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Introduction: In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation. Methods: Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF's estimation capabilities using various datasets. Results and Discussion: The results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R²=0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions. Conclusion: Thus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) offers significant potential as a novel approach for quantifying carbon and water cycling in coastal wetland ecosystems across multiple spatial scales. However, the mechanisms governing these biogeochemical processes remain insufficiently understood, largely due to the periodic influence of tidal inundation. In this study, we investigated the effects and underlying mechanisms of meteorological and tidal factors on the relationships between canopy-level solar-induced chlorophyll fluorescence at 760 nm (SIF760) and key ecosystem processes, including gross primary productivity (GPP) and evapotranspiration (ET), in coastal wetlands. These processes are critical components of the ecosystem carbon and water cycles. Our approach involved a comparative analysis of simulations from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with field measurements. The results showed that: (1) simulations of SIF760 improved following observation-based calibration of the fluorescence photosynthesis module in the SCOPE model; (2) under optimal moisture and temperature conditions (VPD 1.2–1.4 kPa and temperatures of 20–23 °C for air, soil, and water), the simulations of GPP, ET, and SIF760 were most accurate, although salinity stress reduced performance. GPP simulations tended to overestimate under drought stress but improved at higher air temperatures (30–32 °C); (3) during tidal inundation, the SIF760-GPP relationship weakened while the SIF760-ET strengthened. The range of significant correlations between SIF760, water levels, and temperature narrowed, with both relationships becoming more complex due to salinity stress. These findings suggest that tidal inundation can alleviate temperature stress on photosynthesis and transpiration; however, it also decreases photosynthetic efficiency and alters radiative transfer processes due to elevated salinity and water levels. These factors are critical considerations when using SIF to monitor GPP and ET dynamics in coastal wetlands. This study demonstrated that the tidal dynamics significantly affected the SIF760-GPP and SIF760-ET relationships, underscoring the necessity of incorporating tidal influences in the application of SIF remote sensing for monitoring GPP and ET dynamics. The results of this study not only contribute to a deeper understanding of the mechanisms linking SIF760 with GPP and ET but also provide new insights into the development and refinement of SIF-based remote sensing for carbon quantification in coastal blue-carbon ecosystems on a large-scale domain. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis, is one of the most serious diseases on wheat yield. It is of great significance to timely and accurately detect the disease, in order to monitor and prevent the wheat stripe rust. The stripe rust can infect the internal physical and chemical characteristics and external morphological structure of wheat. Solar-induced chlorophyll fluorescence (SIF) can be expected for the remote sensing detection of crop stress. The red-band sunlight-induced chlorophyll fluorescence (RSIF) has more information about photosystem II (PSII), thus sensitively representing the photosynthetic physiological state of plants. The SIF escape rate is closely related to the canopy geometry, leaf optical properties, and light energy utilization efficiency of vegetation. In this study, field-measured data was used to invert and calculate the SIF and its escape rate (εCP) at different scales (canopy scale SIFCanopy and photosystem scale SIFPS) in the red and far-red band. The contents of four wheat pigments were obtained to combine the leaf area index (LAI) closely related to vegetation growth. The physiological basis of RSIF escape rate (RεCP) was determined to monitor the wheat stripe rust. Subsequently, the response characteristics of RεCP under stripe rust stress were explored to compare with the SIF and its derived parameters (fluorescence yield ФF, apparent SIF yield SIFy) in the red and far-red light bands, the normalized difference vegetation index (NDVI), the MERIS terrestrial chlorophyll index (MTCI) and the simple ratio vegetation index (SR). In addition, a systematic analysis was performed on the response characteristics to SL under different disease severity (SL) and different chlorophyll contents (Chl). The results indicate that the correlations between nitrogen balance index (NBI), Chl, flavonoids (Flav), anthocyanins (Anth), and LAI and SL all reached the P<0.01 level, among which the correlation between Chl and SL was the highest. The correlations of RεCP with NBI, Chl, Flav, and Anth increased by 29.06% and 31.52% on average, respectively, compared with photosystem-scale RSIF and photosystem-scale far-red band SIF (far-red solarinduced chlorophyll fluorescence, FRSIF). The correlation with LAI increased by 15.63%, compared with the canopy-scale FRSIF. RεCP better reflected the variation in the crop physiology and canopy structure caused by disease stress. RεCP shared the highest correlation with SL, which was 60.87%, 42.31%, 17.46%, 39.62%, 34.55%, 5.71%, 13.85%, and 21.31% higher than those of canopy-scale FRSIF (FRSIFCanopy), photosystem-scale FRSIF (FRSIFPS), photosystem-scale RSIF (RSIFPS), apparent SIF yield in the red light band (RSIFy), fluorescence yield in the red light band (RФF), NDVI, MTCI and SR, respectively. In the mild to moderate (0%45%) disease conditions, the correlation between RεCP and SL increased by an average of 56.34% and 53.97%, respectively, compared with SIF and their derived parameters and vegetation index at the P<0.01 level. RεCP was sensitively responded to the variation in SL, which was better than the rest parameters. RεCP was most sensitive to the wheat stripe rust stress under the low (Chl≤30) and medium to high chlorophyll content (Chl>30). The correlation with SL increased by an average of 42.77% and 43.25%, respectively, compared with SIF and their derived parameters and vegetation index at the P<0.01 level. RεCP can serve as a suitable factor for remote sensing monitoring of wheat stripe rust. RεCP can greatly contribute to disease prevention for better yields. The finding can also provide a strong reference and powerful tool for remote sensing monitoring of crops in agricultural production. RSIF and escape rate can be incorporated into the remote sensing monitoring, in order to greatly improve the detection and monitoring of plant health status. [ABSTRACT FROM AUTHOR]

Flash droughts adversely affect agriculture and ecosystems due to their rapid depletion of soil moisture (SM). However, few studies assessed the impacts of flash droughts on crops, especially in the agricultural regions of southern China. In this study, we investigated flash droughts using crop root zone SM in the main agricultural region of southern China. Additionally, solar-induced chlorophyll fluorescence (SIF) served as a vegetation index to explore the crop response to flash droughts. The results reveal that the SIF exhibited an upward trend from 2001 to 2020 in the study area, indicating the enhanced photosynthetic capacity of crops and subsequent yield improvement. Hotspots of flash drought frequency occurred in the eastern areas of both the upper and lower Yangtze River regions, specifically in areas where the most rapid types of flash droughts were particularly prevalent. The average duration of flash droughts in the southern agricultural region was 6–12 pentads, a sufficiently long duration to significantly hinder crop photosynthesis, resulting in negative SIF standardized anomalies. The area affected by flash droughts in the southern agricultural region presented a downward trend during 2001–2020, with flash droughts of the longest duration in the recent decade, specifically in 2019, 2010, and 2013. The response frequency and time of SIF to flash droughts were >80% and <2 pentads, respectively, indicating that crops in the study area have a high sensitivity to flash droughts. In the northern part of the middle Yangtze River region and the southwestern and southeastern parts of the South China region, the mean values of the standardized anomalies of the SIF were lower than −0.5 during flash droughts, suggesting that crops in these areas were severely affected by flash droughts. During the late summer of 2019, the study area experienced a precipitation shortage coupled with high evapotranspiration capacity. This unfavorable combination of meteorological conditions can quickly lead to a substantial depletion of SM, ultimately triggering flash droughts that can be devastating for crops. Our findings can enhance the understanding of the impacts of flash droughts on crops in agricultural regions, as well as provide early warning signals of flash droughts for farmers to make appropriate mitigation strategies. [ABSTRACT FROM AUTHOR]

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of "carbon neutrality" in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted "U" shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals. [ABSTRACT FROM AUTHOR]

IntroductionIn the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation.MethodsSolar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF’s estimation capabilities using various datasets.Results and DiscussionThe results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R²=0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions.ConclusionThus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management.

Recently the applications of remotely sensed Solar-Induced chlorophyll Fluorescence (SIF) in the study of photosynthesis, stress conditions, and gross primary production have increased significantly. The full SIF spectrum spans over a spectral region of 650 ∼ 850 nm with two characteristic peaks around 685 nm and 740 nm. Over recent decades, many retrieval algorithms have been developed to estimate SIF at Top-Of-Canopy (TOC) using in-situ measurements of solar irradiance and canopy radiance spectra. Although the majority of retrieval methods retrieve SIF at a narrow spectral window, there exists a potential for retrieval of SIF in the full emission spectrum. Moreover, solar irradiance and canopy radiance spectra should ideally be measured at the same time but are usually measured sequentially with a certain time lag, raising potential errors in SIF retrieval. In this study, an enhanced retrieval algorithm of the full SIF spectrum at TOC is proposed. The proposed algorithm attempts to minimize the errors owing to time mismatch in measurements of solar irradiance and canopy radiance spectra. As an improvement to the previous algorithm (advanced Fluorescence Spectrum Reconstruction, aFSR), this proposed algorithm (aFSR-SVE) models the SIF-free contribution with principal components using the singular value decomposition technique. The optimal parameter settings in the forward model were determined for the experimental data collected by spectrometers used in the study. Firstly, the proposed algorithm was used to reconstruct full SIF spectrum for simulated data. The results were compared with known reference SIF values. After achieving satisfying results from simulated data, the proposed algorithm was compared with retrievals from established algorithms using experimental data. The results show improved SIF retrieval accuracy, without the need to simultaneously measure solar irradiance and canopy radiance spectra. The retrieval values comply with the results of previous algorithms in terms of spectral shape, diurnal trend, and temporal variations. The induced errors in SIF retrievals due to non-simultaneous measurements of solar irradiance and canopy radiance spectra were also investigated and the proposed algorithm was found to be less prone to such errors. Hence, the proposed algorithm is an improvement in reconstructing the full SIF spectrum with near-ground measurements. With the help of the proposed algorithm, field measurements using sequential systems and automated measurements of multiple targets can be performed effectively as it relaxes the requirement of concurrent measurement of solar irradiance and canopy radiance spectra. For future work, the applicability of this method can be investigated under more variable illumination conditions, like high cirrus clouds, passing clouds or persistent thin clouds.

