Variation In Leaf Area Index Research Articles

Limited driving of elevated CO2 on vegetation greening over global drylands

Drylands are the world’s largest biome and dominate the trends and interannual variability of global carbon sinks. Although a ‘greening’ trend of global drylands has been widely reported, large uncertainties remain in attributing its drivers. It is increasingly emphasized that elevated CO2 has greatly contributed to the vegetation greening over global drylands. Here we quantified the contributions of climate change, elevated CO2, and land use and land cover change (LULCC) on leaf area index (LAI) over drylands, using a process-based land surface model Noah-MP to investigate the drivers of vegetation change. The state-of-the-art model shows better performance in simulating the interannual variability of satellite-observed LAI over global drylands compared with that of the multi-model ensemble mean LAI from the TRENDY results. The area that LAI changes dominated by climate change (44.03%) is three times greater than that by CO2 (13.89%), and climate change also contributes most to the global drylands greening trend (55.07%). LULCC shows regional dominance over 13.35% of the global drylands, which is associated with afforestation, woody plant encroachment, and agricultural intensification. Our results imply that the vegetation greening area driven by elevated CO2 is much limited relative to the overwhelming climatic driving, which should be considered in predictions of trends and interannual variations of global carbon sinks.

Implementation of a dynamic specific leaf area (SLA) into a land surface model (LSM) incorporated crop-growth model

Croplands exert a strong influence on the land–atmosphere coupling and the surface exchanges of heat, water vapor, and momentum. The current trend is incorporating crop models into process-based land surface models (LSMs) to capture photosynthesis, crop growth process, and yield process. However, the models still have shortcomings, such as the assumption of constant SLA tends to ignore specific leaf area (SLA) changes caused by the accumulation and transport of photosynthetic products between organs. In this study, a dynamic SLA, which is specified for each crop, was implemented in the Noah-Multiparameterization Land Surface Model (Noah-MP-Crop), a coupled-crop-land-surface model, with the aim of improving the accuracy of estimated leaf area index (LAI) and above-ground biomass (leaf, stem and grain yield) of crops (maize, rice, soybean and winter wheat) in China. By including the dynamic SLA, the new model (Noah-MP-Cropsla) is more than qualified for capturing the measured variability in LAI and crops’ above-ground biomass, with the mean dim (Willmott’s index of agreement for the dynamic SLA scheme) of the four crops were all above 0.7. A remarkable improvement in the simulations of crop LAI and above-ground biomass were found during the crop’s jointing stage as indicated by the mean relative change rate of RMSE (RMSE% > 50%). Excluding the dynamic process of SLA will result in higher biases in the grain yield for most of the study sites. the improvement degree of crop yield was related to the improvement degree of LAI at the jointing stage (heading stage) of maize, rice and soybean (winter wheat). With the improved SLA process, the Noah-MP-Cropsla have a solid ability to capture the spatial pattern of crop yields, the R2 of the simulated and census data of the four major crop yield in China were all above 0.5.

Bahiagrass (Paspalum notatum CV. Inia Sepé) responses to defoliation intensities

AbstractDefoliation management is determinant of forage production and plant persistence. The objective of this study was to identify grazing suitability and stability of herbage production of INIA Sepé subjected to four intensities of defoliation (represented for sward canopies of 3, 10, 17 and 24 cm maintained constant through weekly cuts) and two rates of nitrogen fertilization (100 and 300 kg ha−1). The response variables studied were: botanical/morphological composition of the herbage mass, herbage accumulation, nutritive value, tiller population density (TPD), tiller weight (TW), leaf area index (LAI) and nitrogen nutrition index (NNI) and calculations performed relative to the tiller size × density compensation mechanism (SDC). Taller swards had smaller population of large tillers, with greater individual leaf area, showing a tiller × size density compensation response. Calculations indicated that the regression coefficient for the linear regression between TW and TPD in a log scale was close to −2.5, in agreement with the −5/2 self‐thinning rule, with variable LAI and R (ratio between tiller leaf area and volume). The exception was during year 1 for the 100 kg N ha−1, when the regression coefficient was close to −1.5. During year 2 only one replication of the 3 cm and 100 kg N ha−1 did not follow the SDC line. LAI and herbage accumulation were positively correlated with the distance between points of log(TW) × log(TPD) and an hypothetical −3/2 SDC line (p < .0001). Paspalum notatum cv. INIA Sepé shows stability of herbage accumulation and ability to adjust tiller size and population density.

