Variation In Leaf Area Index Research Articles

A Bayesian network approach to assess the ecosystem integrity of mangroves in Tampamachoco, Veracruz, Mexico

Given the alarming rates of mangrove forest loss, resource managers must count on information regarding the condition of the mangrove forests. We propose a Bayesian network (BN) to assess mangrove forests' ecosystem integrity (EI) to support a mangrove monitoring system in Mexico. This approach allowed us to infer the system's condition based on variables on forest structure and function. We defined the BN structure based on informal interviews with specialists on vegetation and coastal geomorphology. We applied the expectation-maximization learning algorithm to train the model. Data from plots in two mangrove areas of an Avicennia germinans forest, defined based on their undisturbed and disturbed conditions, were used as training datasets. We applied sensitivity analysis to determine the degree of influence of each model variable. We evaluated the prediction capacity of the BN with a k-fold cross-validation (the process is repeated five times, starting from the database in 2 parts). The variables selected for the model were the Holdridge complexity index (HCI, Holdridge et al., 1971), Leaf area index (LAI), litter production (g/month/m2), leaf C, N and P concentration (%), and leaf N:P ratio. The most critical variable to infer mangrove condition was leaf N:P (Variance reduction = 11%), followed by forest structure variables HCI and LAI (Variance reduction > 5%). The cross-validation to test the model resulted in a minimum square error of 0.3, which indicates a reasonable capacity to predict the condition of mangrove integrity. The BN constructed can diagnose the integrity of a monospecific mangrove forest with acceptable precision, considering the environmental factors that define the forest structure and functioning locally. We then asked the experts to review and modify the model to apply to multispecies mangrove ecosystems and environmental contexts.

Effects of Leafy Flexible Vegetation on Bed‐Load Transport and Dune Geometry

AbstractThe development of sustainable river management strategies requires knowledge of the effect of vegetation on hydrodynamics and sediment transport. To date, the complex physical processes involving the combined effects of leafy flexible vegetation and mobile bedforms are not completely understood. Most sediment transport models have been developed for bare bed conditions so that their performance in the presence of leafy flexible vegetation remains unclear. On the other hand, recently developed models consider vegetated conditions but they typically account only for the presence of rigid cylinders and in some cases scour at their base. For this purpose, laboratory experiments were conducted with mobile dune bed conditions and artificial flexible plants with varying Leaf Area Index to investigate the effect of flexible vegetation on bed morphology and sediment transport. Sediment transport rates and bedform characteristics such as height, wavelength and celerity, were measured in specifically designed experimental runs. The collected data show that the presence of leafy vegetation alters bed morphology, tending to reduce the average dune wavelength and leading to the formation of complex 3D geometries. Bed‐shear‐stress‐based models for predicting sediment transport were verified to be valid under conditions of low vegetation roughness density. On the contrary, the collected data emphasize that the measured bed‐load transport rate increased in the presence of leafy flexible vegetation with higher frontal area. Recent bed‐load models for vegetated channels provide a better interpretation for dense leafy vegetation but are less effective when predominant effects related to dunes are present.

Change trend and attribution analysis of leaf area index in the East African Plateau from 1982 to 2020.

The ecosystems on the East African Plateau are crucial for maintaining the biodiversity, water resource balance, and ecological equilibrium of the African continent. However, the spatiotemporal variations of vegetation and the driving factors remain unclear. We analyzed leaf area index (LAI) change trends in the East African Plateau based on the GIMMS LAI4g dataset and further conducted attribution analysis combining temperature and precipitation data, as well as 10 Dynamic Global Vegetation Models (DGVMs) in TRNEDY v9. The results showed that LAI of the East African Plateau had a modest change trend from 1982 to 1999 (2.5×10-3 m2·m-2·a-1), but significantly increased from 2000 to 2020 (5.2×10-3 m2·m-2·a-1), which was 2.1 times faster than that during 1982-1999. Temperature and precipitation had weak correlations with LAI from 1982 to 1999, but showed significant correlations from 2000 to 2020. The DGVMs demonstrated consistent attribution results, with temperature and precipitation contributing significantly more to the LAI variations from 2000 to 2020 compared to the period from 1982 to 1999. The results highlighted the key role of climate change in driving vegetation greening on the East African Plateau during 2000-2020, which could provide important evidence for ecological conservation and sustainable development strategies in the region.

