Solar-induced Chlorophyll Fluorescence Research Articles

Assessing the Potential for Photochemical Reflectance Index to Improve the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity in Crop and Soybean

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

Solar-Induced Chlorophyll Fluorescence (SIF): Towards a Better Understanding of Vegetation Dynamics and Carbon Uptake in Arctic-Boreal Ecosystems

Abstract Purpose of Review Terrestrial ecosystems in the Arctic-Boreal region play a crucial role in the global carbon cycle as a carbon sink. However, rapid warming in this region induces uncertainties regarding the future net carbon exchange between land and the atmosphere, highlighting the need for better monitoring of the carbon fluxes. Solar-Induced chlorophyll Fluorescence (SIF), a good proxy for vegetation CO$$^{2}$$ 2 uptake, has been broadly utilized to assess vegetation dynamics and carbon uptake at the global scale. However, the full potential and limitations of SIF in the Arctic-Boreal region have not been explored. Therefore, this review aims to provide a comprehensive summary of the latest insights into Arctic-Boreal carbon uptake through SIF analyses, underscoring the advances and challenges of SIF in solving emergent unknowns in this region. Additionally, this review proposes applications of SIF across scales in support of other observational and modeling platforms for better understanding Arctic-Boreal vegetation dynamics and carbon fluxes. Recent Findings Cross-scale SIF measurements complement each other, offering valuable perspectives on Arctic-Boreal ecosystems, such as vegetation phenology, carbon uptake, carbon-water coupling, and ecosystem responses to disturbances. By incorporating SIF into land surface modeling, the understanding of Arctic-Boreal changes and their climate drivers can be mechanistically enhanced, providing critical insights into the changes of Arctic-Boreal ecosystems under global warming. Summary While SIF measurements are more abundant and with finer spatiotemporal resolutions, it is important to note that the coverage of these measurements is still limited and uneven in the Arctic-Boreal region. To address this limitation and further advance our understanding of the Arctic-Boreal carbon cycle, this review advocates for fostering a SIF network providing long-term and continuous measurements across spatial scales. Simultaneously measuring SIF and other environmental variables in the context of a multi-modal sensing system can help us comprehensively characterize Arctic-Boreal ecosystems with spatial details in land surface models, ultimately contributing to more robust climate projections.

Impact of atmospheric dryness on solar-induced chlorophyll fluorescence: Tower-based observations at a temperate forest

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

A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)

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.

Integrating multi-source datasets in exploring the covariation of gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) at an Indian tropical forest flux site

Integrating multi-source datasets in exploring the covariation of gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) at an Indian tropical forest flux site

Quantification of uncertainty caused by geometric location mismatch in the validation of TROPOMI solar-induced chlorophyll fluorescence product

ABSTRACT Validation of the TROPOspheric Monitoring Instrument (TROPOMI) SIF product is a necessity to evaluate its feasibility in various applications. A few validation works have been conducted through direct comparison with in-situ SIF retrievals or cross-comparison with similar satellite-based SIF or vegetation index (VI) products. Nevertheless, the influence of the geolocation mismatch between the validation pixel and reference data on validation results was never considered. This study, for the first time, quantifies the geolocation shift of the TROPOMI validation pixel based on a geometric location matching method and then the uncertainty caused by the geolocation mismatch. The results indicate that the geolocation shift of the TROPOMI pixel shows large temporal variations, with a standard deviation of 2.30 km and 1.91 km in the across- and along-track directions. The mean shifts are 0.17 km and 0.28 km in the across- and along-track directions respectively, indicating eastward shifts in the across-track direction and northward shifts in the along-track direction in general. More than half (42/54) of the time periods showed relative uncertainty larger than 5%, with the maximum even reaching 55.7%. Hence, the uncertainty caused by geolocation mismatch should be fully considered in the validation of satellite SIF products, especially over heterogeneous surfaces.

