Claim · model-written summary
Adding land surface emissivity significantly improved LAI prediction accuracy.
- Taxon
- Forestt0
- Outcome
- prediction accuracy of the LAIe0
- Direction
- improvede0
- Value
- R2CV = 0.81, RMSECV = 0.63 m2m−2e0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingQualifier
Verbatim
e0“The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2).”
t0“Forest Leaf Area Index”
Title
Elnaz Neinavaz; Roshanak Darvishzadeh; Andrew K. Skidmore; et al. Remote Sensing 2019. doi:10.3390/rs11040390
Other claims from this paper (4)
- Association Leaf area index related more strongly to emissivity than surface temperature.
- Method / resource Vegetation indices predicted LAI with relatively low accuracy.
- Method / resource The modified vegetation index was the most accurate index for LAI prediction.
- Method / resource The neural network using spectral data improved LAI prediction accuracy.