GPP) based on SIF observations remains unresolved due to the confounding effects of leaf physiology and canopy structure, especially the vertical heterogeneity of plant attributes. Radiative transfer models are ideally suited to investigate the variability in radiance signals including fluorescence, but previous studies lack vertically layered spectra to validate the model. In this study, we developed a vertically layered hyperspectral system and proposed an innovative framework for assessing vertical characteristics of SIF and improving the estimation of GPP based on SIF in a subtropical evergreen forest. The global sensitivity analysis on Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model with vertical profiles showed that chlorophyll content (Cab), leaf inclination distribution (LIDFa), leaf area index (LAI), and senescent material (Cs) dominate the vertical variations in reflectance (Ref) and SIF. We found that the vertical characteristics of SIF were mainly impacted by these parameters of the adjacent vertical layer, except for the effects of LAI across the vertical layers on the observed understory SIF U , which were approximately equivalent. 7.8 ± 1.7% of the SIF U was transferred to the top of canopy, contributing up to 17% of the SIF observed on the top of canopy (SIF TOC) during the year. Furthermore, our findings suggest that substituting the observed SIF TOC with the total emitted SIF, particularly from the simulated overstory and understory SIF, could enhance the correlation between GPP and SIF with an increased R2 (ΔR2 = 0.09). This study highlights the importance of accounting for the contribution of the layered SIF in the quantification of the total SIF emission and can benefit the GPP estimation based on SIF signals in subtropical evergreen forests with high canopy density. [ABSTRACT FROM AUTHOR]

Global observations of solar‐induced chlorophyll fluorescence (SIF) are available from multiple satellite platforms, and SIF is increasingly used as a proxy for photosynthetic activity and ecosystem productivity. Because the relationship between SIF and gross primary productivity (GPP) depends on a variety of factors including ecosystem type and environmental conditions, it is necessary to study SIF observations across various spatiotemporal scales and ecosystems. To explore how SIF signals relate to productivity over a temperate deciduous forest, we deployed a PhotoSpec spectrometer system at the University of Michigan Biological Station AmeriFlux site (US‐UMB) in the northern Lower Peninsula of Michigan during the 2018 and 2019 growing seasons. We found that SIF correlated with GPP across diurnal and seasonal cycles (R2 = 0.61 and 0.64 for 90‐min‐ and daily‐averaged data), but that SIF signals were more strongly related to downwelling radiation than GPP (R2 = 0.91 for daily‐averaged data). The dependence of SIF on radiation obscured the impact of intraseasonal drought in the SIF timeseries, but drought stress was apparent as a decrease in relative SIF, which exhibited a stronger correlation with GPP (R2 = 0.56) than other remotely sensed data over the drought period. These results highlight the potential of SIF for detecting stress‐induced losses in forest productivity. Additionally, we found that the red:far‐red SIF ratio did not exhibit a response to water stress‐induced losses in productivity, but was largely driven by seasonal and interannual changes in canopy structure, as well as by synoptic changes in downwelling radiation. Plain Language Summary: Satellite measurements of solar‐induced chlorophyll fluorescence (SIF), a faint light signal emitted from vegetation during photosynthesis, are increasingly being used to estimate ecosystem productivity and carbon uptake. To accurately do so requires a robust understanding of how the relationship between SIF and plant productivity changes over time, in response to environmental stressors, and across different ecosystems. To better understand SIF signals and how they relate to carbon uptake over a temperate deciduous forest, we used a high‐precision spectrometer system to observe SIF signals at an AmeriFlux site (US‐UMB) in the northern Lower Peninsula of Michigan. While the shared dependence of SIF and ecosystem productivity on sunlight lead to strong daily and seasonal correlations, we found that SIF signals were more closely tied to the amount of incoming sunlight than to ecosystem productivity. Despite the stronger dependence of SIF on sunlight, we show that drought conditions lead to a lower SIF relative to the total light signal. Lastly, we show that the observation of SIF at multiple wavelengths may provide additional information on seasonal and interannual changes in canopy structure. Our results demonstrate the value and limitations in using SIF to assess carbon dynamics over temperate deciduous forest ecosystems. Key Points: Solar‐induced chlorophyll fluorescence above a temperate deciduous forest is more strongly tied to radiation than to productivityRelative solar‐induced fluorescence signals track water stress‐induced summer losses in productivity better than absolute fluorescenceThe ratio of red to far‐red solar‐induced fluorescence is sensitive to phenological changes in canopy structure and downwelling radiation [ABSTRACT FROM AUTHOR]

