The respective effects of atmospheric vapor pressure and soil moisture on ecosystem productivity in Southwest China

doi:10.21203/rs.3.rs-2824753/v1

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The respective effects of atmospheric vapor pressure and soil moisture on ecosystem productivity in Southwest China

https://doi.org/10.21203/rs.3.rs-2824753/v1

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Drought is considered as a key factor restricting the growth of plants. It is critical to investigate the impact of drought stress on vegetation and understand the respective effects of Soil Moisture (SM) and Vapor Pressure Difference (VPD) on ecosystem production for ecological sustainability. For the Southwest China, the respective impacts of VPD and SM on the productive capacity of regional ecosystems are still unknown. To capture the drought stress over Southwest China, Solar-induced fluorescence (SIF) was selected as a reliable indicator of regional ecosystem production and applied to analyze the impact of SM and VPD using data binning. In this study, PAR and FPAR data were incorporated to calculate SIFyield, eliminating the influence of PAR and FPAR on SM and VPD, which was used to analyze the spatiotemporal correlation between SM, VPD, and SIFyield. And then a data-binned method was applied to compare the implications of low SM and high VPD for each SIFyield quantitatively. Finally, vegetation coverage data was used to conduct a statistical analysis of the correlation of VPD, SM, and SIFyield under different vegetation types and further learn the correlation between these parameters. The results revealed that the impact of high VPD on SIFyield was notably more prominent in regions with 75% vegetation coverage. High correlation between SM and SIFyield was found over forest and shrub, and SM large influence on VPD and SIFyield over grassland types. These new findings will be valuable for future agriculture, forestry breeding, and cultivation over this eco-fragile region.

Soil Moisture

Vapor Pressure Difference

SIF

Drought

The productive capacity of an ecosystem is a fundamental metric for examining the carbon balance and water use efficiency of a regional ecosystem(Karjalainen ,et al., 1995), and it is a crucial parameter in the carbon-water coupling of such ecosystems(Kucharik ,et al., 2000). Over the past few years, the occurrence of droughts has become increasingly common on a global scale, resulting in devastating damage to regional ecosystems(Huang ,et al., 2010, Kuleshov ,et al., 2014, Siegert ,et al., 2001). The cumulative impact of extreme drought can severely compromise the production capacity of ecosystems, and consequently, have a significant effect on the carbon sink of those ecosystems(Anderegg ,et al., 2020, Yang ,et al., 2018). Indeed, in the larger context of climate change, ecosystem production serves as a vital indicator to assess how ecosystems respond to the evolving environmental conditions(Rahman ,et al., 2015, Zhang ,et al., 2022). As a result, it has become an essential parameter for evaluating the health and resilience of regional ecosystems.

Soil moisture (SM) and Vapor Pressure Deficit (VPD) are deemed to be the dominant drivers of regional ecosystem production(Liu ,et al., 2020). From the vegetation physiology viewpoint, the low soil moisture and high atmospheric water demand both cause vegetation to suffer from drought stress(Sulman ,et al., 2016), which affects the normal physiological activities of vegetation and hence causes the death of vegetation and impairs the productivity of the ecosystem(Cheng ,et al., 2022). There has been significant debate regarding the respective contributions of soil moisture and atmospheric water demand in studies investigating the response of vegetation to drought(Liu ,et al., 2020). VPD effects the closure of plant stomata and thus controls physiological processes such as transpiration and photosynthesis(Yuan ,et al., 2019). Excessive VPD induces vegetation stomatal closure, thus limiting photosynthesis in vegetation, which in turn leads to a decrease in ecosystem productivity(López ,et al., 2021). Soil moisture is the direct water source of vegetation, and low soil water will lead to agricultural drought, and even cause the death of vegetation(Zhang ,et al., 2022). Therefore, understanding the individual and combined contributions of SM and VPD to ecosystem production is crucial to accurately assess the impact of drought on regional ecosystems. Such knowledge can inform sustainable management practices and ultimately aid in the preservation of vital ecosystem services.