A SIF-based approach for quantifying canopy photosynthesis by simulating the fraction of open PSII reaction centers (qL)

Advances in retrieval of solar-induced chlorophyll fluorescence (SIF) provide a promising and independent approach for quantifying gross primary production (GPP) across spatial scales. Recent studies have highlighted the prominent role of qL, the fraction of open Photosystem II (PSII) reaction centers, in mechanistically modeling GPP from remote sensing SIF. However, due to the limited availability of simulated and experimental data, a comprehensive understanding of qL responses to environmental and physiological variations has yet to emerge, and as a consequence, prediction of qL across leaf and canopy scales is still in an early stage. Based on a global sensitivity analysis of a recently developed mechanical model of photosynthesis, we find that the broadband total SIF emitted from PSII (SIFTOT_FULL_PSII) and leaf temperature (TLeaf) are the two major predictors of qL. A leaf-level instrument is designed to obtain concurrent measurements of qL, SIFTOT_FULL_PSII, and TLeaf over a wide range of environmental conditions. From these measurements, we show that qL can be modelled as a hyperbolic function of SIFTOT_FULL_PSII with only one temperature-related parameter m which increases with temperature, but decreases rapidly as temperatures exceed the optimum temperature. It is suggested that m can be mathematically modelled by a peaked function. The results of the leaf-level experiments on winter wheat demonstrate that the proposed model predicts qL with high accuracy (R2 ≥ 0.91, rRMSE ≤ 8.46%) under diverse light and temperature conditions. The essential steps necessary to apply it at canopy scale, including estimating the escape fraction, removing fluorescence emitted from Photosystem I, and reconstructing SIFTOT_FULL_PSII from top-of-canopy (TOC) narrowband SIF, are also presented. Our results confirm that estimated GPP using SIF-informed qL agrees well with measured GPP at a winter wheat site (R2 = 0.81, rRMSE = 12.03%). The key benefit of SIF-informed approach is that SIFTOT_FULL_PSII provides critical information on the collective influence of the sub-canopy light environment on qL, avoiding the requirement to explicitly estimate qL at different canopy depths, potentially promoting the ability of SIF to mechanistically quantify photosynthetic CO2 assimilation at large scales.

Potential of solar-induced chlorophyll fluorescence (SIF) to access long-term dynamics of soil salinity using OCO-2 satellite data and machine learning method

The accumulation of soil salt becomes a worldwide widespread phenomenon, being a major threat to global production. As an environmental stress, soil salinity can reduce the vegetation photosynthetic activity. Solar-induced chlorophyll fluorescence (SIF) is an electromagnetic signal actively released by vegetation during photosynthesis. SIF not only can capture lower vegetation photosynthetic activity due to environmental stress promptly, but also is less affected by atmosphere and soil background. However, the ability of SIF observation to detect soil salinity remains unclear. Here, we use standardizedsolar-induced chlorophyll iluorescence index (SIFI) from SIF observation time series (2000 ∼ 2020) of global OCO-2 based SIF product (GOSIF) to develop soil salinity model. The results show that: SIF observation can identify salt affected soil (EC ≥ 2 ∼ 4 dS m−1) and salinity class a global scale. The SIFI calculated from SIF observation at May ∼ August (hereafter SIFI5-8) is the optimal sensitivity indices for rainfed cropland, herbaceous cover, irrigated cropland, shrubland, grassland. SIFI10-11 is the optimal sensitivity indices for forest and sparse vegetation; (2) By comparison, the ovrerall classification accuracy of predicted salinity class is above 70 %. The order of classification accuracy is rainfed cropland > irrigated cropland > bare area > forest > grassland > shrubland > herbaceous cover > sparse vegetation; (3) During at least three-quarters of the period from 2000 to 2020, the area of salt affected soil (EC ≥ 2 ∼ 4 dS m−1) was 4.9 Mkm2; (4) The annual change rate of global soil salt content is generally between −0.05 and 0.05 dS m−1 yr−1. Soil salinity in South Africa and West Asia increased greatly with an annual change rate of 0.02 ∼ 0.03 dS m−1 yr−1. These results demonstrate the ability of SIF observation to estimate soil salinity, providing a new perspective for explaining and evaluating soil salinity variation.