Mathematical Modeling for Predicting Growth and Yield of Halophyte Hedysarum scoparium in Arid Regions under Variable Irrigation and Soil Amendment Conditions

Growing degree days (GDDs) and leaf area index (LAI) greatly influence the growth and yield of many crops grown in arid regions. Therefore, variation in LAI due to GDD can provide a theoretical basis for predicting crop growth, water consumption, plant development, and yield in arid agriculture via the development of mathematical growth models. This study described the relationship between plant biomass production and variation in LAI due to GDD in arid regions under different types of irrigation (fresh water and saline water) and soils amended with different substances (manure+sandy soil, compost+sandy soil, clay+sandy soil, and sandy soil). Mathematical models for LAI were established for GDDs. In addition, different water quality irrigation techniques were used as independent variables to calculate the LAI of halophytic plants (Hedysarum scoparium) in arid regions under different soil amendment treatments. Furthermore, mathematical models for plant biomass production were developed by using the LAI and GDDs. For this purpose, Logistic, Gaussian, modified Gaussian, and Cubic polynomial models were used. Modified Gaussian and Cubic polynomial models are the best among all developed models, but Cubic polynomial models are more suitable among all developed models because of their simple quadratic equations that can be solved by using the first derivative. It was observed that with increased salt concentration in the irrigation water, the growth of per plant production decreased. However, soil amendments like manure and compost enhance salt tolerance against salt stress and enable plants to sustain their growth. Furthermore, Hedysarum scoparium attains maximum LAI when its GDD is about 1117.5 °C under both irrigation regimes and in all soil amendment treatments. It was concluded that these predicted mathematical models can provide crucial insights for enhancing production in arid regions by using eco-friendly soil amendments to improve water use efficiency across diverse types of water irrigation.

Enhancing Winter Wheat Representation in Noah‐MP‐Crop for Improved Dynamic Crop Growth Simulation in the North China Plain

AbstractExplicitly representing the world's most frequently cultivated winter wheat in land surface model (LSM) is important for understanding carbon and energy cycling over cropland and its interactions with climate, which is crucial for global food security. However, in the latest version of Noah‐MP‐Crop LSM, winter wheat is significantly underrepresented. This study improved the winter‐wheat parameterization in Noah‐MP‐Crop model by optimizing the phenological scheme, incorporating vernalization process, and calibrating several key parameters associated with winter wheat photosynthesis and carbon allocations. Focusing on the North China Plain as area representative region, model performance in simulating crop dynamic growth, carbon flux, and energy fluxes was validated at both site and regional scales. Results showed that the simulated phenological development matched well with the real‐world phenological records. A comparison between the simulated results by the default and developed parameterizations revealed the significant improvements in the reproductions of leaf area index (LAI) and gross primary production (GPP). The determination coefficient (R2) value of GPP was increased from 0.15 to 0.46 to 0.39–0.91. Simulations of energy fluxes showed smaller improvements, with R2 values increasing from 0.46 to 0.67 to 0.61–0.84 for latent heat (LE) and 0.18–0.55 to 0.25–0.61 for sensible heat. Additionally, the mean average error of net radiation was reduced. Improvements in spatial and temporal variations of LAI, GPP, and LE in regional simulation were also observed. This work can facilitate incorporating winter wheat cultivation and its interactions with climate system, particularly when coupling the Noah‐MP‐Crop model with the widely used Weather Research and Forecasting model.