It is challenging to examine the respective impacts of SM and VPD on ecosystem production at the regional ecosystem scale, however, this research is vital for ecosystem response mechanisms to drought and for environmental protection. Liu et al.(Liu ,et al., 2020) investigated the respective effects of low SM and high VPD on ecosystem production at a global scale using a data splitting box approach. It was discovered that SM plays a dominant role in most terrestrial vegetation ecosystem production subjected to drought stress compared to VPD. Yuan et al.(Lu ,et al., 2022) think that Liu's research has not eliminated the effects of photosynthetic active radiation on VPD and SM, which causes some errors in the results. Yuan et al. therefore used Sun-Induced Chlorophyll Fluorescence yield (SIFyield) to discuss the respective effects of SM and VPD on ecosystem production. The results show that atmospheric drying caused by VPD has a higher impact on ecosystem production than SM.

Southwest China is the major karst landscape distribution area in China, with extremely fragile ecosystems that are strongly vulnerable to extreme hazards(Pang ,et al., 2018). In recent years, there has been a noticeable increase in the frequency and intensity of drought events in Southwest China(Sun ,et al., 2022), causing significant negative impacts on regional ecosystem production as well as the sustainable development of human society(Wang ,et al., 2010). Research on the impact of different drivers on ecosystem production and carbon sinks in Southwest China has been the focus of academic interest(Cao ,et al., 2003, Qiu ,et al., 2020, Wang ,et al., 2007). Chen et al.(Chen ,et al., 2021) investigated the effects of drought on vegetation productivity in Southwest China utilizing Sun-Induced Chlorophyll Fluorescence (SIF) and soil moisture. The results of the research indicate that large-scale drought significantly influences regional ecosystem productivity. Chen et al. (Chen ,et al., 2021)analyzed the impact of VPD on regional primary productivity in three typical ecosystem study areas in China and revealed that the decline in GPP in Southwest China was closely related to SM and VPD, and that more than 50% of the change in GPP was attributable to the combination of SM as well as VPD. Despite the numerous studies on SM and VPD on ecosystem productivity(Cheng ,et al., 2022, Dang ,et al., 2022, Lu ,et al., 2022), fewer studies have clarified the respective influences of SM and VPD on ecosystem production.

In this experiment, we investigate the impact of SM and VPD on ecosystem production in Southwest China using a land surface model (CLM4.5) combined with SIF data. Firstly, this experiment investigated the spatio-temporal correlation between SM and VPD and SIFyield in Southwest China. And then the respective impacts of low SM and high VPD on ecosystem productivity in Southwest China was examined using a data binning approach, and quantified the importance. Finally, this experiment discusses and analyses the effect of SM and VPD on the correlation of both with SIFyield under different vegetation types. This study will provide scientific suggestions and a theoretical foundation for tackling future droughts in the Southwest China.

2.1 CLM4.5

In this experiment, the land surface process model CLM4.5 was applied to simulate soil moisture and other variables in the study area from 2005 to 2020. The CLM4.5 model is among the most developed international models of land surface processes, which is the land surface module of the Common Earth System Model (Fox ,et al., 2018, Montané ,et al., 2017). The CLM4.5 model realizes the heterogeneity of land surface space by nesting grid, that is, the grid contains a variety of land individuals, snow, soil columns and vegetation functional types (Deng ,et al., 2020). In this research we simulated hydrological variables such as soil moisture for 2005 to 2020 in Southwest China utilizing the GLDAS (Rodell ,et al., 2004, Wang ,et al., 2016) data-driven CLM4.5 model.

2.2 Datasets

Solar-induced chlorophyll fluorescence (SIF) has great potential as an effective indicator of plant photosynthesis for monitoring Gross Primary Productivity (GPP)(Sun ,et al., 2018). The relationship between SIF and GPP on a global scale stated that GPP had a significant positive correlation with SIF (p < 0.001) (Gao ,et al., 2021). In this experiment, the global high-resolution SIF data produced by Chen et al.(Chen ,et al., 2022) was selected as an indicator of regional ecosystem production. This data was produced based on the XGBoost machine learning model with a temporal resolution of 8 days and a spatial resolution of 0.05°. The SIF data has a long temporal and high spatial resolution, which is of great value in evaluating long-term terrestrial ecosystem photosynthesis and global carbon water flux.

Additionally, the Photosynthetically Available Radiation (PAR) and Fraction of Absorbed Photosynthetically Active Radiation (fPAR) products was used, which is obtained from GLASS(Jia ,et al., 2019, Liang ,et al., 2013). These two products are based on a look-up table approach combined with MODIS data to produce land surface data products with global coverage. The PAR and fPAR data have a temporal resolution of 1 day and a spatial resolution of 0.05°from 2000 to 2021, and they are widely used in studies of the carbon cycle and carbon driving mechanisms.

