Spatial variation and mechanisms of leaf water content in grassland plants at the biome scale: evidence from three comparative transects

AbstractLeaf water content (LWC) has important physiological and ecological significance for plant growth. However, it is still unclear how LWC varies over large spatial scale and with plant adaptation strategies. Here, we measured the LWC of 1365 grassland plants, along three comparative precipitation transects from meadow to desert on the Mongolia Plateau (MP), Loess Plateau, and Tibetan Plateau, respectively, to explore its spatial variation and the underlying mechanisms that determine this variation. The LWC data were normally distributed with an average value of 0.66 g g−1. LWC was not significantly different among the three plateaus, but it differed significantly among different plant life forms. Spatially, LWC in the three plateaus all decreased and then increased from meadow to desert grassland along a precipitation gradient. Unexpectedly, climate and genetic evolution only explained a small proportion of the spatial variation of LWC in all plateaus, and LWC was only weakly correlated with precipitation in the water-limited MP. Overall, the lasso variation in LWC with precipitation in all plateaus represented an underlying trade-off between structural investment and water income in plants, for better survival in various environments. In brief, plants should invest less to thrive in a humid environment (meadow), increase more investment to keep a relatively stable LWC in a drying environment, and have high investment to hold higher LWC in a dry environment (desert). Combined, these results indicate that LWC should be an important variable in future studies of large-scale trait variations.

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Variation and adaptation of leaf water content among species, communities, and biomes

Environmental Research Letters ◽

10.1088/1748-9326/ac38da ◽

2021 ◽

Vol 16 (12) ◽

pp. 124038

Author(s):

Ruomeng Wang ◽

Nianpeng He ◽

Shenggong Li ◽

Li Xu ◽

Mingxu Li

Keyword(s):

Water Content ◽

Plant Species ◽

Plant Communities ◽

Cost Effective ◽

Terrestrial Ecosystems ◽

Life Forms ◽

Systematic Investigation ◽

Leaf Water ◽

Leaf Water Content ◽

Plant Life Forms

Abstract Leaf water content (LWC) is essential for the physiological activities in plants, but its spatial variation and the underlying mechanisms in natural plant communities are unclear. In this study, we measured the LWC of 5641 plant species from 72 natural communities in China, covering most terrestrial ecosystems, to answer these questions. Our results showed that LWC, on average, was 0.690 g g–1, and was significantly higher in forests and deserts than in grasslands. LWC was significantly different among different plant life forms, and ranked on averages in the following order: herbs > shrubs > trees. Interestingly, LWC decreased with increasing humidity and increased in dry environments. Furthermore, the variations of LWC in plant communities were higher in arid areas and those species with lower LWC in a plant community were more sensitive to changing environments. These results demonstrated the adaptations of plants to water regime in their habitats. Although, phylogeny has no significant effect on LWC, plant species both in forests and grasslands evolve toward higher LWC. Variations of LWC from species to community to biome represent the cost-effective strategy of plants, where plant species in drier environment require higher input to keep higher LWC to balance water availability and heat regulation. This systematic investigation fills the gaps on how LWC varies spatially and clarifies the different adaptation mechanisms regulating LWC across scales.

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Continuous observation of canopy water content changes with GPS sensors

10.5194/egusphere-egu21-15551 ◽

2021 ◽

Author(s):

Vincent Humphrey ◽

Brian L. Dorsey ◽

Christian Frankenberg

Keyword(s):

