Fast response of satellite fluorescence-derived plant physiology to drought stress

Domain: chemistry
Published: 2026-02-27
Editorial outcome: major_revision

Abstract
The differences in response times between vegetation physiology and vegetation structure to water stress at the global scale remain unclear. Here, we integrate solar-induced chlorophyll fluorescence satellite observations, optical remote sensing indices, and hydrometeorological data to globally disentangle the sequence and related factors of vegetation physiological and structural responses to drought. We isolated fluorescence efficiency by normalizing solar-induced chlorophyll fluorescence by absorbed photosynthetically active radiation; We show that fluorescence efficiency is a robust proxy for ecosystem-level vegetation physiology and responds to drought within ~3 days, while structural changes emerge after ~12 days. The contrast in timing is clearest in humid regions, owing to the sustained soil moisture availability during the initial stage of drought. Physiological responses are more temporally aligned with changes in vapor pressure deficit, whereas structural changes coincide more with soil moisture dynamics, reflecting differing patterns of association under increasing drought stress. These findings advance mechanistic understanding of vegetation drought responses across the continuum from physiological to structural processes.

Introduction
Drought is one of the most prevalent extreme climate events globally, with its frequency, intensity, and duration increasing in a warmer world1. Because of prolonged water deficit, drought impairs photosynthetic efficiency2, inhibits carbon fixation in plants3, and may lead to plant hydraulic failure4,5. This can lead to individual plant mortality6 and potentially ecosystem-level dieback7. The response time of vegetation to drought stress reveals the time required for plants to exhibit detectable physiological and structural changes after sensing water scarcity. Quantifying these timescales not only enhances our understanding of ecosystem drought response but also enables more accurate prediction of drought impacts on land surface carbon and water balances.

Reflectance-based optical remote sensing to measure vegetation greenness and structure has been well established. Vegetation indices (VIs) such as the enhanced vegetation index (EVI) or the near-infrared reflectance of vegetation (NIRv) are effective in capturing key physical properties like density and coverage8,9. These indices are therefore widely used as proxies in ecosystem-scale studies of vegetation responses to drought10–13. Since the dynamics of these indices primarily reflect slow changes in plant tissues (e.g., above-ground biomass)9,14, their response to drought is generally lagged at the ecosystem scale14,15. This could introduce delays in representing drought-induced vegetation stress in global carbon cycle modeling15–17, assessments of ecosystem services18,19, and wildfire forecasting20,21.

Vegetation physiology typically exhibits a more rapid response to stress than vegetation greenness and structure22. Specifically, plants respond to increased transpiration demand and soil water deficit by adjusting several physiological processes, such as reducing stomatal conductance23. This reduction subsequently diminishes both transpiration and photosynthesis rates, which in turn leads to a decline in carbon fixation and biomass accumulation24, and triggers a series of ecosystem–atmosphere feedbacks25,26. Hence, physiological adjustments later manifest in greenness and structure. Vegetation physiological parameters, including maximum carboxylation efficiency, electron transport rate, and stomatal conductance, have been shown to exhibit greater sensitivity and faster response to stress than greenness and structure at both leaf and ecosystem levels27,28. However, ecosystem-level physiological drought responses remain understudied as vegetation physiological parameters have so far remained difficult to isolate unambiguously from space-based remote sensing.

Recent advances in solar-induced fluorescence (SIF) satellite observations provide new opportunities to study global physiological responses to drought. SIF is the release of energy in the form of radiation when excited chlorophyll molecules return to their ground state after absorbing sunlight. Closely associated with key physiological processes related to plant photosynthesis, SIF serves as a comprehensive proxy for vegetation physiology by capturing the dynamics of energy partitioning within the photosynthetic apparatus29. Under non-stress conditions, high SIF emission generally indicates active photosynthesis and high light use, whereas under stress (e.g., drought), changes in SIF can indicate shifts toward enhanced non-photochemical energy dissipation and reduced photosynthetic efficiency30. However, factors such as solar radiation and canopy properties govern the SIF signal to a great extent, and the fluorescence quantum yield (SIF_yield), the fraction of absorbed light re-emitted as fluorescence and leaving the canopy, represents only a small portion of the variability in SIF. This is, however, the component that is closely tied to vegetation physiology and regulated by photochemical and non-photochemical quenching. Consequently, isolating the physiological signal from these structural and radiative contributions is essential to accurately extract information on vegetation function and stress responses. Recent studies have also shown the potential of isolating SIF_yield to diagnose gross primary productivity, in specific vegetation types or under regional drought events25,31–39.

Here, we explore the response times of global vegetation physiology, greenness, and structure to drought stress at a daily scale. We consider satellite SIF observations as influenced by three factors: incoming radiation, vegetation canopy properties, and SIF_yield31,40,41. To quantify these factors, we leverage satellite observations of PAR and the fraction of absorbed PAR (i.e., fPAR) to compute the absorbed PAR (i.e., APAR; see “Methods”: Isolating vegetation physiology). Because SIF primarily responds to light regardless of stress conditions, we normalize it to account for variations in incoming radiation (i.e., SIF/PAR). Specifically, we consider SIF/PAR as a mixed proxy of vegetation canopy properties and SIF_yield, while SIF normalized by absorbed photosynthetically active radiation (SIF/APAR) isolates the SIF_yield itself, thus serving as a refined proxy for vegetation physiological functioning. Meanwhile, EVI, NIRv, fPAR and leaf area index (LAI) are considered as indicators of vegetation greenness and/or structure.

While the influence of soil moisture (SM) deficits during drought on vegetation dynamics has been well studied42–44, the impact of high vapor pressure deficit (VPD) during drought on plant physiology is less well understood45–47. As drought persists, SM is slowly depleted at scales that match the gradual structural impacts observed in vegetation. This temporal alignment may lead to a potentially misleading conclusion that SM is the dominant driver of vegetation decay during droughts. In contrast, VPD rapidly responds to factors such as air temperature, evaporation, radiation, and wind speed45,48–50, and it directly influences the plant economics at the leaf level, triggering faster physiological changes47,51. In response to increased VPD during drought, immediate physiological adjustments at the leaf level often precede significant declines in SM47,51. From this perspective, plant physiology may show an earlier response to atmospheric dryness (high VPD) than to SM deficits during droughts. Here, we simultaneously explore the effects of soil and atmospheric drought and leverage vegetation physiological indicators like SIF_yield for detecting earlier vegetation responses that are not reflected by traditional VIs. The evaporative stress index (ESI) is used to identify the most severe drought stress event between 2018 and 2022 for every terrestrial location on Earth. For these drought events, we then explore the covariance of the anomalies in VPD or SM with the anomalies in the SIF-derived and other VIs, and calculate the time lags of the cross-correlations to reveal mean vegetation response times.

Results
Impact of drought on vegetation
About 74% of global vegetated areas experienced at least one drought event persisting for over 30 days during the 5-year study period (Fig. 1). These drought events correspond well with major reported droughts (Fig. 1a), such as the 2019 European52,53, the 2019 eastern China54, or the 2018–2019 Australian55 events. Across both the Northern and Southern Hemispheres, drought occurrences predominantly cluster during summer months (Fig. 1b).

Drought stress is well correlated with vegetation decline globally (Fig. 2). We observe significant correlations (p < 0.01, two-tailed t-test) between the anomalies in SM or VPD and the anomalies in vegetation state across ~83% of the drought-affected land area for at least one VI, with ~93% of the significant correlations found to be positive. Regionally, the VI anomalies in southern North America, southeastern South America, southeastern Africa, Indonesia, and northeastern Australia are most strongly correlated with SM anomalies and VPD anomalies, consistent with previous studies56. Nearly identical patterns emerge when SM is taken from an alternative database (ERA5-Land instead of GLEAM, Supplementary Fig. 1), and they remain independent of the selection of vegetation indices (Supplementary Fig. 2), soil depth (Supplementary Fig. 3), spatial scale (Supplementary Fig. 4), and whether non-linear rather than linear relationships between variables are considered (Supplementary Fig. 5). Negative correlations are primarily concentrated in energy-limited high latitudes in the Northern Hemisphere, where increased radiation during drought may promote vegetation activity by warming temperatures toward, but not beyond, the optimal range for plant growth57. We also find some negative correlations in the Amazon region, in line with the well-documented findings of canopy greening during drought due to enhanced radiation and access to deep water reserves12,58–60.

The strong correlations between anomalies of EVI or LAI and SM anomalies indicate that SM is the primary factor associated with vegetation canopy changes during drought (Fig. 2), or that at least their temporal scale of variability matches, as previously reported by other studies42,56. However, the impact of SM on vegetation physiology (SIF/APAR, as a proxy for SIF_yield) is less pronounced (Fig. 2a), reflecting how vegetation physiology changes are faster than those of SM during drought, and may be governed by other factors such as VPD61.

Response time of vegetation to drought
The kernel density distribution curves of the global vegetation drought response time based on all available pixels are plotted in Fig. 3a, b. The curves indicate a significant global pattern in the response time of vegetation characteristics to drought, from physiology to greenness, and finally to structure. Specifically, among all VIs examined, SIF/APAR shows the most rapid response to drought (~3 days to VPD and ~5 days to SM), whereas LAI shows the most delayed response (~13 days to VPD and ~12 days to SM). This confirms the physiological character of SIF/APAR, which captures plant biochemical processes at the ecosystem scale. Furthermore, the enhanced sensitivity of SIF/APAR to VPD confirms that signal decomposition transforms SIF from a biophysical metric primarily sensitive to SM to a biochemical metric primarily controlled by VPD. These findings remain independent of the choice of SM dataset (Supplementary Fig. 6), specific vegetation index products (Supplementary Fig. 7), soil depth (Supplementary Fig. 8), spatial scale (Supplementary Fig. 9), and whether linear or non-linear relationships between variables are considered (Supplementary Fig. 10). The drought response times of SIF/PAR and SIF resemble those of EVI (Fig. 3a, b), indicating that their behavior is more influenced by variations in vegetation canopy structure and greenness. This is likely because SIF/PAR accounts for changes in incoming radiation but still retains the impact of fPAR, which predominantly reflects canopy structure. In contrast, SIF/APAR removes this structural component, isolating physiological changes.

Spatially, the response time of vegetation physiology to drought shows clear latitudinal variability (Fig. 3c, d). The shortest response time of SIF/APAR to drought occurs in intertropical regions, in line with other studies signaling the high sensitivity of vegetation to water deficits in these areas62–64, mostly driven by limitations in rainfall57,65 and temperature increases over the photosynthetic optimum57,66–69. A similar, albeit less distinct, spatial pattern is observed in vegetation greenness metrics (Fig. 3i, j), which may be attributed to the intrinsic coupling between leaf pigment dynamics and plant physiological mechanisms70.

Vegetation response to drought per biome and hydroclimatic regime
The correlations of the VI anomalies to VPD and SM anomalies across diverse biomes and hydroclimatic regimes remain consistent (Fig. 4a–h), but the response times vary (Fig. 4i–p). The response time of vegetation physiology (i.e. SIF/APAR) exhibits a gradual decrease along the aridity gradient from dry to wet, attributable to the enhanced drought-tolerance mechanisms evolved by vegetation in water-limited regions71–73. In humid environments, vegetation physiology is more responsive to VPD (Fig. 4l, p), consistent with site-level observations27,74. In the early stages of drought, the higher soil water availability in humid regions is insufficient to trigger plant stress responses in response to the declining SM, yet short-term VPD changes can rapidly regulate vegetation physiology at the leaf scale27,46,72,74.

Humid tropical broadleaf forest regions exhibit an almost immediate response to drought stress (Fig. 4l, p), likely due to high hydraulic vulnerability and stomatal sensitivity75,76. Croplands show slower drought responses relative to savannas and grasslands in dry sub-humid regions (Fig. 4o), presumably due to irrigation occasionally offsetting the rise in VPD and the declines in SM46,77. In general, the biome-specific analysis confirms the global observational patterns (Fig. 3), with physiological processes (SIF/APAR anomalies) showing faster response to atmospheric drought while morphological changes appear more in phase with soil water depletion.