Increased impact of the El Niño–Southern Oscillation on global vegetation under future warming environment

There are broad effects of vegetation changes on regional climate, carbon budget, the water cycle, and ecosystems’ productivity. Therefore, further knowledge of the drivers of future vegetation changes is critical to mitigate the influences of global warming. The El Niño–Southern Oscillation (ENSO) is a major mode of interannual climate variability and is likely to affect vegetation on the global scale. Nonetheless, little is known about the causal impacts of ENSO on future vegetation cover with changes in land use and a warming environment. Here, we examined the connections between ENSO and vegetation using leaf area index (LAI) data over the period 2015–2100 from Coupled Modeling Intercomparison Project Phase 6. Our findings indicate that, compared with the historical period 1915–2000, the vegetated areas influenced by ENSO are projected to rise by approximately 55.2% and 20.7% during the twenty-first century of the scenarios SSP2-4.5 and SSP5-8.5, respectively. Though uncertainty for the causal link between ENSO and vegetation changes remains in several regions (i.e., parts of North America, southern Australia, and western Asia), ENSO signature on LAI variations is robust over northern Australia, Amazonia, and parts of Southeast Asia. These results indicate that the influences of ENSO on global vegetation may strengthen in the future.

Leaf area index estimation under wheat powdery mildew stress by integrating UAV‑based spectral, textural and structural features

The infection of powdery mildew changes the internal physiological activity and external morphology of winter wheat, resulting in damage to crop health. Leaf area index (LAI) is an important agronomic parameter to assess crop growth. However, how to accurately and quickly interpret the spatial variation of LAI under powdery mildew stress has become a difficult problem. To detect the spatial variation and improve the estimation accuracy of winter wheat LAI under powdery mildew stress, unmanned aerial vehicle (UAV) multispectral remote sensing technique was used to continuously observe winter wheat infected with diseases in the field. The artificial inoculation experiment of powdery mildew was carried out in the experimental base of the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Xinxiang, China in 2022. The occurrence of powdery mildew changed the canopy spectral reflectance and morphology of winter wheat. Parameters of vegetation indices (VIs), structure (Str) and texture (Tex) features were used to quantify the spectral reflectance and morphology of winter wheat canopy. Sensitivity analysis of different types of features and LAI and crop disease index (DI) was conducted to obtain specific features responding to LAI and DI. Partial least squares method (PLSR) was used to construct LAI estimation model under powdery mildew stress. The results of this study showed that (i) in contrast to crop height, crop coverage and canopy volume maintained consistency with the time change of LAI, which could be utilized to assess LAI variation under powdery mildew stress. (ii) In general, Tex features were more sensitive to LAI and DI than the Str and VIs. (iii) The R2 and RMSE of LAI estimated under powdery mildew stress by Tex, Str and VIs were 0.56, 0.50, 0.45 and 0.32, 0.35, and 0.36, respectively. The combination of VIs, Str, and Tex provided the highest estimation accuracy (R2 = 0.71, RMSE = 0.26). Compared with VIs, Str, Tex, VIs + Str, VIs + Tex, Str + Tex, the R2 of winter wheat LAI estimation under powdery mildew stress was increased by 58%, 42%, 27%, 22%, 15%, and 8%, respectively. This study analyzed the spatial variation of LAI of winter wheat under powdery mildew stress using remote sensing technique, which provided an important theoretical basis for crop disease control in the field.

A novel semi-empirical model for crop leaf area index retrieval using SAR co- and cross-polarizations