Light detection and ranging (LiDAR) offers high-precision, 3D information, and the ability to rapidly acquire data, giving it a significant advantage in timely resource monitoring. Currently, LiDAR is widely utilized in land-cover classification tasks. However, the complexity and uneven distribution of land-cover types in rural and township settings pose additional challenges for fine-scale classification. Although the geometric features of LiDAR can provide valuable insights and have been extensively explored, distinguishing between objects with similar 3D characteristics has considerable room for improvement, particularly in complex scenarios where the introduction of additional attribute information is necessary. To address these challenges, this work proposes the integration of solar-induced chlorophyll fluorescence (SIF) features to assist and optimize LiDAR data for land-cover classification, leveraging the sensitivity of SIF to vegetation physiological characteristics. Moreover, a multi-stage classification strategy is introduced to enhance the utilization of SIF information. The implementation of this approach achieves a maximum classification accuracy of 92.45%, yielding satisfactory results with low computational costs. This outcome validates the feasibility of applying SIF information in land-cover classification. Furthermore, the results obtained through the multi-stage classification strategy demonstrate improvements ranging from 6.65% to 9.12% compared with land-cover classification relying solely on LiDAR, effectively highlighting the optimization role of SIF in enhancing LiDAR-based land-cover classification, particularly in complex rural and township environments. Our approach offers a robust framework for precise and efficient land-cover classification by leveraging the combined strengths of LiDAR and SIF. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Monitoring vegetation photosynthesis in China’s subtropical regions using remote sensing is challenging because of the complex ecosystems and climate variability. Previous studies often pay less attention on the influence of multiple climatic factors on the temporal effects (lag and accumulation) of vegetation photosynthesis, thereby underestimating their impact. This study utilizes a dataset comprising Solar-induced chlorophyll fluorescence (SIF) data (GOSIF product), MODIS Land Cover product (MCD12C1), and various climatic variables. Analytical methods including Theil-Sen Median trend analysis, the Mann-Kendall test, partial correlation analysis, and the optimal parameter-based geographical detector (OPGD) model were employed to explore the temporal dynamics of subtropical vegetation SIF responses to climatic factors and to identify their climate drivers in subtropical China. The study findings indicate that (1) vegetation photosynthesis, as indicated by SIF, exhibited an increasing trend in the majority of Chinese subtropical regions, which constitute over 80 % of the study area, with particularly pronounced enhancements in the southern and central western parts of the Chinese subtropics. (2) Soil moisture primarily exhibits lag effects on SIF, particularly in evergreen needleleaf forests, deciduous broadleaf forests, and mixed forests, whereas temperature does not exhibit significant temporal effects. Solar radiation and vapor pressure deficits impact SIF through both lag and accumulation effects. Under the lag and accumulation effects, the proportion of significant correlations between climatic factors and vegetation SIF increases by 36.71 % ∼ 43.8 %, excluding temperature. (3) Temperature is the dominant factor affecting vegetation SIF, particularly in the evergreen needleleaf forest. Interactions between climatic factors have a significantly stronger influence on SIF than individual factors. Notably, the explanatory power of the vapor pressure deficit increases substantially when it interacts with other factors. Studying the lag and accumulation effects of climatic factors on photosynthesis aids in accurately predicting vegetation responses to climate change, thereby improving the accuracy of global carbon cycle models and guiding the development of carbon sequestration management strategies.

Solar-induced chlorophyll fluorescence (SIF) provides a valuable tool for gross primary production (GPP) monitoring. However, the spatial resolution of satellite SIF products is lower than the kilometer level, which hinders their potential for carbon cycle study at regional scales. This work reconstructed a 0.005° SIF in China during 2019 and 2020 from the 5-km level TROPOMI SIF by a proposed downscaling strategy that corrected the predicted bias when statistics-based machine learning models, such as random forest (RF), were used. Our bias-corrected downscaled SIF (named BCSIF) had an improved capacity of preserving the information of the original TROPOMI SIF than the directly predicted SIF from RF. The BCSIF showed better consistencies with the tower-based SIF than the 0.05° TROPOMI SIF with an averaged R2 increased from 0.590 to 0.798 at two sites since it has a more comparable spatial scale with spectral observations. For the spatial–temporal correlations with FLUXCOM GPP at different biomes in China, BCSIF outperformed the original SIF with the averaged R2 increased from 0.472 to 0.877 due to its reduced noise, also outperformed the near-infrared radiation reflected by vegetation (NIRvP), especially for the savanna type with the R2 increased from 0.828 to 0.889. For the temporal correlations with FLUXCOM GPP, BCSIF gives comparable R2 values as NIRvP in more than half of China (around 65% pixels), not including the needleleaf forest region in the southern Tibetan Plateau and savanna region in Yunnan province where BCSIF greatly outperformed, as well as some alpine meadows regions in Inner Mongolia and Tibetan Plateau where NIRvP outperformed.

Satellite-based solar-induced chlorophyll fluorescence (SIF) has emerged as a valuable tool for monitoring the photosynthetic activity of vegetation at both regional and global scales. Downscaling techniques offer the opportunity to utilize coarse-spatial-resolution SIF products for investigating carbon cycles and ecological processes at finer resolutions. However, the lack of pre-2000 downscaled products and the limited utilization of residual information in existing models pose challenges in fully harnessing the potential of SIF downscaling. In this study, we generated a new monthly SIF product, DSIFRFK*_EA0.05, at a resolution of 0.05° in East Asia from July 1995 to June 2003. Random forest kriging (RFK) was employed, incorporating GOME SIF, AVHRR data, ERA5 climate data, and using the optimal explanatory variables. Compared with other downscaled results, the produced DSIFRFK*_EA0.05 has higher accuracy and more accurate details of SIF distribution with the highest mean R2 value of 0.83 and smallest root mean squared error value of 0.08 mW/m2/nm/sr. The DSIFRFK*_EA0.05 estimates showed a strong correlation with gross primary productivity data from eight flux sites (R2 = 0.73), as well as high correlation coefficient values of 0.73, 0.88, and 0.89 with three other fine-resolution products. This study addresses the gap in statistical downscaling of SIF before 2000 and demonstrates the feasibility of utilizing AVHRR data for fine-resolution SIF prediction. Our developed product offers improved detail compared to the original GOME SIF, thereby enhancing the application of satellite SIF for understanding early carbon cycles and ecological processes at a finer resolution.

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of “carbon neutrality” in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted “U” shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals.