Overall, our global analysis reveals distinct temporal sequences in vegetation responses to drought stress (Fig. 5), with physiological adjustments preceding changes in greenness and structural modifications. By decomposing satellite-derived fluorescence signals, we have demonstrated that vegetation physiology, measured through SIF/APAR, responds to drought immediately, particularly to VPD increases, while morphological changes manifest after weeks. This sequence suggests a mechanistic cascade in which VPD-induced physiological regulations function as early indicators of drought stress before changes in leaf pigments or canopy structure can be observed by traditional optical remote sensing proxies. The response patterns show marked spatial heterogeneity, with intertropical regions exhibiting heightened sensitivity to water stress. Notably, drought impacts exhibit a transition from patterns associated with VPD-linked physiological changes in the early stages to those aligned with SM-related morphological changes as drought stress persists. Factors such as drought frequency and intensity, legacy effects, and canopy overshooting may also influence vegetation responses to drought80,81; these factors are not explicitly accounted for, being a limitation of this study. In addition, compound events such as wildfires, plant diseases and insect pests, or other extreme disturbances occurring concurrently with drought, may affect the strength and timing of vegetation signals, and their potential impacts on vegetation responses warrant further investigation. Nevertheless, our results provide satellite-based evidence for the decoupling of physiological and morphological responses to water stress at the ecosystem scale, providing insights into global vegetation–drought interactions (Fig. 5). This refined mechanistic understanding of how vegetation responds to water stress across temporal and spatial scales at the physiological level has implications for climate prediction and the understanding of carbon and water balances26.

Methods
Vegetation data
A diverse array of satellite-based vegetation data was utilized to conduct a global study on vegetation physiological and morphological responses to drought, including SIF, EVI, NIRv, LAI, and fPAR.

The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5 Precursor provides global daily SIF observations with high spatial resolution82–84. Daily TROPOMI SIF data, retrieved at 743 nm, were gridded to the spatial resolution of 0.25° for the period from 1 May 2018 to 31 December 2022. SIF varies with changes in incoming radiation throughout the day at the leaf scale, even in the absence of drought stress. Since instantaneous SIF is an inadequate representation of the average state of vegetation photosynthetic potential over the course of a day, a day-length corrected SIF product was used82,84,85.

LAI was used as a proxy to measure vegetation structural characteristics. The LAI dataset, sourced from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD15A3H product (4 July 2002—31 December 2022, 500 m, 4-day), was spatially resampled to 0.25° using Google Earth Engine. A temporal linear interpolation was applied to obtain the daily LAI. The fPAR in MCD15A3H was processed in the same way as LAI and used to examine the effect of different vegetation metrics on the results.

EVI and NIRv were used to diagnose vegetation greenness, since they reflect changes in both canopy structure and pigment concentration. Daily EVI and daily NIRv were calculated from the MODIS MCD43C4 products (24 February 2000—31 December 2022, 0.05°, daily) according to Eqs. (1, 2), and then resampled to 0.25°.

EVI = 2.5 * (NIR - R) / (NIR + 6 * R - 7.5 * B + 1) (1)

NIRv = (NIR - R) / (NIR + R) * NIR (2)

where NIR is the near-infrared band, R is the red band, B is the blue band. 0.08 was used to eliminate the influence of soil background8. Only EVI and NIRv values in the range of 0 to 1 were retained.

Hydrometeorological data
ESI, the ratio of actual to potential evaporation, reflects transpiration and soil evaporation decrease under stress conditions86. ESI was employed to identify the most severe droughts during the study period (See Section: Data pre-processing). Daily ESI data were obtained from the Global Land Evaporation Amsterdam Model (GLEAM) v3.7b87,88 at the spatial resolution of 0.25° (1 January 2003—31 December 2022). Using the ESI for drought event identification allows us to examine whether there are differences in vegetation response to soil drought and atmospheric drought within the same drought event.

SM and VPD were used as indicators of soil drought and atmospheric drought, respectively. Surface SM daily data with the same resolution and period as the ESI were also sourced from GLEAM v3.7b. Root zone SM in GLEAM v3.7b was used to test the influence of soil depth. To avoid dependence effects between GLEAM surface SM data and GLEAM ESI data, ERA-5 Land SM data were additionally used.

Global daily 0.25° air temperature (AT) and relative humidity (RH) were obtained by downscaling observations from the Atmospheric Infrared Sounder (AIRS). Global daily 0.25° VPD was then calculated according to Eq. (3)89,90.

VPD = 610.78 * exp (17.2694 * AT / (AT + 237.3)) * (1 - RH / 100) (3)

where VPD (Pa) represents the difference between saturated and actual vapor pressure, AT is near-surface air temperature (°C), and RH is relative humidity (%).

PAR data were sourced from the Clouds and the Earth’s Radiant Energy System (CERES) SYN1deg Ed4.1 product (1 May 2018 – 31 December 2022, 1°, daily). The all-sky surface PAR direct flux and the all-sky surface PAR diffuse flux were summed to represent surface total incoming radiation. The surface total incoming radiation data were resampled to 0.25° using a spatial linear interpolation method.

Data pre-processing
All data were resampled to the spatial resolution of 0.25° and daily temporal resolution. Masks were applied using snow water equivalent (SWE) data from GlobSnow and the land cover mask band in the TROPOMI SIF product to mitigate the effects of snow cover and include only terrestrial ecosystems. Data points with SWE > 0 were excluded, and pixels identified as water bodies or bare land were filtered out. Data with quality control band values below 0.582,84 and cloud fraction greater than 50% were excluded to ensure SIF accuracy. The choice of cloud fraction is motivated by the trade-off between accuracy and amount of data (Supplementary Fig. 11). SIF was further decomposed into SIF/APAR to compute SIF_yield as a proxy of vegetation physiology (see Section: Isolating vegetation physiology).

Our focus is on the interaction between vegetation and water stress during anomalous decreases in ESI. First, long-term trends were extracted using a locally weighted smoothing function91, with the overlap period set to 40% of the observation period; the influence of this parameter is limited (Supplementary Fig. 12). Subsequently, the anomalies were derived by subtracting the long-term trend and the daily average seasonal cycle from the original daily observations, with the period from 1 May 2018 to 31 December 2022 used as reference. A spatiotemporal smoothing using a 15-day and 3×3-pixel moving window was applied to reduce random errors. The 15-day window strikes a balance between capturing short-term dynamics and maintaining data continuity (Supplementary Fig. 13).

To identify the most severe drought event locally between 1 May 2018 and 31 December 2022, fluctuations for which the ESI anomalies remain below zero during at least 30 consecutive days were first detected (Supplementary Fig. 14a). Events separated by less than 30 days were merged into a single episode if no precipitation occurred in the interim. The droughts between 1 July 2018 and 31 October 2022 were further screened out (Supplementary Fig. 14b) to ensure the accuracy of the response time results (see Section: Response time to drought). The drought event with the most negative ESI anomaly was selected as the most severe drought event (Supplementary Fig. 14c). The first date on which the ESI anomaly dropped below 0 during the selected drought event was taken as the drought onset. The entire drought period was defined as extending from 30 days prior to the drought onset until the end of the drought.

The MODIS MCD12C1 land cover product was used to diagnose drought response times across different vegetation types. To minimize the influence of land cover change and non-vegetated surface, only pixels with consistent vegetation type over the five-year period (2018–2022) and a vegetation cover exceeding 50% were included in the analysis. The vegetation cover was defined as the proportion of vegetated pixels within each 0.25° grid in the raw land cover data. The dominant vegetation type corresponds to the one covering >50% of the pixel; mixed pixels without a dominant vegetation type were not further analysed. Ecosystem types in focus are forests, savannas, grasslands and croplands (Supplementary Fig. 15). All data processing was implemented in Python 3.10.4.

Isolating vegetation physiology
The satellite-observed SIF signal is conceptualized in a way that accounts for incoming solar radiation, canopy structure, and SIF_yield92, thereby enabling the isolation of plant physiological signals92:

SIF = PAR * fPAR * SIF_yield = APAR * SIF_yield (4)

where PAR is photosynthetically active radiation, fPAR is the fraction of absorbed photosynthetically active radiation, SIF_yield is the fluorescence quantum yield, known as the SIF photons re-emitted from the canopy, and it was computed as SIF/APAR in this study. APAR is absorbed photosynthetically active radiation. Then SIF/PAR can be computed as:

SIF/PAR = fPAR * SIF_yield (5)

SIF/PAR reflects the product of fPAR and SIF_yield, and was obtained hereby dividing SIF by PAR. SIF/APAR was calculated as follows, with values falling below the 1st percentile or exceeding the 99th percentile being excluded from further analysis.

SIF/APAR = SIF / (PAR * fPAR) (6)

The ratio SIF/APAR used here can be regarded as a combined metric that implicitly incorporates both the intrinsic SIF_yield and the fluorescence escape probability (f_esc), since the f_esc is not explicitly separated in our formulation. This treatment is consistent with the definition of apparent SIF_yield by Wang et al.93