The CLM atmospheric drive data are derived from GLDAS-2.1 with a temporal resolution of 0.25° and a spatial resolution of 3 h(Rodell ,et al., 2004). The atmospheric drive data consist mainly of data on precipitation, air temperature, barometric radiation, etc. Land Cover data is produced by MODIS with IGBP classification standard and a spatial resolution of 500m(Broxton ,et al., 2014).

2.3 Calculation of SIFyield

Simultaneous changes in PAR and fPAR with VPD and SM will interfere with the analysis of VPD and SM effects(Li ,et al., 2018, Pei ,et al., 2022). For example, when VPD rises, the accompanying rise in PAR boosts ecosystem production. That would offset the decline in ecosystem production caused by a rise in VPD(Lu ,et al., 2022). With regard to SM, both FPAR and SM reductions equally cause a reduction in SIF. To better separate the simultaneous effects of PAR and FPAR on ecosystem production, the experiment was conducted with Yuan's method of applying SIFyield instead of SIF(Dang ,et al., 2022). SIFyield was calculated as follows:

$${SIF}_{yield}=\frac{SIF}{PAR\times fPAR}$$

2.4 Data Binning

SM and VPD are strongly coupled, and the experiment require a reduction in the coupling of the two elements to better investigate the influence of each on regional ecosystem production(Liu ,et al., 2020). This experiment employs a data-binning approach to reduce the strong coupling correlation between SM and VPD, which is performed as follows: For each image element, thresholds of 10%, 20%...... 100% of that sequence are determined according to the range of image values of its long time series, and the data of the time series on that image element is divided into 10 bins according to the different thresholds.

The effects of SM and VPD on SIFyield(Lu ,et al., 2022) were counted separately on the basis of data bins, where the effect of VPD on SIFyield was calculated as follows:

$$\varDelta SIFyield\left(VPD|SM\right)=\frac{1}{N}{\sum }_{k=0}^{n}{SIFyield}_{k,{m}_{k,max}}-{SIFyield}_{k,{m}_{k,min}}$$

where N is the number of bins of SM, k is a particular bin number of SM, and ${m}_{k,max}$ and ${m}_{k,min}$ are the maximum and minimum VPD bin numbers of the bins for which SM is k. The meaning of Eq. (2) is to calculate the difference between the SIFyield corresponding to the largest bins number with VPD and the smallest bins number for all SM bins, which is the change in SIFyield variation due to high VPD.

$$\varDelta SIFyield\left(SM|VPD\right)=\frac{1}{N}{\sum }_{l=0}^{n}{SIFyield}_{{m}_{l,min},l}-{SIFyield}_{{m}_{l,max},l}$$

where N is the number of bins of VPD, l is a particular bin number of VPD, and ${m}_{l,max}$ and ${m}_{l,min}$ are the maximum and minimum SM bin numbers of the bins for which VPD is l. The meaning of Eq. (3) is to calculate the difference between the SIFyield corresponding to the smallest sub-box number of the SM and the largest sub-box number for all VPD sub-boxes, which is the change in SIFyield variation caused by the low SM.

Southwest China mainly includes Yunnan, Sichuan, Guizhou, Guangxi and Chongqing provinces(Lin ,et al., 2015). The entire study area lies between $94^\circ {21}^{{\prime }}\text{E}$–$112^\circ {04}^{{\prime }}\text{E}$ and $20^\circ {54}^{{\prime }}\text{E}$–$34^\circ {19}^{{\prime }}\text{N}$ at an altitude of between 0 and 7756m(Zhang ,et al., 2005). Southwest China is the main distribution area of karst landscapes in China, with karst areas covering up to 550,000km²(Zhou ,et al., 2018). Southwest China is also an important forest resource area in China. According to the Sixth Forest Resources Survey, there are approximate 573,800km² of forested land in Southwest China, with a forest cover of 36%(Jiang ,et al., 2014). The extensive distribution of karst landscapes makes the ecosystem in Southwest China very fragile and vulnerable to climatic disasters(Wu ,et al., 2018). Statistics show that Southwest China has suffered around 11 extreme drought events since 2000, with cumulative economic losses of more than 300 billion yuan(Lin ,et al., 2015, Zeng ,et al., 2019). Severe disasters not only entail economic losses, but also threaten people's water security and other aspects, thus gradually attracting the attention of all sectors of society(Guan ,et al., 2015).

