Association of biomass production and canopy spectral reflectance indices in winter wheat

2009 ◽  
Vol 89 (3) ◽  
pp. 485-496 ◽  
Author(s):  
B. Prasad ◽  
M. A. Babar ◽  
B. F. Carver ◽  
W. R. Raun ◽  
A. R. Klatt
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Water Content ◽  
Biomass Production ◽  
Spectral Reflectance ◽  
Water Based ◽  
Canopy Spectral Reflectance ◽  
Reflectance Indices ◽  
Canopy Water ◽  
Spectral Reflectance Indices

Increased biomass production could be an important criterion for future grain yield improvement in wheat (Triticum aestivum L.). Quick assessment of genetic variations for biomass production may become a useful tool for wheat breeders. The potential of using canopy spectral reflectance indices (SRI) to assess genetic variation for biomass production in winter wheat was evaluated. Three experiments were conducted for 2 yr (2003-2004 and 2004-2005) at Oklahoma State University, Stillwater, OK. The first experiment consisted of 25 winter wheat cultivars, and the other two experiments contained two sets of 25 F4:6 and F4:7 recombinant inbred lines from two crosses developed by breeding programs in the great plains of the United States of America. Three groups of SRI (vegetation-based, pigment-based, and water-based) were tested for their ability to assess biomass production at three growth stages (booting, heading, and grainfilling). The water index and the normalized water indices gave stronger genetic correlations (P < 0.01) and linear relationship for biomass production compared with the vegetation-based and pigment-based indices. The strong association of water-based indices with biomass was related to the canopy water content of the genotypes. Canopy water content was significantly (P < 0.05) correlated with biomass production. A strong positive association (P < 0.05) of grain yield and dry biomass was observed at the heading and grainfilling stages. Our study demonstrated the potential of using water-based SRI as a breeding tool to estimate genetic variability and identify genotypes with higher biomass production, and could eventually help to achieve higher grain yield in winter wheat. Key words: Wheat; biomass; grain yield; spectral reflectance index

scholarly journals Genetic Analysis of Indirect Selection for Winter Wheat Grain Yield Using Spectral Reflectance Indices

Crop Science ◽  
2007 ◽  
Vol 47 (4) ◽  
pp. 1416-1425 ◽  
Author(s):  
B. Prasad ◽  
B. F. Carver ◽  
M. L. Stone ◽  
M. A. Babar ◽  
W. R. Raun ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Winter Wheat ◽  
Grain Yield ◽  
Spectral Reflectance ◽  
Indirect Selection ◽  
Wheat Grain ◽  
Selection For ◽  
Reflectance Indices ◽  
Spectral Reflectance Indices

scholarly journals Potential Use of Spectral Reflectance Indices as a Selection Tool for Grain Yield in Winter Wheat under Great Plains Conditions

Crop Science ◽  
2007 ◽  
Vol 47 (4) ◽  
pp. 1426-1440 ◽  
Author(s):  
B. Prasad ◽  
B. F. Carver ◽  
M. L. Stone ◽  
M. A. Babar ◽  
W. R. Raun ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Great Plains ◽  
Spectral Reflectance ◽  
Selection Tool ◽  
Reflectance Indices ◽  
Potential Use ◽  
Spectral Reflectance Indices

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.

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