ObjectiveRice blast is considered as the most destructive disease that threatens global rice production and causes severe economic losses worldwide. The detection of rice blast in an early manner plays an important role in resistance breeding and plant protection. At present, most studies on rice blast detection have been devoted to its symptomatic stage, while none of previous studies have used solar-induced chlorophyll fluorescence (SIF) to monitor rice leaf blast (RLB) at early stages. This research was conducted to investigate the early identification of RLB infected leaves based on solar-induced chlorophyll fluorescence at different leaf positions.MethodsGreenhouse experiments and field trials were conducted separately in Nanjing and Nantong in July and August, 2021, in order to record SIF data of the top 1th to 4th leaves of rice plants at jointing and heading stages with an Analytical Spectral Devices (ASD) spectrometer coupled with a FluoWat leaf clip and a halogen lamp. At the same time, the disease severity levels of the measured samples were manually collected according to the GB/T 15790-2009 standard. After the continuous wavelet transform (CWT) of SIF spectra, separability assessment and feature selection were applied to SIF spectra. Wavelet features sensitive to RLB were extracted, and the sensitive features and their identification accuracy of infected leaves for different leaf positions were compared. Finally, RLB identification models were constructed based on linear discriminant analysis (LDA).Results and DiscussionThe results showed that the upward and downward SIF in the far-red region of infected leaves at each leaf position were significantly higher than those of healthy leaves. This may be due to the infection of the fungal pathogen Magnaporthe oryzae, which may have destroyed the chloroplast structure, and ultimately inhibited the primary reaction of photosynthesis. In addition, both the upward and downward SIF in the red region and the far-red region increased with the decrease of leaf position. The sensitive wavelet features varied by leaf position, while most of them were distributed in the steep slope of the SIF spectrum and wavelet scales 3, 4 and 5. The sensitive features of the top 1th leaf were mainly located at 665-680 nm, 755-790 nm and 815-830 nm. For the top 2th leaf, the sensitive features were mainly found at 665-680 nm and 815-830 nm. For the top 3th one, most of the sensitive features lay at 690 nm, 755-790 nm and 815-830 nm, and the sensitive bands around 690 nm were observed. The sensitive features of the top 4th leaf were primarily located at 665-680 nm, 725 nm and 815-830 nm, and the sensitive bands around 725 nm were observed. The wavelet features of the common sensitive region (665-680 nm), not only had physiological significance, but also coincided with the chlorophyll absorption peak that allowed for reasonable spectral interpretation. There were differences in the accuracy of RLB identification models at different leaf positions. Based on the upward and downward SIF, the overall accuracies of the top 1th leaf were separately 70% and 71%, which was higher than other leaf positions. As a result, the top 1th leaf was an ideal indicator leaf to diagnose RLB in the field. The classification accuracy of SIF wavelet features were higher than the original SIF bands. Based on CWT and feature selection, the overall accuracy of the upward and downward optimal features of the top 1th to 4th leaves reached 70.13%、63.70%、64.63%、64.53% and 70.90%、63.12%、62.00%、64.02%, respectively. All of them were higher than the canopy monitoring feature F760, whose overall accuracy was 69.79%, 61.31%, 54.41%, 61.33% and 69.99%, 58.79%, 54.62%, 60.92%, respectively. This may be caused by the differences in physiological states of the top four leaves. In addition to RLB infection, the SIF data of some top 3th and top 4th leaves may also be affected by leaf senescence, while the SIF data of top 1th leaf, the latest unfolding leaf of rice plants was less affected by other physical and chemical parameters. This may explain why the top 1th leaf responded to RLB earlier than other leaves. The results also showed that the common sensitive features of the four leaf positions were also concentrated on the steep slope of the SIF spectrum, with better classification performance around 675 and 815 nm. The classification accuracy of the optimal common features, ↑WF832,3 and ↓WF809,3, reached 69.45%, 62.19%, 60.35%, 63.00% and 69.98%, 62.78%, 60.51%, 61.30% for the top 1th to top 4th leaf positions, respectively. The optimal common features, ↑WF832,3 and ↓WF809,3, were both located in wavelet scale 3 and 800-840nm, which may be related to the destruction of the cell structure in response to Magnaporthe oryzae infection.ConclusionsIn this study, the SIF spectral response to RLB was revealed, and the identification models of the top 1th leaf were found to be most precise among the top four leaves. In addition, the common wavelet features sensitive to RLB, ↑WF832,3 and ↓WF809,3, were extracted with the identification accuracy of 70%. The results proved the potential of CWT and SIF for RLB detection, which can provide important reference and technical support for the early, rapid and non-destructive diagnosis of RLB in the field.

Flash droughts adversely affect agriculture and ecosystems due to their rapid depletion of soil moisture (SM). However, few studies assessed the impacts of flash droughts on crops, especially in the agricultural regions of southern China. In this study, we investigated flash droughts using crop root zone SM in the main agricultural region of southern China. Additionally, solar-induced chlorophyll fluorescence (SIF) served as a vegetation index to explore the crop response to flash droughts. The results reveal that the SIF exhibited an upward trend from 2001 to 2020 in the study area, indicating the enhanced photosynthetic capacity of crops and subsequent yield improvement. Hotspots of flash drought frequency occurred in the eastern areas of both the upper and lower Yangtze River regions, specifically in areas where the most rapid types of flash droughts were particularly prevalent. The average duration of flash droughts in the southern agricultural region was 6–12 pentads, a sufficiently long duration to significantly hinder crop photosynthesis, resulting in negative SIF standardized anomalies. The area affected by flash droughts in the southern agricultural region presented a downward trend during 2001–2020, with flash droughts of the longest duration in the recent decade, specifically in 2019, 2010, and 2013. The response frequency and time of SIF to flash droughts were >80% and

Light detection and ranging (LiDAR) offers high-precision, 3D information, and the ability to rapidly acquire data, giving it a significant advantage in timely resource monitoring. Currently, LiDAR is widely utilized in land-cover classification tasks. However, the complexity and uneven distribution of land-cover types in rural and township settings pose additional challenges for fine-scale classification. Although the geometric features of LiDAR can provide valuable insights and have been extensively explored, distinguishing between objects with similar 3D characteristics has considerable room for improvement, particularly in complex scenarios where the introduction of additional attribute information is necessary. To address these challenges, this work proposes the integration of solar-induced chlorophyll fluorescence (SIF) features to assist and optimize LiDAR data for land-cover classification, leveraging the sensitivity of SIF to vegetation physiological characteristics. Moreover, a multi-stage classification strategy is introduced to enhance the utilization of SIF information. The implementation of this approach achieves a maximum classification accuracy of 92.45%, yielding satisfactory results with low computational costs. This outcome validates the feasibility of applying SIF information in land-cover classification. Furthermore, the results obtained through the multi-stage classification strategy demonstrate improvements ranging from 6.65% to 9.12% compared with land-cover classification relying solely on LiDAR, effectively highlighting the optimization role of SIF in enhancing LiDAR-based land-cover classification, particularly in complex rural and township environments. Our approach offers a robust framework for precise and efficient land-cover classification by leveraging the combined strengths of LiDAR and SIF.

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.

Plant phenology is essential for projecting changes in the ecosystem including growing seasons, plant communities and carbon storage. However, it is still unclear how the start of growing season (SOS) of tropical plants responds to climate change. This study examined SOS of natural rubber (NR) which grows in tropical areas using long-term satellite solar-induced chlorophyll fluorescence (SIF) data and nineteen monthly climate factors. “Phenofit” (a R package), Pearson correlation coefficient, random forest and structural equation modeling were used to extract SOS and find the relationship between SOS and climate factors at landscape scale. SOS mainly occurred during 53.3–69.9 day of the year, with a slight yet non-significant delaying trend (about 0.2 d/y) across more than 76% of the study area over 2001–2021. The sunshine duration (Sund), ground temperature at 20 cm depth and evaporation in June last year (pre-June) were the three climate drivers with substantial effects on SOS. Sund in pre-June played the most crucial role (r > 0.65, p

Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially within mountain grassland ecosystems. In this study, we used three types of high-spatial-resolution SIF datasets (0.05°), coupled with meteorological and soil moisture datasets, to investigate the characteristics of atmospheric, soil, and compound drought types. We centered this investigation on the years spanning 2001–2020 in the Three-River Headwaters Region (TRHR). Our findings indicate that the TRHR experienced a combination of atmospheric drying and soil wetting due to increases in the standardized saturation vapor pressure deficit index (SVPDI) and standardized soil moisture index (SSMI). In the growing season, atmospheric drought was mainly distributed in the southern and eastern parts of the TRHR (reaching 1.7 months/year), while soil drought mainly occurred in the eastern parts of the TRHR (reaching 2 months/year). Compound drought tended to occur in the southern and eastern parts of the TRHR and trended upward during 2001–2020. All three SIF datasets consistently revealed robust photosynthetic activity in the southern and eastern parts of the TRHR, with SIF values generally exceeding 0.2 mW · m−2 · nm−1 · sr−1. Overall, the rise in SIF between 2001 and 2020 corresponds to enhanced greening of TRHR vegetation. Vegetation photosynthesis was found to be limited in July, attributable to a high vapor pressure deficit and low soil moisture. In the response of CSIF data to a drought event, compound drought (SVPDI ≥ 1 and SSMI ≤ −1) caused a decline of up to 14.52% in SIF across the source region of the Yellow River (eastern TRHR), while individual atmospheric drought and soil drought events caused decreases of only 5.06% and 8.88%, respectively. The additional effect of SIF produced by compound drought outweighed that of atmospheric drought as opposed to soil drought, suggesting that soil moisture predominantly governs vegetation growth in the TRHR. The reduction in vegetation photosynthesis capacity commonly occurring in July, characterized by a simultaneously high vapor pressure deficit and low soil moisture, was more pronounced in Yellow River's source region as well. Compound drought conditions more significantly reduce SIF compared to singular drought events. Soil drought evidently played a greater role in vegetation growth stress than atmospheric drought in the TRHR via the additional effects of compound drought. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has a high correlation with Gross Primary Production (GPP). However, studies focusing on the impact of drought on the SIF-GPP relationship have had mixed results at various scales, and the mechanisms controlling the dynamics between photosynthesis and fluorescence emission under water stress are not well understood. We developed a leaf-scale measurement system to perform concurrent measurements of active and passive fluorescence, and gas-exchange rates for winter wheat experiencing a one-month progressive drought. Our results confirmed that: (1) shifts in light energy allocation towards decreasing photochemistry (the quantum yields of photochemical quenching in PSII decreased from 0.42 to 0.21 under intermediate light conditions) and increasing fluorescence emissions (the quantum yields of fluorescence increased to 0.062 from 0.024) as drought progressed enhance the degree of nonlinearity of the SIF-GPP relationship, and (2) SIF alone has a limited capacity to track changes in the photosynthetic status of plants under drought conditions. However, by incorporating the water stress factor into a SIF-based mechanistic photosynthesis model, we show that drought-induced variations in a variety of key photosynthetic parameters, including stomatal conductance and photosynthetic CO2 assimilation, can be accurately estimated using measurements of SIF, photosynthetically active radiation, air temperature, and soil moisture as inputs. Our findings provide the experimental and theoretical foundations necessary for employing SIF mechanistically to estimate plant photosynthetic activity during periods of drought stress. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been found to be a useful indicator of vegetation's gross primary productivity (GPP). However, the directional SIF observations obtained from a canopy only represent a portion of the total fluorescence emitted by all the leaf photosystems because of scattering and reabsorption effects inside the leaves and canopy. Hence, it is crucial to downscale the SIF from canopy level to leaf level by modeling fluorescence escape probability (fesc) for improved comprehension of the relationship between SIF and GPP. Most methods for estimating fesc rely on the assumption of a "black soil background," ignoring soil reflectance and the effect of scattering between soils and leaves, which creates significant uncertainties for sparse canopies. In this study, we added a correction factor considering soil reflectance, which was modeled using the Gaussian process regression algorithm, to the semi-empirical NIRv/FAPAR model and obtained the improved fesc model accounting for soil reflectance (called the fesc_GPR-SR model), which is suitable for near-infrared SIF downscaling. The evaluation results using two simulation datasets from the Soil–Canopy–Observation of Photosynthesis and the Energy Balance (SCOPE) model and the Discrete Anisotropic Radiative Transfer (DART) model showed that the fesc_GPR-SR model outperformed the NIRv/FAPAR model, especially for sparse vegetation, with higher accuracy for estimating fesc (R2 = 0.954 and RMSE = 0.012 for SCOPE simulations; R2 = 0.982 and RMSE = 0.026 for DART simulations) compared with the NIRv/FAPAR model (R2 = 0.866 and RMSE = 0.100 for SCOPE simulations; R2 = 0.984 and RMSE = 0.070 for DART simulations). The evaluation results using in situ observation data from multi-species canopies also suggested that the leaf-level SIF calculated by the fesc_GPR-SR model tracked better with photosynthetic active radiation absorbed by green components (APARgreen) for sparse vegetation (R2 = 0.937, RMSE = 0.656 mW/m2/nm) compared with the NIRv/FAPAR model (R2 = 0.921, RMSE = 0.904 mW/m2/nm). The leaf-level SIF calculated by the fesc_GPR-SR model was less sensitive to observation angles and differences in canopy structure among multiple species. These results emphasize the significance of accounting for soil reflectance in the estimation of fesc and demonstrate that the fesc_GPR-SR model can contribute to further exploring the physiological mechanism between SIF and GPP. [ABSTRACT FROM AUTHOR]