4.1 Spatio-temporal correlation analysis of VPD, SM and SIFyield in Southwest China

The present study sought to delve into the intricate relationship between the variables of SM, VPD, and SIFyield in the vegetation ecosystem of Southwest China. The investigation entailed a comprehensive examination of the spatial and temporal correlations between SIFyield, SM, and VPD, and the results are elegantly depicted in Figs. 2 and 3. Figure 2 offers a glimpse into the significant positive correlation between SIFyield and SM across Southwest China. Conversely, the results demonstrate an interannual variation of SIFyield and VPD that is moving in an opposing direction. Figure 3, on the other hand, underscores the presence of a significant positive correlation between SM and SIFyield in most regions of Southwest China, with a correlation coefficient that surpasses 0.5. Moreover, when the results are juxtaposed against the land use classification of Southwest China, it is evident that the SM and SIFyield under the grassland cover exhibit a negative correlation, with a correlation coefficient of approximately − 0.25. This observation provides valuable insights into the dynamic interplay between vegetation, soil moisture, and atmospheric conditions, and highlights the significance of considering these variables in studies of the ecosystem in Southwest China.

The spatio-temporal correlation between VPD and SIFyield in Southwest China is the exact opposite of the SM results, with VPD exhibiting a significant negative correlation with SIFyield overall regionally, with a correlation coefficient greater than − 0.5. VPD in the north-western part of Southwest China (grassland cover area) presented a positive correlation with SIFyield, with a correlation coefficient of around 0.3. The results demonstrate that the correlation between SM and SIFyield is characterized by an increase in SM in forest and shrub ecosystems leading to a corresponding increase in SIFyield, while an increase in VPD leads to a decrease in SIFyield. The situation is reversed in grassland ecosystems, where a rise in SM instead causes a fall in SIFyield, while a rise in VPD causes a rise in SIFyield.

The results of the present study(Cheng ,et al., 2022, Fu ,et al., 2022, Yu ,et al., 2022), when taken in conjunction with the findings from related research and the analysis of Figs. 2 and 3, point to the conclusion that VPD and SM have a profound impact on Solar-Induced Fluorescence yield (SIFyield) in the vegetation ecosystem of Southwest China. The strong coupling between VPD, SM, and SIFyield suggests that these variables are closely interconnected and play a critical role in shaping the ecosystem's functioning and productivity. These findings further emphasize the need to explore methods for separating the individual impact of VPD and SM on regional SIFyield and to gain a more comprehensive understanding of the interplay between atmospheric conditions, vegetation, and ecosystem functioning. The examination of the correlation between SM and VPD at different temporal scales unveiled a remarkable trend. As the time resolution increases, the correlation between SM and VPD tends to decrease, and by the time the 8-day scale is reached, the correlation becomes weak, with a coefficient of around − 0.35, as demonstrated in Fig. 4. To effectively mitigate the correlation between the two drivers, a data binning approach was employed on SM and VPD at the 8-day scale. The result of the binning procedure showed that the correlation between SM and VPD was reduced to nearly − 0.1, indicating a successful separation of the two variables at the 8-day scale.

4.2 Analysis of the impact of SM and VPD on SIFyield

After binning the data, we used methods (2) and (3) to calculate the respective effects of SM and VPD on SIFyield in Southwest China. To obtain more spatially comparable results, we normalized the calculation on an image-by-image basis using the image value at 90% of its position. Figure 5 shows that low SM mainly affects the SIFyield in the south-western part of Southwest China, where the main vegetation types are shrubs and forestland, resulting in an increase in SIFyield. In contrast, the effect of low SM on SIFyield reduction was more widely spread. The area where SIFyield was reduced by low SM was about 1.9 times greater than the area where it was increased. Both the increase and decrease in SIFyield caused by low SM were approximately 0.2.

The area where high VPD led to an elevated SIFyield is mainly concentrated in the northwestern part of Southwest China, and is primarily associated with grassland vegetation. Conversely, the area where high VPD caused a reduced SIFyield represents approximately 81.8% of the total area in Southwest China. In general, high VPD and low SM were the main contributors to the decrease in SIFyield in Southwest China. Additionally, it can be observed that high VPD induces more significant changes in SIFyield than low SM.

