Abstract
Abstract Background Leaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants. Results The results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND (1287,1673) and crop water stress index (CWSI) (R 2 = 0.86, RMSE = 0.01). Conclusions The machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.
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Claims 2
Claim · model-written summary
Combining multiple data sources improved rice leaf water content model accuracy by 6–44% against a single ND index.
- Taxon
- rice
Oryza sativae0 - Outcome
- model accuracy of rice LWC estimatione0
- Direction
- improvede0
- Value
- 6–44%e0
- Comparator
- single spectral index normalized difference index (ND)e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND)”
Claim · model-written summary
A GBDT model using ND and CWSI produced optimal rice leaf water prediction accuracy with R2 of 0.86 and RMSE of 0.01.
- Taxon
- riceOryza sativae0
- Outcome
- prediction accuracy of rice LWCe0
- Direction
- optimale0
- Value
- R 2 = 0.86, RMSE = 0.01e0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND (1287,1673) and crop water stress index (CWSI) (R 2 = 0.86, RMSE = 0.01)”