[Objective]Rice blast is considered as the most destructive disease that threatens global rice production and causes severe economic losses worldwide. The detection of rice blast in an early manner plays an important role in resistance breeding and plant protection. At present, most studies on rice blast detection have been devoted to its symptomatic stage, while none of previous studies have used solar-induced chlorophyll fluorescence (SIF) to monitor rice leaf blast (RLB) at early stages. This research was conducted to investigate the early identification of RLB infected leaves based on solar-induced chlorophyll fluorescence at different leaf positions.[Methods]Greenhouse experiments and field trials were conducted separately in Nanjing and Nantong in July and August, 2021, in order to record SIF data of the top 1th to 4th leaves of rice plants at jointing and heading stages with an Analytical Spectral Devices (ASD) spectrometer coupled with a FluoWat leaf clip and a halogen lamp. At the same time, the disease severity levels of the measured samples were manually collected according to the GB/T 15790-2009 standard. After the continuous wavelet transform (CWT) of SIF spectra, separability assessment and feature selection were applied to SIF spectra. Wavelet features sensitive to RLB were extracted, and the sensitive features and their identification accuracy of infected leaves for different leaf positions were compared. Finally, RLB identification models were constructed based on linear discriminant analysis (LDA). [Results and Discussion]The results showed that the upward and downward SIF in the farred region of infected leaves at each leaf position were significantly higher than those of healthy leaves. This may be due to the infection of the fungal pathogen Magnaporthe oryzae, which may have destroyed the chloroplast structure, and ultimately inhibited the primary reaction of photosynthesis. In addition, both the upward and downward SIF in the red region and the far-red region increased with the decrease of leaf position. The sensitive wavelet features varied by leaf position, while most of them were distributed in the steep slope of the SIF spectrum and wavelet scales 3, 4 and 5. The sensitive features of the top 1th leaf were mainly located at 665-680 nm, 755-790 nm and 815-830 nm. For the top 2th leaf, the sensitive features were mainly found at 665-680 nm and 815-830 nm. For the top 3th one, most of the sensitive features lay at 690 nm, 755-790 nm and 815-830 nm, and the sensitive bands around 690 nm were observed. The sensitive features of the top 4th leaf were primarily located at 665-680 nm, 725 nm and 815-830 nm, and the sensitive bands around 725 nm were observed. The wavelet features of the common sensitive region (665-680 nm）, not only had physiological significance, but also coincided with the chlorophyll absorption peak that allowed for reasonable spectral interpretation. There were differences in the accuracy of RLB identification models at different leaf positions. Based on the upward and downward SIF, the overall accuracies of the top 1th leaf were separately 70% and 71%, which was higher than other leaf positions. As a result, the top 1th leaf was an ideal indicator leaf to diagnose RLB in the field. The classification accuracy of SIF wavelet features were higher than the original SIF bands. Based on CWT and feature selection, the overall accuracy of the upward and downward optimal features of the top 1th to 4th leaves reached 70.13%、63.70%、64.63%、64.53% and 70.90%、63.12%、62.00%、64.02%, respectively. All of them were higher than the canopy monitoring feature F760, whose overall accuracy was 69.79%, 61.31%, 54.41%, 61.33% and 69.99%, 58.79%, 54.62%, 60.92%, respectively. This may be caused by the differences in physiological states of the top four leaves. In addition to RLB infection, the SIF data of some top 3th and top 4th leaves may also be affected by leaf senescence, while the SIF data of top 1th leaf, the latest unfolding leaf of rice plants was less affected by other physical and chemical parameters. This may explain why the top 1th leaf responded to RLB earlier than other leaves. The results also showed that the common sensitive features of the four leaf positions were also concentrated on the steep slope of the SIF spectrum, with better classification performance around 675 and 815 nm. The classification accuracy of the optimal common features, ↑WF832, 3 and ↓WF809, 3, reached 69.45%, 62.19%, 60.35%, 63.00% and 69.98%, 62.78%, 60.51%, 61.30% for the top 1th to top 4th leaf positions, respectively. The optimal common features, ↑WF832, 3 and ↓WF809, 3, were both located in wavelet scale 3 and 800-840nm, which may be related to the destruction of the cell structure in response to Magnaporthe oryzae infection.[Conclusions]In this study, the SIF spectral response to RLB was revealed, and the identification models of the top 1th leaf were found to be most precise among the top four leaves. In addition, the common wavelet features sensitive to RLB, ↑WF832, 3 and ↓WF809, 3, were extracted with the identification accuracy of 70%. The results proved the potential of CWT and SIF for RLB detection, which can provide important reference and technical support for the early, rapid and non-destructive diagnosis of RLB in the field. [ABSTRACT FROM AUTHOR]

Spring phenology is a critical indicator to characterize vegetation dynamics and their responses to climate change. Spring phenology on the Tibetan Plateau (TP) has received extensive attentions as it has experienced one of the most rapid warmings. Warming-induced advancement of spring phenology has been revealed by many studies, however, the underlying mechanisms remain obscure. In this article, we derived the start of growing season (SOS) from the satellite solar-induced chlorophyll fluorescence (SIF) and investigated the spatial and temporal variations of SOS over grasslands on the TP during 2001–2020. The temperature sensitivity (St) of SOS was then analyzed, i.e., the slope of a linear regression between the advanced SOS and preseason air temperature. Results showed an average advanced trend of 0.29 days per decade of SOS, although not statistically significant. Spatially, grasslands in eastern TP showed an earlier trend of SOS whilst those in western TP showed a later trend of SOS. The spatial distribution of St was much more affected by precipitation and air temperature, i.e., a 1 mm decrease of precipitation and 1 °C warming incur a decrease in St of 0.02 and 0.54 day/°C, respectively. Temporally, St showed a significant decrease with an average speed of 0.14 day/°C per year during 2001–2020, and the climate controllers show a high spatial heterogeneity. These findings improved our understanding of grasslands spring phenology responses to warming and help us clarify future global water and energy cycles.

