Abstract
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 R 2 of 0.85 and R 2 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 (R 2 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.
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Claims 5
Claim · model-written summary
Canopy spectral reflectance increased as irrigation times increased, especially at 750–1350 nm.
- Taxon
- wheat
Triticum aestivumt0 - Treatment
- irrigation timese0
- Outcome
- canopy spectral reflectancee0
- Direction
- increasede0
- Qualifier
- near-infrared band (750–1350 nm)e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750–1350 nm).”
t0“winter wheat”
Claim · model-written summary
The new DVI index predicted leaf water content more accurately than the existing spectral index, with calibration and validation R² of 0.85 and 0.78.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- prediction accuracye0
- Direction
- highere0
- Value
- 0.85e0
- Qualifier
- 0.78 for the calibration and validation, respectivelye0
- Comparator
- existing spectral indexe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R 2 of 0.85 and R 2 of 0.78 for the calibration and validation, respectively.”
t0“winter wheat”
Claim · model-written summary
The characteristic-band model accuracy was not significantly lower than full-spectrum model accuracy.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- accuracy of the modele0
- Direction
- not significantly lowere0
- Comparator
- full spectrum-based modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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.”
t0“winter wheat”
Claim · model-written summary
ERT-x-Lw performed best among the tested models, with calibration R² and RMSE of 0.88 and 1.46 and validation values of 0.84 and 1.62.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- performed the beste0
- Direction
- performed the beste0
- Value
- 0.88 and 1.46e0
- Qualifier
- 0.84 and 1.62 for the calibration and validation, respectivelye0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Among these models, the ERT- x-Lw model performed the best (R 2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively).”
t0“winter wheat”
Claim · model-written summary
ERT-x-Lw estimated leaf water content more accurately than DVI.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- accuracy of the LWC estimation modele0
- Direction
- highere0
- Comparator
- DVI (R1185, R1307)e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307).”
t0“winter wheat”