Response time to drought
Response times were determined by calculating cross-correlations between the VIs and drought indicators during drought. The drought index anomaly time series was shifted sequentially within a lag window of −5 to +31 days, in 1-day steps. For each shift, Pearson’s correlation and its corresponding p-value between the vegetation index anomalies and the drought index anomalies were calculated. Vegetation activity is theoretically expected to decay after the onset of drought; however, considering measurement errors, the correlation peak (trough for VPD) closest to zero with p-value (two-tailed, t-test) < 0.01 within the lag window range of −5 to +31 days was searched. The correlation and lag of the first local peak (or trough) closest to the 0-point was defined as the covariation and response time between vegetation and drought. In the ideal case, the distribution of values in the correlation array is unimodal (Supplementary Fig. 16a) with a single peak value. However, in practice, a more complex correlation pattern was occasionally observed, with multiple peaks (Supplementary Fig. 16b). To capture the first response of vegetation to drought, we used the peak index closest to zero (zero in the lag range) as the response time within the lag range of −5 to +31 days (the gray area in Supplementary Fig. 16), provided that the p-value (two-tailed, t-test) < 0.01. Pixels where no p-value was significant (i.e., no p-value < 0.01) were removed from further analysis. This rule was used for all vegetation physiological and structural indices in this study. A 3×3-pixel mean filter was applied to spatially smooth the results. Spearman’s correlation was additionally used to examine the influence of potential non-linear relationships between vegetation and drought stress on the results (Supplementary Fig. 5, Supplementary Fig. 10).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript studied the responses of vegetation physiology and vegetation structure during drought events with SIF and other vegetation products. The idea is very interesting. It contributes to fully understand vegetation drought responses across the continuum from physiological to structural process. However, I don not think the current version of the manuscript can meet the acceptation requirements of Nature communication - high impact journal. The main issues include: 1) linear correlation method is not enough to detect the non-linear coupling correlation between vegetation and atmosphere; 2) whether the SIF/APAR, SIF/PAR can comprehensively represent the vegetation physiology and vegetation structure, respectively; 3) both EVI and LAI are frequently used to represent vegetation greenness in various studies, while here the authors used EVI and LAI to represent vegetation structural characteristics and vegetation greenness, respectively; and 4) the observations-based validations needed for confirming the findings. Please see the specific comments below.\n1. SM and VPD were used as indicators of soil drought and atmospheric drought. Actually, there is strong coupling between SM and VPD. For example, in figure 2, there is very similar spatial pattern between SM and VPD. We can not find which factor (SM or VPD) affecting the response time of vegetation during drought.\n2. The definition of drought: the authors use ESI (ET/PET) to identify droughts. It reflects the water stress on ET of land (vegetation and soil). For vegetation, it has the self-regulation function when meeting water deficiency. Sometimes, when drought happens, the transpiration does not decrease despite the stomata closes. See the paper https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.16485. In figure 1, the authors show the drought occurrence time, however, the results do not compare with that of traditional definitions with SPEI/PDSI. The ESI-based drought identifications may be not suitable here. This study focused on meteorological and soil moisture droughts, so it is better to use the indices of the meteorological and soil moisture droughts to identify different droughts. That is, which droughts the authors focused here, meteorological or soil moisture droughts? The ESI-based droughts identifications could cover the meteorological and soil moisture droughts?\n3. In the equation (4), the authors used the LUESIF term. It is weird for me. Because LUE usually represent light use efficiency. This paper used fluorescence quantum yield, rather than LUE (https://www.nature.com/articles/s41467-022-29009-w).\n4. As said above, the indictors for vegetation physiology need to consider more parameters, such as stomatal regulation, water use efficiency. I am worrying about if SIF/APAR, SIF/PAR accurately detect vegetation activities.\n5. For SM and VPD, the authors used surface soil moisture of GLEAM, what depth it can characterize soil moisture variations, why not use rootzone or one-meter soil moisture; the equation 3 was used to calculate VPD, it is my first time to see the equation, do you compare it with other common methods of estimating VPD.\n6. Response time to drought: the authors used the cross-correlations to calculate the response time of vegetation activities to drought. Why not directly use the indicators’ variation for vegetation physiology, vegetation structure to get response time?\n7. The figure 5 shows the vegetation response to drought in different scales. In no drought, the stomatal is opening, while it closed in the early stage of drought. I think it should be calculated with different stomatal models to confirm the changes of stomatal. And, same for chlorophyll degradation and plasmolysis.\n8. Did you just selected the most severe drought to conduct this study? Despite you can get these droughts at each grid cell, the responses of vegetation to droughts may have different responses due to different drought severity. So, how did you consider different drought severity may cause different responses time of vegetation to droughts? In figure 2b, it is better to use the different colors to represent the drought onset.", "reviewer_2": "This study investigates how vegetation responds to drought by distinguishing physiological and structural changes at a global scale. Using satellite-based chlorophyll fluorescence (SIF), optical indices, and environmental data, the authors show that physiological responses (captured by SIF/APAR) occur rapidly—within 2 days—while structural changes (captured by EVI, LAI,…) lag behind, appearing around 12 days later. The authors find that atmospheric conditions (i.e., VPD) initially drive plant stress, shifting to soil moisture control as drought intensifies.\nI congratulate the authors on what is a very interesting piece of work. Although some of the reported knowledge is established through plot scale investigations and via experimental research, this study provides evidence at global scale and improves understanding of the timing and drivers of vegetation drought responses, with implications for monitoring ecosystems at large scale. The data and methods are mostly well described, findings are presented in a comprehensive and high-quality form. Yet more caution in the interpretation of the findings seems required, as the dataset and analysis do not support all conclusions.\nI have two major concerns.\nThe first is the coupling between SM and VPD that does not allow to disentangle the impact of one environmental driver over the other. Attempts at disentangling effects of SM and VPD rely on experimental research where manipulation of VPD is possible independently from SM. Alternatively, under natural conditions the study by Liu et al. 2020 used bins of SM or VPD and assessed at each SM bin the difference in SIF between max and min VPD bins and vice versa for each VPD bin. I encourage the authors to test a similar approach if the aim is to investigate impact / causality and if the time series allows for it. Alternatively, the text should be modified to reflect the discrepancy between individual driver causality and simple temporal alignment. There are several points in the text (including in the Highlights) where this could be clarified (e.g., LL 29-31; 99-102). Relevant references: Liu et al. 2020 (https://doi.org/10.1038/s41467-020-18631-1) and Lu et al. 2022 (https://doi.org/10.1038/s41467-022-29009-w).\nThe second point that deserves attention is the fact that structural and physiological vegetation responses to drought often follow nonlinear temporal trajectories, that vary among ecosystems and species. Often responses are originating from cumulative stress, so drought frequency rather than drought intensity could play a role. Moreover, legacy and carry-over effects could act in both directions, e.g., see structural overshoot—where excessive canopy buildup from abundant precipitation in the previous years can lead to impaired functioning during a subsequent dry year.\nThese study limitations should be acknowledged in the dicussion section.\nDetailed comments to the authors:\nLL11: second Highlight, please add “structure” and/ or “greenness” vegetation indices, as there are optical indices that reflect physiological changes in LUE (e.g. PRI) or in WUE that exhibit a rapid response to drought.\nLL 39: consider replacing “observable” with “detectable” or similar, as physiological changes are mostly pre-visual\nLL 45: consider replacing “high degree of sophistication” with “well established” or other expression.\nLL 47: “density and coverage”, below you write “biomass and morphology”, all correct but confusing, maybe introduce as landscape vs plot scale..\nLL 52: lagged rather than underestimated?\nLL 61: I suggest removing “diagnostics” as it is not the diagnostics that shows higher sensitivity and faster response but rather the measured processes\nLL 66: Do not leave the reader guessing, which are (were) the limitations?\nLL 70-71: This is too vague, please expand a bit to explain the link between SIF and photochemistry under stress and non-stress conditions. The fluorescence pathway accounts for a minor portion of the total excitation energy and is not a major route for energy dissipation, however it offers important quantitative insights into other energy pathways—especially regulated non-photochemical and photochemical processes. I know you partly touch upon it in the Methods, but I think it would be good to briefly set the stage here.\nLL 72-75: Similar comment as above. This sounds too vague, and you leave the reader waiting until reaching the Methods to understand what is meant here. I suggest being specific and still concise, mentioning what are the “other” dominant parts of the SIF signal.\nLL 85-87: Consider clarifying here that independently from (drought) stress level, SIF primarily responds to light so normalization by APAR or PAR would allow to account for this.\nLL 95: Consider replacing “atmospheric dryness” with atmospheric water (or moisture) content.\nFig. 1: Specify year of “major” drought event (from LL335-337: \"The drought event in which the most negative ESI anomaly is located over the entire record was identified as the most severe drought event\"). The term \"onset\" is not needed here.\nLL 131: Are you referring to temperature only above or also below the optimum? In the latter, you would need to replace “exceeding”.\nLL 168: “due to unmitigated vegetation canopy signal effects” is too vague, please be specific.\nLL 175: Double check, this range of subplots does not include only greenness metrics.\nLL 196: Isohydricity is a continuum, and differences exists among tree species and even for the same tree species on different soil types. I suggest to at least add “mostly” or similar when referring to an isohydric behaviour.\nLL 197: I do not see this clearly in Fig. 4l,p. What rather sticks out is the EVI lag for NF.\nFig. 5: Consider placing this figure earlier in the MS. It could help to formulate hypotheses if you wanted to add some. Anyhow, I think currently it is slightly misplaced. LL 247: \"external\" is redundant here, in plant science one usually refers to leaves morphology vs anatomy. In the figure itself, please change \"Pigment response\" to “Chlorophyll pigment response”, to avoid confusion with other sets of pigments (e.g., Xanthophylls) responding to stress at much shorter time scales (hours). I recommend writing out all acronyms used in the figure and/or captions as figure should in general be self-standing.\nLL 262: Consider replacing \"environmental stress\" with drought stress since high irradiance also counts as an environmental stress leaves are subjected to even under \"normal\" water availability conditions. Only a small fraction of absorbed light can be used for photosynthesis, the rest has to be safely dissipated through NPQ pathways.\nLL 265: Daylength-corrected SIF reflects total daily potential for photosynthesis-related fluorescence. I suggest changing \"intensity\" with \"potential\".\nLL 283: If I am not mistaken, ESI was never written out as Evaporative Stress Index. Please carefully check that all acronyms are written out at first mention.\nLL 288: Could you add an explanation how the “onset” was determined and how it is defined also with reference to Extended Data Fig. 10.\nLL 317: here and elsewhere in the text consider referring to “proxy of” vegetation physiology (also structure for EVI, etc.).\nLL 324: Please explicitly define somewhere reference period for anomaly calculation.\nLL 332-333: I am a bit sceptical about this approach, 30 days is a long and arbitrary period. Did you set any additional rule on the period separating two events before merging? For example no Precip above X\nLL 358: Please explain how you came to this fesc value.\nLL 367-368: See previous comment, how was drought onset defined and why 30 days prior to the drought onset?\nExtended Data Fig. 12: I assume this is a schematic representation and not based on real data, please consider writing “Schematic representation of response time determination”.\n(Remarks on code availability)\nI could not access the code. On the Zenodo interface it states: \"The record is publicly accessible, but files are restricted to users with access.\"", "reviewer_author_response": "Beyond the requested changes, we have made stylistic adjustments to enhance the overall readability and flow of the text.\nResponse to Reviewer #1:\nThe manuscript studied the responses of vegetation physiology and vegetation structure during drought events with SIF and other vegetation products. The idea is very interesting. It contributes to fully understand vegetation drought responses across the continuum from physiological to structural process. However, I don not think the current version of the manuscript can meet the acceptation requirements of Nature communication - high impact journal. The main issues include:\nComment #1:\n1) linear correlation method is not enough to detect the non-linear coupling correlation between vegetation and atmosphere;\n– Response: Thank you for your consideration. The advantages of using linear correlation are that it is a simple metric, not prone to overfitting, and easy to interpret. This is why many papers published in high-impact journals in this field use linear correlation methods (Huang et al., 2017; Li et al., 2023; Liu et al., 2020). We agree with the philosophy that if a simple method can effectively address the problem, then more complex methods should be avoided unless strict causal relationships are a requirement to address the scientific hypotheses, which is not really the case in our study.