Background and aims: Solar-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis and can sensitively reflect the growth and health of vegetation. Using the advantages of SIF in photosynthetic physiological diagnosis, this study carried out a collaborative study of SIF, land attributes and image reflectance spectra to estimate soil organic carbon (SOC) content in typical agricultural areas of the Qinghai-Tibet plateau (QTP). Methods: The spectral reflectance (R), first derivative of reflectance (FDR), second derivative of reflectance (SDR) of spectral band of Landsat 8 Operational Land Imager (OLI) data were selected together with land attributes (i.e. elevation, slope, soil temperature, and soil moisture content) and SIF index and vegetation indices to establish the SOC content estimation models using the random forest (RF), back propagation neural network (BPNN) and partial least squares regression (PLSR), respectively. Results: SIF index can significantly improve the SOC content estimation compared to the vegetation indices. The accuracy of the BPNN model established by combining SIF index with the FDR of Landsat 8 OLI data and land attributes was the highest (R2 = 0.977, RMSEC = 2.069 g·kg− 1, MAE = 0.945 g·kg− 1, RPD = 3.970, d-factor = 0.010). Conclusion: This study confirmed the good effect of BPNN model driven by SIF index, land attributes, and Landsat 8 OLI data on the estimation of SOC content, which can provide a new way for the accurate estimation of the soil internal components in the agricultural areas. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) substantially influences the land climate conditions through the co-variability of multiple climate variables, which in turn affect the structural and functional characteristics of terrestrial vegetation. Our study explored the varying responses of vegetation photosynthesis in India to the SST variations in the tropical Indian Ocean during the summer monsoon. To characterise the terrestrial photosynthetic activity, we used solar-induced chlorophyll fluorescence (SIF). Our results demonstrated a significant negative SST-SIF relationship during the onset phase of the summer monsoon: the SIF anomalies in the northern and central Indian regions decrease when strong warm SST anomalies persist in the tropical Indian Ocean. Further, SIF anomalies increase with cold anomalies of SST. However, the negative SST anomalies in the tropical Indian Ocean are less impactful on SIF anomalies relative to the positive SST anomalies. The observed statistically significant SST–SIF link is feasible through atmospheric teleconnections. During monsoon onset, positive SST anomalies in the tropical Indian Ocean favour weakened monsoon flow, decreasing moisture transport from the ocean to the Indian mainland. The resultant water deficiency, along with the high air temperature, created a stress condition and reduced the photosynthetic rate, thus demonstrating negative SIF anomalies across India. Conversely, negative SST anomalies strengthened monsoon winds in the onset period and increased moisture availability across India. Negative air temperature anomalies also dampen water stress conditions and increased photosynthetic activity, resulting in positive SIF anomalies. The identified SST-SIF relationship would be beneficial to generate a simple framework that aids in the detection of the probable impact on vegetation growth across India associated with the rapidly varying climate conditions in the Indian Ocean. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) is a key parameter connecting light absorption and carbon fixation and understanding the relationship between fluorescence and photosynthesis. Based on the absorbed energy dissipation pathways, LUE can be divided into light use efficiency of photosynthetic (LUEp) and light use efficiency of fluorescence (LUEf). The relationship between LUEp and LUEf of poplar and cork oak and the differences between species were explored in this study, to better understand the mechanism links between solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) in deciduous broadleaf forests. In this study, the poplar (Populus alba x Populus gllandulosa) and cork oak (Quercus variabilis BI.) seedlings were used as the objects to measure the divergences in SIF and net photosynthetic rate (Pn) under the same soil moisture and nutrient conditions, to explore the responses of LUEp/LUEf to photosynthetically active radiation (PAR). The results showed that, (1) SIF was driven by incident PAR, SIF and PAR were significantly positively correlated. The coefficient of determination (R²) of poplar and cork oak were 0.965 and 0.973, respectively, and LUEf was higher in poplar. Pn gradually increased with increasing PAR. The light saturation point (LSP) of poplar was significantly higher than that of cork oak, which were 998.59 and 674 μmolCO2·m-2·s-1, respectively. (2) Pn first increased with the increasing SIF, and then the Pn of poplar was gradually saturated, while that of cork oak continued to slowly increase. (3) LUEp and LUEf showed significant negative linear correlation, and LUEp/LUEf decreased exponentially with increasing PAR, there was no significant difference in the change pattern and amplitude between tree species. During the daytime, with the increase of PAR, LUEf changed slightly, while LUEp decreased significantly, and the proportion of light energy allocation to carbon assimilation decreased. Therefore, Pn saturated under high light intensity, SIF and GPP showed exponential correlation in intraday scale, and the exponential relationship was consistent across tree species. [ABSTRACT FROM AUTHOR]

