Search claims · PlantClaims

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Inverted EWT showed good correspondence with measured EWT, with MAPE of 8.49%.

Taxon: Kauri seedlingst0
Outcome: mean absolute percentage error (MAPE)e1
Value: 8.49%e1
Comparator: measured EWTe1

Verbatim

t0
“Kauri Seedlings”
✓ located verbatim in the source · title, characters 35–50

e1
“a comparison of inverted and measured EWT showed good correspondence (mean absolute percentage error (MAPE) = 8.49%”
✓ located verbatim in the source · abstract, characters 2295–2410

Title

Early Detection of Water Stress in Kauri Seedlings Using Multitemporal Hyperspectral Indices and Inverted Plant Traits

Mark Jayson B. Felix; Russell Main; Michael S. Watt; et al. Remote Sensing 2025. doi:10.3390/rs17030463

Other claims from this paper (13)

Method / resourceobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Leaf REWT versus EWT had R2 of 0.86 in LOPEX93.

Outcome: leaf REWTe1
Value: 0.86e0
Comparator: EWTe1

Not reportedTaxon

Verbatim

e0
“0.86 for the Leaf Optical Properties Experiment (LOPEX'93) dataset”
✓ located verbatim in the source · abstract, characters 941–1007

e1
“leaf REWT was 3.3 times larger than the EWT”
✓ located verbatim in the source · abstract, characters 818–861

Title

Detection of leaf and canopy EWT by calculating REWT from reflectance spectra

Liangyun Liu; Jihua Wang; Wenjiang Huang; et al. International Journal of Remote Sensing 2010. doi:10.1080/01431160903085636

Other claims from this paper (6)

Method / resource Leaf and canopy REWT can be calculated from reflectance difference at 945 and 975 nm.
Method / resource Leaf REWT was 3.3 times EWT with R2 of 0.80 in the study dataset.
Method / resource Canopy REWT was 1.4 times EWT in the winter wheat dataset.
Method / resource Canopy REWT and EWT had significant R2 of 0.56 in the 2002 winter wheat dataset.
Method / resource Canopy REWT and EWT had R2 of 0.61 in the 2004 dataset.
Method / resource The ratio of REWT to EWT can calculate near-infrared multiple scattering optical pathlength.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Canopy REWT was 1.4 times EWT in the winter wheat dataset.

Taxon: winter wheatTriticum aestivume1
Outcome: canopy REWTe0
Direction: largere0
Value: 1.4 timese0
Comparator: EWTe0

Verbatim

e0
“the canopy REWT was 1.4 times larger than the EWT”
✓ located verbatim in the source · abstract, characters 1013–1062

e1
“winter wheat canopy spectral dataset”
✓ located verbatim in the source · abstract, characters 1102–1138

Title

Detection of leaf and canopy EWT by calculating REWT from reflectance spectra

Liangyun Liu; Jihua Wang; Wenjiang Huang; et al. International Journal of Remote Sensing 2010. doi:10.1080/01431160903085636

Other claims from this paper (6)

Method / resource Leaf and canopy REWT can be calculated from reflectance difference at 945 and 975 nm.
Method / resource Leaf REWT was 3.3 times EWT with R2 of 0.80 in the study dataset.
Method / resource Leaf REWT versus EWT had R2 of 0.86 in LOPEX93.
Method / resource Canopy REWT and EWT had significant R2 of 0.56 in the 2002 winter wheat dataset.
Method / resource Canopy REWT and EWT had R2 of 0.61 in the 2004 dataset.
Method / resource The ratio of REWT to EWT can calculate near-infrared multiple scattering optical pathlength.

Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

The developed indices can estimate EWT with simple laboratory measurements.

