Close-range Hyperspectral Spectroscopy Reveals Leaf Water Content Dynamics

Water plays a crucial role in maintaining plant functionality and drives many ecophysiological processes. The distribution of water resources is in a continuous change due to global warming affecting the productivity of ecosystems around the globe, but there is a lack of non-destructive methods capable of continuous monitoring of plant and leaf water content that would help us in understanding the consequences of the redistribution of water. We studied the utilization of novel small hyperspectral sensors in the 1350-2450 nm spectral range in non-destructive estimation of leaf water content in laboratory and field conditions. We found that the sensors captured up to 96% of the variation in equivalent water thickness (EWT, g/m2) and up to 90% of the variation in relative water content (RWC). These laboratory findings were supported by field measurements, where repeated leaf spectra measurements were in good agreement (R2=0.79) with a time-lagged change of tree xylem diameter. Further tests were done with an indoor plant (Dracaena marginate Lem.) by continuously measuring leaf spectra while drought conditions developed, which revealed detailed diurnal dynamics of leaf water content. We conclude that close-range hyperspectral spectroscopy can provide a novel tool for continuous measurement of leaf water content at the single leaf level and help us to better understand plant responses to varying environmental conditions.

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Evaluation of Hyperspectral Reflectance Parameters to Assess the Leaf Water Content in Soybean

Water ◽

10.3390/w11030443 ◽

2019 ◽

Vol 11 (3) ◽

pp. 443 ◽

Cited By ~ 13

Author(s):

Marek Kovar ◽

Marian Brestic ◽

Oksana Sytar ◽

Viliam Barek ◽

Pavol Hauptvogel ◽

...

Keyword(s):

Water Content ◽

Precision Agriculture ◽

Irrigation Management ◽

Dry Mass ◽

Leaf Water ◽

Leaf Water Content ◽

Content Assessment ◽

Equivalent Water Thickness ◽

Soybean Plants ◽

Positive Correlations

Nondestructive assessment of water content and water stress in plants is an important component in the rational use of crop irrigation management in precision agriculture. Spectral measurements of light reflectance in the UV/VIS/NIR region (350–1075 nm) from individual leaves were acquired under a rapid dehydration protocol for validation of the remote sensing water content assessment in soybean plants. Four gravimetrical approaches of leaf water content assessment were used: relative water content (RWC), foliar water content as percent of total fresh mass (FWCt), foliar water content as percent of dry mass (FWCd), and equivalent water thickness (EWT). Leaf desiccation resulted in changes in optical properties with increasing relative reflectance at wavelengths between 580 and 700 nm. The highest positive correlations were observed for the relations between the photochemical reflectance index (PRI) and EWT (rP = 0.860). Data analysis revealed that the specific water absorption band at 970 nm showed relatively weaker sensitivity to water content parameters. The prediction of leaf water content parameters from PRI measurements was better with RMSEs of 12.4% (rP = 0.786), 9.1% (rP = 0.736), and 0.002 (rP = 0.860) for RWC, FWCt, and EWT (p < 0.001), respectively. The results may contribute to more efficient crop water management and confirmed that EWT has a statistically closer relationship with reflectance indices than other monitored water parameters.

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Improving the Selection of Vegetation Index Characteristic Wavelengths by Using the PROSPECT Model for Leaf Water Content Estimation

Remote Sensing ◽

10.3390/rs13040821 ◽

2021 ◽

Vol 13 (4) ◽

pp. 821

Author(s):

Jian Yang ◽

Yangyang Zhang ◽

Lin Du ◽

Xiuguo Liu ◽

Shuo Shi ◽

...

Keyword(s):

Water Content ◽

Biochemical Parameters ◽

Leaf Water ◽

Leaf Structure ◽

Carotenoid Content ◽

Leaf Water Content ◽

Dry Matter Content ◽

Structure Parameter ◽

Equivalent Water Thickness ◽

Selection Of

Equivalent water thickness (EWT) is a major indicator for indirect monitoring of leaf water content in remote sensing. Many vegetation indices (VIs) have been proposed to estimate EWT based on passive or active reflectance spectra. However, the selection of the characteristics wavelengths of VIs is mainly based on statistical analysis for specific vegetation species. In this study, a characteristic wavelength selection algorithm based on the PROSPECT-5 model was proposed to obtain characteristic wavelengths of leaf biochemical parameters (leaf structure parameter (N), chlorophyll a + b content (Cab), carotenoid content (Car), EWT, and dry matter content (LMA)). The effect of combined characteristic wavelengths of EWT and different biochemical parameters on the accuracy of EWT estimation is discussed. Results demonstrate that the characteristic wavelengths of leaf structure parameter N exhibited the greatest influence on EWT estimation. Then, two optimal characteristics wavelengths (1089 and 1398 nm) are selected to build a new ratio VI (nRVI = R1089/R1398) for EWT estimation. Subsequently, the performance of the built nRVI and four optimal published VIs for EWT estimation are discussed by using two simulation datasets and three in situ datasets. Results demonstrated that the built nRVI exhibited better performance (R2 = 0.9284, 0.8938, 0.7766, and RMSE = 0.0013 cm, 0.0022 cm, 0.0030 cm for ANGERS, Leaf Optical Properties Experiment (LOPEX), and JR datasets, respectively.) than that the published VIs for EWT estimation. It is demonstrated that the built nRVI based on the characteristic wavelengths selected using the physical model exhibits desirable universality and stability in EWT estimation.

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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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Effect of dust deposition on spectrum-based estimation of leaf water content in urban plant

Ecological Indicators ◽

10.1016/j.ecolind.2019.04.074 ◽

2019 ◽

Vol 104 ◽

pp. 41-47 ◽

Cited By ~ 4

Author(s):

Wenpeng Lin ◽

Yuan Li ◽

Shiqiang Du ◽

Yuanfan Zheng ◽

Jun Gao ◽

...

Keyword(s):

Water Content ◽

Leaf Water ◽

Leaf Water Content ◽

Dust Deposition

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

Scientific Reports ◽

10.1038/s41598-021-88678-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ruomeng Wang ◽

Nianpeng He ◽

Shenggong Li ◽

Li Xu ◽

Mingxu Li

Keyword(s):

Spatial Variation ◽

Water Content ◽

Large Scale ◽

Life Forms ◽

Important Variable ◽

Leaf Water ◽

Leaf Water Content ◽

Desert Grassland ◽

Large Spatial Scale ◽

Grassland Plants

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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