Declining grassland canopy height in China under asymmetric biomass allocation

Domain: chemistry
Published: 2026-03-03
Editorial outcome: minor_revision

Abstract
Grassland canopy height is one of the most important traits for determining plant diversity and community structure, directly affecting the resource use efficiency of livestock in grassland ecosystems. However, broad-scale changes in grassland canopy height are seldom reported due to the complex effects of species aggregation on both interspecific and intraspecific structures. Here, we decouple grassland aboveground biomass into vertical and horizontal allocations, thereby offering a pathway to mirror changes in grassland canopy height. Grassland aboveground biomass is estimated using a machine learning algorithm by fusing climatic factors, satellite-driving metrics, and 8-year consecutive ground-truth surveys; the horizontal allocation of grassland aboveground biomass is derived from optimized linear spectral mixture analysis. We find that changes in horizontal biomass allocation primarily accounted for increases in Chinese grassland aboveground biomass from 2001 to 2022, resulting in a significant decline in grassland canopy height. The decline in grassland canopy height is shaped by reduced radiation and, more importantly, by the combined effects of warming and grazing, while also being related to variations in plant diversity. The dwarfing grassland community with declining canopy height may increase the impact of livestock disturbances, thus diminishing the resistance of grassland ecosystems to climate fluctuations.

Introduction
Grassland canopy height is considered one of the most critical structural traits, serving as a fundamental indicator of functional diversity and resource use efficiency in grassland ecosystems1. At the functional trait level, grassland canopy height significantly influences plant ecological strategies, as taller species gain a competitive advantage in terms of light acquisition, potentially driving species turnover or loss, particularly under environmental pressures and intense grazing2,3. Therefore, grassland canopy height could serve as an underlying trait-based mechanism, reflecting the impact of manifold factors on morphological characteristics in different grassland species. An manipulative study on the Tibetan Plateau (2014–2017) found that rapid alpine warming shifted functional composition from legumes and forbs toward grasses and sedges, accompanied by an increase in canopy height4. By contrast, studies in arid regions report that warming shortens herbaceous growth cycles, limits biomass accumulation prior to seed set, and ultimately leads to vegetation dwarfing5,6. Grassland canopy height is also a key factor in explaining how livestock alter species diversity and community structure, with changes directly reflecting grazing intensity, influencing forage availability and quality, and consequently affecting the resource use efficiency of grassland ecosystems7. Therefore, accurately predicting grassland canopy height is crucial for understanding changes in ecosystem functionality and safeguarding food security in the future.

Since the onset of the satellite era, optical remote sensing data have been widely used in the inversion-based upscaling of ecological metrics (e.g., biomass and cover)8,9 owing to their high spatiotemporal resolution and provision of multidimensional spectral information. However, the effectiveness of optical remote sensing data for estimating canopy height is limited by signal saturation caused by a high level of fractional vegetation cover1,10. In domains with sparse vegetation cover (e.g., under 20%), soil noise interferes with the capture of plant signals, resulting in satellite metrics influenced by the interplay between plants and soils11. Although spaceborne light detection and ranging (LiDAR) instruments, such as the Global Ecosystem Dynamics Investigation (GEDI) mission, provide additional insights and techniques for estimating canopy structure, it is primarily focused on forests and has limited footprint coverage12,13. Compared with woody communities (e.g., forests and shrublands), grassland species are much smaller in size but exhibit complex interspecific and intraspecific structures, posing a challenge for directly extracting grassland canopy height with filtering algorithms because of the low signal-to-noise ratio of LiDAR data14. A study indicates that grassland canopy height models were sensitive to LiDAR point cloud density, as details of the maximum height of grassy canopies are often lost in cases with low-density point clouds15. Overall, existing reports on changes in grassland canopy height solely rely on plot-scale measurements from sparse experimental stations4; however, a robust strategy for tracking dynamics across broader spatial scales is lacking16. This knowledge gap limits our understanding of changes in grassland ecosystem functioning and resilience under climate extremes (e.g., droughts), thereby hindering effective grazing management and sustainable utilization.

Grassland species are primarily composed of herbaceous plants, which exhibit flexible allocational plasticity among their leaves, stems, and roots17,18. To maximize growth, plants in grasslands respond to environmental variations by partitioning biomass among different plant organs to optimize the capture of nutrients, light, water, and carbon dioxide19,20. Previous studies have shown that this allocation strategy is primarily manifested in biomass changes across horizontal and vertical dimensions, which explain a substantial variation in grassland aboveground biomass4,21. However, the direction (positive or negative) and intensity of biomass allocation across the vertical and horizontal dimensions remain controversial. Some studies of rapidly growing annual species have indicated that a competitive trade-off exists between the vertical and horizontal dimensions20, where an increase in leaf area limits resource investment in roots and stems, resulting in shorter plant height21; however, others have argued that the plant community height in grasslands is positively correlated with leaf area3,4. These insights are valuable but are solely confined to the plot scale, creating a barrier to comprehensively understanding the principles of biomass allocation in grassland ecosystems at broader scales under the forcing of environmental change. In this regard, we hypothesize that the spatial allocation of grassland biomass differs between the vertical and horizontal dimensions due to environmental changes and grazing practices22.

Our study is conducted across Chinese grasslands, and 24,125 field measurements of aboveground biomass, fractional vegetation cover, and canopy height were collected during the peak growing season (July–August) from 2009 to 2016 (Supplementary Fig. 1). We focused on China given its multiplex bioclimatic regimes and high stocking rate, which result in diverse species compositions and complex community structures; therefore, the canopy height estimation methods developed in this study can be applied elsewhere8. We propose that grassland aboveground biomass can be allocated across the horizontal fractional vegetation cover and vertical canopy height. Accordingly, the ratio of aboveground biomass to fractional vegetation cover ([aboveground biomass/ fractional vegetation cover]) reflects the vertical allocation of biomass per unit area and indicates variations in canopy height1. The gridded aboveground biomass at a 500 m resolution was simulated using a random forest machine learning algorithm with different assemblies of satellite observations, climate reanalysis data, and field-surveyed aboveground biomass; the horizontal determinant of the fractional vegetation cover was independently obtained using optimized linear spectral mixture analysis (SMA). Thereafter, we map the spatial regime of grassland canopy height and its changes during 2001–2022 using a strategy of [aboveground biomass/fractional vegetation cover] ratio. The main goal of this study is to investigate the spatial and temporal dynamics of grassland canopy height across China over the past 22 years and to elucidate the spatial allocation of aboveground biomass to broaden our understanding of shifts in ecosystem functioning and sustainability in the context of environmental change.

Results
Changes in grassland aboveground biomass
First, the random forest models of grassland aboveground biomass were trained using three forcing ensembles, namely, satellite-driving metrics, climatic factors, and their combination (Supplementary Table 1). The results revealed that the model with combined forcing factors achieved the highest accuracy (R2 = 0.73) in comparison with the models driven by separate climatic factors (R2 = 0.69) and satellite-driving metrics (R2 = 0.55) (Fig. 1a). To further assess the consistency of the aboveground biomass variations from 2001 to 2022, we illustrated the interannual changes in aboveground biomass under different data forcings (Fig. 1b). The results revealed a consistently significant increasing trend, with rates ranging from 0.4 to 0.47 g m-2 y-1 and an ensemble average of 0.44 g m-2 y-1 (t (20) = 3.48, P = 2.4 × 10-3, R2 = 0.38, 95% CI [0.17, 0.70]). Moreover, we generated a trend map based on the ensemble average to examine the spatial changes in aboveground biomass (Fig. 1c). We observed that 63% of the grasslands in China exhibited an increasing aboveground biomass trend, with an average rate of 0.42 g m-2 y-1. Spatially, the aboveground biomass trend showed considerable heterogeneity, with a standard deviation (SD) of 1.25 g m-2 y-1. Significant increases in aboveground biomass were primarily identified in temperate montane meadow on the eastern Tibetan Plateau and in temperate meadow-steppe in eastern Inner Mongolia, with rates of 0.81 and 1.01 g m-2 y-1, respectively. In contrast, the alpine steppe in the central Tibetan Plateau showed a slight declining trend in aboveground biomass, with a rate of -0.05 g m-2 y-1. To further examine the robustness of aboveground biomass estimation, we calculated the SDs of the trends derived from the three forcing systems (Fig. 1d). The analysis revealed that 86% of the grassland aboveground biomass trends presented relatively low SDs (0–2 g m-2 y-1), with an average value of 1.23 ± 0.86 g m-2 y-1. We conclude that the grassland aboveground biomass in China has shown a robust increasing trend over the past 22 years, regardless of the forcing ensemble used.