A Spatial Downscaling Method for Solar-Induced Chlorophyll Fluorescence Product Using Random Forest Regression and Drought Monitoring in Henan Province

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

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Integrating SWAP and SIF anomaly to assess the responses of vegetation to the drought-flood abrupt alternation in the middle and lower reaches of the Yangtze River basin, China

Study regionThe middle and lower reaches of the Yangtze River basin, China. Study focusUnder the background of global warming, drought-flood abrupt alternation (DFAA) events are occurred frequently, with substantial impact on ecosystems and water resources. Therefore, this study calculated the Standard Weighted Average Precipitation (SWAP) based on daily precipitation to verify the applicability of SWAP in the middle and lower reaches of the Yangtze River basin (MLRYRB). The response of Solar-Induced Chlorophyll Fluorescence (SIF) anomaly to DFAA events was determined based on lagged time correlation coefficients. Finally, based on Partial Wavelet Coherence (PWC), the impact of atmospheric circulation factors on the response relationship between DFAA events and vegetation was analyzed. New hydrological insights for the regionThe results indicate that: (1) the evolutionary characteristics of DFAA events determined by SWAP were consistent with historical records. (2) the DFAA events frequency with most regions experiencing 8–11 events during 2001–2019, with intensity of 2–3, and in the northern and southwestern Jiangxi and southeastern Hubei regions were more intensive and frequently. (3) the stronger correlations were mainly in eastern Guizhou and eastern Jiangxi, with SIF anomaly more sensitive to SWAP at 2-month lag time. (4) The El Niño-Southern Oscillation (ENSO) and sunspots have a significant influence on the response relationship between DFAA events and vegetation anomaly. This research can provide a scientific basis for the integrated response to DFAA events and ecological management in the MLRYRB.

Soil salinization poses greater effects than soil moisture on field crop growth and yield in arid farming areas with intense irrigation

Soil moisture and salinization are key environmental factors affecting crop growth and yield in arid farming areas with intense irrigation. Quantifying their effects on crops is beneficial to understanding the principle of soil water-salt interactions. Due to the strong coupling relationship between them, the independent effect on field crops is hard to distinguish clearly. In this study, taking Yongji Sub-irrigation District (YJSID) in Inner Mongolia of China as case study, the sorting bins method and GeoDetector were employed to try to disentangle relative effects of soil moisture and salinization on crop development and yield during growing seasons in 2021–2022. The regional soil water content (SWC) and soil salt content (SSC) were firstly mapped and validated through the inverse density weighted method and random forest model. Separated effects of SWC and SSC on solar-induced chlorophyll fluorescence (SIF), gross primary productivity (GPP), and crop yield were then calculated and presented. Results showed that low SWC inhibited SIF and the ratio of GPP to SIF (GPP/SIF) in SSC bins, as their values declined among 52.989% and 71.801% of YJSID. In SWC bins, the increase of SIF and GPP/SIF covered 91.356% and 50.087% of the area, indicating that low SSC could bring out higher SIF and GPP/SIF for field crops. Relative importance assessment suggested that SSC posed greater impacts than SWC on SIF and GPP/SIF in YJSID with the area of 77.188% and 59.628%, respectively. To the SWC effects, it performed greater for the C3 crops (sunflower, wheat, interplant, others) than the C4 plant (maize) in SIF, while reversely in GPP/SIF. The q value calculated from GeoDetector also indicated that SSC had greater effects than SWC on crop yield. Increased SSC tended to reduce grain yield with linear relationships, and the severity was bigger for maize than sunflower. These results would contribute to further understanding of the key processes involved in soil water-salt interactions and dealing with them better in agricultural practice.