The retrieval of continuous leaf area index (LAI) in space and time from remote sensing is beneficial for cropland monitoring and management. Synthetic Aperture Radar (SAR) with the advantages of all-weather operation and fine spatial resolutions has been utilized in various agricultural applications. Although the water cloud model (WCM) has been extensively used for LAI estimation over croplands, it is modified for application to crop canopies with large gaps. The requirement of prior information on soil moisture is also a hinderance for the model application. In this study, WCM is theoretically modified to consider the correlation of active microwave propagation through the canopy in downward and upward directions. Such a modification is particularly important for sparse vegetation with large gaps between crop plants in the early stage of the growing season. A parameter with explicit physical meaning, i.e. the full-vegetation backscattering coefficient, was defined to simplify our model scheme and make the model applicable for the whole growing season. In addition, a physics-based SAR data processing scheme is developed to entangle the influences of LAI and soil moisture on SAR backscatter by taking advantage of the multiple polarizations of RADARSAT-2 (R2) SAR data. In this way, LAI was estimated using modified WCM without the prior knowledge of soil moisture. To evaluate LAI retrieved from the R2 datasets (R2 LAI), ground-based LAI measurements were made at the experimental area of SMAPVEX16-MB in Canada with twenty approximately 800 m х 800 m plots in soybeans and corn. R2 LAI was well correlated to these ground-based LAI (n = 15, R2 = 0.63, RMSE = 0.99 m2·m−2 for soybeans; n = 5, R2 = 0.66, RMSE = 1.20 m2·m−2 for corn). LAI was also retrieved from optical data acquired by Sentinel-2/MSI (S2), denoted as S2 LAI. The R2 LAI and S2 LAI are well correlated and achieved coefficients of determination (R2) of 0.76 and 0.71 and root mean square errors (RMSE) of 1.1 and 1.4 m2·m−2 for soybeans and corn, respectively. The seasonal variations of R2 LAI and S2 LAI are generally similar except at the end of the growing season when S2 LAI is considerably larger than R2 LAI. S2 LAI followed the trend of the reduction in leaf chlorophyll content, while R2 LAI reduced only slightly due to the decrease in leaf water content near the end the growing season. R2 LAI represents the total standing leaf area useful for surface energy balance estimation, while S2 LAI responds to green leaf area useful for crop productivity modeling.

Influence of hydrothermal factors on a coniferous forest canopy in the semiarid alpine region of Northwest China

Analyzing the physiological response of trees to climate change in the Qilian Mountains region is key to studying the impact of global change on forest ecosystems in the semiarid alpine region of Northwest China. The leaf area index (LAI) of the canopy of a forest is an important input parameter for simulating carbon and water cycles in forest ecosystems. Studying the relationship between LAI and environmental factors can provide a scientific basis for accurately describing the structure, function, and ecohydrological processes of forest ecosystems and theoretically guide for sustainable management of water conservation in forests. Methods: In this study, the LAI of the Picea crassifolia canopy was monitored for 2 years (2015–2016) by field observations, and its dynamic changes were analyzed. The relations between LAI and air temperature (AT), precipitation (P), soil temperature (ST), and soil water content (SWC) were studied using Pearson's correlation and multiple regression analyses. The results were as follows: seasonal variations in LAI showed a single-peak curve, which first increased, reached a maximum, remained relatively stable, and then decreased. The maximum value was 4.02 and 4.18 relatively observed in mid-August 2015 and 2016. The LAI of the P. crassifolia canopy in different months was positively correlated with AT and P. It was correlated between the LAI of the canopy with ST40−60 in May and June (p < 0.05) and was also highly positively correlated between the LAI of the canopy with ST60−80, STmean, and SWC60−80 in July and August (p < 0.01). There was a positive correlation between the LAI of the canopy with SWC0−60 and SWCmean in July and SWC0−60 and SWCmean in August (p < 0.05). The LAI of the canopy was affected by AT and ST in May and July, AT and P in June, P in August, and P and ST in September. Our study implied that the rapid increase period of the LAI of the canopy was from late May to early July. The LAI of the canopy was more influenced by temperature and water in July and August. In addition, the LAI of the canopy has significant seasonal variation although it is evergreen coniferous tree species.

Maize On-Farm Stressed Area Identification Using Airborne RGB Images Derived Leaf Area Index and Canopy Height

The biophysical properties of a crop are a good indicator of potential crop stress conditions. However, these visible properties cannot indicate areas exhibiting non-visible stress, e.g., early water or nutrient stress. In this research, maize crop biophysical properties including canopy height and Leaf Area Index (LAI), estimated using drone-based RGB images, were used to identify stressed areas in the farm. First, the APSIM process-based model was used to simulate temporal variation in LAI and canopy height under optimal management conditions, and thus used as a reference for estimating healthy crop parameters. The simulated LAI and canopy height were then compared with the ground-truth information to generate synthetic data for training a linear and a random forest model to identify stressed and healthy areas in the farm using drone-based data products. A Healthiness Index was developed using linear as well as random forest models for indicating the health of the crop, with a maximum correlation coefficient of 0.67 obtained between Healthiness Index during the dough stage of the crop and crop yield. Although these methods are effective in identifying stressed and non-stressed areas, they currently do not offer direct insights into the underlying causes of stress. However, this presents an opportunity for further research and improvement of the approach.