Disentangling drought impacts on plant photosynthesis is crucial for projecting future terrestrial carbon dynamics. We examined the separate responses of canopy structure and leaf physiology to an extreme summer drought that occurred in 2011 over Southwest China, where the weather was humid and radiation was the main growth-limiting factor. Canopy structure and leaf physiology were, respectively, represented by near-infrared reflectance of vegetation (NIRv) derived from MODIS data and leaf scale fluorescence yield (Φf) derived from both continuous SIF (CSIF) and global OCO-2 SIF (GOSIF). We detected contrasting responses of canopy structure and leaf physiology to drought with a 14.0% increase in NIRv, compared with 12.6 or 19.3% decreases in Φf from CSIF and GOSIF, respectively. The increase in structure resulted in a slight carbon change, due to water deficit-induced physiological constraints. The net ecosystem effect was a 7.5% (CSIF), 1.2% (GOSIF), and −2.96% (EC-LUE GPP) change in photosynthesis. Our study improves understanding of complex vegetation responses of plant photosynthesis to drought and may contribute to the reconciliation of contrasting observed directions in plant responses to drought in cloudy regions via remote sensing.

Drought causes significant losses in vegetation net primary productivity (NPP). However, the lack of real-time, large-scale NPP data poses challenges in analyzing the relationship between drought and NPP. Solar-induced chlorophyll fluorescence (SIF) offers a real-time approach to monitoring drought-induced NPP dynamics. Using two drought events in the Huang–Huai–Hai Plain from 2010 to 2020 as examples, we propose a new SIF/NPP ratio index to quantify and evaluate SIF's capability in monitoring drought-induced NPP dynamics. The findings reveal distinct seasonal changes in the SIF/NPP ratio across different drought events, intensities, and time scales. SIF demonstrates high sensitivity to commonly used vegetation greenness parameters for NPP estimation (R2 > 0.8, p < 0.01 for SIF vs NDVI and SIF vs LAI), as well as moderate sensitivity to land surface temperature (LST) and a fraction of absorbed photosynthetically active radiation (FAPAR) (R2 > 0.5, p < 0.01 for SIF vs FAPAR and R2 > 0.6, p < 0.01 for SIF vs LST). However, SIF shows limited sensitivity to precipitation (PRE). Our study suggests that SIF has potential for monitoring drought-induced NPP dynamics, offering a new approach for real-time monitoring and enhancing understanding of the drought–vegetation productivity relationship. [ABSTRACT FROM AUTHOR]

Spaceborne solar-induced chlorophyll fluorescence (SIF) products have emerged over the past decade. However, limited by the spatial-temporal mismatch between satellite-based and ground measurements or the absence of sufficient in situ SIF observations, no direct validation results of satellite-derived SIF products have been provided to date. The Orbiting Carbon Observatoty-2/3 (OCO-2/3) platforms allow us to validate SIF products using in situ SIF observations benefiting from comparable spatial resolution (1.3 km × 2.25 km and 1.6 km × 2.2 km, respectively) to ground measurements. In addition, the automated in situ SIF measurement technique facilitates accuracy validation of spaceborne SIF dataset. In this communication, we describe a direct validation of OCO-2/3 SIF products using tower-based SIF measurements acquired at six sites across China. There were 1–7 and 1–18 successful observations overpassing six in situ sites for OCO-2 and OCO-3, respectively. An overall consistent temporal variation pattern was observed between the OCO-2/3 SIF products and tower-based SIF measurements. The validation accuracies of OCO-2 and OCO-3 SIF products were 0.26 and 0.33 mW m−2 sr−1 nm−1, respectively. To best use satellite-based SIF products in a wide range of applications, we suggest that further accuracy validation using more in situ SIF observations is required, which in turn requires increased availability of public in situ SIF observations. [ABSTRACT FROM AUTHOR]

Accurate phenological extraction is important for estimating carbon uptake in terrestrial ecosystems under climate change. The emergence of remotely sensed vegetation indices (VIs) and solar‐induced chlorophyll fluorescence (SIF) provides multiple approaches for extracting land surface phenology. However, there is lacking studies to track phenological metrics via multiple VIs and SIF. Therefore, the advantage of combining VIs and SIF to estimate more accurate phenology requires exploration. In this study, we combined the advantages of the normalized difference, enhanced, green‐red, near‐infrared reflectance vegetation indices from MCD43A4 data set, and SIF from CSIF data set to estimate hybrid phenology at 20 eddy flux sites in North America. Results showed that the hybrid phenology derived from the best‐performing start (SOS) and end (EOS) of the growing season among multiple VIs and SIF for each plant functional type and site were both more consistent with those derived from gross primary production (GPP). Specifically, the R2 of hybrid phenology increased by 0.11–0.4 (0.04–0.4) for SOS, 0.01–0.24 (0.09–0.22) for EOS, 0.01–0.7 (0.05–0.34) for the length of the growing season (LOS) based on Gaussian (logistic) method. Moreover, hybrid phenology can improve the explanation of the seasonal and annual variations in GPP. The explanatory power of hybrid phenology for GPP variations increased by 0.05–0.15 (0.02–0.23) for SOS, 0–0.36 (0.11–0.27) for EOS, 0.01–0.51 (0.03–0.4) for LOS, 0.04–0.18 (0.04–0.16) for LOS × ${\times} $ seasonal GPP maximum based on Gaussian (logistic) method. These findings highlight the potential of combining high‐spatiotemporal structural and coarse‐spatiotemporal physiological vegetation indicators in tracking phenology and GPP. Plain Language Summary: Vegetation phenology illustrates the timing of plant growth phases and can serve as a valuable indicator for understanding plant responses and feedback mechanisms to climate change. Consequently, it is important to accurately estimate vegetation phenology in terrestrial ecosystems. The remotely sensed vegetation indices (VIs) and solar‐induced chlorophyll fluorescence (SIF) are widely employed to estimate land surface phenology such as the start (SOS), end (EOS), and thus LOS. However, there is lacking studies to track phenological metrics via multiple VIs and SIF. This study proposes two hybrid phenological metrics estimation approach that combines the best‐performing vegetation indicators estimated SOS and EOS among multiple VIs and SIF for each plant functional type and site. Results reveal that two hybrid phenological metrics significantly enhance the accuracy of phenology estimation, and provide a more robust interpretation of gross primary productivity. Key Points: Hybrid phenological metrics are derived by identifying the most effective vegetation indicators for capturing the start of growing season and the end of growing seasonHybrid phenological metrics can yield superior accuracy compared to single vegetation index or solar‐induced chlorophyll fluorescence derived phenologyHybrid phenological metrics can better track the seasonal and annual gross primary production dynamics [ABSTRACT FROM AUTHOR]

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar‐induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP‐SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP‐SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM‐simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML‐based GPP‐SIF relationship. The ELM model when fed with the ML GPP‐SIF models also can well predict the spatial‐temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball‐Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote‐sensing SIF, which can be further improved in the future with more ground‐ and satellite‐based observations. Plain Language Summary: Model estimation of photosynthesis product, that is, gross primary productivity (GPP), is a challenging but vital task. One of the keys is to find better values for key parameters. This parameter searching process requires good proxies for GPP that can be widely available across space and time, good statistical methods to relate proxies to GPP and to make best estimations that reduce the gaps between modeled results and observations. Here, we designed a new method that use solar‐induced chlorophyll fluorescence (SIF, a good proxy for photosynthesis) as a key input, and employ machine learning (a robust way to relate SIF and GPP) and surrogate modeling (a good method for finding the best parameters), to improve the photosynthesis parameterization in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), a state‐of‐the‐art terrestrial biosphere model. Our results demonstrate that this new integrated approach has great potential for improving the parameterization of key photosynthesis parameters in land models. Key Points: We built a unique method to improve gross primary productivity (GPP) modeling in land modelsThis method integrates solar‐induced chlorophyll fluorescence observations, machine learning, and surrogate modelingThe method reduced posterior uncertainties in simulated GPP and improved the modeling of its spatial patterns [ABSTRACT FROM AUTHOR]

