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EcoSIS UW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data (LMA_gDW_cm2)

leaf · NIR

EcoSIS UW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data (LMA_gDW_cm2). v2.0 standardized NIRS package: 1 spectral source(s), 1 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2leaf
74
samples
2,151
wavelengths
1
sources
1
targets
101
metadata
NIR
family

Dataset property explorer

Mean profile risk0.46
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
EcoSIS UW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data (LMA_gDW_cm2) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS UW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data (LMA_gDW_cm2) profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 0.97PCA outliers: 0.36reference: 0.60repeatability: 0.23structure: 0.55EcoSIS UW-BNL N…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA0.36
Distance à la référence0.60
Répétabilité0.23
Baseline / forme0.97
Structure multi-régimes0.55
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.730.73Erreur calibration / référenc…Erreur calibration / référence blanche: 0.610.61Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.580.58Signature VERA25-likeSignature VERA25-like: 0.550.55Fond différentFond différent: 0.540.54Différence de sonde / géométr…Différence de sonde / géométrie: 0.510.51Mélange feuille + fondMélange feuille + fond: 0.400.40Spectre saturé / clippingSpectre saturé / clipping: 0.380.38
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.73forteSpike rate 1.00, Jump rate 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur calibration / référence blancheX0.61moyenneartefacts locaux 1.00, Baseline/mean/area 0.97, RMS/SAM référence 0.60Décalage systématique entre campagnes, instruments ou référence blanche.
Erreur interpolation / rééchantillonnageX0.58moyenneSpike rate 1.00, Jump rate 1.00, SNR normal/élevé 1.00Artefacts numériques ou traitement spectral incorrect.
Signature VERA25-likeX0.55moyenneSpike rate 1.00, Jump rate 1.00, RMS/SAM référence 0.60Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Fond différentX0.54moyenneBaseline/mean/area 0.97, RMS/SAM référence 0.60, Mahalanobis / T2 0.36Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Différence de sonde / géométrieX0.51moyenneBaseline/mean/area 0.97, RMS/SAM référence 0.60, Mahalanobis / T2 0.36Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Mélange feuille + fondX0.40faibleBaseline/mean/area 0.97, RMS/SAM référence 0.60, Mahalanobis / T2 0.36Couverture partielle du spot; contribution du fond ou du support.
Spectre saturé / clippingX0.38faibleJump rate 1.00, Baseline/mean/area 0.97, PCA Q 0.34Détecteur saturé ou dynamique insuffisante.