Sensitivity of Vegetation to Climate in Mid-to-High Latitudes of Asia and Future Vegetation Projections

Mid- to high-latitude Asia (MHA) is one of the regions with the strongest warming trend and it is also a region where ecosystems are most sensitive to climate variability. However, how the vegetation in the region will change in the future remains uncertain. Using observation-based Leaf Area Index (LAI) and meteorological data and the multiple regression method, this study analyzes the response of vegetation in the MHA to climate elements during 1982–2020. Then, machine learning prediction models based on the Random Forest (RF) and Extreme Random Tree (ERT) algorithms are built and validated. Based on the calibrated meteorological fields from 17 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under intermediate (SSP2-4.5) and high (SSP5-8.5) emission scenarios and the machine learning models, the LAI over the MHA in 2021–2100 is projected. The historical long-term increasing trends of LAI in the MHA since 1982 are found to be mainly caused by the increasing near-surface air temperature, while the interannual variations of LAI are also greatly affected by precipitation and surface downward solar radiation, especially in summer. The LAI over most of the MHA shows a significant increasing trend in the future, except over some dry areas, and the increasing trends are stronger under the SSP5-8.5 scenario than under the SSP2-4.5 scenario.

Multivariate regressions coupling colorimetric and textural features derived from UAV-based RGB images can trace spatiotemporal variations of LAI well in a deciduous forest

ABSTRACT The leaf area index (LAI) is a critical structural variable related to a variety of biophysical processes of vegetation, and its efficient and accurate estimation is of importance to understanding ecosystem dynamics. Despite many direct and indirect methods being well developed, in the more recent unmanned aerial vehicle (UAV)-based RGB data have emerged to be a promising alternative source for LAI estimation due to their low-cost, considerable flexibility, and useful applicability. In this study, we have investigated the feasibility of using multivariate regressions combined with different colour indices and textural-based features extracted from the UAV-based RGB images for tracing the dynamical LAI across different phenological stages in a typical temperate deciduous forest. The results demonstrated that the visible atmospherically resistant index (VARI) and red–green ratio index (RGR) were the best when using colour indices (CIs) alone while the difference (D) performed best if using textural information only to estimate the spatiotemporal changes of LAI. More importantly, the multivariate regressions based on the combination of CIs and texture-based features resulted in greater accuracies for LAI estimation, in which the random forest regression (R2 = 0.85, RMSE = 0.56, RPD = 2.52) outperformed other analytical methods. The findings of this study provide a cost-effective yet reliable approach to trace LAI from a commonly and easily obtained data source, with the capability to extend to ever-large scales for quick vegetation monitoring and management.

Assessing Within-Field Variation in Alfalfa Leaf Area Index Using UAV Visible Vegetation Indices

This study examines the use of leaf area index (LAI) to inform variable-rate irrigation (VRI) for irrigated alfalfa (Medicago sativa). LAI is useful for predicting zone-specific evapotranspiration (ETc). One approach toward estimating LAI is to utilize the relationship between LAI and visible vegetation indices (VVIs) using unmanned aerial vehicle (UAV) imagery. This research has three objectives: (1) to measure and describe the within-field variation in LAI and canopy height for an irrigated alfalfa field, (2) to evaluate the relationships between the alfalfa LAI and various VVIs with and without field average canopy height, and (3) to use UAV images and field average canopy height to describe the within-field variation in LAI and the potential application to VRI. The study was conducted in 2021–2022 in Rexburg, Idaho. Over the course of the study, the measured LAI varied from 0.23 m2 m−2 to 11.28 m2 m−2 and canopy height varied from 6 cm to 65 cm. There was strong spatial clustering in the measured LAI but the spatial patterns were dynamic between dates. Among eleven VVIs evaluated, the four that combined green and red wavelengths but excluded blue wavelengths showed the most promise. For all VVIs, adding average canopy height to multiple linear regression improved LAI prediction. The regression model using the modified green–red vegetation index (MGRVI) and canopy height (R2 = 0.93) was applied to describe the spatial variation in the LAI among VRI zones. There were significant (p < 0.05) but not practical differences (<15%) between pre-defined zones. UAV imagery coupled with field average canopy height can be a useful tool for predicting LAI in alfalfa.