Water Content ◽

Liquid Water ◽

Large Scale ◽

Climatic Conditions ◽

Leaf Water ◽

Global Navigation Satellite Systems ◽

Leaf Water Content ◽

Vegetation Water ◽

Canopy Water

<p>Canopy water content is a direct indicator of vegetation water use and hydraulic stress, reflecting how ecosystems respond and adapt to droughts and heatwaves. It represents an interesting target for Earth system models which attempt to predict the response and resilience of the vegetation in the face of changing climatic conditions. So far, in-situ estimates of vegetation water content often rely on infrequent and time-consuming samplings of leaf water content, which are not necessarily representative of the canopy scale. On the other hand, several satellite techniques have demonstrated a promising potential for monitoring vegetation optical depth and water content, but these large-scale measurements are still difficult to reference against sparse in-situ level observations.</p><p>Here, we present an experimental technique based on Global Navigation Satellite Systems (GNSS) to bridge this persisting scale gap. Because GNSS microwave signals are obstructed and scattered by vegetation and liquid water, placing a GNSS sensor in a forest and measuring changes in signal quality can provide continuous information on canopy water content and forest structure. We demonstrate that variations in GNSS signal attenuation reflect the distribution of biomass density and liquid water in the canopy, consistent with ancillary relative leaf water content measurements, and can be monitored continuously. Of particular interest, this technique can resolve diurnal variations in canopy water content at sub-hourly time steps. The few rainfall events captured during the 8-months observational record also suggest that canopy water interception can be monitored at 5 minutes intervals. We discuss future strategies and requirements for deploying such off-the-shelf passive bistatic radar systems at existing FluxNet sites.</p>

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Leaf water content estimation using top-of-canopy airborne hyperspectral data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2021.102393 ◽

2021 ◽

Vol 102 ◽

pp. 102393

Author(s):

Rahul Raj ◽

Jeffrey P. Walker ◽

Vishal Vinod ◽

Rohit Pingale ◽

Balaji Naik ◽

...

Keyword(s):

Water Content ◽

Hyperspectral Data ◽

Leaf Water ◽

Leaf Water Content

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Capability of leaf water content and its threshold values in reflection of soil–plant water status in maize during prolonged drought

Ecological Indicators ◽

10.1016/j.ecolind.2021.107395 ◽

2021 ◽

Vol 124 ◽

pp. 107395

Author(s):

Huailin Zhou ◽

Guangsheng Zhou ◽

Qijin He ◽

Li Zhou ◽

Yuhe Ji ◽

...

Keyword(s):

Water Content ◽

Water Status ◽

Leaf Water ◽

Plant Water Status ◽

Leaf Water Content ◽

Plant Water ◽

Threshold Values ◽

Prolonged Drought

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Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

Remote Sensing ◽

10.3390/rs13132634 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2634

Author(s):

Qiyuan Wang ◽

Yanling Zhao ◽

Feifei Yang ◽

Tao Liu ◽

Wu Xiao ◽

...

Keyword(s):

Heat Stress ◽

Water Content ◽

Spontaneous Combustion ◽

Leaf Water ◽

Coal Gangue ◽

Leaf Water Content ◽

Spectral Features ◽

Lasso Regression ◽

Stress Assessment ◽

First Derivative

Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.

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Spectral assessments of wheat plants grown in pots and containers under saline conditions

Functional Plant Biology ◽

10.1071/fp12208 ◽

2013 ◽

Vol 40 (4) ◽

pp. 409 ◽

Cited By ~ 16

Author(s):

Harald Hackl ◽

Bodo Mistele ◽

Yuncai Hu ◽

Urs Schmidhalter

Keyword(s):

Water Potential ◽

Water Content ◽

Aboveground Biomass ◽

Leaf Water ◽

Leaf Water Content ◽

Spectral Indices ◽

Fresh Weight ◽

Normalised Difference Vegetation Index ◽

Spectral Sensing ◽

Reflectance Ratio

Spectral measurements allow fast nondestructive assessment of plant traits under controlled greenhouse and close-to-field conditions. Field crop stands differ from pot-grown plants, which may affect the ability to assess stress-related traits by nondestructive high-throughput measurements. This study analysed the potential to detect salt stress-related traits of spring wheat (Triticum aestivum L.) cultivars grown in pots or in a close-to-field container platform. In two experiments, selected spectral indices assessed by active and passive spectral sensing were related to the fresh weight of the aboveground biomass, the water content of the aboveground biomass, the leaf water potential and the relative leaf water content of two cultivars with different salt tolerance. The traits were better ascertained by spectral sensing of container-grown plants compared with pot-grown plants. This may be due to a decreased match between the sensors’ footprint and the plant area of the pot-grown plants, which was further characterised by enhanced senescence of lower leaves. The reflectance ratio R760 : R670, the normalised difference vegetation index and the reflectance ratio R780 : R550 spectral indices were the best indices and were significantly related to the fresh weight, the water content of the aboveground biomass and the water potential of the youngest fully developed leaf. Passive sensors delivered similar relationships to active sensors. Across all treatments, both cultivars were successfully differentiated using either destructively or nondestructively assessed parameters. Although spectral sensors provide fast and qualitatively good assessments of the traits of salt-stressed plants, further research is required to describe the potential and limitations of spectral sensing.

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