Changes in grassland fractional vegetation cover
Second, to reflect changes in horizontal aboveground biomass allocation, the fractional vegetation cover was calculated using SMA based on the satellite normalized difference vegetation index (NDVI). In particular, iterative optimization was employed to determine the endmembers of SMA (i.e., NDVI and NDVI )across eight distinct grassland types (Supplementary Table 2). With these endmembers, the grassland fractional vegetation cover model exhibited strong performance (Supplementary Fig. 2a). In the horizontal dimension, aboveground biomass allocation significantly expanded from 2001 to 2022, with a trend of 0.24% y-1 (t (20) = 4.39, P = 2.8 × 10-4, R2 = 0.49, 95% CI [0.001, 0.004], Fig. 2a). Spatially, horizontal aboveground biomass allocation exhibited a trend similar to that of the total aboveground biomass, and 70% of the grasslands experienced an increase from 2001 to 2022 (Fig. 2b). The significant increases were most evident in the temperate meadow-steppe and temperate steppe of Inner Mongolia, with rates of 0.44 and 0.46% y-1, respectively. Notably, areas of decline were primarily observed in the central Tibetan Plateau, with an fractional vegetation cover trend of -0.17% y-1. To further examine the contribution of horizontal aboveground biomass allocation to overall aboveground biomass changes, we calculated the Pearson correlation coefficient between the trends in fractional vegetation cover and aboveground biomass on a pixel basis (Fig. 2c). The results revealed a significantly positive correlation (t (22079) = 67.5, P < 1 × 10-16, r = 0.41, 95% CI [109.19, 115.72]), with 70% of the grasslands showing a synchronous change. Overall, we found that the horizontal dimension of the aboveground biomass increased significantly and explained the aboveground biomass changes in 54% of the Chinese grasslands (spatial average R2 = 0.26; Fig. 2d). However, the explanatory power in high fractional vegetation cover regions (e.g., the eastern Tibetan Plateau) was relatively low, accounting for less than 18%. We further found that when the fractional vegetation cover reached a plateau, the horizontally increasing capacity of the aboveground biomass was apparently constrained (Supplementary Fig. 3). Given the challenge of selecting strictly pure vegetation endmembers in sparse grasslands, which may bias SMA-based fractional vegetation cover estimates, we instead used the classical gap fraction method to derive fractional vegetation cover, showing a similar result to that of the SMA fractional vegetation cover (Supplementary Note 1: Additional experiment on fractional vegetation cover calculation; Supplementary Figs. 2b and 4). We conclude that horizontal aboveground biomass allocation via fractional vegetation cover had a strongly synchronous response to the changes in the total grassland aboveground biomass from 2001 to 2022.

Changes in grassland canopy height
Finally, grassland canopy height during 2001–2022 was estimated using a strategy involving the [aboveground biomass/fractional vegetation cover] ratio and linear regression analysis for scale convention in cm. On the basis of the order of the field-surveyed canopy height, we achieved an average accuracy of 73% in calculating grassland canopy height (Supplementary Fig. 5a). To further assess the accuracy of the modelled canopy height, we compared it with the surveyed canopy height across different grassland types, revealing a marginal difference of 0.5 cm (left panel in Fig. 3a). The results showed that the grassland canopy height in China ranged from 0 to 40 cm, with an average height of 17.6 ± 8.2 cm (Fig. 3a). Spatially, grassland canopy height displays significant heterogeneity, decreasing from temperate grasslands (27.4 ± 8.5 cm) in Inner Mongolia to alpine grasslands (8.5 ± 3.8 cm) on the Tibetan Plateau. Among the seven grassland types, temperate meadow-steppe and lowland meadow in eastern Inner Mongolia have the highest canopy heights of 32.4 and 24.6 cm, respectively, whereas the alpine steppe and alpine desert-steppe in the western Tibetan Plateau presented the lowest heights (~8.0 cm). To further investigate the changes in grassland canopy height from 2001 to 2022, canopy height trends were calculated (Fig. 3b). We observed a noticeable decline in canopy height across 57% of the grasslands in China, with an average decreasing rate of -0.04 cm y-1. These declining trends in canopy height across different grassland types exhibited notable heterogeneity, with an SD of 0.15 cm y-1. In particular, canopy height in temperate and alpine types in northern and western China consistently decreased, ranging from -0.21 cm y-1 in lowland meadow to -0.01 cm y-1 in alpine steppe and alpine meadow (left panel in Fig. 3b). Interestingly, canopy height in temperate meadow-steppe and temperate montane meadow (0.02 and 0.04 cm y-1) in eastern Inner Mongolia and on the Tibetan Plateau slightly increased, indicating that the increase in aboveground biomass in high fractional vegetation cover areas was attributed primarily to the vertical dimension of biomass allocation. To further verify the robustness of the canopy height trend, we linearly fitted the [aboveground biomass/fractional vegetation cover] with different percentiles of field-surveyed canopy height, and the results were consistent (Supplementary Note 2: Additional experiment on canopy height trending robustness; Supplementary Figs. 5 and 6). In summary, compared with the fractional vegetation cover increases in the horizontal dimension, our simulations reveal an asymmetric biomass allocation in the vertical dimension or a decrease in grassland canopy height across China during the contemporary period of 2001–2022.

Independent evidence for declining canopy height
Previous studies indicate that CO₂ fertilization, together with climate warming-driven extensions of the growing season, has contributed to widespread terrestrial greening in recent decades23, manifested by rising leaf area index (LAI) and concurrent increases in grassland biomass yield24. Our results indicate that horizontal aboveground biomass allocation seemingly accounts for a substantial proportion of biomass accumulation, resulting in a decrease in the vertical dimension of aboveground biomass allocation (i.e., canopy height). To further validate the trends of aboveground biomass changes in the vertical dimension, we collected continuous interannual aboveground biomass and fractional vegetation cover measurements (2001–2018) from six evenly distributed stations across China (Fig. 4). The direct measurements show a consistent declining trend in vertical aboveground biomass allocation during the contemporary period, with an average decreasing rate of -2.27 ± 1.09 g m-2 y-1. To account for the potential limitation of using only the 2009–2016 survey years for temporal extrapolation, we assessed canopy height changes during this period and found that 53% of grasslands showed a decline, with an average trend of -0.02 cm y-1 (Supplementary Fig. 7). For the more recent period of 2019–2022, GEDI-derived canopy height estimates also showed a decreasing trend (-0.09 m y-¹), but substantial uncertainty remains in characterizing grassland canopy height (Supplementary Fig. 8). Similarly, modelling uncertainties in aboveground biomass, fractional vegetation cover, and canopy height remain, particularly in the sparsely vegetated alpine desert steppe (Supplementary Note 3; Uncertainty analysis of canopy height estimation; Supplementary Fig. 9). Nevertheless, because canopy height trends are derived from interannual relative changes, their impact is limited to the magnitude of the rate of canopy height decline rather than the direction of the trend, which remains robust overall.