River Bars and Vegetation Dynamics in Response to Upstream Damming: A Case Study of the Middle Yangtze River

Investigating river bars and their vegetation dynamics in response to upstream damming is important for riverine flood management and ecological assessment. However, our mechanical understanding of the damming-induced changes in river bar and vegetation, such as bar area, morphology, and leaf area index (LAI), remains limited for large river systems. Leveraging satellite images and in situ observed hydrogeomorphic data from, we improve a machine learning-based LAI inversion model to quantify variations in river bar morphology, vegetation distribution, and LAI in the Middle Yangtze River (MYR) following the operation of the Three Gorges Dam (TGD). Then we analyze the mechanisms controlling the bar and vegetation dynamics based on high-resolution river cross-sectional profiles as well as daily discharge, water levels, and sediment in both the pre- and post-TGD periods. Our results indicate that the river bar area decreased by approximately 10% from 2003 to 2020, while the vegetation area and average LAI of these bars increased by >50% and >20%, respectively. Moreover, the plant community on most river bars tended to expand from the bar tail to the bar head and from the edge to the center. The main factor driving vegetation expansion in the MYR after the TGD’s operation was the reduction in bar submergence frequency (by 55%), along with a slight bar erosion. Further analysis revealed that the standard deviation of annual discharge decreased by approximately 37%, and the frequency of vegetation-erosive flow decreased by approximately 74%. Our data highlight the potential impact of large dams downstream flow regimes and vegetation encroachement. Such findings further the understanding of the biogeomorphological impacts of large dams on the river bar vegetation and have important implications for riverine plant flux estimatin, flood management and ecological restoration in dammed river systems.

Development of the grass LAI and CCC remote sensing-based models and their transferability using sentinel-2 data in heterogeneous grasslands

ABSTRACT Estimation of biophysical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) at high spatiotemporal resolution is important for managing natural and heterogeneous environments. However, accurate estimation of biophysical variables particularly over heterogeneous environments remains a challenge. The objective of the study was to develop locally parameterized grass LAI and CCC empirical models using the Sentinel-2 variables combined with the Stepwise multiple linear regression (SMLR) and Random forest (RF) at the Golden Gate Highlands National Park (GGHNP) and Marakele National Park (MNP) in South Africa. Results showed that in MNP, SMLR yielded better LAI estimation with root mean squared error (RMSE) of 0.67 m2.m−2 and mean adjusted error (MAE) of 0.54, explaining 48% of LAI variability, when bands and indices are combined. In contrast, RF gave better CCC estimation i.e. RMSE and MAE of 17.08 µg.cm−2 and 13.18 respectively, explaining about 40% of CCC variability with Sentinel-2 bands only. In GGHNP, the RF models provided the best estimates of both LAI and CCC compared to SMLR models. Furthermore, the CCC and LAI estimation models of GGHNP showed improved model accuracies when 50% and 75% of the MNP field samples were transferred to the GGHNP models. In contrast, the CCC and LAI estimation models of MNP showed a decline in model performance across all scenarios where the GGHNP field samples were transferred to the MNP models. These findings have significant implications for the development of locally parameterized types of models that can provide improved and consistent site-specific accurate estimates of grass biophysical parameters over heterogeneous environments.

Underestimated Interannual Variability of Terrestrial Vegetation Production by Terrestrial Ecosystem Models

AbstractVegetation gross primary production (GPP) is the largest terrestrial carbon flux and plays an important role in regulating the carbon sink. Current terrestrial ecosystem models (TEMs) are indispensable tools for evaluating and predicting GPP. However, to which degree the TEMs can capture the interannual variability (IAV) of GPP remains unclear. With large data sets of remote sensing, in situ observations, and predictions of TEMs at a global scale, this study found that the current TEMs substantially underestimate the GPP IAV in comparison to observations at global flux towers. Our results also showed the larger underestimations of IAV in GPP at nonforest ecosystem types than forest types, especially in arid and semiarid grassland and shrubland. One cause of the underestimation is that the IAV in GPP predicted by models is strongly dependent on canopy structure, that is, leaf area index (LAI), and the models underestimate the changes of canopy physiology responding to climate change. On the other hand, the simulated interannual variations of LAI are much less than the observed. Our results highlight the importance of improving TEMs by precisely characterizing the contribution of canopy physiological changes on the IAV in GPP and of clarifying the reason for the underestimated IAV in LAI. With these efforts, it may be possible to accurately predict the IAV in GPP and the stability of the global carbon sink in the context of global climate change.