The absolute values of the changes in SIFyield due to low SM and high VPD were used to quantify and visualize the differences in this experiment, as shown in Fig. 6. Figure 6 illustrates that the change in SIFyield caused by high VPD is more significant than that caused by low SM. We could find that $\varDelta SIFyield\left(SM|VPD\right)$ was less than $\varDelta SIFyield\left(VPD|SM\right)$ in approximately 75% of the vegetated ecosystems in Southwest China, compared to around 25% of the areas where the effects caused by SM were more significant, mainly involving shrubs and some grassland vegetation types. These findings suggest that VPD is more crucial than SM for ecosystem production in Southwest China.

4.3 Analysis of the influence of the correlation between SIFyield and SM and VPD under different vegetation types

In light of the previous findings and related research, it has become evident that SM and VPD are highly interdependent in Southwest China. In order to gain a more concrete understanding of this phenomenon, we have conducted a detailed analysis on the impact of different SM and VPD bins on the correlation between other variables and SIFyield, using the innovative data binning approach. The outcomes are depicted in Figs. 7 and 8. Figure 7 provides a clear visual representation of the correlation coefficient between VPD and SIFyield, which decreases gradually from − 0.5 to -0.2 with an increase in SM from 10% to approximately 40%. However, the change in correlation is negligible as SM increases from 50% to about 80%. Notably, as SM reaches its maximum at 100%, the correlation coefficient between VPD and SIFyield shows a moderate increase, albeit with a smaller magnitude.

The interplay between VPD and SM on SIFyield is a complex phenomenon, and it appears that as SM levels increase, the correlation between VPD and SIFyield tends to decrease. This effect is particularly pronounced when transitioning from a state of extremely low SM to a state of normal SM. As shown in the vivid depiction of Fig. 8, the correlation coefficient between SM and SIFyield progressively decreases from 0.4 to approximately 0.2 as VPD rises from 10% to around 50%. Thus, as VPD intensifies, the correlation between SM and SIFyield gradually diminishes. It is noteworthy that when VPD reaches its highest point, the correlation coefficient between SM and SIFyield approaches 0.0, suggesting that the relationship between VPD and SIFyield overrides the influence of SM on SIFyield.

By analyzing the data in Figs. 7 and 8, it becomes evident that changes in VPD and SM in Southwest China are associated with a reduction in the correlation between another factor and SIFyield. To comprehensively investigate the impact of different environmental factors on the correlation between SIFyield and SM and VPD under various ecosystems in Southwest China, we conducted a study on the relationship between SM_SIFyield and SM and VPD under different vegetation types using land use classification data. The research findings are illustrated in Figs. 9, revealing interesting results. It can be found that there are some missing under the SM and VPD bins in Fig. 9, which is mainly due to the exclusion of bins with less than 50 data when calculating the correlation. Specifically, as shown in Fig. 9, the correlation between VPD and SIFyield for the forest type is observed to exhibit a downward trend followed by an upward trend as the number of SM bins increases. Conversely, for shrub and grass types, the correlation between VPD and SIFyield displays a trend of first increasing and then decreasing with the number of SM bins.

The correlation between SM and SIFyield under forest type tends to decrease and then increase with increasing VPD bins number. The correlation between SM and SIFyield exhibited highly significant decreasing trends with increasing VPD under shrub type, and the effect of VPD on the correlation between SM and SIFyield was more complex under grassland type. To gain deeper insights into the impact of VPD on the correlation between SM and SIFyield, we calculated the slope of the linear regression line of the SIFyield correlation curve for each vegetation type and employed the magnitude of the slope as a measure of this effect. Our results, as presented in Table 1, illustrate that the effect of VPD on the correlation between SM and SIFyield is significantly greater than the effect of SM on VPD. This phenomenon is particularly apparent under forest and shrub ecosystems, where the difference between the two effects on the correlation with each other and SIFyield is most pronounced. However, in grassland ecosystems, the difference between the effects of SM and VPD on the correlation with each other and SIFyield is not significant, and the effect of SM is more salient.