The declining canopy height trend is also consistent with previous studies based on plot- or station-scale observations. For example, a significant reduction in grassland canopy height was reported under continuous grazing pressure, with a decrease approaching 38% (~-1.87 cm y-1) in three-year experiments (2009–2011) performed in the meadow steppe of Hulunbuir7. Similarly, a study conducted in two temperate grasslands in central and eastern Inner Mongolia indicated a decrease in community height between 2015 and 2018 due to drought, with decreases of -2.7 and -16.2 cm, respectively25. Globally, a meta-analysis of 197 studies reported a widespread shift in grassland species from tall (e.g., erect plants) to short plants (e.g., prostrate plants) under grazing impacts26. Overall, all the above evidence suggests a prevalent decline in grassland canopy height, whereas our study provides the dynamic and real-time evidence of this decline at a broad ecosystem scale.

Driver analysis of asymmetric biomass allocation
Grassland plant morphological characteristics (i.e., fractional vegetation cover and canopy height) are expected to undergo substantial changes under environmental change and grazing disturbances, but the underlying mechanisms remain unknown4,27. Here, we applied pixel-based structural equation modelling (SEM) with four environmental factors and grazing practice to evaluate the path effects of precipitation (P), air temperature (T ), solar radiation (R ), CO fertilization effect (C ), and grazing pressure (G ) on canopy height and fractional vegetation cover. In areas with increased canopy height, the path coefficients indicated that simultaneous rises in R , P, and C promoted the increases in canopy height and fractional vegetation cover, whereas T and G exerted negative effects on canopy height, with rising T emerging as the dominant constraint (Fig. 5a).

Consistently, in areas with decreased canopy height, rising P and C continued to exert positive effects on both canopy height and fractional vegetation cover (Fig. 5b). However, increases in T and G , together with reduced R , negatively influenced both canopy height and fractional vegetation cover, leading to an asymmetric spatial allocation of biomass, characterized by increasing fractional vegetation cover alongside decreasing canopy height. This shift reflects an adaptive trade-off in which plants tend to expand horizontally, as vertical growth requires abundant rainfall and fertile soils, whereas horizontal expansion improves access to photosynthetic resources (radiation and CO )2 and alleviates grazing damage28. Grazing manipulation experiments show that the combined effects of climate warming and grazing profoundly reshape grassland community composition and phenotypic traits, reduce plant diversity (e.g., species richness) and ecosystem multifunctionality, and ultimately lead to declines in biomass, canopy height, and community coverage26,29. Our results are consistent with previous studies, demonstrating that fractional vegetation cover, canopy height, and aboveground biomass are strongly influenced by climate warming and grazing disturbance, which jointly shape community structure and constrain canopy height development. Numerous studies indicate that rising C and P promoted fractional vegetation cover, canopy height, and biomass across different plant functional groups and species30–33. However, warming-induced water deficits can intensify grazing pressure, drive shifts in species composition34, and ultimately suppress vegetation growth, manifested as grassland dwarfing and declines in canopy height26.

We further investigated the interactive effects of climate warming and other driving factors on the spatial distribution of biomass using linear mixed-effects models (LMMs)29. The LMM results revealed consistently negative effects arising from increases in T and G (Supplementary Table 3). Moreover, we found a pervasive negative interaction between rising T and other driving factors on both canopy height and fractional vegetation cover. In areas with decreased canopy height, the amplified impacts of the interaction between T and G were evident, exceeding their individual constraints on canopy height development. Collectively, we suggest that CO fertilization, climate change, and grazing pressure jointly shape the spatial allocation of grassland biomass29,31,35. Among these factors, higher R , P, and C promoted canopy height and fractional vegetation cover, whereas increases in T and G , individually and in combination, exerted antagonistic effects29,36, with canopy height being more strongly affected.

Previous studies have also shown that decreasing plant diversity weakens the stability and resistance of grassland ecosystems to environmental change37,38. Here, we examine the relationships between surveyed species richness, GEDI-derived foliage height diversity (FHD)13, and canopy height trends. Our results indicate that canopy height trends respond linearly and significantly to species richness and FHD, with correlation coefficients of 0.87 (t (23) = 8.35, P = 2 × 10-8, 95% CI [0.002, 0.004]) and 0.94 (t (26) = 13.44, P = 3.3 × 10-13, 95% CI [0.019, 0.025]), respectively (Fig. 6). Meanwhile, a tipping point was observed whereby species richness below 11 showing an apparent decline in canopy height (Fig. 6a). Similarly, grassland canopy height with FHD below 2.47 exhibited a negative trend (Fig. 6b). These findings suggest grazing‐induced species loss can disrupt asynchronous responses among species to climate variability, thereby intensifying canopy height reductions, particularly during drought years22,39.

In conclusion, the grassland biome in China, as an important part of the Eurasian steppes, plays a crucial role in maintaining ecological services, ensuring food security, and promoting sustainable development, serving as an important component for achieving the global Sustainable Development Goals (SDGRs)40. Previous studies have consistently reported that grassland productivity and greenness have increased over the past few decades23, seemingly illustrating a positive signal of ecosystem evolution in the context of climate warming24,41. Unfortunately, we observed a significant decline in grassland canopy height from 2001 to 2022, reflecting an emerging alteration in plant morphological characteristics. The decrease in grassland canopy height will not only cause ruminants (e.g., cattle and sheep) to increase the difficulty and grazing time for forage accessibility, but also enhance the risk of disturbance to grassland roots during grazing26. Moreover, decreased grassland canopy height can further reduce ecosystem structural diversity and functional sustainability, resulting in strong productivity fluctuations in response to climate extremes and human activities42. Our findings reveal that grassland canopy height decline is notably shaped by ongoing climate warming and grazing pressure, and is closely associated with shifts in species diversity. We advocate climate-adaptive management that pairs rotational and rest grazing with revegetation to sustain species richness and structural diversity, thereby enhancing ecosystem resistance and resilience to achieve the SDGs.

Methods
Datasets
Optical remote sensing images were obtained from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS), which is operated by the National Aeronautics and Space Administration (NASA), as part of the Earth Observing System (https://modis.gsfc.nasa.gov). We used two products, MOD09A1 and MOD15A2H, covering the peak growing season (July–August) from 2001 to 2022. MOD09A1 was used to calculate 11 satellite-driving metrics (see Supplementary Table 1). The spectral bands used included seven bands at a 500 m resolution: blue (469 nm), green (555 nm), red (645 nm), near-infrared (859 nm), shortwave infrared 1 (1240 nm), shortwave infrared 2 (1640 nm), and shortwave infrared 3 (2130 nm). We used MOD15A2H LAI data to calculate fractional vegetation cover using the classical gap fraction method from 2001 to 2022. GEDI Level 2A and Level 2B products were employed to extract relative height metrics and FHD13, respectively, from 2019 to 2022. All the remote sensing data were processed using maximum value compositing for July and August via the Google Earth Engine (GEE) platform.

Climate data were derived from the ERA5-Land dataset provided by the Copernicus Climate Change Service through the European Centre for Medium-Range Weather Forecasts (https://cds.climate.copernicus.eu/). This dataset provides monthly meteorological data on a 0.1° (~10 km) latitude/longitude grid. We utilized monthly averages for P, T , and R to calculate the 22-year mean annual precipitation, mean annual temperature, mean annual vapor pressure deficit, and mean annual R , as well as interannual and seasonal anomalies (June–September) relative to the 22-year averages from 2001 to 2022 (see Supplementary Table 1). Additionally, we applied a linear correction to ERA5 precipitation data via station data to eliminate systematic errors. The station data of monthly precipitation from 2001 to 2019 were obtained from 134 long-term monitoring meteorological stations managed by the China Meteorological Data Service Center (https://data.cma.cn/en). The monthly averaged gridded C data at 0.75° spatial resolution from 2003 to 2020 were obtained from the Copernicus Atmosphere Monitoring Service (CAMS) global greenhouse gas reanalysis43.