Spectral sources

UW-BNL_NASA_HyspIRI_Airborne_Campaign_Compiled_Leaf_and_Canopy_Spectra

X · NIR · SpecEvo_PSM3500
UW-BNL_NASA_HyspIRI_Airborne_Campaign_Compiled_Leaf_and_Canopy_Spectra spectra0.00.20.40.60.801,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm350nm — median 0.1046 (q25–q75 0.05206–0.2049)365nm — median 0.09286 (q25–q75 0.04716–0.1375)381nm — median 0.07345 (q25–q75 0.04331–0.09709)396nm — median 0.04926 (q25–q75 0.04014–0.06571)412nm — median 0.04678 (q25–q75 0.03914–0.06432)427nm — median 0.0471 (q25–q75 0.04032–0.05937)443nm — median 0.05062 (q25–q75 0.04332–0.06394)458nm — median 0.0518 (q25–q75 0.04437–0.07093)474nm — median 0.05229 (q25–q75 0.04464–0.07323)489nm — median 0.05299 (q25–q75 0.04443–0.07528)505nm — median 0.05933 (q25–q75 0.04814–0.07981)520nm — median 0.08642 (q25–q75 0.07118–0.1103)536nm — median 0.1228 (q25–q75 0.1084–0.1435)551nm — median 0.134 (q25–q75 0.1181–0.1538)567nm — median 0.1224 (q25–q75 0.1041–0.142)582nm — median 0.1005 (q25–q75 0.08071–0.1166)597nm — median 0.09086 (q25–q75 0.07382–0.1085)613nm — median 0.0798 (q25–q75 0.06672–0.09878)628nm — median 0.0737 (q25–q75 0.06109–0.09088)644nm — median 0.06553 (q25–q75 0.0552–0.08377)659nm — median 0.05672 (q25–q75 0.0476–0.07361)675nm — median 0.05174 (q25–q75 0.04361–0.06689)690nm — median 0.06811 (q25–q75 0.05501–0.08168)706nm — median 0.1918 (q25–q75 0.1693–0.2267)721nm — median 0.3448 (q25–q75 0.3241–0.3811)737nm — median 0.4639 (q25–q75 0.4352–0.5044)752nm — median 0.5063 (q25–q75 0.4715–0.5457)768nm — median 0.5127 (q25–q75 0.4798–0.5577)783nm — median 0.515 (q25–q75 0.4814–0.5598)799nm — median 0.5162 (q25–q75 0.4822–0.5601)814nm — median 0.5162 (q25–q75 0.4828–0.56)829nm — median 0.5159 (q25–q75 0.4837–0.5605)845nm — median 0.5152 (q25–q75 0.4852–0.5602)860nm — median 0.5159 (q25–q75 0.4852–0.5593)876nm — median 0.5156 (q25–q75 0.4845–0.5581)891nm — median 0.5148 (q25–q75 0.4834–0.5567)907nm — median 0.5136 (q25–q75 0.4828–0.5547)922nm — median 0.5122 (q25–q75 0.4828–0.5528)938nm — median 0.5083 (q25–q75 0.4798–0.5492)953nm — median 0.5002 (q25–q75 0.4756–0.5421)969nm — median 0.4932 (q25–q75 0.464–0.5327)984nm — median 0.4926 (q25–q75 0.4609–0.5298)1,000nm — median 0.4897 (q25–q75 0.4571–0.5234)1,015nm — median 0.4947 (q25–q75 0.4662–0.5328)1,031nm — median 0.5002 (q25–q75 0.4709–0.5379)1,046nm — median 0.5029 (q25–q75 0.4745–0.5394)1,062nm — median 0.5031 (q25–q75 0.4751–0.5416)1,077nm — median 0.5033 (q25–q75 0.475–0.5429)1,092nm — median 0.5026 (q25–q75 0.4739–0.5415)1,108nm — median 0.5001 (q25–q75 0.4715–0.5377)1,123nm — median 0.496 (q25–q75 0.466–0.5321)1,139nm — median 0.4804 (q25–q75 0.4477–0.5181)1,154nm — median 0.4622 (q25–q75 0.4318–0.4953)1,170nm — median 0.452 (q25–q75 0.4244–0.4828)1,185nm — median 0.4463 (q25–q75 0.42–0.4771)1,201nm — median 0.4444 (q25–q75 0.4187–0.4749)1,216nm — median 0.4451 (q25–q75 0.4208–0.4787)1,232nm — median 0.4496 (q25–q75 0.424–0.4834)1,247nm — median 0.4525 (q25–q75 0.4271–0.4883)1,263nm — median 0.454 (q25–q75 0.4276–0.4895)1,278nm — median 0.4534 (q25–q75 0.4254–0.4883)1,294nm — median 0.4494 (q25–q75 0.421–0.4838)1,309nm — median 0.4408 (q25–q75 0.412–0.47)1,324nm — median 0.4222 (q25–q75 0.3946–0.4484)1,340nm — median 0.3973 (q25–q75 0.3728–0.4268)1,355nm — median 0.3755 (q25–q75 0.3512–0.4051)1,371nm — median 0.3466 (q25–q75 0.321–0.3769)1,386nm — median 0.2865 (q25–q75 0.2552–0.3164)1,402nm — median 0.1975 (q25–q75 0.1716–0.2239)1,417nm — median 0.1473 (q25–q75 0.1262–0.1692)1,433nm — median 0.1267 (q25–q75 0.1084–0.146)1,448nm — median 0.1216 (q25–q75 0.1024–0.1412)1,464nm — median 0.1227 (q25–q75 0.1021–0.142)1,479nm — median 0.1348 (q25–q75 0.11–0.1539)1,495nm — median 0.1491 (q25–q75 0.1268–0.1748)1,510nm — median 0.1678 (q25–q75 0.1422–0.1938)1,526nm — median 0.1856 (q25–q75 0.1606–0.2152)1,541nm — median 0.2018 (q25–q75 0.1739–0.2351)1,556nm — median 0.2178 (q25–q75 0.188–0.2514)1,572nm — median 0.2326 (q25–q75 0.2017–0.2655)1,587nm — median 0.2448 (q25–q75 0.2134–0.2777)1,603nm — median 0.2561 (q25–q75 0.224–0.2884)1,618nm — median 0.2638 (q25–q75 0.234–0.2963)1,634nm — median 0.2715 (q25–q75 0.2415–0.3026)1,649nm — median 0.2765 (q25–q75 0.2433–0.3054)1,665nm — median 0.2767 (q25–q75 0.2439–0.3063)1,680nm — median 0.2748 (q25–q75 0.2442–0.3045)1,696nm — median 0.2693 (q25–q75 0.238–0.2986)1,711nm — median 0.26 (q25–q75 0.2291–0.2921)1,727nm — median 0.2516 (q25–q75 0.222–0.2845)1,742nm — median 0.2469 (q25–q75 0.2164–0.2783)1,758nm — median 0.237 (q25–q75 0.207–0.2698)1,773nm — median 0.2296 (q25–q75 0.2001–0.2629)1,788nm — median 0.2262 (q25–q75 0.1979–0.2585)1,804nm — median 0.2268 (q25–q75 0.1976–0.2593)1,819nm — median 0.2286 (q25–q75 0.1995–0.2603)1,835nm — median 0.2278 (q25–q75 0.1987–0.258)1,850nm — median 0.2146 (q25–q75 0.1872–0.2443)1,866nm — median 0.1764 (q25–q75 0.1515–0.2027)1,881nm — median 0.1105 (q25–q75 0.09395–0.1274)1,897nm — median 0.04688 (q25–q75 0.04048–0.05462)1,912nm — median 0.03446 (q25–q75 0.02766–0.04145)1,928nm — median 0.03294 (q25–q75 0.0252–0.03995)1,943nm — median 0.0337 (q25–q75 0.02608–0.04168)1,959nm — median 0.0367 (q25–q75 0.02968–0.04469)1,974nm — median 0.04051 (q25–q75 0.0348–0.04954)1,990nm — median 0.04618 (q25–q75 0.04027–0.05624)2,005nm — median 0.05395 (q25–q75 0.04614–0.06398)2,021nm — median 0.06063 (q25–q75 0.05131–0.07277)2,036nm — median 0.06913 (q25–q75 0.05631–0.08142)2,051nm — median 0.07449 (q25–q75 0.06067–0.08666)2,067nm — median 0.07956 (q25–q75 0.06464–0.09513)2,082nm — median 0.08607 (q25–q75 0.06851–0.1047)2,098nm — median 0.09241 (q25–q75 0.07301–0.1123)2,113nm — median 0.09723 (q25–q75 0.07662–0.1182)2,129nm — median 0.1009 (q25–q75 0.08067–0.1244)2,144nm — median 0.1038 (q25–q75 0.08449–0.129)2,160nm — median 0.1089 (q25–q75 0.08868–0.1341)2,175nm — median 0.113 (q25–q75 0.09277–0.137)2,191nm — median 0.118 (q25–q75 0.097–0.1422)2,206nm — median 0.1213 (q25–q75 0.1001–0.147)2,222nm — median 0.1234 (q25–q75 0.1016–0.1484)2,237nm — median 0.1197 (q25–q75 0.09894–0.1454)2,253nm — median 0.1111 (q25–q75 0.09138–0.1346)2,268nm — median 0.1012 (q25–q75 0.08194–0.1268)2,283nm — median 0.09521 (q25–q75 0.07714–0.1182)2,299nm — median 0.0892 (q25–q75 0.07083–0.1115)2,314nm — median 0.08463 (q25–q75 0.06749–0.1058)2,330nm — median 0.08114 (q25–q75 0.06402–0.1001)2,345nm — median 0.07761 (q25–q75 0.05996–0.09317)2,361nm — median 0.07464 (q25–q75 0.05799–0.08758)2,376nm — median 0.06945 (q25–q75 0.05372–0.08153)2,392nm — median 0.06474 (q25–q75 0.04966–0.07482)2,407nm — median 0.05806 (q25–q75 0.04682–0.06866)2,423nm — median 0.05397 (q25–q75 0.04253–0.06318)2,438nm — median 0.04931 (q25–q75 0.04132–0.05985)2,454nm — median 0.04522 (q25–q75 0.03739–0.05515)2,469nm — median 0.04069 (q25–q75 0.03358–0.04976)2,485nm — median 0.04014 (q25–q75 0.03327–0.04989)2,500nm — median 0.04397 (q25–q75 0.03679–0.05408)

Sampling

Wavelengths2,151
Axis range350–2,500 nm
Mean spacing1 nm
Griduniform
Observations97

Signal & quality

Value range0.0144 – 0.731
Mean range0.0334 – 0.526
Mean level0.2353
Area506
PTP0.4927
Noise RMS3.5157e-05
SNR6.7e+03
SNR dB8e+01 dB
Dynamic range0.493
Smoothness0.000621
Saturated0.0%
X-outliers37

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count12,466
Spike rate5.98%
Jump count7,791
Jump rate3.74%
Clip fraction0.00%

Shape & reference

Baseline slope-0.23867
Curvature RMS0.00058107
D1 RMS0.0018132
RMS to mean0.032671
RMS p950.058961
SAM to mean0.078836
SAM p950.13036
Affine offset p950.039628
Affine gain p95 Δ0.22952
Affine residual p950.031086
Xcorr lag p953

Outliers & repeatability

PCA Q p95/median2.8
Hotelling T2 p95/median2.1
Mahalanobis H p95/median1.5
Repeat groups23
RMS intra-ID0.011366
SAM intra-ID0.026164
CV intra-ID0.040607

Dimensionality (PCA)