Shrubs Compensate for Tree Leaf Area Variation and Influence Vegetation Indices in Post‐Fire Siberian Larch Forests

AbstractIn post‐fire Siberian larch forests, where tree density can vary within a burn perimeter, shrubs constitute a substantial portion of the vegetation canopy. Leaf area index (LAI), defined as the one‐sided total green leaf area per unit ground surface area, is useful for characterizing variation in plant canopies. We estimated LAI with allometry for trees and tall shrubs (>0.5 and <1.5 m) across 26 sites with varying tree stem density (0.05–3.3 stems/m2) and canopy cover (4.6%–76.9%) in a uniformly‐aged mature Siberian larch forest that regenerated following a fire ∼75 years ago. We investigated relationships between tree density, tree LAI, and tall shrub LAI, and between LAI and satellite observations of Normalized Difference and Enhanced Vegetation Indices (NDVI and EVI). Across the density gradient, tree LAI increases with increasing tree density, while tall shrub LAI decreases, exhibiting no patterns in combined tree‐shrub LAI. We also found significant positive relationships between tall shrub LAI and NDVI/EVI from PlanetScope and Landsat imagery. These findings suggest that tall shrubs compensate for lower tree LAI in tree canopy gaps, forming a canopy with contiguous combined tree‐shrub LAI across the density gradient. Our findings suggest that NDVI and EVI are more sensitive to variation in tall shrub canopies than variation in tree canopies or combined tree‐shrub canopies in these ecosystems. The results improve our understanding of the relationships between forest density and tree and shrub leaf area and have implications for interpreting spatial variability in LAI, NDVI, and EVI in Siberian boreal forests.

Climate Change and CO2 Fertilization Have Played Important Roles in the Recent Decadal Vegetation Greening Trend on the Chinese Loess Plateau

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint.

Retrieval of Mangrove leaf area index and its response to typhoon based on WorldView-3 image

In recent decades, mangroves have become one of the world's most threatened and vulnerable ecosystems due to human disturbance and climate change. Remote sensing techniques have helped to obtain biophysical parameters of forests on a large scale. However, to our knowledge, mangrove leaf area index (LAI) inversion using texture features and its scale dependency is rarely discussed. Whether the dynamics of remotely sensed LAI could reveal the responses of mangrove species to typhoon disturbance remain unknown. In this study, we classified mangrove species, estimated the LAI of each species based on high-resolution WorldView-3 (WV-3) images, and evaluated the performance of different object-based classifiers and the effects of texture features and scale dependency on the LAI model. We characterized typhoon disturbance on different species using interannual LAI variation. Random forest (RF) has the highest accuracy in mangrove species classification, with an overall accuracy of 82.29% after post-classification, compared with naive Bayes (NB), classification and regression trees (CART), k-nearest neighbor (KNN), and support vector machines (SVM). Texture features significantly increased the accuracy of LAI estimates, and the optimal prediction result was generated at a 2-m resampling rate (R2 = 0.52; MSE = 0.56). The mean LAIs of B. gymnorrhiza and S. apetala assemblages were significantly higher than those of K. obovata, S. caseolaris, and A. marina. LAI variation demonstrates a disturbance pattern caused by Typhoon Mangkhut. This typhoon had the greatest impact on K. obovata, while S. apetala and S. caseolaris showed good resistance to wind damage. We concluded that RF was the best classifier for mapping mangrove species distribution using object-based classification algorithms with WV-3 image. The texture features play a key role in constructing the estimation model of mangrove LAI with scale dependency.