Divergence in Autumn Phenology Extracted From Different Satellite Proxies Reveals the Timetable of Leaf Senescence Over Deciduous Forests

AbstractRemote sensing detection of autumn phenology is challenging and highly uncertain, as exemplified by the observed divergence in autumn phenology extracted from different proxies. Here, we compared the autumn phenology derived from Solar‐Induced chlorophyll Fluorescence (SIF), Chlorophyll/Carotenoid Index (CCI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) over deciduous forest sites. We observed a clear temporal sequence in the derived autumn phenology from various proxies: SIF < CCI < EVI < NDVI. Comparison with field measurements supported that SIF, EVI, and NDVI can successfully capture the attenuation of photosynthetic activity, leaf coloration, and leaf fall, respectively. The sequence among the autumn phenology derived from those proxies was also consistent with their responses to climate cues, where SIF had the highest partial correlation coefficient to solar radiation in autumn, followed by CCI, EVI, and NDVI, while NDVI was more correlated with temperature, followed by EVI, CCI, and SIF.

Global ecosystem responses to flash droughts are modulated by background climate and vegetation conditions

AbstractFlash droughts and their physical processes have received increasing attention in recent years due to concerns about the potential of flash droughts to affect water resources and ecosystems. Yet to date, the response of ecosystems during flash drought events, particularly on a large scale, and the determinants of the ecosystem responses to flash droughts have been underexplored. Here we analyse temporal variations in vegetation anomalies during flash drought events at a global scale between 2001 and 2020 using observation-based leaf area index, gross primary productivity, and solar-induced chlorophyll fluorescence data. We identify divergent ecosystem responses in terms of the timing and intensification of drought-induced vegetation stress across different regions around the world. Furthermore, we find that these regional differences are largely modulated by background climate and vegetation conditions, rather than meteorological conditions, with ecosystems being subjected to more rapidly developing and greater degrees of vegetation stress in arid and short vegetation-dominated regions as compared to humid forests. Our results highlight the spatially heterogeneous ecological impacts of flash droughts, implying the need to comprehensively integrate aspects of both atmospheric and bioclimatic properties in flash drought monitoring and forecasting systems to improve our ability to track their evolution and impacts.

Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR2 = 0.85), followed by NIRV (meanR2 = 0.84), NDVI (meanR2 = 0.76), and kNDVI (meanR2 = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR2 = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR2 = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes.

The Relationship of Gross Primary Productivity with NDVI Rather than Solar-Induced Chlorophyll Fluorescence Is Weakened under the Stress of Drought

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

Spatial-temporal heterogeneity of spring phenology in boreal forests as estimated by satellite solar-induced chlorophyll fluorescence and vegetation index

Changes in spring phenology in boreal forests around the subarctic region have a profound impact on the global carbon and water cycles. To date, photosynthesis and greenness dynamics in evergreen coniferous forests have been found to be decoupled on a large scale, but whether the spatial heterogeneity of seasonal dynamics in boreal forests is coupled in various datasets remains unclear. Therefore, based on the solar-induced chlorophyll fluorescence (SIF) retrieved by satellite and the modelled SIF (GOME-2 SIF and GOSIF) during 2007–2018, we calculated the start of the growing season (SOS) of boreal forests (45–70°N) and compared the results with 2-band Enhanced Vegetation Index (EVI2) phenological products (MCD12Q2 C6) and flux data (Gross primary productivity, GPP) phenology. The spatial-temporal variations in the SOS were analyzed by linear regression and piecewise linear regression. The results exhibited that GOME-2 SIF showed an SOS of 11 days earlier than GOSIF, 8 days earlier than EVI2 in North America, and 5 days earlier than GOSIF, 3 days earlier than EVI2 in Eurasia. However, all relationships between SOSs of the three remote sensing datasets were significant in North America and Eurasia, and the significance was higher in Eurasia. With increasing latitude and longitude, almost all SOSs estimated from the three datasets significantly increased. Also, both in North America and Eurasia, the SOSs exhibited a trend of being advanced to being delayed from the western to the eastern parts of the continents over 2007–2018. This indicated the spatial consistency of photosynthesis and greenness dynamics in the spring phenology of boreal forests, especially in Eurasia. In this study, the validity of the spatial-temporal variation in spring phenology of boreal forests based on GOME-2 SIF retrieved from satellites and MCD12Q2 C6 was confirmed, and further exploration is needed to determine their potential advantages.