Monitoring Biophysical Variables (FVC, LAI, LCab, and CWC) and Cropland Dynamics at Field Scale Using Sentinel-2 Time Series

Biophysical variables play a crucial role in understanding phenological stages and crop dynamics, optimizing ultimate agricultural practices, and achieving sustainable crop yields. This study examined the effectiveness of the Sentinel-2 Biophysical Processor (S2BP) in accurately estimating crop dynamics descriptors, including fractional vegetation cover (FVC), leaf area index (LAI), leaf chlorophyll a and b (LCab), and canopy water content (CWC). The evaluation was conducted using estimation quality indicators (EQIs) and comprehensive ground throughout the entire growing season at the field scale. To identify soil and vegetation pixels, the spectral unmixing technique was employed. According to the EQIs, the best retrievals were obtained for FVC in around 99.9% of the 23,976 pixels that were analyzed during the growth season. For LAI, LCab, and CWC, over 60% of the examined pixels had inputs that were out-of-range. Furthermore, in over 35% of the pixels, the output values for LCab and CWC were out-of-range. The FVC, LAI, and LCab estimates agreed well with ground measurements (R2 = 0.62–0.85), whereas a discrepancy was observed for CWC estimates when compared with ground measurements (R2 = 0.51). Furthermore, the uncertainties of FVC, LAI, LCab, and CWC estimates were 0.09, 0.81 m2/m2, 60.85 µg/cm2, and 0.02 g/cm2 through comparisons to ground FVC, LAI, Cab, and CWC measurements, respectively. Considering EQIs and uncertainty metrics, the order of the estimation accuracy of the four variables was FVC > LAI > LCab > CWC. Our analysis revealed that temporal variations of FVC, LAI, and LCab were primarily driven by field-scale events like sowing date, growing period, and harvesting time, highlighting their sensitivity to agricultural practices. The robustness of S2BP results could be enhanced by implementing a pixel identification algorithm, like embedding spectral unmixing. Overall, this study provides detailed, pixel-by-pixel insights into the performance of S2BP in estimating FVC, LAI, LCab, and CWC, which are crucial for monitoring crop dynamics in precision agriculture.

Influence of climate, soil and vegetation diversity on the leaf area index as measured by canopy hemispherical photography in tropical rainforests across a wide elevational gradient

Tropical rainforests (TRFs) play an important role in regulating the global climate and conserving biodiversity. Leaf area index (LAI) determines the capacity of TRFs for radiation interception and carbon sequestration via photosynthesis while also influencing the water loss from TRF canopies via transpiration. Elevational gradients in tropical environments provide an approach to investigate the influence of environmental variations on key vegetation properties such as the LAI. In this work we determined the concurrent, interactive influences of the elevational gradients of the environment (i.e. selected climatic and soil properties) and vegetation (tree diversity and structure) in determining the variation of LAI of one-hectare permanent sampling plots (PSPs) within TRFs across a wide elevational gradient (from 117 m to 2132 m above sea level). Eight rounds of canopy hemispherical photography were carried out at three-month intervals over a 24-month period from 2019 to 2022. Canopy LAI was estimated using HemiView image processing software. Long-term mean climatic data from 1990 to 2021 were extracted for the PSPs from monthly global weather database of CRU-TS-4.06-bias corrected with WorldClim-2.1. Key soil properties were measured at 0–15 cm and 15–30 cm depths via soil sampling. Tree diversity was determined by a complete census of all trees with equal or greater than 10 cm in diameter at breast height. Total tree biomass was estimated using allometric equations. Linear mixed model analysis of LAI showed significant (p < 0.05) variance for the random effects of elevation and elevation × time interaction. In a majority of measurement rounds, variance of LAI due to elevation was significant while at a majority of elevations, the fixed effect of measurement round on LAI was significant. Leaf area index showed significant linear reductions with elevation in all rounds, at rates ranging from 0.175 to 0.357 per 1000 m. Linear and multiple regression analyses identified increasing maximum cumulative soil water deficit, CSWDMax (an index of water deficit computed as the difference between evapotranspiration and precipitation), as the major climatic factor responsible for the reduction of LAI with elevation. Decreasing solar irradiance (SR) and increasing day-night temperature difference (DTR) with elevation also contributed to the observed LAI trend. Multiple regression analysis showed soil exchangeable potassium (KEx) and available phosphorus (PAv) influencing LAI positively while electrical conductivity (EC) and total nitrogen (NTot) influencing LAI negatively. Variation of Shannon-Wiener tree diversity index (H) and total tree biomass per hectare (TBM) contributed positively to the observed elevational variation of LAI. Factor analysis extracted two underlying factor constructs which accounted for 86 % of the combined variation in climate, soil and vegetation across the elevational gradient. Plot-wise scores of Factor 1, which had CSWDMax, SR, DTR, soil EC, NTot and H loading strongly and accounting for 70 % of the variation, showed a significant linear relationship with LAI. A cluster analysis incorporating the elevational variation of LAI, climate, soil and vegetation clearly classified the TRFs into four ranges of elevation at a linkage distance of 0.75. We conclude that LAI of TRFs in our study decreases with increasing elevation and identify increasing water deficits with elevation as the major climatic factor controlling this reduction.