Integrated effects of co-evolutions among climate, land use and vegetation growing dynamics to changes of runoff quantity and quality

Climates, Land use/land cover (LULC) and vegetation growing dynamics have been regarded as the main factors affecting terrestrial hydrological process. However, the mechanisms underlying their integrated effects on terrestrial runoff and nutrient dynamics are not understood well. Here, we constructed a framework to disentangle and quantify the independent and coupled contributions of climate, LULC and vegetation leaf area index (LAI) changes to watershed runoff and nutrient yields changes. Long series of changing meteorological, LULC and LAI data between 1990 and 2020 were integrated into a factor-controlled simulation protocol in a distributed hydrological model, to quantify their comprehensive contributions (individual contribution of single factor change and coupling contribution of multiple factor synchronous changes) to runoff and nutrient changes. The results showed that changes of runoff and nutrient yields are more induced by climate change, rather than LULC and LAI transformations. Increase in annual precipitation significantly elevated runoff and nutrient yields. TP yield was more sensitive to climate change than runoff and TN yields. LULC transformation and climate change have synergistic effects on runoff and nutrient yields. Shift of vegetation areas to construction lands will amplify the effect of climate change on runoff and nutrient yields. Single LAI change has weak effect on runoff and nutrient yields, but it can significantly alter the hydrological effects derived from climate change and the synergistic effects between climate change and LULC transformation. This study considered the coupling and potential synergistic effects among climate change, LULC conversion and LAI variation, which elucidated the comprehensive effects of changing environment on runoff and nutrients evolutions in a more systematic and integrated perspective.

Natural decadal variability of global vegetation growth in relation to major decadal climate modes

The ongoing climate change can modulate the behavior of global vegetation and influence the terrestrial biosphere carbon sink. Past observation-based studies have mainly focused on the linear trend or interannual variability of the vegetation greenness, but could not explicitly deal with the effect of natural decadal variability due to the short length of observations. Here we put the variabilities revealed by remote sensing-based global leaf area index (LAI) from 1982 to 2015 into a long-term perspective with the help of ensemble Earth system model simulations of the historical period 1850–2014, with a focus on the low-frequency variability in the global LAI during the growing season. Robust decadal variability in the observed and modelled LAI was revealed across global terrestrial ecosystems, and it became stronger toward higher latitudes, accounting for over 50% of the total variability north of 40°N. The linkage of LAI decadal variability to major natural decadal climate modes, such as the El Niño–Southern Oscillation decadal variability (ENSO-d), the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO), was analyzed. ENSO-d affects LAI by altering precipitation over large parts of tropical land. The PDO exerts opposite impacts on LAI in the tropics and extra-tropics due to the compensation between the effects of temperature and growing season length. The AMO effects are mainly associated with anomalous precipitation in North America and Europe but are mixed with long-term climate change impacts due to the coincident phase shift of the AMO which also induces North Atlantic basin warming. Our results suggest that the natural decadal variability of LAI can be largely explained by these decadal climate modes (on average 20% of the variance, comparable to linear changes, and over 40% in some ecosystems) which also can be potentially important in inducing the greening of the Earth of the past decades.

Improving Leaf Area Index Estimation With Chlorophyll Insensitive Multispectral Red-Edge Vegetation Indices

As an essential vegetation biophysical trait that determines the plant's structure and photosynthetic capacity, characterizing of leaf area index (LAI) is important for vegetation growth and health monitoring. The empirical models based on vegetation indices (VIs) from remote sensing images is an effective method for deriving LAI. However, due to the coupled impacts of LAI and leaf chlorophyll content (LCC) on canopy reflectance and saturation effect, most VIs cannot achieve a good accuracy of LAI estimation. The remotely sensed red-edge reflectance can provide valuable information to delineate the LAI, therefore a series of leaf chlorophyll insensitive red-edge VIs by using the Sentinel-2 and GF-6 multispectral images are developed in this work to improve the LAI estimation accuracy. The potentials of reflecting LAI variations and sensitivity to LCC changes for each Sentinel-2 and GF-6 red-edge band are comprehensively analyzed based on the PROSAIL model to select the optimal band in VIs design. The proposed VIs are then evaluated in multiple ways, including with PROSAIL simulated datasets, ground measured LAI with canopy spectra and real satellite images. The evaluation results based on field LAI measurements indicate that the proposed red-edge VIs can effectively improve crop LAI estimation accuracy with the best regression coefficient ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.81 for Sentinel-2 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.65 for GF-6) among all comparative VIs. Our work showcases incorporating red-edge bands with suitable formula is promising for improving VI-based LAI retrieval, and they offer a practicable solution to fast achieve decameter LAI maps by using the Sentinel-2 or GF-6 images.