Effective rank2.9
PCs → 95% var4
PCs → 99% var7
Top-10 cum. var99.7%
Computed metric scores 29worst 1.00
FamilleMétrique calculéeValeurScoreNiveauInterprétation datasetCauses typiquesCalcul / scoring
Intégrité des donnéesNaN ratiointegrity.nan_ratio0%0.00faibleSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countintegrity.inf_count00.00faibleNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratiointegrity.zero_ratio0%0.00faibleNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceamplitude.mean_reflectance0.235290.97fortValeur atypique: Trop clair / fond visible ou Trop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveamplitude.area_under_curve506.020.97fortValeur atypique: Différence d'éclairement ou NormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)amplitude.peak_to_peak0.492680.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance0.0332610.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms3.5157e-050.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr6692.60.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min27.1670.18faibleZone fiableDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countartefacts.spike_count12,4661.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate5.98%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count7,7911.00fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate3.74%1.00fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.000959%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope-0.238670.97fortDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.000581070.12faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.00181320.07faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio2.75360.34faibleConformeArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio2.12950.27faibleCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio1.45780.36faiblePopulation normaleDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p950.0589610.48moyenSpectre différentDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)reference.sam_to_mean_spectrum_p950.130360.37faibleSimilaireFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDrepeatability.rms_intra_id0.0113660.23faibleStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDrepeatability.sam_intra_id0.0261640.17faibleStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDrepeatability.cv_intra_id0.0406070.16faibleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densitystructure.pca_score_density1.34260.55moyenSous-populationsLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)structure.local_outlier_factor_p951.6530.33faiblePopulation normaleCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scorestructure.isolation_forest_score_p950.547550.55moyenSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-5.0-2.50.02.55.07.5-2-1012PC1 -1.911 · PC2 -1.418PC1 -0.3501 · PC2 -0.3882PC1 -0.3904 · PC2 -0.7068PC1 -1.23 · PC2 -0.8082PC1 -0.944 · PC2 -0.9775PC1 -1.166 · PC2 -1.014PC1 -0.9313 · PC2 -0.8365PC1 -1.325 · PC2 -1.154PC1 0.9902 · PC2 -1.431PC1 1.593 · PC2 -0.5169PC1 1.071 · PC2 -1.041PC1 1.074 · PC2 -0.8298PC1 1.545 · PC2 -0.7968PC1 1.588 · PC2 -0.6853PC1 -0.3322 · PC2 -1.632PC1 1.571 · PC2 -0.3588PC1 0.9264 · PC2 -1.176PC1 0.3301 · PC2 -0.936PC1 0.4911 · PC2 -1.127PC1 0.8224 · PC2 -0.8718PC1 1.516 · PC2 -0.1263PC1 0.2485 · PC2 -0.4666PC1 1.874 · PC2 1.123PC1 0.6723 · PC2 0.6613PC1 1.468 · PC2 -0.6457PC1 1.504 · PC2 0.1585PC1 1.047 · PC2 0.5746PC1 1.919 · PC2 0.4234PC1 0.1662 · PC2 -0.2717PC1 5.177 · PC2 0.4499PC1 0.7094 · PC2 0.5877PC1 1.73 · PC2 0.6633PC1 -1.55 · PC2 -1.374PC1 1.527 · PC2 1.912PC1 -1.316 · PC2 1.373PC1 -1.852 · PC2 1.769PC1 -1.97 · PC2 0.9003PC1 -0.7951 · PC2 1.419PC1 -3.203 · PC2 1.045PC1 0.6463 · PC2 1.201PC1 1.017 · PC2 -1.162PC1 0.6379 · PC2 -1.135PC1 0.1175 · PC2 -1.499PC1 1.103 · PC2 1.385PC1 0.5351 · PC2 -0.9095PC1 -0.1674 · PC2 -0.8725PC1 0.03611 · PC2 -0.7268PC1 1.14 · PC2 -0.8605PC1 -0.07222 · PC2 0.3835PC1 0.1795 · PC2 0.02791PC1 0.1215 · PC2 -1.019PC1 -0.4434 · PC2 -1.132PC1 -0.4434 · PC2 -1.132PC1 0.9131 · PC2 -0.2298PC1 -2.534 · PC2 -0.9414PC1 0.6499 · PC2 -0.09151PC1 -2.23 · PC2 -0.6172PC1 0.1556 · PC2 -0.4072PC1 -0.946 · PC2 -0.2927PC1 -0.6146 · PC2 0.6217PC1 -1.938 · PC2 0.4596PC1 0.8189 · PC2 0.696PC1 0.8595 · PC2 0.09066PC1 -0.5444 · PC2 -0.6898PC1 0.3207 · PC2 -0.2683PC1 -1.022 · PC2 -0.6302PC1 0.3597 · PC2 -0.9502PC1 1.233 · PC2 1.585PC1 0.5246 · PC2 1.772PC1 1.339 · PC2 1.891PC1 2.243 · PC2 1.539PC1 1.143 · PC2 1.006PC1 -0.8808 · PC2 1.297PC1 -0.2191 · PC2 1.218PC1 -0.4165 · PC2 0.9399PC1 -4.722 · PC2 0.6101PC1 0.4697 · PC2 -0.6526PC1 -1.194 · PC2 -1.104PC1 -2.272 · PC2 1.034PC1 -0.5658 · PC2 -0.9241PC1 -0.2148 · PC2 0.2572PC1 -3.745 · PC2 0.7175PC1 1.233 · PC2 0.4545PC1 1.461 · PC2 -0.09503PC1 0.6949 · PC2 0.006244PC1 -0.1954 · PC2 0.9023PC1 -0.954 · PC2 0.9178PC1 0.102 · PC2 0.8217PC1 -0.01716 · PC2 0.9733PC1 -0.2669 · PC2 0.8753PC1 -0.328 · PC2 -0.176PC1 -1.014 · PC2 0.9812PC1 -1.744 · PC2 0.724PC1 0.8506 · PC2 0.1266PC1 -1.292 · PC2 0.7838PC1 -2.359 · PC2 0.4713PC1 0.1526 · PC2 0.2794PC1 (61.9%)PC2 (27.3%)97 scores
PCA explained variance0%25%50%75%100%PC1: 61.9% (cumulative 61.9%)1PC2: 27.3% (cumulative 89.2%)2PC3: 4.8% (cumulative 94.0%)3PC4: 3.0% (cumulative 97.0%)4PC5: 1.2% (cumulative 98.1%)5PC6: 0.7% (cumulative 98.8%)6PC7: 0.4% (cumulative 99.2%)7PC8: 0.3% (cumulative 99.5%)8PC9: 0.1% (cumulative 99.6%)9PC10: 0.1% (cumulative 99.7%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
X-Y spectral correlation 1
X · LMA_gDW_cm2 spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
LMA_gDW_cm20.7682,3020.45740.3%