New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

Enhanced Leaf Area Index Estimation in Rice by Integrating UAV-Based Multi-Source Data

The monitoring of crop growth, particularly the estimation of Leaf Area Index (LAI) using optical remote sensing techniques, has been a continuous area of research. However, it has become a challenge to accurately and rapidly interpret the spatial variation of LAI under nitrogen stress. To tackle these issues, this study aimed to explore the potential for precise LAI estimation by integrating multiple features, such as average spectral reflectance (ASR), vegetation index, and textures, obtained through an unmanned aerial vehicle (UAV). The study employed the partial least squares method (PLS), extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) to build the LAI estimation model under nitrogen stress. The findings of this study revealed the following: (i) texture features generally exhibited greater sensitivity to LAI compared to ASR and VIs. (ii) Utilizing a multi-source feature fusion strategy enhanced the model’s accuracy in predicting LAI compared to using a single feature. The best RP2 and RMSEP of the estimated LAI were 0.78 and 0.49, respectively, achieved by RF through the combination of ASR, VIs, and textures. (iii) Among the four machine learning algorithms, RF and SVM displayed strong potential in estimating LAI of rice crops under nitrogen stress. The RP2 of the estimated LAI using ASR + VIs + texture, in descending order, were 0.78, 0.73, 0.67, and 0.62, attained by RF, SVM, PLS, and ELM, respectively. This study analyzed the spatial variation of LAI in rice using remote sensing techniques, providing a crucial theoretical foundation for crop management in the field.

Spatio-temporal analysis of LAI using multisource remote sensing data for source region of Yellow River Basin

Introduction: The Leaf area index (LAI) of source region of yellow river basin is an important indicator for environmental sustainability. Most studies focus on the trend of LAI in Yellow River Source Region (YRSR) in accordance with both climate change and human actives. However, quantifying the effect of human activities on LAI is difficult but urgently needed. Specifically, Particle Matter 2.5 (PM2.5) can be an indirect indicator of human activities.Methods: In this study, we explored the potential dependence of LAI on temperature, precipitation, and PM2.5 in different land cover types in YRSR with linear regression and correlation analysis.Results: Over the period of 2001–2020, the climate in the region has been warming and becoming more humid, leading to overall improvements in vegetation. The mean LAI values varied between seasons, with summer having the highest and winter having the lowest LAI. The analysis of the LAI trends revealed that the mean LAI has been steadily increasing, particularly in the eastern region. The correlation analysis showed a significant positive correlation between annual average LAI and both annual precipitation and temperature, indicating that temperature has a greater impact on vegetation growth. The analysis of land cover types showed that most types exhibited a unimodal trend in LAI throughout the year, except for construction land which had two distinct peaks. Human-induced land cover change had a small impact on the overall increase in LAI. Furthermore, the interannual variation of PM2.5 showed a downward trend, with a strong correlation with the trend of LAI. Additionally, multiple linear regression analysis and residual trend analysis showed that climate factors had the strongest impact on LAI.Conclusion: The study highlights the spatiotemporal variations of LAI in the YRSR and its correlation with climatic and human factors. The findings suggest that climate change plays a crucial role in the vegetation growth and LAI in the region.