Exploring evapotranspiration stress in China: A blending approach employing multi-source remote sensing proxies

Study regionChina Study focusAn effective evapotranspiration stress assessment system requires reliable estimates actual evapotranspiration (ETa), and potential evapotranspiration (ETp), and should consider multiple influencing indicators. This study explores the consistency of multiple evaporation reanalysis datasets across mainland China and fuses them with multi-model ensemble method. To address the uncertainty inherent in the traditional evapotranspiration stress index (ESI) that only considers the ETa and ETp relationship, we employ Pearson Correlation Analysis and a Random Forest-based Boruta Algorithm to propose new evapotranspiration stress proxies blending multiple remote sensing indicators, named as MESI-P and MESI-B, respectively. Then, we verified their performance in depicting vegetation production capacity based on Solar-Induced Chlorophyll Fluorescence (SIF) and Gross Primary Productivity (GPP). New hydrological insights for the regionThe patterns of ETa from GLDAS, GLEAM, and Harvard Dataverse in China show consistent, but their threshold values differ. The synthesized ETa obtained after using a multi-model ensemble (MME) method is more adaptable in China, with a range of 0.45–1485.31 mm and an increase of 11.34 mm/10a. Following the same pattern as GLDAS and CRU, the synthetic ETp experiences an increasing trend (11.24 mm/10a) during 2000–2019. Compared with the traditional ESI, the MESI-P and MESI-B proposed in this study have superior signal convergence with ETa, SIF, and GPP due to the higher correlation coefficients (＞0.95) in China. Furthermore, during the calculation of MESI-B, soil moisture and solar radiation are identified as dominate factor in Northwest and Southern China respectively.

Seasonal drought severity identification using a modified multivariate index: a case study of Indo-Gangetic Plains in India

Effective drought monitoring is crucial for mitigation efforts and the implementation of early warning systems. Due to the intricate nature of drought phenomena, its impact cannot be accurately characterized solely through the use of univariate indicators. This study introduces a composite version of the modified multivariate standardized drought index (MMSDI) for the Indo-Gangetic Plains (IGP) in India. The MMSDI integrates precipitation, soil moisture, and solar-induced chlorophyll fluorescence datasets from 2001 to 2020, utilizing a nonparametric joint probabilistic framework. It combines three consolidated drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized solar-induced chlorophyll fluorescence Index (SSIFI). The drought patterns among SPI, SSI, SSIFI, and MMSDI indices are compared for short, and mid-term time scales (herein 1, 3 and 6 month) over IGP. The performance of MMSDI is evaluated through quantitative metrics e.g. probability of detection (POD), false alarm ration (FAR) and critical success index (CSI) at various time scales. Results reveal that MMSDI provides a reliable estimation of the severity and spatial coverage of major drought events over IGP at various time scales. The MMSDI demonstrates superior effectiveness in identifying and characterizing the spatial severity of drought, i.e. the visibility of the drought hotspots. The MMSDI has the capability to assess agrometeorological drought, indicating its potential as a valuable tool for identifying regions vulnerable to drought. Overall, this modified drought index derived from multi-sensor datasets produced comprehensive insights for policymakers in implementing effective agricultural drought management practices to understand intricate drought phenomena over IGP region in India.

Impacts of Sea Surface Temperature Variability in the Indian Ocean on Drought Conditions over India during ENSO and IOD Events

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