Table 1 Statistics on the trend of correlation between VPD, SM and SIFyield for different vegetation types as influenced by another element

	Forest	Shrub	Grassland
VPD_bins	0.082766	0.063476	0.018023
SM_bins	0.042719	0.025902	0.024029

In this experiment, we meticulously explored and examined the criticality of the influence of low SM and high VPD on regional ecosystem productivity, utilizing a comprehensive set of soil moisture data, VPD, and SIF data. This finding unequivocally demonstrated that high VPD had a more substantial impact on the production of ecosystems in Southwest China than low SM. Further scrutinizing the data with respect to various vegetation types in the region, the effect of VPD variations on the correlation between SM and SIFyield was found to be significantly more prominent, particularly in the forest and shrub vegetation types. Conversely, the influence of SM on the correlation between VPD and SIFyield was more potent under grassland types. These compelling results confirm that VPD has a significantly higher influence on regional ecosystem production than SM, thereby establishing its crucial role in regulating the intricate dynamics of ecosystem productivity in Southwest China.

This experiment has provided a comprehensive analysis of the respective impacts of VPD and SM on ecosystem production in Southwest China, thus shedding new light on regional drought mechanisms and promoting future drought resilience. In contrast to previous research conducted by Liu and Yuan, this study combines the important principles and methods of both studies. Moreover, to reinforce the robustness of our results, we validated our findings by examining the changes in SIFyield under different vegetation types with varying VPD and SM bins. Furthermore, this study also incorporated a state-of-the-art land surface process model to simulate soil moisture and VPD, thereby circumventing the potential data errors caused by disparate sources.

Although the present study offers valuable insights, it is important to note some limitations. The spatial resolution of the driving data used was relatively low, resulting in a corresponding low spatial resolution of the soil moisture and VPD data. As a result, upscaling the SIF data was necessary, which may introduce some inaccuracies in the results. Furthermore, while the study accounted for the impacts of SM and VPD, other factors such as temperature were not considered. Temperature, however, can significantly influence the effects of SM and VPD on SIFyield, and future research may benefit from investigating this relationship.

This study simulated the soil moisture and VPD data of Southwest China from 2005 to 2020 by using the land surface process. Using the simulated data, we studied the effects of low SM and high VPD on regional ecosystem production in Southwest China. Through this research, we mainly found the following conclusions:

(1) In Southwest China, the correlations between SM and VPD for SIFyield exhibited completely opposite phenomena. Under forest and shrub ecosystems in Southwest China, SM presented a positive correlation with SIFyield, while under grassland systems, SM posted a negative correlation with SIFyield, while VPD demonstrated the opposite.

(2) The correlation between SM and VPD exhibited a very significant decrease as the temporal resolution of the data increased. The correlation between SM and SIFyield and VPD and SIFyield exhibited a decrease as the bin numbers of VPD and SM increased.

(3) The correlation of SIFyield with SM under forest and shrub types was influenced by VPD bins to a greater extent than the correlation of SIFyield with VPD was influenced by SM bins. Under grassland types, SM has a more significant effect on the correlation between VPD and SIFyield.

Author Contributions: Conceptualization, X.H.; data curation, X.S. and M.M.; formal analysis, Y.X. And J.W.; funding acquisition, M.M. and X.H.; methodology, X.S. and M.Z.; project administration.; software, X.S.; supervision, M.M. and X.H.; validation, X.Y. and M.M.; visualization, X.S. and X.H.; writing—original draft, X.S.; writing—review and editing, J.H. and X.H. All authors have read and agreed to the published version of the manuscript.

Funding: This work is supported by the NSFC (National Science Foundation of China) project (grant number: 41830648, U2244216)

Acknowledgments: In this study, the Atmospheric Drive Data used came from the Global Land Data Assimilation System (GLDAS Noah Land Surface Model L4 3 hourly 0.25 x 0.25-degree V2.1 Greenbelt) and the statistical year data used came from the Chinese Government Statistical Yearbook. Comparison of soil moisture data from GLDAS with ERA5. We sincerely thank the anonymous reviewers.

Conflicts of Interest: The authors declare no conflict of interest.

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The respective effects of atmospheric vapor pressure and soil moisture on ecosystem productivity in Southwest China

Status:

Version 1

Abstract

Figures

1 Introduction

2 Methods And Data

2.1 CLM4.5

2.2 Datasets

2.3 Calculation of SIFyield

2.4 Data Binning

3 Study Area

4 Results

4.1 Spatio-temporal correlation analysis of VPD, SM and SIFyield in Southwest China

4.2 Analysis of the impact of SM and VPD on SIFyield

5 Discussion

6 Conclusion

Declarations

References

Status:

Version 1