A multi-angular invariant spectral index for the estimation of leaf water content across a wide range of plant species in different growth stages

2021 ◽  
Vol 253 ◽  
pp. 112230
Author(s):  
Xiao Li ◽  
Zhongqiu Sun ◽  
Shan Lu ◽  
Kenji Omasa
Keyword(s):  
Water Content ◽  
Plant Species ◽  
Spectral Index ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Growth Stages ◽  
Wide Range ◽  
Different Growth Stages

scholarly journals Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

2021 ◽  
Vol 13 (20) ◽  
pp. 4125
Author(s):  
Weiping Kong ◽  
Wenjiang Huang ◽  
Lingling Ma ◽  
Lingli Tang ◽  
Chuanrong Li ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Water Content ◽  
Near Infrared ◽  
Field Experiments ◽  
Estimation Method ◽  
Middle Layer ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Growth Stages ◽  
Spectral Indices

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

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

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.

scholarly journals Comparison of the Prediction of Winter Wheat Leaf Water Content by Using New Hyperspectral Index and Machine Learning Models

2020 ◽  
Author(s):  
Juanjuan Zhang ◽  
Wen Zhang ◽  
Shuping Xiong ◽  
Zhaoxiang Song ◽  
Wenzhong Tian ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Water Content ◽  
Spectral Index ◽  
Spectral Reflectance ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Full Band ◽  
Wheat Leaf ◽  
Canopy Spectral Reflectance ◽  
Better Than

Abstract In this study, hyperspectral technology was used to establish the winter wheat leaf water content inversion model to provide technical reference for winter wheat precision irrigation. In a field experiment, seven different wheat varieties for different irrigation times were treated during two consecutive years. The data onto canopy spectral reflectance and leaf water content (LWC) of winter wheat were collected. Five different modeling methods, Spectral index, partial least squares (PLSR), random forest (RF), extreme random tree (ERT) and k-nearest neighbor (KNN) were used to construct LWC estimation models. The results showed that the canopy spectral reflectance was directly proportional to the irrigation times, especially in the near infrared band. As for LWC, the prediction effect of the newly differential spectral index DVI (R1185, R1308) is better than the existing spectral index, and R2 are 0.78. Because of the large amount of hyperspectral data. The correlation coefficient method (CA) and loading weight (x-Lw) are used to select the water characteristic bands from the full band. The results show that the accuracy of the model based on the characteristic band is not significantly lower than that of the full band. Among these models, the ERT- x-Lw model performs best (R2 and RMSE of 0.88 and 1.81; 0.84 and 1.62 for calibration and validation, respectively). In addition, the accuracy of LWC estimation model constructed by ERT-x-Lw was better than that of DVI (R1185, R1307). The results provide technical reference and basis for crop water monitoring and diagnosis under similar production conditions.

scholarly journals Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Juanjuan Zhang ◽  
Wen Zhang ◽  
Shuping Xiong ◽  
Zhaoxiang Song ◽  
Wenzhong Tian ◽  
...  
Keyword(s):  
Water Content ◽  
Correlation Coefficient ◽  
Spectral Index ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Estimation Model ◽  
Full Spectrum ◽  
Calibration And Validation ◽  
Wheat Leaf ◽  
Coefficient Method

Abstract Background The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. Methods In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018–2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models. Results The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750–1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R2 of 0.85 and R2 of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307). Conclusion The two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.

scholarly journals Critical Leaf Water Content for Maize Photosynthesis under Drought Stress and Its Response to Rewatering

2021 ◽  
Vol 13 (13) ◽  
pp. 7218
Author(s):  
Xingyang Song ◽  
Guangsheng Zhou ◽  
Qijin He
Keyword(s):  
Drought Stress ◽  
Water Content ◽  
Global Climate ◽  
Leaf Water ◽  
Extreme Weather Events ◽  
Leaf Water Content ◽  
Growth Stages ◽  
Stomatal Limitation ◽  
Jointing Stage ◽  
Maize Photosynthesis

Crop photosynthesis is closely related to leaf water content (LWC), and clarifying the LWC conditions at critical points in crop photosynthesis has great theoretical and practical value for accurately monitoring drought and providing early drought warnings. This experiment was conducted to study the response of LWC to drought and rewatering and to determine the LWC at which maize photosynthesis reaches a maximum and minimum and thus changes from a state of stomatal limitation (SL) to non-stomatal limitation (NSL). The effects of rehydration were different after different levels of drought stress intensity at different growth stages, and the maize LWC recovered after rewatering following different drought stresses at the jointing stage; however, the maize LWC recovered more slowly after rewatering following 43 days and 36 days of drought stress at the tasselling and silking stages, respectively. The LWC when maize photosynthesis changed from SL to NSL was 75.4% ± 0.38%, implying that the maize became rehydrated under physiologically impaired conditions. The LWCs at which the maize Vcmax25 reached maximum values and zero differed between the drought and rewatering periods. After exposure to drought stress, the maize exhibited enhanced drought stress tolerance, an obviously reduced suitable water range, and significantly weakened photosynthetic capacity. These results provide profound insight into the turning points in maize photosynthesis and their responses to drought and rewatering. They may also help to improve crop water management, which will be useful in coping with the increased frequency of drought and extreme weather events expected under global climate change.

Various responses of sunflower genotypes to water stress on newly reclaimed sandy soil

2013 ◽  
Vol 61 (1) ◽  
pp. 55-69
Author(s):  
A. Salem ◽  
A. Omar ◽  
M. Ali
Keyword(s):  
Water Stress ◽  
Water Content ◽  
Transpiration Rate ◽  
Leaf Water ◽  
Oil Yield ◽  
Leaf Water Content ◽  
Growth Stages ◽  
Severe Drought ◽  
Water Supplies ◽  
Water Regimes

Water stress is a severe limitation for crop growth especially in arid and semi-arid regions of the world, as it has a vital role in plant growth and development at all growth stages. The aim of the present study was to evaluate the differential responses of twelve sunflower genotypes to three levels of water supply and select the most suitable one for such conditions. Two field experiments were conducted under adequate (7140 m3/ha), moderate (4760 m3/ha) and severe (2380 m3/ha) water regimes to evaluate the chlorophyll index, transpiration rate, leaf water content, plant height, head diameter, seeds/head, 1000-seed weight, seed and oil yield of the genotypes. Moderate and severe levels of drought had a significant impact on the transpiration rate, leaf water content, yield-contributing characters and oil yield of all the sunflower genotypes. However, the sunflower genotypes showed different responses to the different water regimes. The highest seed and oil yields were attained in L990 and Giza 102 in the case of adequate water supplies, while L38 was the best under moderate and severe drought conditions. On the basis of the results, sunflower genotype L990 could be recommended for growing when adequate water supplies are available, and L38 under moderate and severe water regimes to obtain high seed and oil yields.

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

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

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

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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