Metric interpretation reference

Metric catalog 29
FamilleMétriqueCe qu’elle détecteForte valeur =Faible valeur =Causes typiquesCalcul / score
Intégrité des donnéesNaN ratioDonnées manquantesSpectre corrompuSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countValeurs infiniesCorruptionNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratioColonnes ou cellules nullesSpectre tronquéNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceNiveau moyenTrop clair / fond visibleTrop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveIntensité globaleDifférence d'éclairementNormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)DynamiqueVariabilité forteSpectre platSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceVariabilité spectraleNormal ou hétérogèneSpectre platMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSBruit haute fréquenceBruitéStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRQualité signalBon signalMauvais signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRBruit localiséZone fiableZone problématiqueDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countPics étroitsArtefactsSpectre propreCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateDensité de picsSpectre suspectNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countDiscontinuitésRaccord détecteurContinuSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateFréquence de sautsProblème spectralNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionSaturationClippingNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopePente globaleDériveStableÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSCourbureForme inhabituelleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSVariabilité localeSpectre structuréPlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)Non expliqué par PCASpectre atypiqueConformeArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²Extrême dans PCAExtrême mais cohérentCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis HDistance au nuageOutlier globalPopulation normaleDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumDistance moyenneSpectre différentTypiqueDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)Différence de formeForme différenteSimilaireFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDReproductibilitéMauvaise répétabilitéStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDVariation de formeInstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDVariabilité interneMauvais contrôleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densityClustersSous-populationsHomogèneLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)Anomalie localeSpectre isoléPopulation normaleCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scoreAnomalie globaleSpectre atypiqueNormalDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions
TechnologieAdaptations / métriquesAnomalies cibléesCommentaire pratique
UV-Vis 300-1000 nmBaseline, pente globale, dérive aux bords 300-350 et 900-1000; métriques par zonesLumière parasite, mauvais blanc, saturation, faible signal aux extrémitésLes bords sont souvent instables; calculer aussi des scores edge/middle.
UV-Vis 300-1000 nmSaturation / clipping proche absorbance max ou réflectance maxSignal écrêtéTrès important si absorption forte.
UV-Vis 300-1000 nmRed-edge, position de maximum, ratios de bandes si végétalDécalage biologique ou artefact optiqueAide à distinguer changement réel et problème d'acquisition.
UV-Vis 300-1000 nmSmoothness / roughness indexBruit haute fréquenceSouvent plus informatif que le SNR seul.
MIR / ATR-FTIRATR contact quality index: intensité globale, aire totale, profondeur des bandes clésMauvais contact cristal-échantillonCrucial: beaucoup d'anomalies viennent du contact ATR.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsMauvaise correction atmosphériquePics parasites fréquents.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, dérive baselineTrès utile avant PCA.
MIR / ATR-FTIRPeak position shiftMauvais alignement spectral / calibrationImportant en FTIR car de petits shifts comptent.
MIR / ATR-FTIRBand area ratios sur bandes connuesSpectre chimiquement incohérentÀ adapter par matrice: polysaccharides, protéines, lipides, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Injection faible, ionisation instableÉquivalent MS du niveau global spectral.
HS-MSNombre de pics détectésSpectre pauvre ou trop bruitéTrop peu = mauvais signal; trop = bruit/contamination.
HS-MSMass accuracy / m/z driftProblème calibration masseFondamental en HRMS.
HS-MSRetention time drift si LC/GC-MSDérive chromatographiqueÀ suivre sur standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overComparer échantillons vs blancs.
HS-MSInternal standard CVVariabilité instrumentaleTrès robuste si standards disponibles.
HS-MSMissingness par featureInstabilité de détectionCrucial pour filtrer les variables.
Avec répétitionsRMS intra-échantillonRépétabilité globaleApplicable à toutes les technologies.
Avec répétitionsSAM / corrélation intra-échantillonRépétabilité de formeTrès utile pour spectres.
Avec répétitionsCV intra-échantillon par bande / featureRépétabilité localeDétecte les zones instables.
Avec répétitionsICC ou variance componentsPart variance échantillon vs techniqueTrès utile si plusieurs répétitions par sample.
Avec répétitionsDistance au centroïde intra-IDRépétition aberrantePermet de flagger la mauvaise répétition plutôt que le sample entier.
Bug-hunting / supervised audits
Famille de bug potentielMéthodes à ajouterCe que ça détecteÉtat dans l’explorateur
Shift spectral globalCorrélation spectre moyen inter-dataset, DTW, cross-correlation, comparaison positions de picsDécalage en longueur d'onde, mauvais alignement, interpolation différentePartiellement calculé: cross-correlation lag et dispersion des positions de pics vs spectre moyen.
Baseline / offset / gainRégression chaque spectre vs spectre moyen: x = a + b ref + residual; suivi de a, b, RMS résiduelOffset additif, effet multiplicatif, dérive de baselineCalculé dans reference.affine_*.
Mélange de lignes / mauvais appariement X-M-YVérification index, hash des lignes, duplication ID, distance spectrale intra-ID, labels incohérentsLignes mélangées, metadata mal alignées, Y attribué au mauvais spectrePartiellement couvert par répétabilité intra-ID; checks index/hash à ajouter au pipeline canonical.
Fuite d'information / répétitions mal splitéesGroupKFold par sample_id vs StratifiedKFold random; audit des partitions par sample_idPerformance artificiellement bonne due aux répétitionsNécessite splits et benchmark modèle; non calculé par la carte descriptive.
Label bugsÉchantillons proches en X mais Y différents, confident learning, erreurs systématiques FP/FNY inversés, erreurs de saisie, classes ambiguësNécessite Y et/ou modèle; recommandé pour l'explorateur supervisé.
Sous-domaines cachésPCA/UMAP/t-SNE + clustering non supervisé + association avec dataset/Y/date/operatorLots, campagnes, sondes, backgrounds non renseignésPartiellement calculé par structure PCA/LOF; UMAP/t-SNE hors carte statique.
Artefacts localisés inconnusCarte wavelength x dataset: différence moyenne, différence variance, KS par longueur d'ondeRégions spectrales anormales non anticipéesÀ calculer au niveau banque quand plusieurs datasets partagent un axe spectral.
Ruptures instrumentalesDiscontinuités dans dérivées, changepoint detectionSplice, raccord détecteur, saut local non prévuCalculé par jump/spike rates; changepoint plus avancé à ajouter.
Mélange / contamination spectraleNMF / unmixing / reconstruction par convex hullComposante externe: fond, plastique, solNon calculé automatiquement; nécessite hypothèses de composants ou grande bibliothèque.
Features instables mais prédictivesImportance modèle vs instabilité QC par variableModèle qui apprend un artefact plutôt qu'un signal biologiqueNécessite modèle supervisé; recommandé pour rapports de benchmark.

Variables

Targets 1

LMA_gDW_cm2

target · numeric
LMA_gDW_cm2 distribution05100.003656 – 0.005141: 50.005141 – 0.006626: 80.006626 – 0.008111: 40.008111 – 0.009595: 90.009595 – 0.01108: 40.01108 – 0.01257: 30.01257 – 0.01405: 70.01405 – 0.01553: 50.01553 – 0.01702: 10.01702 – 0.0185: 10.0185 – 0.01999: 20.01999 – 0.02147: 10.02147 – 0.02296: 10.02296 – 0.02444: 30.02444 – 0.02593: 40.02593 – 0.02741: 20.02741 – 0.0289: 40.0289 – 0.03038: 10.03038 – 0.03187: 20.03187 – 0.03335: 20.03335 – 0.03484: 20.03484 – 0.03632: 00.03632 – 0.03781: 20.03781 – 0.03929: 10.000.010.020.030.04
n / missing74 / 0
Mean ± SD0.01636 ± 0.00991
Median0.0138
Range0.003656 – 0.03929
CV0.606
Skew / kurtosis0.61 / -0.88
Normal?no

Metadata 69

Unique_Sample_ID

metadata · categorical
n / missing74 / 0
Classes74
Balance (entropy)1
Imbalance ratio1
Top classCVARS_Plot1_CAAN4_L1T1N_LC_RG_41430 (1)