The measurements of aboveground biomass, fractional vegetation cover, canopy height and species richness were derived from the National Inventory of Grassland Resources by the National Forestry and Grassland Administration of China from 2009 to 2016 (http://www.forestry.gov.cn), and 24,125 field plots were collected during the peak growing season (July–August) (Supplementary Fig. 1). The aboveground biomass was measured by harvesting the aboveground portion of the plants and oven-drying them to obtain the dry mass weight. The fractional vegetation cover was measured using the point quadrat method, in which a grid with needles was placed over the plot, and the ratio of needle hits on vegetation versus soil was recorded to estimate cover. Canopy height was measured at the centre of the plot with a ruler, and species richness was assessed through field quadrat surveys. The grazing intensity data were collected from ref. 44, with a spatial resolution of ~250 m, covering the period from 2001 to 2022.

The shapefile data for grassland types at a 1:1 million scale were utilized, spanning temperate meadow-steppe, temperate steppe, temperate desert-steppe, alpine steppe, alpine desert-steppe, lowland meadow, temperate montane meadow, and alpine meadow. The data for continuous interannual measurements of aboveground biomass and fractional vegetation cover at six observation stations were primarily sourced from the China Ecosystem Positioning Observation and Research Dataset (www.nesdc.org.cn), with additional data from ref. 45.

Aboveground biomass modelling
Grassland aboveground biomass was estimated through random forest models driven by per se satellite-driving metrics, climatic factors, and their combination, respectively (Supplementary Table 1). The holdout and 5-fold cross-validation schemes were employed for model training. First, the total number of training samples was randomly divided, with 10% allocated for independent validation and the remaining 90% used for iterative training with 5-fold cross-validation. In each iteration, four subsets of samples were sequentially selected for model training, and the single remaining subset was used for model cross-validation8. The training was completed four times to calculate the average validation error across the holdout and 5-fold cross-validation datasets. To improve the underestimation of high aboveground biomass values during model training, the random oversampling method was employed to balance the training samples by increasing the number of minority data instances (i.e., samples with high aboveground biomass values)46. Moreover, to avoid excessive dependence on individual trees, Bayesian model averaging47 was applied to calculate the results of the random forest model. Given the availability of ground-truth data, the final aboveground biomass upscaling models with three forcing ensembles were established using the full dataset through a 5-fold cross-training scheme. Using the ensemble average of the aboveground biomass models, we generated gridded aboveground biomass for Chinese grasslands at a spatial resolution of 500 m and yearly time steps from 2001 to 2022, which were then used to calculate interannual changes, spatial trends, and the SD map reflecting different forcing ensembles. The above calculation was performed using the RandomForestRegressor module from scikit-learn 1.6.1 and the RandomOverSampler module from imbalanced-learn 0.13.0 in Python 3.10.16.

Fractional vegetation cover calculation
The grassland fractional vegetation cover was calculated for each of the eight grassland types using the SMA method9. During the SMA process, based on the survey data, an iterative optimization method was employed to determine the optimal endmembers (i.e., NDVI and NDVI ) for each grassland type, as detailed in Supplementary Table 2. With these optimal endmembers and NDVI composite values, fractional vegetation cover was calculated on the GEE platform using JavaScript with the normalizedDifference function.

Canopy height calculation
Grassland canopy height (cm) was calculated using a linear model on the basis of the relationship between the surveyed canopy height and the ratio of aboveground biomass to fractional vegetation cover (Supplementary Fig. 5a). The linear quantile regression of the surveyed canopy height with the [aboveground biomass/fractional vegetation cover] ratio was determined to control the systematic errors in canopy height estimation across the eight grassland types. With the linear model for each grassland type, we converted gridded [aboveground biomass/fractional vegetation cover] ratios to canopy heights at a spatial resolution of 500 m and yearly time steps from 2001–2022. To further assess the robustness of the trends in the canopy height calculation, the linear model was tested with different statistical schemes (e.g., average), as detailed in Supplementary Note 2: Additional experiment on canopy height trending robustness, and Supplementary Figs. 5b and 6.

