Claim · model-written summary
IVSO fusion features gave the highest leaf water prediction accuracy.
- Taxon
- winter wheat
Triticum aestivumt0 - Treatment
- Fusion Features selected by IVSOe0
- Outcome
- accuracye0
- Value
- R 2 Test = 0.956e0
- Comparator
- higheste0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Fusion Features selected by IVSO achieved the highest accuracy, evaluated by R 2 , RMSE, and Akaike information criterion (AIC) (R 2 Train = 0.960, RMSE Train = 0.016, AIC Train = -1010.061; R 2 Test = 0.956, RMSE Test = 0.015, AIC Test = -98.362)”
t0“winter wheat leaf water content”
t1“winter wheat leaf water content”
Title
Zhigang Wang; Sha Yang; Qing Liang; et al. International Journal of Applied Earth Observation and Geoinformation 2026. doi:10.1016/j.jag.2026.105110
Other claims from this paper (6)
- Method / resource First derivative preprocessing, IVSO selection and BPNN modeling yielded the best leaf water monitoring results.
- Method / resource Band replacement improved leaf water model robustness.
- Method / resource Band fusion improved leaf water model robustness.
- Method / resource Band replacement addressed spectral sensitivity issues.
- Method / resource Band fusion addressed spectral sensitivity issues.
- Method / resource The proposed replacement and constrained fusion framework improved leaf water estimation accuracy.