Spectra_Name

metadata · categorical
n / missing74 / 0
Classes74
Balance (entropy)1
Imbalance ratio1
Top classCVARS_Plot1_CAAN4_L1T1N_LC_RG (1)

Sample_Name

metadata · categorical
Sample_Name classesMCON_Tower_QUKE_L1T1MCON_Tower_QUKE_L1T1: 22SJER_Tower_QUDO_L1T1SJER_Tower_QUDO_L1T1: 22SJER_Plot361_CECU_L1T4chemSJER_Plot361_CECU_L1T4chem: 22CVARS_Plot1_CAAN4_L1T1NCVARS_Plot1_CAAN4_L1T1N: 11CVARS_Plot1_CAAN4_L2M1OCVARS_Plot1_CAAN4_L2M1O: 11CVARS_Plot2_CAAN4_L1T1NCVARS_Plot2_CAAN4_L1T1N: 11CVARS_Plot2_CAAN4_L1T2NCVARS_Plot2_CAAN4_L1T2N: 11CVARS_Plot2_CAAN4_L2T2NCVARS_Plot2_CAAN4_L2T2N: 11CVARS_na_Grape_L1T1chemCVARS_na_Grape_L1T1chem: 11CVARS_Plot1_Grape_L1TNCVARS_Plot1_Grape_L1TN: 11+10 more+10 more: 1010
n / missing74 / 0
Classes71
Balance (entropy)1
Imbalance ratio2
Top classMCON_Tower_QUKE_L1T1 (2)

Site

metadata · categorical
Site classesSJERSJER: 2424MCONMCON: 1212CVARSCVARS: 1111LomaRidgeLomaRidge: 99SJJRSJJR: 99SCRecSCRec: 55KAREKARE: 44
n / missing74 / 0
Classes7
Balance (entropy)0.92
Imbalance ratio6
Top classSJER (24)

Site_Long

metadata · categorical
Site_Long classesSan Joaquin Experimental RangeSan Joaquin Experimental Range: 2424Mixed Conifer - Providence Cr…Mixed Conifer - Providence Creek Sierra CZO: 1212Coachella Valley Agricultural…Coachella Valley Agricultural Research Station: 1111Loma Ridge Coastal SagebrushLoma Ridge Coastal Sagebrush: 99San Jacinto James ReserveSan Jacinto James Reserve: 99South Coast Research and Exte…South Coast Research and Extension Center: 55Kearney Agricultural Research…Kearney Agricultural Research and Extension Center: 44
n / missing74 / 0
Classes7
Balance (entropy)0.92
Imbalance ratio6
Top classSan Joaquin Experimental Range (24)

Sample_Date

metadata · categorical
Sample_Date classes3/26/133/26/13: 996/14/136/14/13: 886/21/136/21/13: 774/4/134/4/13: 776/8/136/8/13: 664/3/134/3/13: 556/26/136/26/13: 443/28/133/28/13: 446/6/136/6/13: 336/19/136/19/13: 33+10 more+10 more: 1818
n / missing74 / 0
Classes20
Balance (entropy)0.93
Imbalance ratio9
Top class3/26/13 (9)

USDA_Species_Code

metadata · categorical
USDA_Species_Code classesQUDOQUDO: 77CECUCECU: 77GrapeGrape: 66CAAN4CAAN4: 55MALA6MALA6: 55QUKEQUKE: 55PEAM3PEAM3: 55QUWI2QUWI2: 55PISA2PISA2: 55PIME4PIME4: 44+8 more+8 more: 2020
n / missing74 / 0
Classes18
Balance (entropy)0.96
Imbalance ratio7
Top classQUDO (7)

Latitude

metadata · numeric
Latitude distribution0204033.52 – 33.67: 1033.67 – 33.82: 2333.82 – 33.97: 033.97 – 34.12: 034.12 – 34.27: 034.27 – 34.42: 034.42 – 34.57: 034.57 – 34.72: 034.72 – 34.87: 034.87 – 35.02: 035.02 – 35.17: 035.17 – 35.32: 035.32 – 35.47: 035.47 – 35.62: 035.62 – 35.77: 035.77 – 35.92: 035.92 – 36.07: 036.07 – 36.22: 036.22 – 36.37: 036.37 – 36.52: 036.52 – 36.67: 436.67 – 36.82: 036.82 – 36.97: 036.97 – 37.12: 36333435363738
n / missing74 / 1
Mean ± SD35.52 ± 1.69
Median36.59
Range33.52 – 37.12
CV0.0475
Skew / kurtosis-0.19 / -2
Normal?no

Longitude

metadata · numeric
Longitude distribution0102030-119.7 – -119.6: 24-119.6 – -119.4: 4-119.4 – -119.3: 0-119.3 – -119.1: 12-119.1 – -119: 0-119 – -118.8: 0-118.8 – -118.7: 0-118.7 – -118.5: 0-118.5 – -118.4: 0-118.4 – -118.2: 0-118.2 – -118.1: 0-118.1 – -117.9: 0-117.9 – -117.8: 0-117.8 – -117.7: 14-117.7 – -117.5: 0-117.5 – -117.4: 0-117.4 – -117.2: 0-117.2 – -117.1: 0-117.1 – -116.9: 0-116.9 – -116.8: 9-116.8 – -116.6: 0-116.6 – -116.5: 0-116.5 – -116.3: 0-116.3 – -116.2: 10-120-119-118-117-116
n / missing74 / 1
Mean ± SD-118.4 ± 1.37
Median-119.2
Range-119.7 – -116.2
CV0.0116
Skew / kurtosis0.46 / -1.4
Normal?no

Instrument

metadata · categorical
Instrument classesASD_FS3ASD_FS3: 4242SpecEvo_PSM3500SpecEvo_PSM3500: 2525ASD_FS4ASD_FS4: 77
n / missing74 / 0
Classes3
Balance (entropy)0.83
Imbalance ratio6
Top classASD_FS3 (42)

GasExchange_Leaf

metadata · categorical
GasExchange_Leaf classesyesyes: 5151nono: 2323
n / missing74 / 0
Classes2
Balance (entropy)0.89
Imbalance ratio2
Top classyes (51)

Spectra

metadata · categorical
n / missing74 / 0
Classes74
Balance (entropy)1
Imbalance ratio1
Top classCVARS_Plot1_CAAN4_L1T1N_LC_RG (1)

SLA_cm2_g_DW

metadata · numeric
SLA_cm2_g_DW distribution05101525.45 – 35.79: 1235.79 – 46.12: 1246.12 – 56.46: 356.46 – 66.8: 466.8 – 77.13: 777.13 – 87.47: 587.47 – 97.8: 297.8 – 108.1: 7108.1 – 118.5: 3118.5 – 128.8: 3128.8 – 139.1: 1139.1 – 149.5: 2149.5 – 159.8: 0159.8 – 170.2: 3170.2 – 180.5: 2180.5 – 190.8: 1190.8 – 201.2: 2201.2 – 211.5: 1211.5 – 221.8: 2221.8 – 232.2: 0232.2 – 242.5: 0242.5 – 252.8: 1252.8 – 263.2: 0263.2 – 273.5: 10100200300
n / missing74 / 0
Mean ± SD91.57 ± 60.1
Median72.46
Range25.45 – 273.5
CV0.656
Skew / kurtosis1 / 0.38
Normal?no