The driving factor analysis of canopy height and fractional vegetation cover
The piecewise SEMs48 were applied to evaluate the effects (i.e., path coefficients) of environmental factors and grazing intensity on canopy height and fractional vegetation cover in relation to changes in aboveground biomass. First, pixel-based trends of explanatory factors (R , P, T , C , and G ) and response variables (canopy height, fractional vegetation cover, and aboveground biomass) were calculated for the period 2001–2022. For cross-comparison of the effects, explanatory factors were normalized using the z-score method by subtracting the mean of each term and dividing by the standard deviation. Second, pixel-scale SEMs were constructed for Chinese grasslands classified by increasing or decreasing canopy height, using normalized explanatory and response trends, with sample sizes of 4.7 and 6.3 million, respectively. In addition, we incorporated trends of explanatory factors as pairwise combinations (e.g., ΔT × ΔG ), and applied Ms to examine both their independent and interactive effects on the response variables (Supplementary Table 3). The above analyses were performed using R statistical software version 4.5.1. The SEMs were carried out using the psem function of the piecewiseSEM package, and LMMs were performed using the lme function within the nlme package.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Your work shows a detailed and interesting study on a very important topic currently. I am sending some questions and\nsuggestions to clarify some points that might be better explained and contribute even more to the subject.\n1. Regarding the estimate of CH from the relation/equation AGB/FVC: please provide reference and example of its use\nelsewhere.\n2. Line 88: please add “respectively” for FVC and CH.\n3. How did you manage the different range of field data (2009 to 2016) and period of study (2001 to 2022)? In addition, in\nwhich context data from the six stations of NESDC were used?\n4. Information about LAI was measured also in the field? Please explain further the use of MOD15A2H 289 LAI data to\ncalculate FVC using the classical gap fraction method.\n5. The collection of pure pixels was made for the green pixels, and the soil pixels were derived from them? It is difficult to\nimagine pure pixels for a 500 m spatial resolution.\n6. It is not clear why allocation of light varied or have an impact on CH under fertilizing effect of rising CO2.\n7. It would be important to add a comment on the effect of diminishing CH on the diversity of grass species.", "reviewer_2": "The study reveals China's grassland canopy height (CH) is declining, affecting ecosystem function and food security. It\nhighlights vulnerability to climate change and grazing, guiding adaptive management strategies.\nThe AGB/FVC ratio method for estimating CH in grasslands addresses remote sensing challenges due to low-density point\nclouds in LiDAR data, integrating machine learning with multi-source data for high-resolution monitoring.\nThis study uses the AGB/FVC ratio to estimate grassland CH at a national scale, addressing a gap in previous studies. It\nprovides broad-scale evidence of CH decline, offering a dynamic perspective not previously captured in plot-scale studies.\nThe work is unique in its methodology for CH estimation and comprehensive analysis of China's grasslands over two\ndecades, building on established remote sensing and ecological principles.\n+ The study’s reliance on field data from 2009–2016, which does not span the full analysis period (2001–2022), may\nintroduce bias in trend extrapolation, and the use of 500 m resolution data may miss fine-scale variations in CH. To address\nthese limitations, it is recommended to extend the field dataset by including earlier or more recent measurements or validate\nfindings using independent datasets. Using higher-quality satellite data like Sentinel-2 can provide more detailed\ninformation, and carefully adding GEDI LiDAR data can help confirm canopy height measurements, even though there are\nsome issues with grasslands. A dedicated section or supplementary analysis should be included to quantify the impacts of\nthese temporal and spatial constraints.\n+ The manuscript does not provide a detailed analysis of the uncertainties in CH estimates, especially in places with low\nFVC, where optical remote sensing can give misleading results. To make this better, the authors should analyze how errors\naffect the CH estimates that come from the AGB/FVC ratio, taking into account the uncertainties in both the AGB and FVC\nmodels. Uncertainty should also be explicitly assessed across FVC gradients, particularly in sparse vegetation regions, and\nconfidence intervals should be reported. Additionally, cross-validation metrics such as RMSE and bias should be expanded\nacross grassland types. A dedicated subsection with visualizations like uncertainty maps is recommended to present these\nresults clearly.\n+ The paper’s claims about reduced ecosystem resilience, biodiversity loss, and grazing impacts are plausible but lack\ndirect supporting evidence, particularly given the non-significant grazing variable . To make these points stronger, the\nauthors should include or reference data on biodiversity, functional traits, or soil carbon to show how the CH decline is\nconnected to ecosystem damage. More detailed grazing data or sensitivity analyses could clarify grazing’s role. The authors\nare also encouraged to propose actionable management strategies, such as rotational grazing or restoration of tall-grass\nspecies—and relate these to relevant Sustainable Development Goals.\n+ The current explanation of asymmetric biomass allocation in the study is overly simplified, emphasizing light optimization\nwithout addressing underlying physiological or ecological mechanisms. To deepen the analysis, the authors should\nintegrate data or models on traits like leaf area index (LAI), root biomass, or photosynthetic efficiency to explain growth\npatterns, and consider interaction effects among drivers such as CO₂ and precipitation.\n+ The paper should improve its global relevance by integrating its findings on China's declining grassland canopy height\nwith global trends. It should compare China's CH decline with other grassland ecosystems, using references like Sloat et al.\n(2018) and Díaz et al. (2007). Also, highlight unique factors like intensive grazing or climate variability."}, "author_response": "We thank the reviewers for the positive reviews and constructive comments on our manuscript.\nIn preparing this revision, we have fully considered the reviewers’ comments,\nconducted additional experiments and analyses as suggested, and revised the manuscript\naccordingly. We summarize our responses to these comments below in blue text, with reference\nto additional response figures and tables (Figs. R1 to R8, Tables R1 and R2). All line numbers\nrefer to numbers in the revised manuscript unless noted otherwise.\nReviewer #1 (Remarks to the Author):\nYour work shows a detailed and interesting study on a very important topic currently. I am\nsending some questions and suggestions to clarify some points that might be better explained and\ncontribute even more to the subject.\n1. Regarding the estimate of CH from the relation/equation AGB/FVC: please provide reference\nand example of its use elsewhere.\nResponse: Thanks for the suggestion. We revised the manuscript to further clarify the definition\nof AGB/FVC, and explained why it can be used to derive CH, with supporting evidence from the\nreferences. First, we clarified that AGB/FVC represents the biomass per unit area, which we use\nas an indicator of the vertical allocation of AGB, rather than CH itself. Many studies have\ndemonstrated a strong linear positive correlation between aboveground biomass and plant height\n(Lang et al., 2023; Proulx, 2021). These evidences provide the basis and likelihood for\nestimating CH using vertical biomass and linear regression. Our results also show a significantly\nlinear relationship between the ratios of AGB/FVC and surveyed CH across different grassland\ntypes (Supplementary Fig. 5). These results further demonstrate the rationality of estimating CH\nusing AGB/FVC. We have modified the main document to clarify the definition and application\nof AGB/FVC (lines 93–94).\nReferences:\n1. Lang, Nico, et al. A high-resolution canopy height model of the Earth. Nature Ecology &\nEvolution 7.11 (2023): 1778-1789.\n2. Proulx, Raphaël. On the general relationship between plant height and aboveground biomass\nof vegetation stands in contrasted ecosystems. PLoS One 16.5 (2021): e0252080.\n2. Line 88: please add “respectively” for FVC and CH.\nResponse: We restructured this sentence in the main text to avoid semantic ambiguity (lines 91–\n92).\n3. How did you manage the different range of field data (2009 to 2016) and period of study\n(2001 to 2022)? In addition, in which context data from the six stations of NESDC were used?\nResponse: Thank you for raising this critical issue. Initially, the surveyed samples of AGB, CH,\nand FVC from 2009–2016 were modeled with the expectation of reflecting long-term changes\nover 2001–2022. However, this inevitably raises the question of how to validate changes outside\nthe surveyed window, both prior to 2009 and after 2016.\nTo address this concern, we conducted segmented experiments, as direct field measurements\ncovering the entire study period are almost unavailable. For the early period (2001–2018), we\ncollected vertical biomass data from six long-term NESDC observation sites to corroborate the\ndeclining trend during gaps in the early years (Fig. R1). We also compared the observed decline\nwith model-extrapolated results, and found consistent trends, despite some biases at two of the\nsix sites caused by scale effects and station continuity.