Vegetation phenology is a crucial biological indicator for monitoring changes in terrestrial ecosystems and global climate. Currently, there are limitations in using traditional vegetation indices for phenology monitoring (e.g., greenness saturation in high-density vegetation areas). Solar-induced chlorophyll fluorescence (SIF), a novel remote sensing product, has great potential in depicting seasonal vegetation dynamics across various regions with different vegetation covers and latitudes. In this study, based on the GOSIF and MODIS NDVI data from 2001 to 2020, we extracted vegetation phenological parameters in Northeastern China by using Double Logistic (D-L) fitting function and the dynamic threshold method. Then, we analyzed the discrepancy in phenological period and temporal trend derived from SIF and NDVI data at multiple spatiotemporal scales. Furthermore, we explored the response of vegetation phenology to climate change and the persistence of phenological trends (Hurst exponent) in Northeastern China. Generally, there is a significant difference in trends between SIF and NDVI, but with similar spatial patterns of phenology. However, the dates of key phenological parameters are distinct based on SIF and MODIS NDVI data. Specifically, the start of season (SOS) of SIF started later (about 10 days), and the end of season (EOS) ended earlier (about 36 days on average). In contrast, the fall attenuation of SIF showed a lag process compared to NDVI. This implies that the actual period of photosynthesis, that is, length of season (LOS), was shorter (by 46 days on average) than the greenness index. The position of peak (POP) is almost the same between them. The great difference in results from SIF and NDVI products indicated that the vegetation indexes seem to overestimate the time of vegetation photosynthesis in Northeastern China. The Hurst exponent identified that the future trend of SOS, EOS, and POP is dominated by weak inverse sustainability, indicating that the future trend may be opposite to the past. The future trend of LOSSIF and LOSNDVI are opposite; the former is dominated by weak inverse sustainability, and the latter is mainly weak positive sustainability. In addition, we speculate that the difference between SIF and NDVI phenology is closely related to their different responses to climate. The vegetation phenology estimated by SIF is mainly controlled by temperature, while NDVI is mainly controlled by precipitation and relative humidity. Different phenological periods based on SIF and NDVI showed inconsistent responses to pre-season climate. This may be the cause of the difference in the phenology of SIF and NDVI extraction. Our results imply that canopy structure-based vegetation indices overestimate the photosynthetic cycle, and the SIF product can better track the phenological changes. We conclude that the two data products provide a reference for monitoring the phenology of photosynthesis and vegetation greenness, and the results also have a certain significance for the response of plants to climate change. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) can accurately reflect the photosynthetic capacity of vegetation and information on the physiological status of vegetation, which is of great research significance and application value. However, the low resolution of the solar-induced chlorophyll fluorescence product of the inverse performance makes it difficult to perform fine-scale studies. Therefore, concerning the above problem, this study proposes a random-forest-based downscaling method based on random forest. We used MODIS reflectance data to analyze GOME-2 SIF data at 0.5° resolution over the Chinese mainland in 2011 and created a monthly SIF product at 0.05° resolution for the Chinese mainland through downscaling. Then we performed a veracity check on the downscaled SIF data, analyzed factors (land cover type, climate zone type, and DEM) that could affect its accuracy, and explored the feasibility of using MODIS data in future GOME-2 SIF downscaling studies. The results show that the downscaled SIF is in remarkable agreement with the GOME-2 SIF, with an improved spatial resolution from 0.5° to 0.05°, and that the structural and physiological information of the SIF is well represented in the downscaled SIF, which is essential for assessing global photosynthesis. In addition, in the region with grassland land cover type, temperate grassland climate region, alpine vegetation climate region of Qinghai–Tibet Plateau, as well as the region with high altitude and complex terrain, the accuracy of using MODIS to downscale the GOME-2 SIF data is low. [ABSTRACT FROM AUTHOR]