SLA_m2_kgDW

metadata · numeric
SLA_m2_kgDW distribution0510152.545 – 3.579: 123.579 – 4.612: 124.612 – 5.646: 35.646 – 6.68: 46.68 – 7.713: 77.713 – 8.747: 58.747 – 9.78: 29.78 – 10.81: 710.81 – 11.85: 311.85 – 12.88: 312.88 – 13.91: 113.91 – 14.95: 214.95 – 15.98: 015.98 – 17.02: 317.02 – 18.05: 218.05 – 19.08: 119.08 – 20.12: 220.12 – 21.15: 121.15 – 22.18: 222.18 – 23.22: 023.22 – 24.25: 024.25 – 25.28: 125.28 – 26.32: 026.32 – 27.35: 10102030
n / missing74 / 0
Mean ± SD9.157 ± 6.01
Median7.246
Range2.545 – 27.35
CV0.656
Skew / kurtosis1 / 0.38
Normal?no

LMA_gDW_m2

metadata · numeric
LMA_gDW_m2 distribution051036.56 – 51.41: 551.41 – 66.26: 866.26 – 81.11: 481.11 – 95.95: 995.95 – 110.8: 4110.8 – 125.7: 3125.7 – 140.5: 7140.5 – 155.3: 5155.3 – 170.2: 1170.2 – 185: 1185 – 199.9: 2199.9 – 214.7: 1214.7 – 229.6: 1229.6 – 244.4: 3244.4 – 259.3: 4259.3 – 274.1: 2274.1 – 289: 4289 – 303.8: 1303.8 – 318.7: 2318.7 – 333.5: 2333.5 – 348.4: 2348.4 – 363.2: 0363.2 – 378.1: 2378.1 – 392.9: 10100200300400
n / missing74 / 0
Mean ± SD163.6 ± 99.1
Median138
Range36.56 – 392.9
CV0.606
Skew / kurtosis0.61 / -0.88
Normal?no

CHN_Sample_Name

metadata · categorical
n / missing74 / 17
Classes57
Balance (entropy)1
Imbalance ratio1
Top classCVARS_Plot1_CAAN4_L1T1N (1)

CHN_USDA_Species_Code

metadata · categorical
CHN_USDA_Species_Code classesGrapeGrape: 66QUDOQUDO: 66CECUCECU: 66QUWI2QUWI2: 55PISA2PISA2: 55PIME4PIME4: 44ABCOABCO: 44CAAN4CAAN4: 33MALA6MALA6: 33QUKEQUKE: 33+7 more+7 more: 1212
n / missing74 / 17
Classes17
Balance (entropy)0.95
Imbalance ratio6
Top classGrape (6)

CHN_Tree_Plant_Number

metadata · numeric
CHN_Tree_Plant_Number distribution01020301 – 1.125: 221.125 – 1.25: 01.25 – 1.375: 01.375 – 1.5: 01.5 – 1.625: 01.625 – 1.75: 01.75 – 1.875: 01.875 – 2: 02 – 2.125: 102.125 – 2.25: 02.25 – 2.375: 02.375 – 2.5: 02.5 – 2.625: 02.625 – 2.75: 02.75 – 2.875: 02.875 – 3: 03 – 3.125: 53.125 – 3.25: 03.25 – 3.375: 03.375 – 3.5: 03.5 – 3.625: 03.625 – 3.75: 03.75 – 3.875: 03.875 – 4: 412510
n / missing74 / 33
Mean ± SD1.78 ± 1.01
Median1
Range1 – 4
CV0.569
Skew / kurtosis1.1 / -0.027
Normal?no

CHN_Leaf_Number

metadata · numeric
CHN_Leaf_Number distribution01020301 – 1.292: 281.292 – 1.583: 01.583 – 1.875: 01.875 – 2.167: 92.167 – 2.458: 02.458 – 2.75: 02.75 – 3.042: 23.042 – 3.333: 03.333 – 3.625: 03.625 – 3.917: 03.917 – 4.208: 14.208 – 4.5: 04.5 – 4.792: 04.792 – 5.083: 25.083 – 5.375: 05.375 – 5.667: 05.667 – 5.958: 05.958 – 6.25: 26.25 – 6.542: 06.542 – 6.833: 06.833 – 7.125: 17.125 – 7.417: 07.417 – 7.708: 07.708 – 8: 102468
n / missing74 / 28
Mean ± SD2.022 ± 1.79
Median1
Range1 – 8
CV0.888
Skew / kurtosis2 / 3.1
Normal?no

CHN_Canopy_Position

metadata · categorical
CHN_Canopy_Position classesTT: 4747MM: 66BB: 44
n / missing74 / 17
Classes3
Balance (entropy)0.53
Imbalance ratio1e+01
Top classT (47)

CHN_Leaf_Age

metadata · categorical
CHN_Leaf_Age classesNewNew: 343411: 2323
n / missing74 / 17
Classes2
Balance (entropy)0.97
Imbalance ratio1
Top classNew (34)

CHN_Conifer_Broadleaf

metadata · categorical
CHN_Conifer_Broadleaf classesBB: 4848CC: 99
n / missing74 / 17
Classes2
Balance (entropy)0.63
Imbalance ratio5
Top classB (48)

C_mass_g_g

metadata · numeric
C_mass_g_g distribution025840.72 – 41.18: 141.18 – 41.64: 041.64 – 42.1: 042.1 – 42.56: 142.56 – 43.02: 143.02 – 43.48: 043.48 – 43.94: 143.94 – 44.4: 444.4 – 44.86: 444.86 – 45.32: 445.32 – 45.78: 245.78 – 46.24: 346.24 – 46.7: 746.7 – 47.16: 547.16 – 47.62: 247.62 – 48.08: 648.08 – 48.54: 248.54 – 49: 649 – 49.46: 149.46 – 49.92: 249.92 – 50.38: 150.38 – 50.84: 050.84 – 51.3: 051.3 – 51.76: 4102050100
n / missing74 / 17
Mean ± SD46.84 ± 2.31
Median46.91
Range40.72 – 51.76
CV0.0493
Skew / kurtosis0.013 / 0.16
Normal?yes

N_mass_g_g

metadata · numeric
N_mass_g_g distribution02460.57 – 0.7471: 20.7471 – 0.9242: 20.9242 – 1.101: 21.101 – 1.278: 41.278 – 1.455: 21.455 – 1.632: 61.632 – 1.81: 21.81 – 1.987: 21.987 – 2.164: 42.164 – 2.341: 32.341 – 2.518: 52.518 – 2.695: 42.695 – 2.872: 52.872 – 3.049: 03.049 – 3.226: 33.226 – 3.403: 23.403 – 3.58: 13.58 – 3.757: 13.757 – 3.935: 03.935 – 4.112: 04.112 – 4.289: 24.289 – 4.466: 24.466 – 4.643: 24.643 – 4.82: 10246
n / missing74 / 17
Mean ± SD2.367 ± 1.06
Median2.25
Range0.57 – 4.82
CV0.45
Skew / kurtosis0.56 / -0.31
Normal?yes