\nFor the middle period (2009–2016), where the models were directly constrained by field data, we\nderived CH trends from simulated gridded data inferred from AGB/FVC. The results confirmed\na consistent decreasing trend, with an average rate of -0.02 cm yr-¹, and ~53% of China’s\ngrasslands exhibited decline (Fig. R2).\nFor the late period (2019–2022), we used GEDI Level-2A data to extract relative canopy height\nmetrics. GEDI-derived CH also showed an overall decline of -0.09 m yr-¹, with consistent\ndecreases detected across five grassland types (Fig. R3).\nIn summary, although the magnitude of decline varied slightly among datasets, all independent\nCH estimates consistently revealed a robust decreasing trend. To highlight this convergence of\nevidence, we have revised the main document (lines 209–218), added Fig. R1 (renumbered as\nFig. 4), and updated the Supplementary Material with Fig. R2 and Fig. R3 (now Supplementary\nFigs. 7 and 8).\nFig. R1. Surveyed and modelled changes in the vertical allocation of AGB from the long-term\ngrassland ecosystem observation stations across China. The map in the right panel shows the\nspatial distribution of stations.\nFig. R2. Spatiotemporal changes in grassland CH in China from 2009 to 2016. a, Interannual\nchanges. b, Rates of grassland CH change for seven grassland types (left panel) and the trend\nmap (right panel), where the trend is dotted if statistically significant (P < 0.05); the embedded\nhistograms in the right panels are statistics for the area percentage aggregated by binning pixel\nvalues.\nFig. R3. Changes of CH derived from the GEDI dataset across Chinese grasslands from 2019 to\n2022. a, Interannual changes of CH. b, Rates of CH change for seven grassland types.\n4. Information about LAI was measured also in the field? Please explain further the use of\nMOD15A2H 289 LAI data to calculate FVC using the classical gap fraction method.\nResponse: As noted, the SMA approach is one of the most widely used methods for estimating\nFVC; however, selecting strictly pure endmembers can be challenging in sparse grasslands. To\novercome this limitation, we also applied the classical gap fraction method, which does not rely\non endmember selection but instead requires LAI to derive FVC (see Supplementary Section 1).\nSpecifically, we used MOD15A2H LAI data to calculate FVC and validated the results against\nfield-measured FVC, which showed high accuracy (Supplementary Fig. 2b).\nWe further compared annual FVC estimates derived from SMA and the classical gap fraction\nmethod across different grassland types and for all Chinese grasslands. The two methods were\nstrongly consistent, with a correlation coefficient of 0.98 (P < 0.05, Fig. R4), and the modeled\nFVC for each grassland type also showed significant correlations (P < 0.05).\nThese results confirm the robustness of our FVC estimates. In the revised manuscript, we have\nclarified the rationale for recalculating FVC using MOD15A2H LAI data and the classical gap\nfraction method, as now stated in lines 155–158 of the main text.\nFig. R4. Comparison of FVC estimates for eight grasslands from 2001 to 2022 derived using the\nSMA and the classical gap fraction method.\n5. The collection of pure pixels was made for the green pixels, and the soil pixels were derived\nfrom them? It is difficult to imagine pure pixels for a 500 m spatial resolution.\nResponse: We agree that identifying pure vegetation and soil endmembers from coarse-\nresolution remote sensing images is challenging due to mixed-pixel effects. Previous studies\nhave typically defined endmembers using constant values (Li et al., 2014) or percentile-based\nthresholds (e.g., the 5th percentile for NDVI and the 75th percentile for NDVI ) (Wittich and\nsoil veg\nHansing, 1995; Zeng et al., 2000), largely because of the lack of direct ground-truth\nobservations.\nIn this study, we applied an iterative optimization method to refine endmember values for\ndifferent grassland types, aiming to achieve the best fit between modeled FVC and field survey\ndata. The optimized endmembers (Table R1) produced compelling accuracy (Supplementary Fig.\n2a). To further evaluate the robustness of SMA-derived FVC, we also applied the classical gap\nfraction method, which avoids endmember selection. Results again showed strong consistency,\nwith r = 0.98 (P < 0.05; Fig. R4).\nOverall, these analyses provide multiple lines of evidence supporting the robustness of our FVC\nestimates across the study region. In the revised manuscript, we have clarified this point in the\nmain text (lines 386–391), added Table R1 in the Supplementary Material (now Supplementary\nTable 2), and included the supporting figure (Supplementary Fig. 2).\nTable R1. Parameters of SMA and classical gap fraction for FVC calculation for different\ngrassland types across China.\nGrassland types NDVIsoil NDVIveg G (0, θ₁)\nTemperate meadow-steppe (TMS) 0.02 0.895 1.00\nTemperate steppe (TS) 0.01 0.88 0.42\nTemperate desert-steppe (TDS) 0.145 0.515 1.00\nAlpine steppe (AS) 0.01 0.5 1.00\nAlpine desert-steppe (ADS) 0.01 0.5 0.63\nLowland meadow (LM) 0.13 0.875 0.67\nTemperate montane meadow (TMM) 0.01 0.83 1.00\nAlpine meadow (AM) 0.01 0.715 1.00\nReferences:\n1. Li, Fei, et al. Improving estimates of grassland fractional vegetation cover based on a pixel\ndichotomy model: A case study in Inner Mongolia, China. Remote Sensing 6.6 (2014): 4705-\n4722.\n2. Wittich, K. P., and O. Hansing. Area-averaged vegetative cover fraction estimated from\nsatellite data. International Journal of Biometeorology 38.4 (1995): 209-215.\n3. Zeng, Xubin, et al. Derivation and evaluation of global 1-km fractional vegetation cover data\nfor land modeling. Journal of Applied Meteorology 39.6 (2000): 826-839.\n6. It is not clear why allocation of light varied or have an impact on CH under fertilizing effect of\nrising CO2.\nResponse: We agree. In the revision, we have re-evaluated the effects of environmental changes\nand grazing pressure on CH and FVC. Instead of relying on aggregated annual changes (2001–\n2022) for statistical analysis, which provided only 22 samples for model fitting, we performed\npixel-scale trend calculations. This yielded much larger sample sizes of 4.7 million and 6.3\nmillion pixels for areas with increasing and decreasing CH, respectively. We then applied\npiecewise SEMs to quantify the independent contributions of environmental and grazing drivers\nto changes in CH and FVC, and subsequently to AGB. Compared with the earlier analysis based\non limited annual samples, this approach more comprehensively captures the regional impacts of\nthe driving factors.\nThe results revealed that rising CO , precipitation, and radiation generally promoted CH and\ndecline, alongside reduced radiation limitation. To further explore interactions among drivers, we\nalso conducted analyses using LMMs. These indicated significant antagonistic effects of rising\ntemperature on other factors, particularly amplifying the negative impacts of grazing (Table R2).\nThis finding is consistent with previous plot-scale studies (Maestre et al., 2022; Zhang et al.,\n2023), which reported that intensified warming increases the risks of grazing-induced losses in\nbiodiversity and ecosystem function, altering both community composition and plant traits.\nWe have incorporated these findings into the discussion (lines 240–276) and added the new\nresults as Fig. R5 (numbered as Fig. 5) in the main text, with corresponding materials provided\nas Table R2 (numbered as Supplementary Table 3).\nFig. R5. Responses of CH and FVC to environmental changes in R , P, T , C , and G . a, b, Path\na r a a p\ncoefficients for grassland areas with increased CH (a), and decreased CH (b), with the\ncalculation of SEM, where the coefficients are asterisked (*) if statistically significant (P < 0.05).\nThe embedded area charts present the overall and median statistics of the 2001–2022 average\nFVC for these two contrasting regions.\nTable R2. Effective coefficients of change rates (Δ) in CH and FVC, resulting from\nenvironmental factors and grazing pressure, independently and interactively, across areas with\nincreased and decreased CH from 2001 to 2022. The relationships among environmental factors,\ngrazing pressure, CH, and FVC were calculated using linear mixed effects models (LMMs),\nwhere the environmental factors and grazing pressure were normalized using the z-score method\nby subtracting the mean of each term and dividing by the standard deviation for cross-\ncomparison of the independent and interactive effects. A higher absolute value of independent\nand interactive effects indicates a stronger effect on promoting or diminishing CH and FVC\nchanges. The independent and interactive effects are asterisked (*) if statistically significant (P <\n0.05).\nAreas with increased CH Areas with decreased CH\nDriving factors\nEffects on ΔCH (+) Effects on ΔFVC (+) Effects on ΔCH (-) Effects on ΔFVC (+)\nΔTa -0.38* -0.19 -0.59* -0.45*\nΔGp -0.19 -0.16 -0.43* -0.37*\nΔPr 0.50* 0.27* 0.18 0.20*\nΔRa 0.58* 0.18 0.66* 0.24*\nΔCa 0.26* 0.20* 0.29* 0.27*\nΔTa × ΔGp -0.35* -0.14 -0.67* -0.29*\nΔTa × ΔPr -0.26* -0.20* -0.06 -0.04\nΔTa × ΔRa -0.03 -0.13 -0.05 -0.01\nΔTa × ΔCa -0.90* -0.21* -0.77* -0.70*\nΔGp × ΔPr 0.04 -0.09 0.06 0.26*\nΔGp × ΔRa -0.09 -0.06 -0.18 -0.41*\nΔGp × ΔCa 0.09 -0.26* -0.17 -0.29*\nΔPr × ΔRa 0.20 0.15 0.03 -0.02\nΔPr × ΔCa 0.03 0.15 0.38* -0.33*\nΔRa × ΔCa 0.30* 0.15 0.56* 0.13\nReferences:\n1. Maestre, Fernando T., et al. Grazing and ecosystem service delivery in global drylands.