Hybrid inversion of radiative transfer models based on topographically corrected Landsat surface reflectance improves leaf area index and aboveground biomass retrievals of grassland on the hilly Loess Plateau

ABSTRACT Accurate monitoring of the leaf area index (LAI) and aboveground biomass (AGB) using remote sensing at a fine scale is crucial for understanding the spatial heterogeneity of vegetation structure in mountainous ecosystems. Understanding discrepancies in various retrieval strategies considering topographic effects or not is necessary to improve LAI and AGB estimations over mountainous areas. In this study, the performances of the look-up table method (LUT) using radiative transfer model (RTM), machine learning algorithms (MLAs), and hybrid RTM integrating RTM and MLAs based on Landsat surface reflectance (SR) before and after topographic correction were compared and analyzed. The results show that topographic correction improves the accuracies of retrieval methods involving RTM more significantly than the MLAs, meanwhile, it reduces the performance variability of different MLAs. Based on the topographically corrected Landsat SR, the random forest (RF) combined with RTM improves the retrieval accuracy of RTM-based LUT by 7.7% for LAI and 13.8% for AGB, and reduces the simulation error of MLA by 15.1% for LAI and 20.1% for AGB. Compared with available remote sensing products, the hybrid RTM based on Landsat SR with topographic correction has better feasibility to capture LAI and AGB variation at 30 m scale over mountainous areas.

Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation.

Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area index (LAI), which can be accurately assessed using proximal or remote sensing data based on plant canopies. This study aimed to (1) develop a machine learning-based method for estimating the LAI in rice and soybean crops using proximal sensing data and (2) evaluate the performance of a Remote Sensing-Integrated Crop Model (RSCM) when integrated with the ML algorithms. To achieve these objectives, we analyzed rice and soybean datasets to identify the most effective ML algorithms for modeling the relationship between LAI and vegetation indices derived from canopy reflectance measurements. Our analyses employed a variety of ML regression models, including ridge, lasso, support vector machine, random forest, and extra trees. Among these, the extra trees regression model demonstrated the best performance, achieving test scores of 0.86 and 0.89 for rice and soybean crops, respectively. This model closely replicated observed LAI values under different nitrogen treatments, achieving Nash-Sutcliffe efficiencies of 0.93 for rice and 0.97 for soybean. Our findings show that incorporating ML techniques into RSCM effectively captures seasonal LAI variations across diverse field management practices, offering significant potential for improving crop growth and productivity monitoring.

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

In addition to serving as North China’s water supply and ecological barrier, the mountainous area of the Haihe River basin (MHRB) is a crucial location for the application of ecological engineering. Vegetation is an important component in the ecological conservation and eco-hydrological progress of the MHRB. A better understanding of regional vegetation growth can be achieved by a thorough investigation of vegetation indicators. In this research, the leaf area index (LAI) and gross primary productivity (GPP) were chosen as vegetation indicators. The characteristics and driving forces of the spatiotemporal variations of LAI and GPP in the MHRB were explored through Sen’s slope, the Mann–Kendall test, the optimal parameter-based geographical detector model, and correlation analysis. From 2001 to 2018, the annual LAI and GPP increased significantly on the regional scale. The areas with significantly increased vegetation accounted for more than 81% of the MHRB. Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial one among climate indicators. Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. The lowest vegetation cover was found in dry regions with annual precipitation below 407 mm and the humidity index under 0.41; while in both forests and large undulating mountains, higher LAI and GPP were observed. About 87% of the significantly increased vegetation was found in areas with unaltered land use. The increase in vegetation in the MHRB from 2001 to 2018 was promoted by the increased precipitation and humidity index and the reduced vapor pressure deficit. The sensitivity of GPP to climate change was stronger than that of LAI. These findings can serve as a theoretical guide for the application of ecological engineering and ecological preservation in the MHRB.

Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in Shared Socioeconomic Pathways

Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1–2.6 but decrease under SSP2–4.5, SSP3–7.0, and SSP5–8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2–4.5, SSP3–7.0, and SSP5–8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.

Intercomparison of leaf area index observed by destructive sampling, plant canopy analyzer and tracing radiation and architecture of canopies in paddy fields

ABSTRACT Leaf area index (LAI) is a canopy structure parameter used to characterize vegetation growth. It is increasingly important to obtain continuous and accurate ground LAI measurement data for the validation of remote-sensing products. In this study, direct destructive sampling method and indirect optical measurement including LAI-2200 plant canopy analyser (PCA) and Tracing Radiation and Architecture of Canopies (TRAC) were used for long-term continuous observation of paddy fields in southern China, to explore the variation of LAI during the growth period of paddy, and to quantitatively analyse the errors of indirect optical measurement. The results show that (1) LAI and Plant area index (PAI) increased first and then decreased in the growing period. The proportion of green leaf area gradually decreased at reproductive growth stage (less than 50%). There was a linear relationship between the proportion of green leaf area and PAI at this stage, with a good correlation (R2 = 0.73). This linear relationship can be used to convert PAI into LAI more conveniently. (2) Effective PAI (PAIe) calculated by the two methods (2000 method and Lang method) from PCA showed good consistency between each other in the growing season (the maximum value of R2 is 0.99). With the increase of zenith angle, the consistency between 2000 method and Lang method decreases (R2 = 0.83). Multiple scattering has an effect on PCA measurement, and this effect is more obvious at high zenith angle. Lang method is more sensitive to the change of zenith angle. (3) Compared to the destructive values, both 2000 method and Lang method are able to compute accurate LAI using four ring sensors (4 R), while TRAC underestimates the LAI and PAI from destructive sampling method. This study provided a new idea for reducing the impact of ground reference data errors on LAI product validation.

Greening of China and possible vegetation effects on soil moisture

Wetting and drying climate sequences and anthropogenic vegetation changes can have profound effects on the hydrological cycle and sustainability of terrestrial ecosystems. China contains many different climates, from humid to extremely arid and it contributes to the global mitigation strategies of climate change. China is also an important contributor to global greening and carbon sequestration. In view of this, we estimated the spatial and temporal variation of leaf area index (LAI), dryness/wetness index (DWI), and soil moisture (SM) in arid (A), semi-arid (SA), semi-humid (SH), and humid (H) climate zones during 1981–2018 in China and analyzed the patterns of DWI and LAI effects on SM under different vegetation greenness. The results showed a significantly increasing trend of LAI in all climatic zones, and the contribution was from highest to lowest: SH (8.17 × 10-3 m2 m−2 year−1), SA (7 × 10-3 m2 m−2 year−1), H (4.71 × 10-3 m2 m−2 year−1), and A (3.98 × 10-3 m2 m−2 year−1). The SA and A zones are becoming wetter, while the soil of SH and H zones are drying. The dry/wet climate variation plays a decisive role in soil moisture, while the role of vegetation is limited. Additionally, the LAI3 (mean LAI from 2016 to 2018) thresholds were 0.25–0.41 and 0.84–0.86 for A and SA as a whole, respectively, as well as 0.47–0.54 and 0.93–1.18 for grassland, respectively. When vegetation exceeded this threshold, the effect of vegetation on soil moisture showed a shift from increasing to decreasing change. Overall, the results of the study provide improved understanding of the atmosphere-soil-vegetation interactions under climate change, as well as effects of vegetation restoration and water conservation efforts.