Although water availability strongly controls gross primary production (GPP), the impact of soil moisture content (SMC) (wilting point) is poorly quantified on regional and global scales. In this study, we used 10 years of observations of solar‐induced chlorophyll fluorescence (SIF) from the Greenhouse gases Observing Satellite (GOSAT) satellite to estimate the wilting point of a semiarid grassland on the Mongolian Plateau. Radiative‐transfer model inversion and soil‐vegetation‐atmosphere transfer simulation were sequentially conducted to distinguish the drought impacts on plant physiology from the changes in the leaf‐canopy optical properties. We modified an existing inversion algorithm and the widely used Soil‐Canopy Observation of Photosynthesis and Energy fluxes model to adequately evaluate dryland features, for example, sparse canopy and strong convection. The modified model, with retrieved parameters and calibration to GOSAT SIF, predicted realistic GPP values. We found that (a) the SIF yield estimated from GOSAT showed a clear sigmoidal pattern in relation to drought, and the estimated wilting point matched ground‐based observations in the literature within ∼0.01 m3 m−3 for the SMC, (b) tuning the maximum carboxylation rate improved the SIF prediction after considering the changes in the leaf‐canopy optical properties, implying that GOSAT detected drought stress in leaf‐level photosynthesis, and (c) the surface energy balance significantly impacted the grassland's SIF; the modified model reproduced observed SIF well (mean bias = 0.004 mW m−2 nm−1 sr−1 in summer), whereas the original model predicted substantially low values under weak horizontal wind conditions. Some model‐observation mismatches in the SIF suggest that more research is needed for fluorescence parametrization (e.g., photoinhibition) and for additional observation constraints. Plain Language Summary: Solar‐induced chlorophyll fluorescence, a weak radiation emitted as a byproduct of photosynthesis, can potentially be used to assess plant physiological status, which is especially promising for evaluating poorly quantified soil drought (wilting) impacts on the carbon cycle. However, the potential of satellite‐observed fluorescence to improve wilting prediction by vegetation models has not been sufficiently explored because of the confounding effects of plant physiological stress and visible damage (i.e., leaf browning and defoliation). In this study, we distinguished physiological wilting from visible damage by estimating leaf pigment contents and leaf amounts from satellite‐observed reflectance with the aid of a radiative transfer model and a state‐of‐the‐art vegetation model. We found that some model modifications were necessary to adequately evaluate the dryland features, for example, sparse vegetation cover and thermally induced atmospheric flow. The observed fluorescence showed a clear nonlinear response to the soil moisture content, which is characteristic of wilting. The model‐based analysis suggested that the nonlinear response resulted from physiological stress, and the estimated wilting point matched the ground‐based observations in the literature well. Since our approach is based on biophysics and satellite data, our findings and methods should help in understanding and predicting terrestrial water and carbon cycles in other regions. Key Points: Satellite‐observed chlorophyll fluorescence showed a nonlinear wilting pattern in response to soil droughts on the Mongolian PlateauWe modified the Soil‐Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model and its ancillary radiative‐transfer inversion algorithm to adequately evaluate dryland featuresThe modifications enabled assessment of the physiological control of photosynthesis and retrieval of the wilting point of the study area [ABSTRACT FROM AUTHOR]

Drought is the most common and complex meteorological disaster in the world, which weakens the carbon sink function of terrestrial ecosystem. Exploring the response of vegetation to drought and choosing sensitive factors for drought detection will be helpful to obtain the impact of drought on vegetation and to understand the response process and the mechanism of vegetation to drought stress. Based on the Solar-Induced chlorophyll Fluorescence (SIF) and the standardized precipitation evapotranspiration index (SPEI), a maximum correlation coefficient method was used to investigate the response of vegetation photosynthesis to meteorological drought in China during the growing season from 2000 to 2018. Sensitivity and response time scale of vegetation to drought was compared for different drought levels and different vegetation types. The results showed that: (1) about 75.05% of total areas of China had a significant positive correlation between SIF and SPEI. These areas were mainly distributed in the northeast, southwest and Qinghai Tibet Plateau of China. The response time scale of most regions to SPEI was mainly medium and short term. (2) The proportion of SIF to SPEI was the lowest in spring, the highest in summer, and slightly decreased in autumn. The response time scale to drought was mainly short-term in spring, while the region with long response time scale in summer was increased compared with in spring. (3) The semi-arid region was the most sensitive to drought, while the arid region was the weakest. The response time scale of different climatic regions to drought was mainly short-term. (4) The selected vegetation types responded to drought in a short time scale. Grassland was the most sensitive to drought, while woodland and cropland were relatively weak. Besides, broad-leaved forest was more sensitive to drought than coniferous forest. The results showed that under different drought gradients and different vegetation types, SIF could quickly reflect the impact of environmental stress on vegetation photosynthesis. [ABSTRACT FROM AUTHOR]

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

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

The lack of long-term high-resolution data makes it difficult to determine historical and future trends in net primary productivity (NPP). This study used tree rings as a proxy to investigate the dynamics of NPP in Tianshan forests where coniferous forests are the major species and the other species are deficient. All trees and some tree cores from five sample plots in different geographic locations in the western Tianshan Mountains were selected to reconstruct forest NPP data from 1950 to 2020. Multiple historical events that resulted in large-scale terrestrial carbon fluxes were identified and the existence of 28a and 17a time-scale cycles of historical forest NPP was observed. We discovered that the reconstructed forest NPP in the western Tianshan Mountains did not significantly correlate with satellite-based products (e.g., MODIS NPP, solar-induced chlorophyll fluorescence data). This result was attributed to the lag of forest growth for climate, the accuracy of the satellite-based products and statistical errors due to the short overlap time. We analysed the uncertainties in reconstructing historical forest NPP using tree ring widths and proposed corresponding solutions. We concluded that the reconstructed data remain the ideal proxy for regions lacking long-term empirical data and exhibit a high degree of confidence for expressing trends in forest productivity change over long time series.

Solar‐induced chlorophyll fluorescence (SIF) has been used as a proxy for gross primary productivity (GPP) estimations. However, knowledge on how links between SIF and GPP across different plant types vary in response to sky conditions remain unclear. Here, we investigated the effects of sky conditions on the GPP‐SIF relationship based on continuous measurements of SIF and flux across four different plant types. Our analysis shows that the GPP‐SIF links are affected by sky conditions and these linking patterns respond differently across plant types. We propose that the inconsistent responses of SIF and GPP to sky conditions are primarily driven by variations in light use efficiency (LUE = GPP/absorbed photosynthetic active radiation (APAR)). Furthermore, we explore a quantitative variation in LUE and SIFyield (SIF/APAR) separately via a decoupling of clearness index (CI) and photosynthetic active radiation under different sky conditions. LUE is more sensitive to sky conditions for the C3 plants (Forest, Wheat and Rice) than the C4 plant (Maize), and SIFyield shows more sensitivity to sky conditions for the forest than croplands. Due to the tight link between CI and other environmental factors, the incorporation of CI into the SIF‐based GPP model improves GPP estimates for all C3 plants at both instantaneous and daily scales. Our study implies that a consideration of sky conditions into the SIF‐based GPP model can significantly advance the GPP modeling under all sky conditions. Plain Language Summary: An emission of radiation by chlorophyll under the solar light, known as solar‐induced chlorophyll fluorescence (SIF), has been widely used to track ecosystem carbon uptake by plants (gross primary productivity, GPP). However, it remains unclear that how responses of links between SIF and GPP to sky conditions vary across plant types. This study explains the responses of GPP‐SIF links to various sky conditions across four plant types based on continuous measurements of in situ SIF and flux (the amount of gases exchanged between land surface and atmosphere). We conclude that light use efficiency (LUE, the efficiency of vegetation converting absorbed light into biochemical energy through photosynthesis) dominates the responses of GPP‐SIF links to sky conditions for the C3 plants (such as forest, rice, and wheat). However, LUE for the C4 plant (Maize) is less sensitive to sky conditions than the C3 plants, indicating the negligible effects of sky conditions on GPP‐SIF links for the C4 plant. Furthermore, our study also suggests that the integration of sky conditions can improve the accuracy of SIF‐based GPP estimation models. Key Points: Light use efficiency (LUE) dominates the responses of gross primary productivity (GPP)‐solar‐induced chlorophyll fluorescence (SIF) relationship to sky conditionsSIFyield shows more sensitivity to sky conditions for forest than croplandsVariations of SIF, SIFyield, GPP and LUE associated with sky conditions are quantified [ABSTRACT FROM AUTHOR]

Extreme climate events, particularly droughts, pose significant threats to vegetation, severely impacting ecosystem functionality and resilience. However, the limited temporal resolution of current satellite data hinders accurate monitoring of vegetation's diurnal responses to these events. To address this challenge, we leveraged the advanced satellite ECOSTRESS, combining its high-resolution evapotranspiration (ET) data with a LightGBM model to generate the hourly continuous ECOSTRESS-based ET (HC-ET ECO ) for the middle and lower reaches of the Yangtze River Basin (YRB) from 2015 to 2022. This dataset showed strong agreement with both ground-based and satellite observations. Utilizing the SPEI, we identified the significant drought period: September to November 2019 and August to September 2022. By integrating hourly Solar-Induced Chlorophyll Fluorescence (SIF) data, we observed that during drought period, the typical afternoon peak in SIF was absent. In contrast to non-drought period, morning photosynthesis and SIF-based Water Use Efficiency (WUE SIF ) anomalies were primarily driven by high Vapor Pressure Deficit (VPD), while the afternoon reductions were influenced by both high VPD and low Soil Moisture (SM) as the drought progressed. Our simulated HC-ET ECO data revealed that ET in the middle and lower reaches of the YRB was consistently lower than normal during drought period. Attribution analysis indicated that this reduction was primarily driven by midday temperature increases and high VPD, suggesting that vegetation in the region copes with drought stress predominantly by limiting water loss. These findings highlight the utility of the generated high-resolution ET dataset in advancing our understanding of vegetation dynamics under drought climate conditions. This work provides critical insights for enhancing climate adaptation strategies and enhancing ecosystem management practices in the face of increasing climate variability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation., Methods: Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF's estimation capabilities using various datasets., Results and Discussion: The results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R ² =0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions., Conclusion: Thus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Li, Wu, Xiang, Jiao and Chai.)