\nWe also note that we do not compute the correlations using the raw variables, but rather their anomaly values, since we are interested in dry extremes relative to the local climatology. This helps to eliminate some of the non-linear relationships present in the raw variables.\nNonetheless, we recalculated the correlations and response times using a non-linear correlation method (Spearman’s correlation), and the results do not change our main conclusions.\n– Action: We revised the manuscript to ensure that no explicit claims of causality were introduced based on the computation of correlations. Moreover, the results of non-linear correlations are now included in the Supplementary Information (Supplementary Fig. 5, Supplementary Fig. 10).\nWe also made changes to the following sentences:\nLL 126 The statement “Nearly identical patterns emerge based on ERA5-Land SM (Supplementary Fig. 1), and they remain independent of the selection of vegetation morphological indices (Supplementary Fig. 2) or spatial scale (Supplementary Fig. 3).”\nWas changed for LL 117 “Nearly identical patterns emerge when SM is taken from ERA-5-Land instead of GLEAM (Supplementary Fig. 1), and they remain independent of the selection of vegetation indices (Supplementary Fig. 2), soil depth (Supplementary Fig. 3), spatial scale (Supplementary Fig. 4), and whether non-linear rather than linear relationships between variables are considered (Supplementary Fig. 5).”\nLL 163 The statement “These findings remain independent of the choice of SM dataset (Supplementary Fig. 4), vegetation morphological indices (Supplementary Fig. 5), and spatial scale (Supplementary Fig. 6).”\nWas changed for LL 157 “These findings remain independent of the choice of SM dataset (Supplementary Fig. 6), specific vegetation index products (Supplementary Fig. 7), soil depth (Supplementary Fig. 8), spatial scale (Supplementary Fig. 9), and whether linear or non-linear relationships between variables are considered (Supplementary Fig. 10).”\nLL 379 New content was added “Spearman’s correlation was additionally used to examine the influence of potential non-linear relationships between vegetation and drought stress on the results (Supplementary Fig. 5, Supplementary Fig. 10).”\nComment #2:\n2) whether the SIF/APAR, SIF/PAR can comprehensively represent the vegetation physiology and vegetation structure, respectively;\n– Response: Thank you for your comment.\nSIF/APAR (i.e., SIFyield) represents the proportion of chlorophyll-absorbed light energy that is re-emitted as fluorescence, i.e., fluorescence yield. It serves as an integrated indicator of the physiological status related to photosynthesis. For example, (Fu et al., 2021)) found through field measurements that SIF/APAR is strongly correlated with the maximum carboxylation rate (Vcmax) and the maximum electron transport rate (Jmax).\nSIF/PAR represents SIF corrected for PAR, providing a way to normalize SIF and make it comparable across time and space. It is basically the product of fPAR and SIFyield, thus integrating both canopy structural and physiological information of vegetation (Maes et al., 2020; Pagán et al., 2019). Therefore, one can expect the drought response of SIF/PAR to fall between that of structural indicators (like LAI) and physiological indicators (like SIFyield). Our results successfully support this hypothesis.\n– Action: The original sentences are somewhat unclear. We made changes as follows.\nLL 85 The statement “We consider SIF/PAR as a diagnostic of both vegetation structure and fluorescence emission efficiency…”\nWas changed for LL 91 “Specifically, we consider SIF/PAR as a mixed proxy of vegetation canopy properties and SIFyield…”\nLL 317 The statement “SIF was further decomposed into SIF/PAR and SIF/APAR to measure vegetation physiology (see Section: Isolating vegetation physiology).”\nWas changed for LL 314 “SIF was further decomposed into SIF/APAR to compute SIFyield as a proxy of vegetation physiology (see Section: Isolating vegetation physiology).”\nComment #3:\n3) both EVI and LAI are frequently used to represent vegetation greenness in various studies, while here the authors used EVI and LAI to represent vegetation structural characteristics and vegetation greenness, respectively;\n– Response: Thank you for your consideration.\nLAI, defined as the total leaf area per unit ground surface area, directly reflects the geometric structure of vegetation canopy. Based on this definition, we use LAI to describe vegetation canopy structural characteristics (the coverage of vegetation canopy leaves), e.g. diagnosing whether leaves have fallen or are still on the branches. That is what is frequently referred to as “structure” or “morphology”.\nEVI is an optical reflectance-based index. Because it incorporates both the NIR and red bands in its calculation, EVI captures mixed information related to vegetation, including both structure (e.g. leaf coverage) and pigment concentration. Therefore, we use EVI as an indicator of “greenness”.\n– Action: We made the following changes to the original content:\nLL 267 The statement “LAI and fPAR are measures of vegetation structural characteristics, describing both the vertical and horizontal distribution of the canopy. The LAI and fPAR dataset sourced from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD15A3H product (4 July 2002 – 31 December 2022, 500 m, 4-day) were spatially resampled to 0.25° using Google Earth Engine and a temporal linear interpolation was applied to obtain the daily LAI and daily fPAR.”\nWas changed for LL 265 “LAI is used as a proxy for to measure vegetation structural characteristics. The LAI dataset, sourced from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD15A3H product (4 July 2002 – 31 December 2022, 500 m, 4-day), was spatially resampled to 0.25° using Google Earth Engine. A temporal linear interpolation was applied to obtain the daily LAI. The fPAR in MCD15A3H was processed in the same way as LAI and used to examine the effect of different vegetation metrics on the results.”\nLL 273 The statement “EVI and NIRv are optical expressions of vegetation greenness.”\nWas changed for LL 271 “EVI and NIRv are used to diagnose vegetation greenness, since they reflect changes in both canopy structure and pigment concentration.”\nComment #4:\nand 4) the observations-based validations needed for confirming the findings.\n– Response: Thank you for your consideration.\nWe acknowledge the reviewer's concern, which is a common limitation of remote sensing-based approaches. Indeed, remote sensing data are not a replacement for ground-based measurements but rather a complementary tool that enables large-scale and consistent monitoring of land surface processes. Validation in global-scale remote sensing studies is a widely acknowledged challenge. Conducting field observations at a global scale would require tremendous resources, which is precisely why remote sensing techniques are deployed – to allow us to observe and understand surface processes from a broader perspective and at a lower cost.\nThe use of remote sensing-based analysis without conducting specific ground validation is supported by numerous peer-reviewed studies in this field of research (Feldman et al., 2024; Huang & Zhai, 2025; Jiao et al., 2021; Li et al., 2023; Liu et al., 2020; Smith & Boers, 2023; Yao et al., 2023). Here, we have ensured that the observed patterns are robust by (1) using multiple vegetation indices, (2) using different soil moisture datasets, (3) applying them over different spatial scales, (4) applying linear and non-linear correlation methods, and (5) conducting cross-correlation analysis to link vegetation signals specifically to drought events, minimizing the influence of unrelated disturbances and small-scale processes that may not be representative at pixel scales.\nFrom a data science standpoint, our study treats remote sensing data as large-scale observational data for analyzing the physiological dynamics of vegetation canopies. In this sense, we believe our findings are themselves observation-based. We also believe that our findings provide valuable insights into large-scale vegetation dynamics under drought.\nWe note that standardized ground-based observational datasets (e.g., FLUXNET) do not include SIF measurements, making the validation of our results at the global scale virtually impossible at present.\nComment #5:\nPlease see the specific comments below.\n1. SM and VPD were used as indicators of soil drought and atmospheric drought. Actually, there is strong coupling between SM and VPD. For example, in figure 2, there is very similar spatial pattern between SM and VPD. We can not find which factor (SM or VPD) affecting the response time of vegetation during drought.\n– Response: Thank you for your consideration.\nSoil and atmospheric drought are dependent, so are vapor pressure deficit (VPD) and soil moisture (SM). However, the coupling between SM and VPD needs to be considered in the context of their specific temporal scales. In the long term period, VPD is well correlated with SM. However, as we introduced in the main text, short-term variations in SM during drought are relatively slow, whereas VPD responds quickly to changes in meteorological conditions, in particular to air temperature, so the coupling between VPD and SM tends to weaken over shorter timescales (Novick et al., 2016).\nSM and VPD exhibit similar spatial patterns in Figure 2, but it is important to note that the correlation shown for each pixel is based on a different time lag (see Section: Response time to drought). For each pixel, we calculated the time lag as the response time, and used the correlation at that lag to represent the relationship between vegetation and VPD (or SM). The results show that, after accounting for the time lag, both increasing VPD and decreasing SM seem to suppress vegetation activity. This explains why similar spatial patterns for VPD and SM are observed in Figure 2.\nWe calculated the correlation between vegetation and VPD (or SM) assuming a zero time lag for all pixels, and found that the resulting spatial pattern is different (see Figure R1), especially for LAI. This figure is provided solely to support our response and is not included in the Supplementary Information.\nFigure R1. Correlations between vegetation physiology and morphology and drought under a zero time lag\nThe key message from Figure 2 is that both soil and atmospheric drought exert negative impacts on vegetation. As for the time scales at which these two factors begin to affect vegetation, this is addressed in the following subsection that is specifically dedicated to exploring the response time.\nImportantly, Figure 2 does not provide information about “which factor (SM or VPD) affects the response time of vegetation during drought.” Our intention is not to make such a claim. Instead, in this work, our goal is to characterize the response time of vegetation indices to SM and VPD, respectively, rather than to determine which factor controls the response time of vegetation the most. We agree that the latter would require a causal inference analysis that would go beyond the use of correlations (see response to Comment #1); however, the peak time of cross correlation is a robust means to estimate the timing of vegetation response (Papagiannopoulou et al., 2017).\n– Action: In the caption of Figure 2, we specifically clarified that the correlation values shown in the figure refer to the results at the peak position of the cross-correlation.\nLL 138 New content was added “The correlations refer to the correlation values at the peak position in the cross-correlation results (See Method: Response time to drought).”\nComment #6:\n2. The definition of drought: the authors use ESI (i.e., ET/PET) to identify droughts. It reflects the water stress on ET of land (vegetation and soil). For vegetation, it has the self-regulation function when meeting water deficiency. Sometimes, when drought happens, the transpiration does not decrease despite the stomata closes. See the paper https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.16485. In figure 1, the authors show the drought occurrence time, however, the results do not compare with that of traditional definitions with SPEI/PDSI. The ESI-based drought identifications may be not suitable here. This study focused on meteorological and soil moisture droughts, so it is better to use the indices of the meteorological and soil moisture droughts to identify different droughts. That is, which droughts the authors focused here, meteorological or soil moisture droughts? The ESI-based droughts identifications could cover the meteorological and soil moisture droughts?\n– Response: Thank you for your consideration. Plants play a critical role in the global water cycle, as a substantial portion of water moves from the soil to the atmosphere through vegetation. Both soil moisture status and atmospheric moisture conditions can influence the water transport process. Therefore, when analyzing the response time of vegetation to drought stress, we aim to consider drought as a combined effect of both soil and atmospheric water stress. The definition of the ESI aligns well with our objective, as ET represents the land surface’s capacity to supply water, while PET reflects the atmospheric demand for water. It is an indicator of vegetation/ecosystem drought, not an indicator of meteorological or agricultural drought like the more traditional drought indices that the referee mentions.\nIf stomata close, transpiration goes down; simply based on the definition of resistance in a diffusion process. We believe that what the referee refers to is the compensating effect of VPD on transpiration, which on the one hand increases resistance and on the other hand rhz demand. If the demand-effect were to increase beyond the resistance-effect, leading to an increase in transpiration despite the stomatal closure, this would still reflect as a decline in ESI: PET would increase disproportionally compared to ET. This is what makes ESI such a good index, able to reflect the actual VPD and soil moisture constraints on ecosystem function.