CN_Ratio

metadata · numeric
CN_Ratio distribution05108.896 – 12.31: 812.31 – 15.72: 715.72 – 19.14: 919.14 – 22.55: 922.55 – 25.96: 425.96 – 29.37: 229.37 – 32.79: 532.79 – 36.2: 336.2 – 39.61: 239.61 – 43.03: 343.03 – 46.44: 146.44 – 49.85: 049.85 – 53.26: 153.26 – 56.68: 156.68 – 60.09: 060.09 – 63.5: 063.5 – 66.92: 166.92 – 70.33: 070.33 – 73.74: 073.74 – 77.16: 077.16 – 80.57: 080.57 – 83.98: 083.98 – 87.39: 087.39 – 90.81: 10255075100
n / missing74 / 17
Mean ± SD25.33 ± 15.2
Median20.2
Range8.896 – 90.81
CV0.599
Skew / kurtosis2 / 5.5
Normal?no

CHN_Comments

metadata · categorical
n / missing74 / 72
Classes2
Balance (entropy)1
Imbalance ratio1
Top classLeaf N seems a bit high (1)

GE_Sample_Name

metadata · categorical
n / missing74 / 60
Classes14
Balance (entropy)1
Imbalance ratio1
Top classCVARS_Plot1_CAAN4_L1T1_GE (1)

GE_USDA_Species_Code

metadata · categorical
GE_USDA_Species_Code classesGrapeGrape: 55CAAN4CAAN4: 44PIME4PIME4: 33PEAM3PEAM3: 22
n / missing74 / 60
Classes4
Balance (entropy)0.96
Imbalance ratio2
Top classGrape (5)

GE_Tree_Plant_Number

metadata · numeric
GE_Tree_Plant_Number distribution01231 – 1.125: 31.125 – 1.25: 01.25 – 1.375: 01.375 – 1.5: 01.5 – 1.625: 01.625 – 1.75: 01.75 – 1.875: 01.875 – 2: 02 – 2.125: 12.125 – 2.25: 02.25 – 2.375: 02.375 – 2.5: 02.5 – 2.625: 02.625 – 2.75: 02.75 – 2.875: 02.875 – 3: 03 – 3.125: 13.125 – 3.25: 03.25 – 3.375: 03.375 – 3.5: 03.5 – 3.625: 03.625 – 3.75: 03.75 – 3.875: 03.875 – 4: 112510
n / missing74 / 68
Mean ± SD2 ± 1.26
Median1.5
Range1 – 4
CV0.632
Skew / kurtosis0.89 / -0.78

GE_Leaf_Number

metadata · numeric
GE_Leaf_Number distribution02461 – 1.292: 51.292 – 1.583: 01.583 – 1.875: 01.875 – 2.167: 32.167 – 2.458: 02.458 – 2.75: 02.75 – 3.042: 13.042 – 3.333: 03.333 – 3.625: 03.625 – 3.917: 03.917 – 4.208: 04.208 – 4.5: 04.5 – 4.792: 04.792 – 5.083: 15.083 – 5.375: 05.375 – 5.667: 05.667 – 5.958: 05.958 – 6.25: 16.25 – 6.542: 06.542 – 6.833: 06.833 – 7.125: 17.125 – 7.417: 07.417 – 7.708: 07.708 – 8: 102468
n / missing74 / 61
Mean ± SD3.077 ± 2.53
Median2
Range1 – 8
CV0.823
Skew / kurtosis0.99 / -0.54
Normal?yes

GE_Canopy_Position

metadata · categorical
GE_Canopy_Position classesTopTop: 1212MidMid: 11BotBot: 11
n / missing74 / 60
Classes3
Balance (entropy)0.46
Imbalance ratio12
Top classTop (12)

GE_Leaf_Age

metadata · categorical
GE_Leaf_Age classesNewNew: 88OldOld: 11
n / missing74 / 65
Classes2
Balance (entropy)0.5
Imbalance ratio8
Top classNew (8)

GE_Measurement_Date

metadata · categorical
GE_Measurement_Date classes6/8/136/8/13: 446/5/136/5/13: 226/6/136/6/13: 226/26/136/26/13: 226/7/136/7/13: 116/25/136/25/13: 113/22/133/22/13: 113/23/133/23/13: 11
n / missing74 / 60
Classes8
Balance (entropy)0.94
Imbalance ratio4
Top class6/8/13 (4)

Mean_Tair

metadata · numeric
Mean_Tair distribution01219.4 – 19.77: 119.77 – 20.15: 020.15 – 20.53: 020.53 – 20.9: 020.9 – 21.28: 121.28 – 21.65: 021.65 – 22.03: 022.03 – 22.4: 022.4 – 22.78: 222.78 – 23.15: 123.15 – 23.53: 123.53 – 23.9: 223.9 – 24.28: 024.28 – 24.65: 124.65 – 25.03: 025.03 – 25.4: 025.4 – 25.78: 125.78 – 26.16: 126.16 – 26.53: 126.53 – 26.91: 026.91 – 27.28: 027.28 – 27.66: 027.66 – 28.03: 128.03 – 28.41: 1102050100
n / missing74 / 60
Mean ± SD24.18 ± 2.51
Median23.72
Range19.4 – 28.41
CV0.104
Skew / kurtosis0.041 / -0.16
Normal?yes

Mean_Tleaf

metadata · numeric
Mean_Tleaf distribution01224.76 – 25.03: 125.03 – 25.29: 025.29 – 25.56: 125.56 – 25.83: 025.83 – 26.09: 126.09 – 26.36: 126.36 – 26.63: 026.63 – 26.89: 026.89 – 27.16: 027.16 – 27.43: 027.43 – 27.7: 027.7 – 27.96: 127.96 – 28.23: 128.23 – 28.5: 228.5 – 28.76: 028.76 – 29.03: 229.03 – 29.3: 129.3 – 29.56: 029.56 – 29.83: 129.83 – 30.1: 030.1 – 30.37: 030.37 – 30.63: 030.63 – 30.9: 030.9 – 31.17: 22426283032
n / missing74 / 60
Mean ± SD28.15 ± 1.97
Median28.47
Range24.76 – 31.17
CV0.07
Skew / kurtosis-0.26 / -0.67
Normal?yes

PercCV_Tleaf

metadata · numeric
PercCV_Tleaf distribution01230.288 – 0.3892: 10.3892 – 0.4904: 00.4904 – 0.5916: 10.5916 – 0.6928: 10.6928 – 0.794: 10.794 – 0.8952: 30.8952 – 0.9964: 20.9964 – 1.098: 01.098 – 1.199: 01.199 – 1.3: 11.3 – 1.401: 11.401 – 1.502: 11.502 – 1.604: 01.604 – 1.705: 11.705 – 1.806: 01.806 – 1.907: 01.907 – 2.009: 02.009 – 2.11: 02.11 – 2.211: 02.211 – 2.312: 02.312 – 2.413: 02.413 – 2.515: 02.515 – 2.616: 02.616 – 2.717: 10123
n / missing74 / 60
Mean ± SD1.069 ± 0.607
Median0.8781
Range0.288 – 2.717
CV0.568
Skew / kurtosis1.6 / 3.4
Normal?no