The impacts of drought on the terrestrial gross primary production (GPP) are the most intense and widespread in all extreme climate events. Solar-induced chlorophyll fluorescence (SIF) is considered as a direct representative of actual vegetation photosynthesis and has better performance in monitoring vegetation conditions than greenness-based vegetation indices (VIs) during drought events. Based on the spatially downscaled SIF (SIFds), VIs and GPP products, we explored the potential of SIFds to monitor drought effects on GPP in winter wheat. First, the spatiotemporal dynamics of hydrometeorological factors and vegetation variables in winter wheat during drought events were observed. Then, the SIFds—GPP relationships in different phenological stages were examined in the rainfed area. Finally, the drought-induced GPP losses in different phenological stages were evaluated by scaling SIFds to GPP based on the linear SIFds–GPP relationship in the rainfed area. Results showed that SIFds could capture the spatiotemporal dynamics of drought-induced GPP variations in winter wheat during drought events, and it could quantify accurately the drought-induced GPP losses, with higher sensitivities to GPP changes during the vigorous growing periods. Our study reveals the applicability of SIFds to achieve regional agricultural drought detection and drought-induced GPP loss assessment, which can provide some help for crop adaptation management.

Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially within mountain grassland ecosystems. In this study, we used three types of high-spatial-resolution SIF datasets (0.05°), coupled with meteorological and soil moisture datasets, to investigate the characteristics of atmospheric, soil, and compound drought types. We centered this investigation on the years spanning 2001–2020 in the Three-River Headwaters Region (TRHR). Our findings indicate that the TRHR experienced a combination of atmospheric drying and soil wetting due to increases in the standardized saturation vapor pressure deficit index (SVPDI) and standardized soil moisture index (SSMI). In the growing season, atmospheric drought was mainly distributed in the southern and eastern parts of the TRHR (reaching 1.7 months/year), while soil drought mainly occurred in the eastern parts of the TRHR (reaching 2 months/year). Compound drought tended to occur in the southern and eastern parts of the TRHR and trended upward during 2001–2020. All three SIF datasets consistently revealed robust photosynthetic activity in the southern and eastern parts of the TRHR, with SIF values generally exceeding 0.2 mW· m−2·nm−1·sr−1. Overall, the rise in SIF between 2001 and 2020 corresponds to enhanced greening of TRHR vegetation. Vegetation photosynthesis was found to be limited in July, attributable to a high vapor pressure deficit and low soil moisture. In the response of CSIF data to a drought event, compound drought (SVPDI ≥ 1 and SSMI ≤ −1) caused a decline of up to 14.52% in SIF across the source region of the Yellow River (eastern TRHR), while individual atmospheric drought and soil drought events caused decreases of only 5.06% and 8.88%, respectively. The additional effect of SIF produced by compound drought outweighed that of atmospheric drought as opposed to soil drought, suggesting that soil moisture predominantly governs vegetation growth in the TRHR. The reduction in vegetation photosynthesis capacity commonly occurring in July, characterized by a simultaneously high vapor pressure deficit and low soil moisture, was more pronounced in Yellow River’s source region as well. Compound drought conditions more significantly reduce SIF compared to singular drought events. Soil drought evidently played a greater role in vegetation growth stress than atmospheric drought in the TRHR via the additional effects of compound drought.

Solar-induced chlorophyll fluorescence (SIF) has a high correlation with Gross Primary Production (GPP). However, studies focusing on the impact of drought on the SIF-GPP relationship have had mixed results at various scales, and the mechanisms controlling the dynamics between photosynthesis and fluorescence emission under water stress are not well understood. We developed a leaf-scale measurement system to perform concurrent measurements of active and passive fluorescence, and gas-exchange rates for winter wheat experiencing a one-month progressive drought. Our results confirmed that: (1) shifts in light energy allocation towards decreasing photochemistry (the quantum yields of photochemical quenching in PSII decreased from 0.42 to 0.21 under intermediate light conditions) and increasing fluorescence emissions (the quantum yields of fluorescence increased to 0.062 from 0.024) as drought progressed enhance the degree of nonlinearity of the SIF-GPP relationship, and (2) SIF alone has a limited capacity to track changes in the photosynthetic status of plants under drought conditions. However, by incorporating the water stress factor into a SIF-based mechanistic photosynthesis model, we show that drought-induced variations in a variety of key photosynthetic parameters, including stomatal conductance and photosynthetic CO2 assimilation, can be accurately estimated using measurements of SIF, photosynthetically active radiation, air temperature, and soil moisture as inputs. Our findings provide the experimental and theoretical foundations necessary for employing SIF mechanistically to estimate plant photosynthetic activity during periods of drought stress.

Solar-induced chlorophyll fluorescence (SIF) has been found to be a useful indicator of vegetation’s gross primary productivity (GPP). However, the directional SIF observations obtained from a canopy only represent a portion of the total fluorescence emitted by all the leaf photosystems because of scattering and reabsorption effects inside the leaves and canopy. Hence, it is crucial to downscale the SIF from canopy level to leaf level by modeling fluorescence escape probability (fesc) for improved comprehension of the relationship between SIF and GPP. Most methods for estimating fesc rely on the assumption of a “black soil background,” ignoring soil reflectance and the effect of scattering between soils and leaves, which creates significant uncertainties for sparse canopies. In this study, we added a correction factor considering soil reflectance, which was modeled using the Gaussian process regression algorithm, to the semi-empirical NIRv/FAPAR model and obtained the improved fesc model accounting for soil reflectance (called the fesc_GPR-SR model), which is suitable for near-infrared SIF downscaling. The evaluation results using two simulation datasets from the Soil–Canopy–Observation of Photosynthesis and the Energy Balance (SCOPE) model and the Discrete Anisotropic Radiative Transfer (DART) model showed that the fesc_GPR-SR model outperformed the NIRv/FAPAR model, especially for sparse vegetation, with higher accuracy for estimating fesc (R2 = 0.954 and RMSE = 0.012 for SCOPE simulations; R2 = 0.982 and RMSE = 0.026 for DART simulations) compared with the NIRv/FAPAR model (R2 = 0.866 and RMSE = 0.100 for SCOPE simulations; R2 = 0.984 and RMSE = 0.070 for DART simulations). The evaluation results using in situ observation data from multi-species canopies also suggested that the leaf-level SIF calculated by the fesc_GPR-SR model tracked better with photosynthetic active radiation absorbed by green components (APARgreen) for sparse vegetation (R2 = 0.937, RMSE = 0.656 mW/m2/nm) compared with the NIRv/FAPAR model (R2 = 0.921, RMSE = 0.904 mW/m2/nm). The leaf-level SIF calculated by the fesc_GPR-SR model was less sensitive to observation angles and differences in canopy structure among multiple species. These results emphasize the significance of accounting for soil reflectance in the estimation of fesc and demonstrate that the fesc_GPR-SR model can contribute to further exploring the physiological mechanism between SIF and GPP.

Drought causes significant losses in vegetation net primary productivity (NPP). However, the lack of real-time, large-scale NPP data poses challenges in analyzing the relationship between drought and NPP. Solar-induced chlorophyll fluorescence (SIF) offers a real-time approach to monitoring drought-induced NPP dynamics. Using two drought events in the Huang–Huai–Hai Plain from 2010 to 2020 as examples, we propose a new SIF/NPP ratio index to quantify and evaluate SIF’s capability in monitoring drought-induced NPP dynamics. The findings reveal distinct seasonal changes in the SIF/NPP ratio across different drought events, intensities, and time scales. SIF demonstrates high sensitivity to commonly used vegetation greenness parameters for NPP estimation (R2 > 0.8, p < 0.01 for SIF vs NDVI and SIF vs LAI), as well as moderate sensitivity to land surface temperature (LST) and a fraction of absorbed photosynthetically active radiation (FAPAR) (R2 > 0.5, p < 0.01 for SIF vs FAPAR and R2 > 0.6, p < 0.01 for SIF vs LST). However, SIF shows limited sensitivity to precipitation (PRE). Our study suggests that SIF has potential for monitoring drought-induced NPP dynamics, offering a new approach for real-time monitoring and enhancing understanding of the drought–vegetation productivity relationship.