Interannual land cover and vegetation variability based on remote sensing data in the HTESSEL land surface model: implementation and effects on simulated water dynamics

Abstract. Vegetation largely controls land surface–atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived from satellite observations on modeled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated interannually varying land cover from the European Space Agency Climate Change Initiative and interannually varying leaf area index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data on the fraction of green vegetation cover (FCover) from CGLS were used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analyzed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The interannually varying land cover consistently altered the evaporation and soil moisture in regions with major land cover changes. The interannually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with the largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture–evaporation feedbacks during vegetation-water-stressed periods with interannually varying LAI in combination with interannually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation water stress periods.

Spatio-temporal variation in leaf area index in the Yan Mountains over the past 40 years and its relationship to hydrothermal conditions

Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection.

Impact of Updating Vegetation Information on Land Surface Model Performance

AbstractVegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modeled by Land Surface Models (LSMs), which are an essential part of numerical weather prediction and data assimilation. However, most current LSMs implemented specifically in weather forecasting systems use climatological vegetation indices, and land use/land cover data sets in these models are often outdated. In this study, we update land surface data in the European Centre for Medium‐range Weather Forecast (ECMWF) land surface modeling system (ECLand) using Earth observation‐based time varying leaf area index and land use/land cover data, and evaluate the impact of vegetation dynamics on model performance. The performance of the simulated latent heat flux and soil moisture is then evaluated against global gridded observation‐based data sets. Updating the vegetation information does not always yield better model performances because the model's parameters are adapted to the previously employed land surface information. Therefore we recalibrate key soil and vegetation‐related parameters at individual grid cells to adjust the model parameterizations to the new land surface information. This substantially improves model performance and demonstrates the benefits of updated vegetation information. Interestingly, we find that a regional parameter calibration outperforms a globally uniform adjustment of parameters, indicating that parameters should sufficiently reflect spatial variability in the land surface. Our results highlight that newly available Earth‐observation products of vegetation dynamics and land cover changes can improve land surface model performances, which in turn can contribute to more accurate weather forecasts.

Spatiotemporal variation of LAI in different vegetation types and its response to climate change in China from 2001 to 2020

Vegetation in China has experienced significant improvements under the combined effects of human activities and climate change. However, when exploring the effects of climate change on vegetation dynamics, existing studies mainly focus on two meteorological factors (temperature and precipitation) and rarely consider aerosol optical depth (AOD) and photosynthetically active radiation (PAR). Therefore, in this study, we further explored the potential dependence of leaf area index (LAI) on temperature (Tem), precipitation (Pre), AOD, and PAR, based on the analysis of the spatiotemporal variations of LAI in different vegetation types in China. Results showed that the LAI increased significantly from 2001 to 2020, with an overall growth rate of 0.0202/yr; the response of LAI for different vegetation types to each influencing factor varied greatly, and the deciduous broadleaf forests were more sensitive to climate change. Quantitative analysis based on the GeoDetector method showed that the most important driving force of LAI change is Pre (0.596); while Tem (0.394) and AOD (0.325) showed similar effects; by contrast, PAR (0.279) had the lowest driving force on LAI. Among them, the change of Pre is the dominant factor affecting LAI for most vegetation types. From the perspective of regional research, we found that the increase of LAI in the South was greater than that in North China, while human activities (urbanization) were unfavorable to the increase of LAI, such as the Yangtze River Delta where the LAI decreased significantly. This study contributes to a better understanding of the response of different vegetation types to climate and atmospheric changes in China over the past 20 years and provides a scientific reference for developing and implementing ecological restoration measures and promoting sustainable development. Overall, China is 'turning green', and air quality is improving.