Outcome: estimate EWTe0

Not reportedComparatorValue

Verbatim

e0
“The newly developed indices can be used to estimate EWT using simple laboratory measurements”
✓ located verbatim in the source · abstract, characters 2026–2118

Title

Estimation of Leaf Water Content of Different Leaves from Different Species Using Hyperspectral Reflectance Data

Yasir QM; Zhang W Annals of Agricultural & Crop Sciences 2022. doi:10.26420/annagriccropsci.2021.1111

Other claims from this paper (3)

Method / resource EWT was more sensitive than GWC and PWC to leaf water status.
Method / resource The proposed hyperspectral indices outperformed other indices for EWT.
Method / resource Validation on LOPEX and ANGERS databases yielded better results for the indices.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

For dicotyledons, expressing Cw as EWT gave higher precision in the multiple-variable model.

Taxon: dicotyledonouse0
Outcome: precisione1
Comparator: EWTe0

Verbatim

e0
“dicotyledonous Cw is expressed as EWT”
✓ located verbatim in the source · abstract, characters 973–1010

e1
“Multiple variable model indicates that precision is higher”
✓ located verbatim in the source · abstract, characters 865–923

Title

Establishment of Leaf Water Content Models Based on Derivative Spectrum Variables

Yan Shen; Zheng Niu 2005

Other claims from this paper (6)

Method / resource D2, D6 and D8 identified leaf type.
Method / resource EWT correlated with spectral parameters more strongly than FMC did in the single-variable model.
Method / resource Monocotyledon correlations exceeded dicotyledon correlations.
Method / resource A simple unitary linear model showed the broadest forecast applicability.
Method / resource For monocotyledons, expressing Cw as FMC gave higher precision in the multiple-variable model.
Method / resource Overall forecast precision was greater for monocotyledons than dicotyledons.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

FMCw was best predicted, followed by EWT and FMCd.

Taxon: vegetationt0
Outcome: FMCw sees the best prediction, followed by EWT and lastly FMCde0
Comparator: EWT and lastly FMCde0

Verbatim

e0
“FMCw sees the best prediction, followed by EWT and lastly FMCd”
✓ located verbatim in the source · abstract, characters 1595–1657

t0
“vegetation leaf water content”
✓ located verbatim in the source · title, characters 9–38

Title

Deriving vegetation leaf water content from spectrophotometric data with orthogonal signal correction-partial least square regression

Jie Zhang; Jianjun Wu; Lei Zhou International Journal of Remote Sensing 2011. doi:10.1080/01431161.2010.524677

Other claims from this paper (8)

Method / resource OSC-PLSR predicted all leaf water indicators well.
Method / resource One latent variable greatly reduced OSC-PLSR complexity compared with simple PLSR.
Method / resource One-variable loading analysis retrieved vegetation moisture feature bands.
Observation EWT-sensitive bands occurred in one NIR and two SWIR regions.
Observation FMCw and FMCd-sensitive bands occurred in two SWIR regions.
Observation EWT moisture-insensitive band was near 1340 nm.
Observation FMCw moisture-insensitive band was near 1150 nm.
Observation FMCd moisture-insensitive band was near 1330 nm.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The ratio of REWT to EWT can calculate near-infrared multiple scattering optical pathlength.

Outcome: multiple scattering optical pathlengthe0
Comparator: ratio of REWT to EWTe0

Not reportedTaxon

Verbatim

e0
“the multiple scattering optical pathlength in the near-infrared (NIR) bands can also be calculated by the ratio of REWT to EWT”
✓ located verbatim in the source · abstract, characters 1420–1546

Title

Detection of leaf and canopy EWT by calculating REWT from reflectance spectra

Liangyun Liu; Jihua Wang; Wenjiang Huang; et al. International Journal of Remote Sensing 2010. doi:10.1080/01431160903085636

Other claims from this paper (6)

Method / resource Leaf and canopy REWT can be calculated from reflectance difference at 945 and 975 nm.
Method / resource Leaf REWT was 3.3 times EWT with R2 of 0.80 in the study dataset.
Method / resource Leaf REWT versus EWT had R2 of 0.86 in LOPEX93.
Method / resource Canopy REWT was 1.4 times EWT in the winter wheat dataset.
Method / resource Canopy REWT and EWT had significant R2 of 0.56 in the 2002 winter wheat dataset.
Method / resource Canopy REWT and EWT had R2 of 0.61 in the 2004 dataset.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Joint inversion of EWT and LMA achieved a satisfactory R2.