\nScience 378.6622 (2022): 915-920.\n2. Zhang, Minna, et al. Experimental impacts of grazing on grassland biodiversity and function\nare explained by aridity. Nature Communications 14.1 (2023): 5040.\n7. It would be important to add a comment on the effect of diminishing CH on the diversity of\ngrass species.\nResponse: Agreed. Exploring the relationship between CH changes and plant diversity is indeed\nimportant. Unfortunately, long-term in situ observations of plant diversity are rarely available.\nTo address this, we analyzed CH changes across different levels of plant diversity, which is\ndirectly relevant to the focus of this study. Plant diversity was represented by surveyed species\nrichness and GEDI-derived FHD (Bu and Xiao, 2025).\nOur results showed that CH trends were significantly and positively correlated with species\nrichness and FHD (r = 0.87 and 0.94) (Fig. R6). Moreover, CH exhibited a declining trend once\nthese diversity indicators dropped below a certain threshold. Previous studies have demonstrated\nthat plant diversity is strongly linked to ecosystem stability, as well as resistance and resilience to\nenvironmental change (Yu et al., 2025; Isbell et al., 2015). Our findings further corroborate that\nCH dynamics are broadly and strongly associated with variations in grassland species diversity,\nunderscoring the importance of biodiversity conservation.\nWe sincerely thank the reviewer for this constructive comment, which has substantially\nimproved our study. In the revision, we have added a new section on the relationship between\nplant diversity and CH changes (lines 284–293 in the main text), together with a new figure (Fig.\nR6, now Fig. 6 in the main text).\nFig. R6. Correlation between 2001–2022 CH trends and plant diversity across Chinese\ngrasslands. a, Species richness. b, GEDI-derived FHD.\nReferences:\n1. Bu, Jingyi, and Jingfeng Xiao. Upscaling eddy covariance measurements of carbon and water\nfluxes to the continental scale by incorporating GEDI-derived canopy structural complexity\nmetrics. Remote Sensing of Environment 329 (2025): 114930.\n2. Yu, Qiang, et al. Contrasting drought sensitivity of Eurasian and North American grasslands.\nNature 639.8053 (2025): 114-118.\n3. Isbell, Forest, et al. Biodiversity increases the resistance of ecosystem productivity to climate\nextremes. Nature 526.7574 (2015): 574-577.\nReviewer #2 (Remarks to the Author):\nThe study reveals China's grassland canopy height (CH) is declining, affecting ecosystem\nfunction and food security. It highlights vulnerability to climate change and grazing, guiding\nadaptive management strategies.\nThe AGB/FVC ratio method for estimating CH in grasslands addresses remote sensing\nchallenges due to low-density point clouds in LiDAR data, integrating machine learning with\nmulti-source data for high-resolution monitoring.\nThis study uses the AGB/FVC ratio to estimate grassland CH at a national scale, addressing a\ngap in previous studies. It provides broad-scale evidence of CH decline, offering a dynamic\nperspective not previously captured in plot-scale studies.\nThe work is unique in its methodology for CH estimation and comprehensive analysis of China's\ngrasslands over two decades, building on established remote sensing and ecological principles.\n1.+ The study’s reliance on field data from 2009–2016, which does not span the full analysis\nperiod (2001–2022), may introduce bias in trend extrapolation, and the use of 500 m resolution\ndata may miss fine-scale variations in CH. To address these limitations, it is recommended to\nextend the field dataset by including earlier or more recent measurements or validate findings\nusing independent datasets. Using higher-quality satellite data like Sentinel-2 can provide more\ndetailed information, and carefully adding GEDI LiDAR data can help confirm canopy height\nmeasurements, even though there are some issues with grasslands. A dedicated section or\nsupplementary analysis should be included to quantify the impacts of these temporal and spatial\nconstraints.\nResponse: Thank you for providing such valuable comments. While the field measurements\nfrom 2009–2016 are highly unique and rare, we agree that it is not sufficient to extrapolate\nchanges across the entire study period based solely on this middle window. Following the\nsuggestion, we validated the CH declining trend during both the early and late periods using\nindependent datasets: long-term observational station data (2001–2018) and GEDI data (2019–\n2022), respectively. We also recalculated CH trends for the measurement period itself, thereby\nverifying the robustness of the decline across the full study duration.\nFor the early period (2001–2018), observations from six independent long-term monitoring sites,\ntogether with modeled vertical biomass (AGB/FVC), consistently indicated a decline (Fig. R1).\nFor the recent period (2019–2022), GEDI-derived CH also showed a decreasing trend, with\nconsistent declines across five grassland types (Fig. R3), though we acknowledge notable\nuncertainties, as the reviewer pointed out. During the measurement period (2009–2016), CH\ntrends derived from modeled data revealed a consistent decrease of -0.02 cm yr-¹, with ~53% of\nChina’s grasslands exhibiting decline (Fig. R2).\nAlthough the 500 m dataset may not fully capture fine-scale CH variations, we believe this does\nnot undermine the overarching conclusion of widespread CH decline. Regarding finer-resolution\ndata from Sentinel-2 (2017–present), its use for interannual trend analysis may introduce biases,\nas multiple years are typically required to aggregate complete annual coverage (Li et al., 2024).\nIn this revision, we have added a section discussing the multiple lines of evidence for CH decline\n(main text, lines 209–218), along with new figures: Fig. R1, Fig. R2, and Fig. R3 (now Fig. 4,\nSupplementary Fig. 7, and Supplementary Fig. 8).\nReference:\n1. Li, Huaqiang, et al. A machine learning scheme for estimating fine-resolution grassland\naboveground biomass over China with Sentinel-1/2 satellite images. Remote Sensing of\nEnvironment 311 (2024): 114317.\n2.+ The manuscript does not provide a detailed analysis of the uncertainties in CH estimates,\nespecially in places with low FVC, where optical remote sensing can give misleading results. To\nmake this better, the authors should analyze how errors affect the CH estimates that come from\nthe AGB/FVC ratio, taking into account the uncertainties in both the AGB and FVC models.\nUncertainty should also be explicitly assessed across FVC gradients, particularly in sparse\nvegetation regions, and confidence intervals should be reported. Additionally, cross-validation\nmetrics such as RMSE and bias should be expanded across grassland types. A dedicated\nsubsection with visualizations like uncertainty maps is recommended to present these results\nclearly.\nResponse: Agreed. Following the suggestion, we evaluated the uncertainties of different\nmethods for estimating AGB, FVC, and CH across FVC gradients, and extended this assessment\nto the grassland-type scale using R2 and relative error as evaluation metrics. Given the\npronounced FVC gradient among Chinese grassland types (Fig. R7), we ranked the modeling\nuncertainties of the seven grassland types accordingly.\nOverall, the three response indicators showed good consistency, with comparable R2 values.\nIn particular, AGB simulations that combined climatic factors with satellite-derived metrics\nperformed best, while satellite metrics alone introduced larger biases in AGB (Fig. R8a). FVC\nmodelling showed a lower performance in sparsely vegetated alpine desert steppe (ADS), as the\nreviewer noted (Fig. R8b). However, the classical gap fraction method partly alleviated these\nbiases, improving both R2 and relative error for ADS. These results suggest that errors in FVC\nare most evident in sparsely vegetated areas and may propagate into CH estimation (Fig. R8c).\nHowever, ADS accounts for only 5.2% of the total grassland area, mainly on the western Tibetan\nPlateau, and thus is more likely to affect the magnitude of CH change rates rather than the\noverall declining trend.\nIn summary, while optical remote sensing inevitably carries uncertainties in sparsely vegetated\nareas, the relative temporal changes are expected to remain robust. This conclusion is further\nsupported by multiple lines of evidence, including continuous plot-scale observations and GEDI\ndata (Fig. R1 and Fig. R3). To address this point, we have added a new section and Fig. R8 in the\nSupplementary Material (now Supplementary Section 3 and Supplementary Fig. 9) and discussed\nit in the main text (lines 218–222).\nFig. R7. Histogram of FVC gradients across seven grassland types.\nFig. R8. Modeling uncertainties across different grassland types ranked by FVC gradients,\nranging from 28% in ADS to 88% in TMM. The left panel shows the histogram of R2, and the\nright panel presents the corresponding relative errors. a, AGB. b, FVC. c, CH.\n3.+ The paper’s claims about reduced ecosystem resilience, biodiversity loss, and grazing\nimpacts are plausible but lack direct supporting evidence, particularly given the non-significant\ngrazing variable. To make these points stronger, the authors should include or reference data on\nbiodiversity, functional traits, or soil carbon to show how the CH decline is connected to\necosystem damage. More detailed grazing data or sensitivity analyses could clarify grazing’s\nrole. The authors are also encouraged to propose actionable management strategies, such as\nrotational grazing or restoration of tall-grass species—and relate these to relevant Sustainable\nDevelopment Goals.