
A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing
- Author
- Jingjing Wang, Yu Lou, Wentao Wang, Suyi Liu, Haohui Zhang, Xin Hui, Yunling Wang, Haijun Yan and Wouter Maes (UGent)
- Organization
- Abstract
- Diagnosing water conditions timely and accurately is crucial for seasonal irrigation scheduling in crop production. The purpose of this study was to establish robust water deficit models of winter wheat in different growing seasons by combining unmanned aerial vehicle (UAV) multispectral and thermal images. In 2021 and 2022, a water deficit field experiment on winter wheat was carried out in Hebei Province, China. Five-band multispectral and thermal images were obtained with a UAV at six key growth stages of winter wheat. Fourteen vegetation indices (VIs) and two thermal indices (TIs) were calculated. Simultaneously, wheat stomatal conductance and soil water content were measured. On this basis, normalized stomatal conductance (NGS) and effective water content (EWC) were calculated. TIs had the highest correlation with NGS and EWC at early growth stages, whereas ratio vegetation index (RVI), modified simple ratio index (MSR) and normalized difference vegetation index (NDVI) were highly correlated at later stages. Partial least squares (PLS), support vector machine (SVM) and gradient boosting decision tree (GBDT) were used to predict NGS and EWC for each growth stage, with the data of 2021 as the training set and the data of 2022 as independent test set. In general, GBDT outperformed PLS and SVM, and NGS was better predicted than EWC. Including VIs and TIs effectively improved the estimation accuracy of the predictive models. The test set results of the NGS and EWC models built by GBDT achieved the best performance in flowering stage (coefficient of determination (R2) = 0.88, root mean square error (RMSE) = 0.08, normalized root mean square error (NRMSE) = 14.7%) and filling stage (R2 = 0.90, RMSE = 0.05, NRMSE = 15.9%), respectively. The models of the post-heading stage were better than those of the pre-heading stage for both NGS and EWC. This study provides a robust method for diagnosing water stress only using UAV remote sensing data.
- Keywords
- BE Clothier, Multispectral, Thermal, Normalized stomatal conductance, Effective water content, Machine learning, Robustness, VEGETATION INDEX, AREA INDEX, IRRIGATION, EVAPOTRANSPIRATION, TEMPERATURE
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HNZGFEX6PNV19A425GD5Z7A9
- MLA
- Wang, Jingjing, et al. “A Robust Model for Diagnosing Water Stress of Winter Wheat by Combining UAV Multispectral and Thermal Remote Sensing.” AGRICULTURAL WATER MANAGEMENT, vol. 291, 2024, doi:10.1016/j.agwat.2023.108616.
- APA
- Wang, J., Lou, Y., Wang, W., Liu, S., Zhang, H., Hui, X., … Maes, W. (2024). A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing. AGRICULTURAL WATER MANAGEMENT, 291. https://doi.org/10.1016/j.agwat.2023.108616
- Chicago author-date
- Wang, Jingjing, Yu Lou, Wentao Wang, Suyi Liu, Haohui Zhang, Xin Hui, Yunling Wang, Haijun Yan, and Wouter Maes. 2024. “A Robust Model for Diagnosing Water Stress of Winter Wheat by Combining UAV Multispectral and Thermal Remote Sensing.” AGRICULTURAL WATER MANAGEMENT 291. https://doi.org/10.1016/j.agwat.2023.108616.
- Chicago author-date (all authors)
- Wang, Jingjing, Yu Lou, Wentao Wang, Suyi Liu, Haohui Zhang, Xin Hui, Yunling Wang, Haijun Yan, and Wouter Maes. 2024. “A Robust Model for Diagnosing Water Stress of Winter Wheat by Combining UAV Multispectral and Thermal Remote Sensing.” AGRICULTURAL WATER MANAGEMENT 291. doi:10.1016/j.agwat.2023.108616.
- Vancouver
- 1.Wang J, Lou Y, Wang W, Liu S, Zhang H, Hui X, et al. A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing. AGRICULTURAL WATER MANAGEMENT. 2024;291.
- IEEE
- [1]J. Wang et al., “A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing,” AGRICULTURAL WATER MANAGEMENT, vol. 291, 2024.
@article{01HNZGFEX6PNV19A425GD5Z7A9, abstract = {{Diagnosing water conditions timely and accurately is crucial for seasonal irrigation scheduling in crop production. The purpose of this study was to establish robust water deficit models of winter wheat in different growing seasons by combining unmanned aerial vehicle (UAV) multispectral and thermal images. In 2021 and 2022, a water deficit field experiment on winter wheat was carried out in Hebei Province, China. Five-band multispectral and thermal images were obtained with a UAV at six key growth stages of winter wheat. Fourteen vegetation indices (VIs) and two thermal indices (TIs) were calculated. Simultaneously, wheat stomatal conductance and soil water content were measured. On this basis, normalized stomatal conductance (NGS) and effective water content (EWC) were calculated. TIs had the highest correlation with NGS and EWC at early growth stages, whereas ratio vegetation index (RVI), modified simple ratio index (MSR) and normalized difference vegetation index (NDVI) were highly correlated at later stages. Partial least squares (PLS), support vector machine (SVM) and gradient boosting decision tree (GBDT) were used to predict NGS and EWC for each growth stage, with the data of 2021 as the training set and the data of 2022 as independent test set. In general, GBDT outperformed PLS and SVM, and NGS was better predicted than EWC. Including VIs and TIs effectively improved the estimation accuracy of the predictive models. The test set results of the NGS and EWC models built by GBDT achieved the best performance in flowering stage (coefficient of determination (R2) = 0.88, root mean square error (RMSE) = 0.08, normalized root mean square error (NRMSE) = 14.7%) and filling stage (R2 = 0.90, RMSE = 0.05, NRMSE = 15.9%), respectively. The models of the post-heading stage were better than those of the pre-heading stage for both NGS and EWC. This study provides a robust method for diagnosing water stress only using UAV remote sensing data.}}, articleno = {{108616}}, author = {{Wang, Jingjing and Lou, Yu and Wang, Wentao and Liu, Suyi and Zhang, Haohui and Hui, Xin and Wang, Yunling and Yan, Haijun and Maes, Wouter}}, issn = {{0378-3774}}, journal = {{AGRICULTURAL WATER MANAGEMENT}}, keywords = {{BE Clothier,Multispectral,Thermal,Normalized stomatal conductance,Effective water content,Machine learning,Robustness,VEGETATION INDEX,AREA INDEX,IRRIGATION,EVAPOTRANSPIRATION,TEMPERATURE}}, language = {{eng}}, pages = {{14}}, title = {{A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing}}, url = {{http://doi.org/10.1016/j.agwat.2023.108616}}, volume = {{291}}, year = {{2024}}, }
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