Taxon: Vegetationt0
Outcome: R2e0
Comparator: concurrently inverting EWT and LMAe0

Verbatim

t0
“Leaf Biochemistry Parameters Estimation of Vegetation”
✓ located verbatim in the source · title, characters 0–53

e0
“concurrently inverting EWT and LMA can achieve a satisfactory R2”
✓ located verbatim in the source · abstract, characters 1478–1542

Title

Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy

Lin Du; Jian Yang; Jia Sun; et al. Frontiers in Plant Science 2020. doi:10.3389/fpls.2020.00533

Other claims from this paper (3)

Method / resource FW and PCA neural network models improved inversion performance with fewer spectral characteristics.
Method / resource Separate inversion yielded optimal R2 for Cab and Car.
Method / resource Reflectance, transmittance, and combined spectra performed differently in parameter inversion.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Physical models outperformed source-trained models for EWT and LMA.

Outcome: EWT and LMAe0
Comparator: source-trained modelse0

Verbatim

e0
“physical models outperform source-trained models for EWT and LMA”
✓ located verbatim in the source · abstract, characters 1394–1458

Title

From Physical Models to Transfer Learning: Flexible Strategies for Leaf Functional Trait Estimation across Global Optical Properties Datasets

Shuaipeng Fei; Mian Chen; Zhi Wang; et al. Journal of Remote Sensing 2026. doi:10.34133/remotesensing.1050

Other claims from this paper (6)

Method / resource Fine-tuned transfer learning surpassed physical models, with GLM approaches reaching peak accuracy.
Method / resource Physical models sometimes exceeded optimal data-driven performance in specific datasets.
Method / resource Hybrid models generally underperformed pure data-driven methods.
Method / resource The model selection framework achieved 97% accuracy.
Method / resource Bayesian model averaging improved estimation accuracy of CHL, CAR and LMA.
Method / resource The authors developed guidelines for leaf trait estimation emphasizing limited manual measurements.

Method / resourceobservedoriginal resultno effectnot significant (as stated)measured

Claim · model-written summary

Canopy level correlations of SR2 with EWT and water potential were nonsignificant.

Organ / stage: canopye0
Outcome: EWT and water potentiale0
Comparator: SR2e0

Verbatim

e0
“Correlations with EWT and water potential at the canopy level for SR2 were nonsignificant”
✓ located verbatim in the source · abstract, characters 953–1042

Title

Evaluation of Hyperspectral Reflectance Indexes to Detect Grapevine Water Status in Vineyards

José R. Rodríguez-Pérez; David Riaño; Eli Carlisle; et al. American Journal of Enology and Viticulture 2007. doi:10.5344/ajev.2007.58.3.302

Other claims from this paper (9)

Method / resource At leaf level, SR2 correlated highly with equivalent water thickness, with R2 = 0.916.
Method / resource At leaf level, CRA MBD970 correlated highly with EWT, with R2 = 0.917.
Method / resource At leaf level, CRA BA1160 correlated highly with EWT, with R2 = 0.897.
Method / resource MTVI2 best fit canopy predawn water potential with R2 = 0.360.
Method / resource RGI best fit canopy midday stem water potential with R2 = 0.462.
Method / resource RGI estimated the canopy water potential difference with R2 = 0.619.
Method / resource SIPI estimated the canopy water potential difference with R2 = 0.541.
Method / resource CRA BA1600 estimated canopy water status with R2 = 0.477.
Method / resource CRA MBD970 estimated canopy water status with R2 = 0.509.

“EWT”

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