\nAlthough sometimes stomatal closure cannot entirely prevent transpiration, in this study, we do not directly investigate the vegetation response time to ESI itself. Instead, ESI is used solely to identify stress periods. Within these ESI-defined drought periods, we calculate the response times of vegetation to soil moisture deficit (represented by SM) and atmospheric water deficiency (represented by VPD) respectively. This approach ensures the comparability of response times and facilitates our analysis of the sequence in which vegetation responds to changes in soil and atmospheric water.\nWhen analyzing vegetation response time to drought stress, it is essential to consider the differences between vegetation physiology and structure. It is well established that vegetation physiological processes respond to drought stress earlier than structural changes. However, physiological changes often occur rapidly, which requires the use of high-temporal-resolution indicators to identify real-time water deficits at the daily scale. This enables us to capture the short-term dynamics between drought stress and physiological responses. Therefore, while indices like SPEI and PDSI are highly effective for characterizing long-term drought, they are not suitable for our research objective.\nIn conclusion, we believe it is more appropriate to use an index that reflects ecological or vegetation drought, which is the central focus of this study. A recent review has categorized ESI as an indicator of ecological drought (Novick et al., 2024), making it indeed more suitable for our research than SPEI or PDSI.\nComment #7:\n3. In the equation (4), the authors the LUESIF term. It is weird for me. Because LUE usually represent light use efficiency. This paper used fluorescence quantum yield, rather than LUE (https://www.nature.com/articles/s41467-022-29009-w).\n– Response: Thank you for your careful comment. “LUESIF” is replaced with “SIFyield”. Please see LL 352.\nComment #8:\n4. As said above, the indicators for vegetation physiology need to consider more parameters, such as stomatal regulation, water use efficiency. I am worrying about if SIF/APAR, SIF/PAR accurately detect vegetation activities.\n– Response: Thank you for your consideration. As we have already answered in Comment #2, we agree that SIF/PAR does not relate strongly to vegetation physiology, but SIF/APAR can be considered a proxy for vegetation physiology. For the potential of SIF/APAR as a physiological indicator, we refer to Fig. 3 and Fig. 5 in Fu et al., 2021.\nVariables such as stomatal regulation and water use efficiency cannot be directly measured through remote sensing. To break this bottleneck, one of the core objectives of our study is to show that SIF/APAR can serve as an effective proxy for canopy-level physiological activity at the global scale. Our results successfully support the physiological relevance of SIF/APAR across global ecosystems, with dynamics and response times that agree with common expectations based on ground-based studies (Chen et al., 2021; De Cannière et al., 2021; Helm et al., 2020).\nTherefore, SIF/APAR represents the most advanced remotely sensed measurement currently available for large-scale plant physiological studies.\nComment #9:\n5-1. For SM and VPD, the authors used surface soil moisture of GLEAM, what depth it can characterize soil moisture variations, why not use rootzone or one-meter soil moisture;\n– Response: Thank you for your valuable comment.\n– Action: We replaced surface soil moisture with root zone soil moisture and the results did not change substantially. We included new figures to support this claim in the supplementary material. We referred to them in the main text:\nLL 126 The statement “Nearly identical patterns emerge based on ERA5-Land SM (Supplementary Fig. 1), and they remain independent of the selection of vegetation morphological indices (Supplementary Fig. 2) or spatial scale (Supplementary Fig. 3).”\nWas changed for LL 117 “Nearly identical patterns emerge when SM is taken from ERA-5-Land instead of GLEAM (Supplementary Fig. 1), and they remain independent of the selection of vegetation indices (Supplementary Fig. 2), soil depth (Supplementary Fig. 3), spatial scale (Supplementary Fig. 4), and whether non-linear rather than linear relationships between variables are considered (Supplementary Fig. 5).”\nLL 163 The statement “These findings remain independent of the choice of SM dataset (Supplementary Fig. 4), vegetation morphological indices (Supplementary Fig. 5), and spatial scale (Supplementary Fig. 6).”\nWas changed for LL 157 “These findings remain independent of the choice of SM dataset (Supplementary Fig. 6), specific vegetation index products (Supplementary Fig. 7), soil depth (Supplementary Fig. 8), spatial scale (Supplementary Fig. 9), and whether linear or non-linear relationships between variables are considered (Supplementary Fig. 10).”\nLL 291 New content “Root zone SM in GLEAM v3.7b was used to test the influence of soil depth.”\nComment #10:\n5-2. the equation 3 was used to calculate VPD, it is my first time to see the equation, do you compare it with other common methods of estimating VPD.\n– Response: Thank you for your consideration. We calculated the saturated vapor pressure using the Tetens equation, and then derived VPD using relative humidity (RH). Please see the references (Murray, 1967; Valiantzas, 2013). The value “238.3” in the equation was a typographical error, the correct constant should be “237.3”.\n– Actions: We have corrected the equation. Moreover, we added the references to the studies mentioned above. Please see LL 297-298.\nComment #11:\n6. Response time to drought: the authors used the cross-correlations to calculate the response time of vegetation activities to drought. Why not directly use the indicators’ variation for vegetation physiology, vegetation structure to get response time?\n– Response: Thank you for your consideration. By computing cross-correlations we are in fact using the indicator's covariation. Besides, we are comparing anomalies, not just the raw variables, and then using cross-correlations to determine the delay in response between the two variables.\nComment #12:\n7. The figure 5 shows the vegetation response to drought in different scales. In no drought, the stomatal is opening, while it closed in the early stage of drought. I think it should be calculated with different stomatal models to confirm the changes of stomatal. And, same for chlorophyll degradation and plasmolysis.\n– Response: Thank you for your consideration.\nWe appreciate your suggestion to validate the stages shown in Figure 5 through stomatal modeling and simulations of chlorophyll degradation and plasmolysis. However, we would like to clarify that Figure 5 is intended as a conceptual illustration, not a mechanistic or quantitative representation. Its purpose is to visually communicate the general progression of plant physiological and structural responses to increasing drought stress, as widely recognized in the plant physiology literature.\nStomatal closure in response to drought is a well-established and broadly accepted physiological process. Plants will close stomata at different stages of drought as a function of isohydricity and plant access to water, it is not in question. Similarly, chlorophyll degradation and eventual leaf senescence or abscission under prolonged drought are commonly observed patterns in plant drought response.\nTherefore, we respectfully believe that adding stomatal modeling or simulation-based validation would go beyond the intended scope of this conceptual figure. We have revised the figure caption, where appropriate, to emphasize its conceptual nature and avoid any misunderstanding that it represents modeled or empirical data.\n– Action: We modified the caption of Figure 5.\nLL 244 The caption “Vegetation response to drought from leaf scale to canopy scale”\nWas changed for LL 242 “Conceptualization of vegetation responses to drought across scales”\nComment #13:\n8-1. Did you just selected the most severe drought to conduct this study? Despite you can get these droughts at each grid cell, the responses of vegetation to droughts may have different responses due to different drought severity. So, how did you consider different drought severity may cause different responses time of vegetation to droughts?\n– Response: Thank you for your comments.\nIn this study, we intentionally selected the most severe drought event within each 5-year window at each pixel. This approach aims to maximize the likelihood of detecting a clear and consistent vegetation response, since vegetation typically exhibits stronger and more detectable physiological or structural changes under severe stress. By focusing on the most extreme events, we reduce noise from weaker or ambiguous responses that may not be attributable to drought alone.\nWe acknowledge that drought severity (e.g., intensity, duration) can influence vegetation response dynamics. However, our primary focus is on the onset and early phase of drought, which is most critical for detecting the timing of physiological responses. At this stage, variation in severity across events is less likely to result in large differences in the timing of response, although it may affect the magnitude of response, which is not the central focus of our study.\nWe agree that exploring how vegetation response time scales with varying drought severity is an interesting and important question, but it would require a different experimental design and analytical framework. We see it as a valuable direction for future work, beyond the scope of the current study.\n– Action: Based on your consideration and the comment from Reviewer 2, we clarified this limitation in the discussion.\nLL 231 New content was added “Factors such as drought frequency and intensity, legacy effects, and canopy overshooting may also influence vegetation responses to drought (Liu et al., 2025; Zhang et al., 2021); these factors are not explicitly accounted for, being a limitation of this study.”\nComment #14:\n8-2. In figure 2b, it is better to use the different colors to represent the drought onset.\n– Response: Thank you for your comment. It is more logical to have a colour scheme where the difference between December and January is limited, because these are just adjacent months in the southern hemisphere's summer season. We revised the color in Figure 1b to make it more consistent with Figure 1a (We think it’s Figure 1b, not Figure 2b). Please note that a close comparison between the two figures reveals slight differences in the results, for example, in southern Africa (red rectangles in the following figures). This is because we updated the rule for merging adjacent drought events based on Reviewer 2’s suggestion.\nOriginal:\nUpdated:\nReference list\nChen, J. D., Liu, X. J., Du, S. S., Ma, Y., & Liu, L. Y. (2021). Effects of Drought on the Relationship Between Photosynthesis and Chlorophyll Fluorescence for Maize. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 11148-11161. https://doi.org/10.1109/Jstars.2021.3123111\nDe Cannière, S., Herbst, M., Vereecken, H., Defourny, P., & Jonard, F. (2021). Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence. Remote Sensing of Environment, 267, 112722. https://doi.org/10.1016/j.rse.2021.112722\nFeldman, A. F., Konings, A. G., Gentine, P., Cattry, M., Wang, L. X., Smith, W. K.,…Poulter, B. (2024). Large global-scale vegetation sensitivity to daily rainfall variability. Nature, 636(8042), 380-384. https://doi.org/10.1038/s41586-024-08232-z\nFu, P., Meacham-Hensold, K., Siebers, M. H., & Bernacchi, C. J. (2021). The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: benefits for field phenotyping. Journal of Experimental Botany, 72(4), 1295-1306. https://doi.org/10.1093/jxb/eraa537\nHelm, L. T., Shi, H. Y., Lerdau, M. T., & Yang, X. (2020). Solar-induced chlorophyll fluorescence and short-term photosynthetic response to drought. Ecological Applications, 30(5), e02101. https://doi.org/10.1002/eap.2101\nHuang, M., Piao, S., Janssens, I. A., Zhu, Z., Wang, T., Wu, D.,…Peñuelas, J. (2017). Velocity of change in vegetation productivity over northern high latitudes. Nature Ecology & Evolution, 1(11), 1649-1654. https://doi.org/10.1038/s41559-017-0328-y\nJiao, W. Z., Wang, L. X., Smith, W. K., Chang, Q., Wang, H. L., & D'Odorico, P. (2021). Observed increasing water constraint on vegetation growth over the last three decades. Nature Communications, 12(1), 3777. https://doi.org/10.1038/s41467-021-24016-9\nLi, Y., Zhang, W., Schwalm, C. R., Gentine, P., Smith, W. K., Ciais, P.,…Wu, X. C. (2023). Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems. Nature Climate Change, 13(2), 182-188. https://doi.org/10.1038/s41558-022-01584-2\nLiu, L. B., Gudmundsson, L., Hauser, M., Qin, D. H., Li, S. C., & Seneviratne, S. I. (2020). Soil moisture dominates dryness stress on ecosystem production globally. Nature Communications, 11(1), 4892. https://doi.org/10.1038/s41467-020-18631-1\nLiu, Y., Zhang, Y., Penuelas, J., Kannenberg, S. A., Gong, H. B., Yuan, W. P.,…Piao, S. L. (2025). Drought legacies delay spring green-up in northern ecosystems. Nature Climate Change, 15(4), 444–451. https://doi.org/10.1038/s41558-025-02273-6\nMaes, W. H., Pagan, B. R., Martens, B., Gentine, P., Guanter, L., Steppe, K.,…Miralles, D. G. (2020). Sun-induced fluorescence closely linked to ecosystem transpiration as evidenced by satellite data and radiative transfer models [Article]. Remote Sensing of Environment, 249, 112030, Article 112030. https://doi.org/10.1016/j.rse.2020.112030\nMurray, F. W. (1967). On the Computation of Saturation Vapor Pressure. Journal of Applied Meteorology and Climatology, 6(1), 203-204. https://doi.org/10.1175/1520-0450(1967)006<0203:OTCOSV>2.0.CO;2\nNovick, K. A., Ficklin, D. L., Grossiord, C., Konings, A. G., Martínez-Vilalta, J., Sadok, W.,…Mcdowell, N. G. (2024). The impacts of rising vapour pressure deficit in natural and managed ecosystems. Plant Cell and Environment, 47(9), 3561-3589. https://doi.org/10.1111/pce.14846\nNovick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A. C.,…Phillips, R. P. (2016). The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nature Climate Change, 6(11), 1023-1027. https://doi.org/10.1038/Nclimate3114\nPagán, B. R., Maes, W. H., Gentine, P., Martens, B., & Miralles, D. G. (2019). Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates. Remote Sensing, 11(4), 413. https://doi.org/10.3390/rs11040413\nPapagiannopoulou, C., Miralles, D. G., Dorigo, W. A., Verhoest, N. E. C., Depoorter, M., & Waegeman, W. (2017). Vegetation anomalies caused by antecedent precipitation in most of the world. Environmental Research Letters, 12(7), 074016. https://doi.org/10.1088/1748-9326/aa7145\nSmith, T., & Boers, N. (2023). Global vegetation resilience linked to water availability and variability. Nature Communications, 14(1), 498. https://doi.org/10.1038/s41467-023-36207-7\nValiantzas, J. D. (2013). Simplified forms for the standardized FAO-56 Penman-Monteith reference evapotranspiration using limited weather data. Journal of Hydrology, 505, 13-23. https://doi.org/10.1016/j.jhydrol.2013.09.005\nYao, Y., Liu, Y. X., Zhou, S., Song, J. X., & Fu, B. J. (2023). Soil moisture determines the recovery time of ecosystems from drought. Global Change Biology, 29, 3562–3574. https://doi.org/10.1111/gcb.16620\nZhang, Y., Keenan, T. F., & Zhou, S. (2021). Exacerbated drought impacts on global ecosystems due to structural overshoot. Nature Ecology & Evolution, 5(11), 1490-1498. https://doi.org/10.1038/s41559-021-01551-8\nResponse to Reviewer #2:\nThis study investigates how vegetation responds to drought by distinguishing physiological and structural changes at a global scale. Using satellite-based chlorophyll fluorescence (SIF), optical indices, and environmental data, the authors show that physiological responses (captured by SIF/APAR) occur rapidly—within 2 days—while structural changes (captured by EVI, LAI,…) lag behind, appearing around 12 days later. The authors find that atmospheric conditions (i.e., VPD) initially drive plant stress, shifting to soil moisture control as drought intensifies.\nI congratulate the authors on what is a very interesting piece of work. Although some of the reported knowledge is established through plot scale investigations and via experimental research, this study provides evidence at global scale and improves understanding of the timing and drivers of vegetation drought responses, with implications for monitoring ecosystems at large scale. The data and methods are mostly well described, findings are presented in a comprehensive and high-quality form. Yet more caution in the interpretation of the findings seems required, as the dataset and analysis do not support all conclusions.11\nI have two major concerns.\nComment #1:\nThe first is the coupling between SM and VPD that does not allow to disentangle the impact of one environmental driver over the other. Attempts at disentangling effects of SM and VPD rely on experimental research where manipulation of VPD is possible independently from SM. Alternatively, under natural conditions the study by Liu et al. 2020 used bins of SM or VPD and assessed at each SM bin the difference in SIF between max and min VPD bins and vice versa for each VPD bin. I encourage the authors to test a similar approach if the aim is to investigate impact / causality and if the time series allows for it. Alternatively, the text should be modified to reflect the discrepancy between individual driver causality and simple temporal alignment. There are several points in the text (including in the Highlights) where this could be clarified (e.g., LL 29-31; 99-102). Relevant references: Liu et al. 2020 (https://doi.org/10.1038/s41467-020-18631-1) and Lu et al. 2022 (https://doi.org/10.1038/s41467-022-29009-w).\n– Response: Thank you for prompting this important clarification. We would like to clarify that the primary focus of our study is on characterizing the response time (lag) of vegetation to drought stress, rather than establishing causal relationships between individual environmental drivers (SM or VPD) and vegetation activity. Our aim is to identify how fast vegetation responds to drought, not to quantify how much of that response is attributable to either SM or VPD independently.\nGiven this focus, and the constraints of our observational dataset, we have decided not to implement a binning-based separation approach, which would require a different experimental design and would shift the emphasis of the manuscript away from the temporal dynamics we are most interested in.\nThat said, we fully agree with you that the current manuscript could better reflect the distinction between individual driver causality and simple temporal alignment.\n– Action: In response, we revised the relevant statements.\nLL13-14 The statement “Vegetation physiology is more responsive to atmospheric drought than to soil drought.”\nWas changed for “The response of vegetation physiology to atmospheric drought occurs earlier than to soil drought.” (But the Highlights section has been removed in the revised manuscript.)\nLL 29-31 The statement “Physiological responses are predominantly controlled by vapour pressure deficit (VPD), whereas structural changes align with SM dynamics, highlighting a transition from atmospheric to soil drought regulation as water stress intensifies.”\nWas changed for LL 12-14 “Physiological responses are more temporally aligned with changes in vapor pressure deficit, whereas structural changes coincide more with SM dynamics, reflecting differing patterns of association under increasing drought stress.”\nLL 99-102 The statement “From this perspective, plant physiology may respond to atmospheric dryness during droughts faster than it responds to SM deficits, with this influence being sensed in physiological processes but not yet as plant morphological responses.”\nWas changed for LL 90-92 “From this perspective, plant physiology may show an earlier response to atmospheric dryness (high VPD) than to SM deficits during droughts.”\nLL 135-136 The statement “The strong correlations between EVI or LAI anomalies and SM anomalies indicate that SM dominates the morphological changes of vegetation during drought…”\nWas changed for LL 129-130 “The strong correlations between anomalies of EVI or LAI and SM anomalies indicate that SM is the primary factor associated with vegetation canopy changes during drought…”\nLL 232-234 The statement “Notably, drought impacts transition from VPD-driven physiological constraints in early stages to SM-regulated structural decline during prolonged water stress.”\nWas changed for LL 229-231 “Notably, drought impacts exhibit a transition from patterns associated with VPD-linked physiological changes in the early stages to those aligned with SM-related morphological changes as drought stress persists.”\nComment #2:\nThe second point that deserves attention is the fact that structural and physiological vegetation responses to drought often follow non-linear temporal trajectories, that vary among ecosystems and species. Often responses are originating from cumulative stress, so drought frequency rather than drought intensity could play a role. Moreover, legacy and carry-over effects could act in both directions, e.g., see structural overshoot—where excessive canopy buildup from abundant precipitation in the previous years can lead to impaired functioning during a subsequent dry year.\nThese study limitations should be acknowledged in the discussion section.\n– Response: Thank you for your consideration. It is true that we did not consider the effects of drought frequency and intensity, legacy effects, or canopy overshooting.\n– Action: Based on your consideration and the comment from Reviewer 1, we added these limitations in the discussion section.\nLL 231 New content was added “Factors such as drought frequency and intensity, legacy effects, and canopy overshooting may also influence vegetation responses to drought (Liu et al., 2025; Zhang et al., 2021); these factors are not explicitly accounted for, being a limitation of this study.”\nDetailed comments to the authors:\nComment #3:\nLL11: second Highlight, please add “structure” and/ or “greenness” vegetation indices, as there are optical indices that reflect physiological changes in LUE (e.g. PRI) or in WUE that exhibit a rapid response to drought.\n– Response: Thank you for your comment.\n– Action: We added “EVI” and “LAI”.\nLL 11 The statement “SIF efficiency exhibits a more rapid response to drought than vegetation indices based on optical remote sensing.”\nWas changed for “SIF efficiency exhibits a more rapid response to drought than EVI and LAI.” (But the Highlights section has been removed in the revised manuscript.)\nComment #4:\nLL 39: consider replacing “observable” with “detectable” or similar, as physiological changes are mostly pre-visual\n– Response: Thank you for your comment.\n– Action: We replaced “observable” with “detectable”.\nLL 39 The statement “The response time of vegetation to water stress reveals the time required for plants to exhibit observable physiological and structural changes after sensing water scarcity.”\nWas changed for LL 22 “The response time of vegetation to drought stress reveals the time required for plants to exhibit detectable physiological and structural changes after sensing water scarcity.”\nComment #5:\nLL 45: consider replacing “high degree of sophistication” with “well established” or other expression.\n– Response: Thank you for your comment.\n– Action: We replaced “high degree of sophistication” with “well established”.\nLL 45 The statement “Reflectance-based optical remote sensing for measuring vegetation greenness and structure has reached a high degree of sophistication.”\nWas changed for LL 27 “Reflectance-based optical remote sensing to measure vegetation greenness and structure has been well established.”\nComment #6:\nLL 47: “density and coverage”, below you write “biomass and morphology”, all correct but confusing, maybe introduce as landscape vs plot scale..\n– Response: Thank you for your comment. If using “landscape vs plot scale”, it may introduce additional scale confusion.\n– Action: We replaced “plant biomass and morphology” with “plant tissues (e.g. above-ground biomass)”.\nLL 47 The statement “Since the dynamics of these indices primarily reflect changes in plant biomass and morphology….”\nWas changed for LL 32 “Since the dynamics of these indices primarily reflect slow changes in plant tissues (e.g. above-ground biomass), ...”\nComment #7:\nLL 52: lagged rather than underestimated?\n– Response: Thank you for your comment.\n– Action: We replaced “underestimated” with “delay”.\nLL 52 The statement “This could introduce an underestimation of the drought-induced stress on vegetation in ...”\nWas changed for LL 34 “This could introduce delays in representing drought-induced vegetation stress in global carbon cycle modelling...”\nComment #8:\nLL 61: I suggest removing “diagnostics” as it is not the diagnostics that shows higher sensitivity and faster response but rather the measured processes\n– Response: Thank you for your comment.\n– Action: We replaced “diagnostics” with “parameters”.\nLL 61 The statement “Vegetation physiological diagnostics, including maximum carboxylation efficiency...”\nWas changed for LL 44 “Vegetation physiological parameters, including maximum carboxylation efficiency...”\nComment #9:\nLL 66: Do not leave the reader guessing, which are (were) the limitations?\n– Response: Thank you for your comment.\n– Action: We clearly identified the current limitations.\nLL 66 The statement “However, ecosystem level physiological drought responses remain understudied due to the current limitations of satellite-based approaches for directly measuring plant physiology.”\nWas changed for LL 47 “However, ecosystem-level physiological drought responses remain understudied as vegetation physiological parameters have so far remained difficult to isolate unambiguously from space-based remote sensing.”\nComment #10:\nLL 70-71: This is too vague, please expand a bit to explain the link between SIF and photochemistry under stress and non-stress conditions. The fluorescence pathway accounts for a minor portion of the total excitation energy and is not a major route for energy dissipation, however it offers important quantitative insights into other energy pathways—especially regulated non-photochemical and photochemical processes. I know you partly touch upon it in the Methods, but I think it would be good to briefly set the stage here.\n– Response: Thank you for your comment.\n– Action: We added the corresponding content.\nLL 70-71 The statement “Closely associated with key physiological processes related to plant photosynthesis, SIF serves as a comprehensive proxy for vegetation physiology.”\nWas changed for LL 53-59 “Closely associated with key physiological processes related to plant photosynthesis, SIF serves as a comprehensive proxy for vegetation physiology by capturing the dynamics of energy partitioning within the photosynthetic apparatus. Under non-stress conditions, high SIF emission generally reflects efficient photochemical activity, whereas under stress (e.g., drought), changes in SIF can indicate shifts toward enhanced non-photochemical energy dissipation and reduced photosynthetic efficiency (Xu et al., 2021).”\nComment #11:\nLL 72-75: Similar comment as above. This sounds too vague, and you leave the reader waiting until reaching the Methods to understand what is meant here. I suggest being specific and still concise, mentioning what are the “other” dominant parts of the SIF signal.\n– Response: Thank you for your comment.\n– Action: We added the corresponding content.\nLL 72-75 The statement “Nonetheless, the fluorescence emission efficiency, which is directly linked to vegetation physiology, forms only a part of the SIF signal. Therefore, decoupling the SIF signal from the other dominant parts of the SIF signal is necessary to distill physiological information.”\nWas changed for LL 59-66 “However, factors such as solar radiation and canopy properties govern the SIF signal to a great extent, and the fluorescence quantum yield (SIF ), the yield fraction of absorbed light re-emitted as fluorescence and leaving the canopy, represents only a small portion of the variability in SIF. This is however the component that is closely tied to vegetation physiology, and regulated by photochemical and non-photochemical quenching. Therefore, isolating the physiological signal from these structural and radiative contributions is essential to accurately extract information on vegetation function and stress responses.”\nComment #12:\nLL 85-87: Consider clarifying here that independently from (drought) stress level, SIF primarily responds to light so normalization by APAR or PAR would allow to account for this.\n– Response: Thank you for your comment.\n– Action: We added the corresponding content.\nLL 85-87 The statement “We consider SIF/PAR as a diagnostic of both vegetation structure and fluorescence emission efficiency, and SIF/APAR as a specific diagnostic of emission efficiency, which can thus be regarded as a distilled measure of vegetation physiology.”\nWas changed for LL 74-78 “Because SIF primarily responds to light regardless of stress conditions, we normalize it to account for variations in incoming radiation. Specifically, we consider SIF/PAR as a mixed proxy of vegetation canopy properties and SIFyield, while SIF/APAR isolates the SIFyield itself, thus serving as a refined proxy for vegetation physiological functioning.”\nComment #13:\nLL 95: Consider replacing “atmospheric dryness” with atmospheric water (or moisture) content.\n– Response: Thank you for your comment.\n– Action: We replaced “atmospheric dryness” with “VPD”.\nLL 95 The statement “In contrast, atmospheric dryness rapidly responds to…”\nWas changed for LL 86 “In contrast, VPD rapidly responds to…”\nComment #14:\nFig. 1: Specify year of “major” drought event (from LL335-337: \"The drought event in which the most negative ESI anomaly is located over the entire record was identified as the most severe drought event\"). The term \"onset\" is not needed here.