• SIF performs well in tracking GPP during dry conditions in a semi-arid grassland. • SIF and GPP exhibit parallel responses to environmental variables under dry conditions. • Compared to wet conditions, the role of physiological information regulation in SIF and GPP increases under dry conditions. Solar-induced chlorophyll fluorescence (SIF) has recently emerged as a promising tool for estimating gross primary production (GPP). To date, there is ongoing debate regarding whether the strong correlations between SIF and GPP persist under dry conditions. Here, we conducted continuous far-red SIF measurements in a semi-arid grassland from 2017 to 2019 to investigate its association with GPP. Our findings revealed strong correlations in the seasonal patterns of SIF and GPP during dry conditions (R2=0.79). After disentangling the effects of photosynthetically active radiation and soil water content, we observed consistent responses to environmental variables in both SIF and GPP under dry conditions. Furthermore, we conducted a dominance analysis to assess the contributions of physiological and non-physiological components to the variations in SIF and GPP. Our results demonstrated a substantial increase in the contributions of physiological components to both SIF (wet: 19.29%; dry: 60.23%) and GPP (wet: 20.89%; dry: 28.38%) during dry conditions, highlighting a shift towards enhanced physiological regulation of SIF and GPP in response to dry conditions. In conclusion, our findings provide valuable insights into the GPP-SIF relationships in semi-arid grasslands under dry conditions. These insights hold the potential to refine and constrain model predictions under climate change. [ABSTRACT FROM AUTHOR]

• Over 80 % of subtropical China exhibited increased photosynthetic activity, particularly in southern and central-western regions. • Soil moisture showed significant lag effects, while solar radiation and vapor pressure deficit had combined lag and cumulative effects on photosynthesis. • Considering lag and cumulative effects, the area with significant correlations between climatic factors and vegetation SIF increased substantially. • The interaction of climatic factors, especially involving vapor pressure deficit, significantly enhanced their impact on photosynthesis. Monitoring vegetation photosynthesis in China's subtropical regions using remote sensing is challenging because of the complex ecosystems and climate variability. Previous studies often pay less attention on the influence of multiple climatic factors on the temporal effects (lag and accumulation) of vegetation photosynthesis, thereby underestimating their impact. This study utilizes a dataset comprising Solar-induced chlorophyll fluorescence (SIF) data (GOSIF product), MODIS Land Cover product (MCD12C1), and various climatic variables. Analytical methods including Theil-Sen Median trend analysis, the Mann-Kendall test, partial correlation analysis, and the optimal parameter-based geographical detector (OPGD) model were employed to explore the temporal dynamics of subtropical vegetation SIF responses to climatic factors and to identify their climate drivers in subtropical China. The study findings indicate that (1) vegetation photosynthesis, as indicated by SIF, exhibited an increasing trend in the majority of Chinese subtropical regions, which constitute over 80 % of the study area, with particularly pronounced enhancements in the southern and central western parts of the Chinese subtropics. (2) Soil moisture primarily exhibits lag effects on SIF, particularly in evergreen needleleaf forests, deciduous broadleaf forests, and mixed forests, whereas temperature does not exhibit significant temporal effects. Solar radiation and vapor pressure deficits impact SIF through both lag and accumulation effects. Under the lag and accumulation effects, the proportion of significant correlations between climatic factors and vegetation SIF increases by 36.71 % ∼ 43.8 %, excluding temperature. (3) Temperature is the dominant factor affecting vegetation SIF, particularly in the evergreen needleleaf forest. Interactions between climatic factors have a significantly stronger influence on SIF than individual factors. Notably, the explanatory power of the vapor pressure deficit increases substantially when it interacts with other factors. Studying the lag and accumulation effects of climatic factors on photosynthesis aids in accurately predicting vegetation responses to climate change, thereby improving the accuracy of global carbon cycle models and guiding the development of carbon sequestration management strategies. [ABSTRACT FROM AUTHOR]

Alpine meadow ecosystems are extremely vulnerable to climate change and serve an essential function in terrestrial carbon sinks. Accurately estimating their gross primary productivity (GPP) is essential for understanding the global carbon cycle. Solar-induced chlorophyll fluorescence (SIF), as a companion product directly related to plant photosynthesis process, has become an attractive pathway for estimating GPP accurately. To date, the quantitative SIF-GPP relationship in terrestrial ecosystems is not yet clear. Especially, red SIF and far-red SIF present differences in their ability to track GPP under different environmental conditions. In this study, we investigated the performance of SIF at both red and far-red band in monitoring the GPP of an alpine meadow ecosystem based on continuous tower-based observations in 2019 and 2020. The results show that the canopy red SIF ( SIF Red ) and far-red SIF ( SIF Far-red ) were both strongly correlated with GPP. SIF Red was comparable to SIF Far-red for monitoring GPP based on comparisons of both half-hourly averaged and daily averaged datasets. Moreover, the relationship between SIF Red and GPP was linearly correlated, while the relationship between SIF Far-red and GPP tended to be nonlinear. At a diurnal scale, dramatic changes in photosynthetically active radiation (PAR), air temperature (Ta), and vapor pressure deficit (VPD) all had effects on the slope of the linear fitted line with zero intercept for SIF Red -GPP and SIF Far-red -GPP, and the effect on the slope of the linear fitted line with zero intercept for SIF Far-red -GPP was obviously stronger than that for SIF Red -GPP. PAR was the dominant factor among the three environmental factors in determining the diurnal variation of the slope of SIF-GPP. At a seasonal scale, the SIF Far-red /GPP was susceptible to PAR, Ta, and VPD, while the SIF Red /GPP remained relatively stable at different levels of Ta and VPD, and it was only weakly affected by PAR, suggesting that SIF Red was more consistent than SIF Far-red with GPP in response to seasonal variations in environmental factors. These results indicate that SIF Red has more potential than SIF Far-red for monitoring the GPP of alpine meadow ecosystems and can also assist researchers in gaining a more comprehensive understanding of the diversity of SIF-GPP relationships in different ecosystems. [ABSTRACT FROM AUTHOR]

Summary: Solar‐induced Chl fluorescence (SIF) offers the potential to curb large uncertainties in the estimation of photosynthesis across biomes and climates, and at different spatiotemporal scales. However, it remains unclear how SIF should be used to mechanistically estimate photosynthesis.In this study, we built a quantitative framework for the estimation of photosynthesis, based on a mechanistic light reaction model with the Chla fluorescence of Photosystem II (SIFPSII) as an input (MLR‐SIF). Utilizing 29 C3 and C4 plant species that are representative of major plant biomes across the globe, we confirmed the validity of this framework at the leaf level.The MLR‐SIF model is capable of accurately reproducing photosynthesis for all C3 and C4 species under diverse light, temperature, and CO2 conditions. We further tested the robustness of the MLR‐SIF model using Monte Carlo simulations, and found that photosynthesis estimates were much less sensitive to parameter uncertainties relative to the conventional Farquhar, von Caemmerer, Berry (FvCB) model because of the additional independent information contained in SIFPSII.Once inferred from direct observables of SIF, SIFPSII provides 'parameter savings' to the MLR‐SIF model, compared to the mechanistically equivalent FvCB model, and thus avoids the uncertainties arising as a result of imperfect model parameterization. Our findings set the stage for future efforts to employ SIF mechanistically to improve photosynthesis estimates across a variety of scales, functional groups, and environmental conditions. [ABSTRACT FROM AUTHOR]

Solar‐induced chlorophyll fluorescence (SIF) has been used to infer photosynthetic capacity parameters (e.g., the maximum carboxylation rate Vcmax, and the maximum electron transport rate Jmax). However, the precise mechanism and practical utility of such approach under dynamic environments remain unclear. We used the balance between the light and carbon reactions to derive theoretical equations relating chlorophyll a fluorescence (ChlF) emission and photosynthetic capacity parameters, and formulated testable hypotheses regarding the dynamic relationships between the true total ChlF emitted from PSII (SIFPSII) and Vcmax and Jmax. We employed concurrent measurements of gas exchanges and ChlF parameters for 15 species from six biomes to test the formulated hypotheses across species, temperatures, and limitation state of carboxylation. Our results revealed that SIFPSII alone is incapable of informing the variations in Vcmax and Jmax across species, even when SIFPSII is determined under the same environmental conditions. In contrast, the product of SIFPSII and the fraction of open PSII reactions qL, which indicates the redox state of PSII, is a strong predictor of both Vcmax and Jmax, although their precise relationships vary somewhat with environmental conditions. Our findings suggest the redox state of PSII strongly influences the relationship between SIFPSII and Vcmax and Jmax. Summary statement: Our theoretical and measurement results revealed that the fraction of open PSII reaction centers qL, which indicates the redox state of PSII plays an important role when chlorophyll afluorescence is used to infer Vcmax25 and Jmax25. The limitation state of carboxylation must be considered if observed chlorophylla fluorescenceis utilized to infer Vcmax25 (or Jmax25) under dynamic environmental conditions. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) from ground, airborne, and satellite-based observations has been increasingly used in drought monitoring recently due to its close relationship with photosynthesis. SIF emissions respond rapidly to droughts, relative to the widely used vegetation indices (VIs), thus indicating their potential for early drought monitoring. The response of SIF to droughts can be attributed to the confounding effects of both the physiology and canopy structure. In order to reduce the reabsorption and scattering effects, the total emitted SIF (SIFtot) was proposed and served as a better tool to estimate GPP compared with the top-of-canopy SIF (SIFtoc). However, the response time and response magnitude of SIFtot to droughts and its relationships with the environmental parameters and soil moisture (SM) (i.e., the knowledge of drought monitoring using SIFtot) remains unclear. Here, the continuous ground data of F760toc (SIFtoc at 760 nm) from a nadir view that was downscaled to F760tot (SIFtot at 760 nm), NIRv, and the NDVI, SM, meteorological, and crop growth parameters were measured from four winter wheat plots with different intensities of drought (well-watered, moderate drought, severe drought, and extreme drought) over 2 months. The results indicated that F760tot was more closely correlated with the SM than the VIs at short time lags but weaker at longer time lags. The daily mean values of F760tot and NIRv were able to distinguish the differences between different drought levels, and F760tot responded quickly to the onset of drought, especially for the moderate drought intensity. These findings demonstrated that F760tot has potential for early drought monitoring and may contribute to mitigating the risk of agricultural drought. [ABSTRACT FROM AUTHOR]