\nResponse: We agree. In the revision, we incorporated both plot-scale surveyed species richness\nand GEDI-derived FHD as indicators of species diversity and functional traits, and quantified\ntheir relationships with CH trends. The results showed significant positive correlations,\ndemonstrating that variations in species richness and FHD are strongly associated with changes\nin CH (Fig. R6).\nPrevious studies have shown that declines in plant diversity induced by overgrazing reduce\necosystem resistance and resilience to climate change (Isbell et al., 2015; Bu and Xiao, 2025).\nTo better capture the influence of grazing, we replaced the previously used annual livestock\nstatistics with nationally gridded 250-m grazing data over the region. Using SEM and LMM\napproaches at the pixel scale, we examined both the independent and interactive effects of\ngrazing pressure. The results revealed that intensive grazing consistently exerted negative effects\non CH and FVC, with particularly strong impacts in areas where CH is declining (Fig. R5).\nFurthermore, we found that rising temperature amplified the negative influence of grazing (Table\nR2).\nTaken together, these findings provide direct evidence of the ecological risks associated with CH\ndecline, driven not only by grazing pressure but also by climate warming and concurrent changes\nin species diversity. To mitigate these adverse effects, we propose several actionable\nmanagement strategies, including rotational and rest grazing combined with revegetation to\nsustain plant diversity (lines 312–314). While soil carbon is indeed an important topic, given\ncurrent data limitations and the focus of this study on grassland CH decline (dwarfing), we did\nnot extend our analysis to this perspective at this time.\nIn summary, the revision re-assesses the impacts of environmental changes and grazing on\ngrassland CH (lines 240–276), and further extends the analysis to associations with species and\nstructural diversity (lines 284–293). These revisions are supported by two new figures and a\ntable: Fig. R5 and Fig. R6 in the main text (numbered as Fig. 5 and Fig. 6), and Table R2 in\nSupplementary Material (numbered as Supplementary Table 3).\nReferences:\n1. Isbell, Forest, et al. Biodiversity increases the resistance of ecosystem productivity to climate\nextremes. Nature 526.7574 (2015): 574-577.\n2. Bu, Jingyi, and Jingfeng Xiao. Upscaling eddy covariance measurements of carbon and water\nfluxes to the continental scale by incorporating GEDI-derived canopy structural complexity\nmetrics. Remote Sensing of Environment 329 (2025): 114930.\n4.+ The current explanation of asymmetric biomass allocation in the study is overly simplified,\nemphasizing light optimization without addressing underlying physiological or ecological\nmechanisms. To deepen the analysis, the authors should integrate data or models on traits like\nleaf area index (LAI), root biomass, or photosynthetic efficiency to explain growth patterns, and\nconsider interaction effects among drivers such as CO₂ and precipitation.\nResponse: Thank you for these insightful suggestions. To better clarify the mechanisms\nunderlying asymmetric biomass allocation, we employed SEM and LMM approaches to examine\nthe independent and interactive effects of environmental changes and grazing activity on CH,\nFVC, and AGB. For this analysis, we re-collected gridded CO and grazing data covering the\n2\nentire region and conducted pixel-scale assessments separately for areas with increasing and\ndecreasing CH, resulting from sample sizes of 4.7 and 6.3 million, respectively. This\nsubstantially enhanced both the stability and comprehensiveness of our results over the region.\nThe findings indicate that, in addition to the previously noted role of reduced radiation, rising\ntemperature and grazing pressure emerged as the primary drivers of CH decline, with their\ncombined effects further intensifying adverse impacts (Fig. R5, Table R2). In contrast, increased\nprecipitation and elevated CO concentrations had significant positive effects on CH and FVC.\n2\n(Ainsworth and Long, 2005), global warming can amplify the negative impacts of grazing on\ngrasslands (Maestre et al., 2022; Zhang et al., 2023). By comparison, changes in horizontal\nallocation (FVC) were not as strong as vertical allocation (CH), likely because vegetation tends\nto expand horizontally under CO fertilization and increased precipitation, whereas vertical\n2\ngrowth requires both sufficient moisture and fertile soils but is also constrained by grazing\nintensity (Falster and Westoby, 2003).\nWe acknowledge the importance of LAI as a growth indicator. Although LAI was not directly\nused in this study, the FVC derived from MODIS LAI via the classical gap fraction method\nshowed strong consistency with our SMA-derived FVC (Fig. R4), supporting the robustness of\nour results. Since the main objective of this work is to report CH decline, we did not extend the\nanalysis to root biomass or photosynthetic efficiency, given both the difficulty of obtaining\ninterannual measurements and the need to maintain a focused narrative.\nWe sincerely thank the reviewer for helping us refine the mechanistic explanation of CH decline.\nIn the revision, we have substantially improved the discussion of the independent and interactive\neffects of environmental changes and grazing practices on CH, FVC, and AGB (lines 240–276),\nsupported by the addition of Fig. R5 in the main text (numbered as Fig. 5), and Table R2 in\nSupplementary Material (i.e., Supplementary Table 3).\nReferences:\n1. Ainsworth, Elizabeth A., and Stephen P. Long. What have we learned from 15 years of free‐\nair CO enrichment (FACE)? A meta‐analytic review of the responses of photosynthesis,\n2\ncanopy properties and plant production to rising CO . New phytologist 165.2 (2005): 351-\n2\n372.\n2. Maestre, Fernando T., et al. Grazing and ecosystem service delivery in global drylands.\nScience 378.6622 (2022): 915-920.\n3. Zhang, Minna, et al. Experimental impacts of grazing on grassland biodiversity and function\nare explained by aridity. Nature Communications 14.1 (2023): 5040.\n4. Falster, Daniel S., and Mark Westoby. Plant height and evolutionary games. Trends in\necology & evolution 18.7 (2003): 337-343.\n5.+ The paper should improve its global relevance by integrating its findings on China's\ndeclining grassland canopy height with global trends. It should compare China's CH decline with\nother grassland ecosystems, using references like Sloat et al. (2018) and Díaz et al. (2007). Also,\nhighlight unique factors like intensive grazing or climate variability.\nResponse: In the revision, we compared our findings with several global studies to strengthen\ntheir worldwide relevance. Using SEMs, we examined the effects of intensive grazing and\nclimate variability, and highlighted that rising temperature and grazing pressure substantially\nreduce grassland CH, with amplified effects identified through pairwise LMM analysis (Fig. R5,\nTable R2).\nOur results are consistent with reports elsewhere. For instance, a global meta-analysis of 197\nstudies by Díaz et al. (2007) showed a widespread shift in grassland species from tall to short\nforms under grazing pressure. Other studies demonstrated that sustained warming limits biomass\naccumulation and leads to vegetation dwarfing in global grasslands (Arft et al., 1999; Li, 2018).\nMore recently, Maestre et al. (2022) and Zhang et al. (2023) reported that warming amplifies the\nnegative effects of grazing across the world’s native grasslands, driving declines in biodiversity\nand ecosystem function (Fig. R6). As stated in the main text, our study provides the first\ndynamic, real-time evidence of CH decline and the adverse impacts of climate warming and\ngrazing at a broad ecosystem scale.\nIn the revision, we have incorporated these global perspectives and citations, expanding the\ndiscussion on CH decline (lines 224–234), driving mechanisms (lines 240–276), and plant\ndiversity (lines 284–293).\nReferences:\n1. Díaz, Sandra, et al. Plant trait responses to grazing–a global synthesis. Global change biology\n13.2 (2007): 313-341.\n2. Arft, A. M., et al. Responses of tundra plants to experimental warming: meta‐analysis of the\ninternational tundra experiment. Ecological monographs 69.4 (1999): 491-511.\n3. Li, Dan. Grassland Plants under Warming and Precipitation Change. Plant Growth and\nRegulation: Alterations to Sustain Unfavorable Conditions (2018): 1.\n4. Maestre, Fernando T., et al. Grazing and ecosystem service delivery in global drylands.\nScience 378.6622 (2022): 915-920.\n5. Zhang, Minna, et al. Experimental impacts of grazing on grassland biodiversity and function\nare explained by aridity. Nature Communications 14.1 (2023): 5040."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I acknowledge and thank you for the careful review you made to the manuscript. The questions and suggestions I made\nwere sorted and helped to make the manuscript more relevant and understandable. Also, the reproductive potential of the\nsteps you took were also assured, which make an article effective and useful. As it addresses important questions regarding\nclimate change, it is vital to make it accessible to a broader audience that can make a difference for our future."}, "author_response": ""}]

Decision letter