scholarly journals Remote Estimation of Leaf Water Content Using Spectral Index Derived From Hyperspectral Data

2015 ◽  
Author(s):  
Chengfang Zhang ◽  
Zhiyuan Pan ◽  
Heng Dong ◽  
Fangjian He ◽  
Xingbang Hu
Keyword(s):  
Water Content ◽  
Spectral Index ◽  
Hyperspectral Data ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Remote Estimation

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

A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves

2017 ◽  
Vol 196 ◽  
pp. 13-27 ◽  
Author(s):  
Meihong Fang ◽  
Weimin Ju ◽  
Wenfeng Zhan ◽  
Tao Cheng ◽  
Feng Qiu ◽  
...  
Keyword(s):  
Water Content ◽  
Hyperspectral Data ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Spectral Similarity ◽  
Water Index

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 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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