\n– Response: Thank you for your consideration. To clarify, we identified drought events on a per-pixel basis. Therefore, the operation described as \"The drought event in which the most negative ESI anomaly is located over the entire record was identified as the most severe drought event\" was applied individually to the time series of ESI anomalies at each grid cell, allowing us to identify the most severe drought event for each pixel during the 2018–2022 period.\nThe following figure (Supplementary Fig. 10) illustrates an example of drought identification for a grid cell (lon: 5.125, lat: 48.125, located in Europe). The blue lines represent the detrended ESI anomalies, and the orange lines indicate the drought events:\nFirst, all dates with ESI anomalies below 0 were identified. If the ESI anomaly remained below 0 for 30 consecutive days, the period was marked as a drought event. Adjacent drought events were then merged (the merging rule was updated based on your suggestions; please see Comment #30), resulting in panel (a).\nNext, drought events occurring between 1 July 2018 and 31 October 2022 were selected, resulting in panel (b).\nFinally, among these, the drought event containing the lowest ESI anomaly value was identified as the most severe event. This event, marked in orange in panel (c), was considered the selected drought event for this pixel.\nThe first date on which the ESI anomaly dropped below 0 during the most severe drought event was recorded as the drought start date (onset). Therefore, for this particular pixel, the drought start year is 2019 (represented by the light blue color in Fig. 1a in the main text), and the drought start month is June (represented by the orange tone for June in Fig. 1b in the main text).\nWe consider the term ‘onset’ to be necessary, because Figure 1 represents the timing of drought start rather than other time points (such as the center of the event).\nComment #15:\nLL 131: Are you referring to temperature only above or also below the optimum? In the latter, you would need to replace “exceeding”.\n– Response: Thank you for your comment.\n– Action: We modified the text.\nLL 131 The statement “Negative correlations are primarily concentrated in energy-limited high latitudes in the Northern Hemisphere, where increased radiation during drought can stimulate vegetation activity before exceeding optimal growth temperature ranges.”\nWas changed for LL 122 “Negative correlations are primarily concentrated in energy-limited high latitudes in the Northern Hemisphere, where increased radiation during drought may promote vegetation activity by warming temperatures toward, but not beyond, the optimal range for plant growth.”\nComment #16:\nLL 168: “due to unmitigated vegetation canopy signal effects” is too vague, please be specific.\n– Response: Thank you for your comment.\n– Action: We modified the text.\nLL 168 The statement “The drought response times of SIF/PAR and SIF closely resemble those of EVI (Fig. 3a-b), indicating that their physical interpretations align more with vegetation greenness characterization due to unmitigated vegetation canopy signal effects which are less present in SIF/APAR.”\nWas changed for LL 161 “The drought response times of SIF/PAR and SIF closely resemble those of EVI (Fig. 3a–b), indicating that their behaviour is more strongly influenced by variations in vegetation canopy structure and greenness. This is likely because SIF/PAR accounts for changes in incoming radiation but still retains the impact of fPAR, which largely reflects canopy structure. In contrast, SIF/APAR removes this structural component, effectively isolating physiological changes.”\nComment #17:\nLL 175: Double check, this range of subplots does not include only greenness metrics.\n– Response: Thank you for your careful comment.\n– Action: We replaced “(Fig. 3e-j)” with “(Fig. 3i-j)”. Please see LL 172.\nComment #18:\nLL 196: Isohydricity is a continuum, and differences exists among tree species and even for the same tree species on different soil types. I suggest to at least add “mostly” or similar when referring to an isohydric behaviour.\n– Response: Thank you for your comment. The original sentence was changed. Please see Comment #19.\nComment #19:\nLL 197: I do not see this clearly in Fig. 4l,p. What rather sticks out is the EVI lag for NF.\n– Response: Thank you for your comment. We reprocessed the data based on your suggestions and those of Reviewer 1. Accordingly, the content in Figure 4 has changed, the updated Figure 4 is shown as follows. The original sentence is no longer applicable.\n– Action: We modified the sentence.\nLL 197 The statement “Across biomes, forests exhibit a faster physiological response to VPD but slower response to SM due to their isohydric behaviour and deep root systems. This is especially clear in humid regions (Fig. 4l, p)”.\nWas changed for LL 192 “Humid tropical broadleaf forest regions exhibit an almost immediate response to drought stress (Fig. 4l, p), likely due to high hydraulic vulnerability and stomatal sensitivity (Liu et al., 2022; Martínez-Vilalta et al., 2014).”\nThe original Figure 4：\nThe updated Figure 4:\nComment #20:\nFig. 5: Consider placing this figure earlier in the MS. It could help to formulate hypotheses if you wanted to add some. Anyhow, I think currently it is slightly misplaced.\n– Response: Thank you for your consideration. This figure indeed serves more as a graphical abstract, aiming to summarise the core findings and conceptual implications of our study, including key insights such as the response time. However, placing this figure at the beginning of the manuscript might compromise the logical flow, as it reveals central conclusions that are only fully developed in the later sections. Therefore, we prefer to keep it at the end, where it can effectively synthesise the findings and provide a visual summary without preempting the results and discussion. Also, Nature Communications does not allow a graphical abstract currently.\nComment #21:\nLL 247: \"external\" is redundant here, in plant science one usually refers to leaves morphology vs anatomy.\n– Response: Thank you for your comment.\n– Action: We removed “external”. Please see LL 245.\nComment #22:\nIn the figure itself, please change \"Pigment response\" to “Chlorophyll pigment response”, to avoid confusion with other sets of pigments (e.g., Xanthophylls) responding to stress at much shorter time scales (hours).\n– Response: Thank you for your comment.\n– Action: We changed \"Pigment response\" to “Chlorophyll pigment response”. Please see LL 241.\nThe original Figure 5:\nThe updated Figure 5:\nComment #23:\nI recommend writing out all acronyms used in the figure and/or captions as figure should in general be self-standing.\n– Response: Thank you for your comment.\n– Action: We added the full form of the abbreviation in the captions. Please see LL 243-244、 LL 250-251.\nComment #24:\nLL 262: Consider replacing \"environmental stress\" with drought stress since high irradiance also counts as an environmental stress leaves are subjected to even under \"normal\" water availability conditions. Only a small fraction of absorbed light can be used for photosynthesis, the rest has to be safely dissipated through NPQ pathways.\n– Response: Thank you for your comment.\n– Action: We replaced \"environmental stress\" with “drought stress”. Please see LL 261.\nComment #25:\nLL 265: Daylength-corrected SIF reflects total daily potential for photosynthesis-related fluorescence. I suggest changing \"intensity\" with \"potential\".\n– Response: Thank you for your comment.\n– Action: We replaced \"intensity\" with \"potential\". Please see LL 263.\nComment #26:\nLL 283: If I am not mistaken, ESI was never written out as Evaporative Stress Index. Please carefully check that all acronyms are written out at first mention.\n– Response: Thank you so much for your reminder.\n– Action: We added the full name. Please see LL 94.\nComment #27:\nLL 288: Could you add an explanation how the “onset” was determined and how it is defined also with reference to Supplementary Fig. 10.\n– Response: Thank you for your comment. Lines 329-337 describe the identification of drought events. The \"onset\" refers to the day when the ESI anomalies first drop below zero within the identified drought event. For a detailed explanation, please refer to Comment #14.\n– Action: We revised the sentence in LL 285.\nLL 285 The statement “ESI was employed to identify the most severe droughts during the study period.”\nWas changed for LL 283 “ESI was employed to identify the most severe droughts during the study period (See Section: Data pre-processing).”\nWe added the definition of “onset” at LL 335, new text “The first date on which the ESI anomaly dropped below 0 during the selected drought event was taken as the drought onset.”\nWe added the following text to the caption of Supplementary Fig. 14: “The dashed line in (c) indicates the onset of the drought event for this pixel.”\nComment #28:\nLL 317: here and elsewhere in the text consider referring to “proxy of” vegetation physiology (also structure for EVI, etc.).\n– Response: Thank you for your comment.\n– Action: We modified the context.\nLL 317 The statement “SIF was further decomposed into SIF/PAR and SIF/APAR to measure vegetation physiology (see Section: Isolating vegetation physiology).”\nWas changed for LL 314-315 “SIF was further decomposed into SIF/APAR to compute SIFyield as a proxy of vegetation physiology (see Section: Isolating vegetation physiology).”\nComment #29:\nLL 324: Please explicitly define somewhere reference period for anomaly calculation.\n– Response: Thank you for your comment.\n– Action: We modified the context.\nLL 324 The statement “Subsequently, the anomalies were derived by subtracting the long-term trend and daily average seasonal cycle from the original daily observations over the available data period.”\nWas changed for LL 320-323 “Subsequently, the anomalies were derived by subtracting the long-term trend and the daily average seasonal cycle from the original daily observations, with the period from 1 May 2018 to 31 December 2022 used as reference.”\nComment #30:\nLL 332-333: I am a bit sceptical about this approach, 30 days is a long and arbitrary period. Did you set any additional rule on the period separating two events before merging? For example no Precip above X\n– Response: Thank you for your valuable comment.\n– Action: Following your suggestion, we now merge drought events only if no precipitation was recorded during the 30-day separation period.\nLL 332-333 The statement \"Events separated by less than 30 days were merged and considered as a single drought episode.\"\nWas changed for LL 329-331 “Events separated by less than 30 days were merged into a single episode, if no precipitation occurred in the interim.”\nComment #31:\nLL 358: Please explain how you came to this fesc value.\n– Response: Thank you for your comment. We set it to 1 following the approach adopted in earlier study, that is “The SIF data used in this study are retrieved at 743 nm, at which the fesc can be set to 1 due to low leaf absorption in the near-infrared spectrum (Guanter et al., 2014)”.\nHowever, Reviewer 1 suggested that a distinction should be made between LUESIF and SIFyield, recommending the use of SIFyield. We fully agree with this suggestion.\n– Action: We have accordingly revised the original formula following the previous studies (Lu et al., 2022; Zhang et al., 2014) to:\nSIF = PAR · fPAR · SIFyield\nWe have also added a description in the main text to mention fesc. Please see LL 360-364 “The ratio SIF/APAR used here can be regarded as a combined metric that implicitly incorporates both the intrinsic SIF yield and the fluorescence escape probability (fesc), since the fesc not explicitly separated in our formulation. This treatment is consistent with the definition of apparent SIFyield by Wang et al.(Wang et al., 2020)”.\nComment #32:\nLL 367-368: See previous comment, how was drought onset defined and why 30 days prior to the drought onset?\n– Response: Thank you for your consideration. For the drought onset, please see the response in Comment #27.\nWe aim to capture the complete initial response of vegetation to drought, which is why we use “30 days prior to the drought onset” This also helps us more accurately pinpoint the first response time of vegetation to drought stress through correlation analysis.\nComment #33:\nSupplementary Fig. 12: I assume this is a schematic representation and not based on real data, please consider writing “Schematic representation of response time determination”.\n– Response: Thank you for your comment. Yes, it’s a schematic figure.\n– Action: We replaced “Response time determination” with “Schematic representation of response time determination”.\nReviewer #2 (Remarks on code availability):\nComment #34:\nI could not access the code. On the Zenodo interface it states: \"The record is publicly accessible, but files are restricted to users with access.\"\n– Response: The codes and data now are provided in a zip file.\nReference list\nGuanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A.,…Griffis, T. J. (2014). Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences, 111(14), E1327-E1333. https://doi.org/10.1073/pnas.1320008111\nLiu, L. Y., Chen, X. Z., Ciais, P., Yuan, W. P., Maignan, F., Wu, J.,…Su, Y. X. (2022). Tropical tall forests are more sensitive and vulnerable to drought than short forests. Global Change Biology, 28(4), 1583-1595. https://doi.org/10.1111/gcb.16017\nLiu, Y., Zhang, Y., Penuelas, J., Kannenberg, S. A., Gong, H. B., Yuan, W. P.,…Piao, S. L. (2025). Drought legacies delay spring green-up in northern ecosystems. Nature Climate Change, 15(4), 444–451. https://doi.org/10.1038/s41558-025-02273-6\nLu, H. B., Qin, Z. C., Lin, S. R., Chen, X. Z., Chen, B. Z., He, B.,…Yuan, W. P. (2022). Large influence of atmospheric vapor pressure deficit on ecosystem production efficiency. Nature Communications, 13(1), 1653. https://doi.org/10.1038/s41467-022-29009-w\nMartínez-Vilalta, J., Poyatos, R., Aguadé, D., Retana, J., & Mencuccini, M. (2014). A new look at water transport regulation in plants. New Phytologist, 204(1), 105-115. https://doi.org/10.1111/nph.12912\nWang, C., Guan, K. Y., Peng, B., Chen, M., Jiang, C. Y., Zeng, Y. L.,…Miao, G. F. (2020). Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the US Midwest. Remote Sensing of Environment, 241, 111728. https://doi.org/10.1016/j.rse.2020.111728\nXu, S., Atherton, J., Riikonen, A., Zhang, C., Oivukkamäki, J., MacArthur, A.,…Porcar-Castell, A. (2021). Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop. Remote Sensing of Environment, 263, 112555. https://doi.org/10.1016/j.rse.2021.112555\nZhang, Y., Keenan, T. F., & Zhou, S. (2021). Exacerbated drought impacts on global ecosystems due to structural overshoot. Nature Ecology & Evolution, 5(11), 1490-1498. https://doi.org/10.1038/s41559-021-01551-8\nZhang, Y. G., Guanter, L., Berry, J. A., Joiner, J., van der Tol, C., Huete, A.,…Köhler, P. (2014). Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models. Global Change Biology, 20(12), 3727-3742. https://doi.org/10.1111/gcb.12664"}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "Although the authors have addressed most of questions proposed in the last review round, there still existed some issues in this version. So, I would like to recommend a moderate revision.\n1. Did the authors consider the impact of wildfires, land use/cover change and insect infestations? Specifically, were these disturbed pixels excluded? If not, it could potentially lead to significant uncertainties in the results for some regions.\n2. I still disagree with the authors' use of the ESI to characterize drought. Based on the formula provided for it, the ESI generally represents the evaporative fraction. This index is frequently used to screen for the intensity of moisture stress on the evapotranspiration (ET) process. Furthermore, according to the authors' description on line 793, the ESI reflects transpiration under moisture stress. However, ET includes not only transpiration but also soil evaporation, canopy interception, and other components. Therefore, this statement is not precise.\nIn GLEAM 3.7b, PET (Potential Evapotranspiration) is estimated using the PT (Priestley-Taylor) model, which does not account for differences in vegetation types. Variations in vegetation type lead to significant differences in factors such as vegetation roughness and stomatal conductance, consequently causing substantial spatial variation in PET. The PT model relies solely on inputs like radiation data and thus cannot reflect the true underlying surface conditions. Consequently, even if the ESI is used as an indicator, PET should have been calculated using a PET model that considers the actual underlying surface.\nMaybe, the best method to identify the drought events through combining vegetation productivity, SM, VPD, and other drought indicators. Then, you can use the identified drought events to examine whether there are differences in vegetation response to soil drought and atmospheric drought within the same drought event.", "reviewer_2": "I am satisfied with the way the authors have addressed my comments and made changes in the manuscript accordingly. I have only one remaining remark concerning their revision in response to Comment 10.\nIn the revised statement the “high SIF emission reflects efficient photochemical activity” is partially misleading. SIF intensity does not directly increase with photosynthetic efficiency. When photochemical efficiency is high, a larger fraction of absorbed light is used for photochemistry, leaving less energy available for fluorescence and heat dissipation. Therefore, SIF yield tends to decrease when photochemistry is most efficient, even though total SIF emission may still appear high under strong illumination because of increased light absorption. So, high SIF emission generally indicates active photosynthesis and high light use, but not necessarily high efficiency."}, "author_response": "Response to Reviewer #1:\nAlthough the authors have addressed most of questions proposed in the last review round, there still existed some issues in this version. So, I would like to recommend a moderate revision.\nComment #1:\n1) 1. Did the authors consider the impact of wildfires, land use/cover change and insect infestations? Specifically, were these disturbed pixels excluded? If not, it could potentially lead to significant uncertainties in the results for some regions.\n– Response: We appreciate the reviewer’s insightful comment. Regarding the potential influence of land use and cover change, we have already taken this factor into account in our analysis, see LL 342-345 “To minimize the influence of land cover change and non-vegetated surface, only pixels with consistent vegetation type over the five-year period (2018–2022) and a vegetation cover exceeding 50% were included in the analysis”. This filtering approach effectively excludes areas that have undergone transitions between vegetated and non-vegetated states, which represent the main pathways through which land use and cover change affect vegetation signals. In addition, disturbances such as wildfires often lead to vegetation loss and thus can be partially captured through our exclusion of pixels with inconsistent vegetation types.\nNote that the scale level of our study (spatial resolution of 0.25° - about 27.5x27.5 k² at the equator) makes it relatively less likely that pests or diseases would have a major impact at the pixel level. Nevertheless, we acknowledge that in some cases, these disturbances may occur within vegetated areas during drought periods, leading to compound events. Such compound events could indeed have impacts on both the magnitude and timing of vegetation responses compared to droughts alone. However, besides wildfires and insect infestations, droughts can also coincide with other disturbances such as heatwaves, human activities, and various extreme events. It is difficult to comprehensively account for all these interacting factors within a single analysis, and addressing them will require dedicated studies in the future. While we did not consider these effects explicitly in the present analysis, we have added a note in the Discussion to highlight their potential influence and to emphasize that vegetation responses to such compound events warrant further investigation.\n– Action:\nLL 235-238 New content was added “In addition, compound events such as wildfires, plant diseases and insect pests, or other extreme disturbances occurring concurrently with drought may occasionally affect the strength and timing of vegetation signals, and their potential impacts on vegetation responses warrant further investigation.”\nComment #2:\n2) I still disagree with the authors' use of the ESI to characterize drought. Based on the formula provided for it, the ESI generally represents the evaporative fraction. This index is frequently used to screen for the intensity of moisture stress on the evapotranspiration (ET) process. Furthermore, according to the authors' description on line 793, the ESI reflects transpiration under moisture stress. However, ET includes not only transpiration but also soil evaporation, canopy interception, and other components. Therefore, this statement is not precise.\n– Response: We thank the reviewer for this valuable comment and for highlighting the need for clarification. We have revised the description to avoid suggesting that the ESI represents transpiration alone. The revised statement now reads that the ESI reflects decreases in both transpiration and soil evaporation under stress conditions, which may arise from various environmental factors.\nWhile we fully agree that evapotranspiration (ET) comprises several components, including transpiration, soil evaporation, and canopy interception, it is well established that during drought periods in vegetated regions, transpiration is by far the dominant component. Canopy interception is minimal by definition, and the topsoil is typically dry. Therefore, the ESI largely reflects land surface constraints on transpiration during drought, making it well suited for the purpose of our analysis.\n– Action: We made changes as follows.\nLL 281-283 The statement “ESI, the ratio of actual to potential evaporation, provides a comprehensive account of the effect of soil moisture and meteorology during drought, and it reflects the degree of stress on actual plant transpiration.”\nWas changed for LL 285-286 “ESI, the ratio of actual to potential evaporation, reflects transpiration and soil evaporation decrease under stress conditions.”\nComment #3:\n3) In GLEAM 3.7b, PET (Potential Evapotranspiration) is estimated using the PT (Priestley-Taylor) model, which does not account for differences in vegetation types. Variations in vegetation type lead to significant differences in factors such as vegetation roughness and stomatal conductance, consequently causing substantial spatial variation in PET. The PT model relies solely on inputs like radiation data and thus cannot reflect the true underlying surface conditions. Consequently, even if the ESI is used as an indicator, PET should have been calculated using a PET model that considers the actual underlying surface.\n– Response: Thank you for your consideration. We agree that accounting for land-surface differences is important when estimating PET. However, we note that in GLEAM v3.7b, the Priestley–Taylor (PT) model is not implemented as a purely atmosphere-only radiation equation. The PT coefficient (α) and the ground heat flux (G) are parameterized per land-cover type, along with albedo (Martens et al. 2017, https://doi.org/10.5194/gmd-10-1903-2017). And near-surface meteorological conditions are logically influenced by the actual land cover. So, the GLEAM PT-based potential evaporation includes a component of landcover dependence beyond radiation alone. Further, research comparing different PET models based on Eddy covariance data showed that the land cover-adjusted PT model is among the best in representing PET, actually outperforming more complex Penman–Monteith models (Maes et al. 2019, https://doi.org/10.5194/hess-23-925-2019). The reason for this is in fact that the assumption of constant biome-specific stomatal conductance in Penman–Monteith PET models, is not completely correct. Therefore, using PET derived from the PT model ensures that ET/PET serves as a meaningful indicator consistent with the objectives of this study.\nComment #4:\n4) Maybe, the best method to identify the drought events through combining vegetation productivity, SM, VPD, and other drought indicators. Then, you can use the identified drought events to examine whether there are differences in vegetation response to soil drought and atmospheric drought within the same drought event.\n– Response: Thank you for your consideration. However, we strongly believe that including SM or VPD in the definition of drought would result in a circular approach, since anomalies in these variables would by definition be incorporated into the drought metric. Similarly, incorporating vegetation productivity or other vegetation indices into the drought identification would also compromise the independence of the drought metric, making it unsuitable for analysing vegetation response times.\nOur focus in this study is not on the method to isolate or define major drought events, but on the coupling between SIF (and its derived products) and the drought drivers such as SM and VPD. We believe the way we define drought stress is solid, but are also convinced that using an alternative drought metric would not substantially affect our results, as the primary aim is to investigate the physiological and morphological responses of vegetation to these drivers under drought stress.\nResponse to Reviewer #2:\nI am satisfied with the way the authors have addressed my comments and made changes in the manuscript accordingly. I have only one remaining remark concerning their revision in response to Comment 10.\nComment #1:\n1) In the revised statement the “high SIF emission reflects efficient photochemical activity” is partially misleading. SIF intensity does not directly increase with photosynthetic efficiency. When photochemical efficiency is high, a larger fraction of absorbed light is used for photochemistry, leaving less energy available for fluorescence and heat dissipation. Therefore, SIF yield tends to decrease when photochemistry is most efficient, even though total SIF emission may still appear high under strong illumination because of increased light absorption. So, high SIF emission generally indicates active photosynthesis and high light use, but not necessarily high efficiency.\n– Response: Thank you for your careful comment. We changed the statement.\n– Action:\nLL 56 The statement “Under non-stress conditions, high SIF emission generally reflects efficient photochemical activity, whereas under stress (e.g., drought), changes in SIF can indicate shifts toward enhanced non-photochemical energy dissipation and reduced photosynthetic efficiency.”\nWas changed for LL 56 “Under non-stress conditions, high SIF emission generally indicates active photosynthesis and high light use, whereas under stress (e.g., drought), changes in SIF can indicate shifts toward enhanced non-photochemical energy dissipation and reduced photosynthetic efficiency.”"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The author has largely revised the manuscript according to the previous comments.\nComment #1:\nHowever, the explanation provided by the author for using EF to characterize drought is not sufficiently convincing. The author may consider discussing and evaluating the uncertainties associated with using EF to represent drought. Ideally, it would be beneficial to compare EF with other drought indices or soil moisture data to mitigate the impact of drought indicator selection on the results. As for whether to accept the paper, the final decision rests with the editor."}, "author_response": "Response to Reviewer #1:\nThe author has largely revised the manuscript according to the previous comments.\nComment #1:\nHowever, the explanation provided by the author for using EF to characterize drought is not sufficiently convincing. The author may consider discussing and evaluating the uncertainties associated with using EF to represent drought. Ideally, it would be beneficial to compare EF with other drought indices or soil moisture data to mitigate the impact of drought indicator selection on the results. As for whether to accept the paper, the final decision rests with the editor.\n– Response: Thank you for your consideration. We would like to emphasize again that ESI is used solely as a screening tool to identify time windows of stress. Within these identified windows, we explicitly analyse the vegetation response to soil moisture (SM) and vapor pressure deficit (VPD) independently. Therefore, the specific dynamics we report are driven by the underlying physical variables (SM and VPD), ensuring that the results are not artifacts of the ESI definition itself. We also would like to note that we have already conducted extensive sensitivity analyses to ensure robustness (Supplementary Fig. 1−10). Given that our previous sensitivity analyses (e.g., using different SM datasets) confirmed that the observed physiological and structural decoupling is robust across different datasets and definitions, we believe the current results are reliable. Introducing additional drought indices might confuse meteorological anomalies with the specific ecological stress we aim to study. That said, we agree the reviewer's comment highlights a valuable direction and will consider addressing it in future research."}]

Decision letter