Mean_RH_R

metadata · numeric
Mean_RH_R distribution024-0.374 – 2.875: 12.875 – 6.124: 06.124 – 9.373: 09.373 – 12.62: 012.62 – 15.87: 015.87 – 19.12: 019.12 – 22.37: 022.37 – 25.62: 025.62 – 28.87: 028.87 – 32.12: 032.12 – 35.37: 035.37 – 38.61: 038.61 – 41.86: 041.86 – 45.11: 045.11 – 48.36: 048.36 – 51.61: 151.61 – 54.86: 254.86 – 58.11: 358.11 – 61.36: 161.36 – 64.61: 164.61 – 67.86: 167.86 – 71.1: 071.1 – 74.35: 074.35 – 77.6: 4-20020406080
n / missing74 / 60
Mean ± SD58.58 ± 19.6
Median58.42
Range-0.374 – 77.6
CV0.335
Skew / kurtosis-2.2 / 6.5
Normal?no

Mean_RH_S

metadata · numeric
Mean_RH_S distribution02416.43 – 19.16: 119.16 – 21.88: 021.88 – 24.6: 024.6 – 27.32: 027.32 – 30.04: 030.04 – 32.76: 032.76 – 35.49: 035.49 – 38.21: 038.21 – 40.93: 040.93 – 43.65: 043.65 – 46.37: 046.37 – 49.1: 049.1 – 51.82: 051.82 – 54.54: 054.54 – 57.26: 057.26 – 59.98: 259.98 – 62.7: 062.7 – 65.43: 265.43 – 68.15: 368.15 – 70.87: 170.87 – 73.59: 073.59 – 76.31: 176.31 – 79.04: 079.04 – 81.76: 4020406080100
n / missing74 / 60
Mean ± SD66.3 ± 16.6
Median66.23
Range16.43 – 81.76
CV0.25
Skew / kurtosis-2.2 / 6.5
Normal?no

Mean_VpdA

metadata · numeric
Mean_VpdA distribution0120.8498 – 0.9447: 20.9447 – 1.04: 11.04 – 1.135: 01.135 – 1.229: 11.229 – 1.324: 01.324 – 1.419: 21.419 – 1.514: 21.514 – 1.609: 11.609 – 1.704: 21.704 – 1.799: 11.799 – 1.894: 01.894 – 1.989: 01.989 – 2.084: 02.084 – 2.179: 02.179 – 2.274: 02.274 – 2.368: 02.368 – 2.463: 02.463 – 2.558: 02.558 – 2.653: 02.653 – 2.748: 02.748 – 2.843: 02.843 – 2.938: 02.938 – 3.033: 03.033 – 3.128: 10.10.20.512510
n / missing74 / 61
Mean ± SD1.481 ± 0.571
Median1.435
Range0.8498 – 3.128
CV0.385
Skew / kurtosis2.1 / 6.1
Normal?no

Mean_VpdL

metadata · numeric
Mean_VpdL distribution01231.012 – 1.114: 11.114 – 1.216: 01.216 – 1.318: 01.318 – 1.42: 21.42 – 1.522: 11.522 – 1.624: 01.624 – 1.726: 31.726 – 1.828: 01.828 – 1.93: 11.93 – 2.032: 22.032 – 2.134: 32.134 – 2.236: 02.236 – 2.338: 02.338 – 2.44: 02.44 – 2.542: 02.542 – 2.644: 02.644 – 2.746: 02.746 – 2.848: 02.848 – 2.95: 02.95 – 3.052: 03.052 – 3.154: 03.154 – 3.256: 03.256 – 3.358: 03.358 – 3.46: 112510
n / missing74 / 60
Mean ± SD1.847 ± 0.575
Median1.793
Range1.012 – 3.46
CV0.311
Skew / kurtosis1.6 / 4.5
Normal?no

PercCV_VpdL

metadata · numeric
PercCV_VpdL distribution0121.265 – 1.666: 21.666 – 2.067: 22.067 – 2.468: 12.468 – 2.869: 22.869 – 3.27: 03.27 – 3.671: 03.671 – 4.072: 04.072 – 4.473: 24.473 – 4.874: 04.874 – 5.275: 05.275 – 5.676: 05.676 – 6.077: 06.077 – 6.478: 06.478 – 6.879: 06.879 – 7.28: 07.28 – 7.681: 07.681 – 8.082: 18.082 – 8.483: 28.483 – 8.884: 08.884 – 9.286: 09.286 – 9.687: 09.687 – 10.09: 010.09 – 10.49: 110.49 – 10.89: 10.02.55.07.510.012.5
n / missing74 / 60
Mean ± SD4.835 ± 3.51
Median3.398
Range1.265 – 10.89
CV0.726
Skew / kurtosis0.64 / -1.3
Normal?yes

Mean_PARi

metadata · numeric
Mean_PARi distribution01231999 – 1999: 11999 – 1999: 01999 – 1999: 21999 – 1999: 01999 – 1999: 01999 – 1999: 11999 – 2000: 22000 – 2000: 12000 – 2000: 02000 – 2000: 12000 – 2000: 02000 – 2000: 02000 – 2000: 12000 – 2000: 02000 – 2000: 02000 – 2000: 32000 – 2000: 02000 – 2000: 02000 – 2000: 12000 – 2000: 02000 – 2000: 02000 – 2000: 02000 – 2001: 02001 – 2001: 11,999.01,999.52,000.02,000.52,001.0
n / missing74 / 60
Mean ± SD2000 ± 0.425
Median2000
Range1999 – 2001
CV0.000212
Skew / kurtosis0.4 / -0.52
Normal?yes

Mean_Ci_over_Ca

metadata · numeric
Mean_Ci_over_Ca distribution0120.5262 – 0.5385: 10.5385 – 0.5508: 00.5508 – 0.5631: 00.5631 – 0.5754: 00.5754 – 0.5877: 00.5877 – 0.6: 00.6 – 0.6123: 00.6123 – 0.6246: 10.6246 – 0.6369: 00.6369 – 0.6492: 10.6492 – 0.6615: 00.6615 – 0.6738: 10.6738 – 0.6861: 00.6861 – 0.6984: 00.6984 – 0.7107: 20.7107 – 0.723: 10.723 – 0.7353: 10.7353 – 0.7476: 10.7476 – 0.7599: 00.7599 – 0.7722: 00.7722 – 0.7845: 00.7845 – 0.7968: 20.7968 – 0.8091: 00.8091 – 0.8214: 20.10.20.51
n / missing74 / 61
Mean ± SD0.7119 ± 0.084
Median0.7153
Range0.5262 – 0.8214
CV0.118
Skew / kurtosis-0.76 / 0.54
Normal?yes

PercCV_Ci_Ca

metadata · numeric
PercCV_Ci_Ca distribution012315.04 – 16.36: 316.36 – 17.67: 017.67 – 18.99: 018.99 – 20.3: 020.3 – 21.62: 221.62 – 22.94: 022.94 – 24.25: 024.25 – 25.57: 025.57 – 26.88: 026.88 – 28.2: 028.2 – 29.51: 029.51 – 30.83: 130.83 – 32.15: 232.15 – 33.46: 133.46 – 34.78: 034.78 – 36.09: 136.09 – 37.41: 137.41 – 38.73: 038.73 – 40.04: 040.04 – 41.36: 141.36 – 42.67: 042.67 – 43.99: 043.99 – 45.31: 045.31 – 46.62: 1102050100
n / missing74 / 61
Mean ± SD28.75 ± 10.2
Median30.95
Range15.04 – 46.62
CV0.356
Skew / kurtosis0.051 / -1
Normal?yes

Mean_Cond

metadata · numeric
Mean_Cond distribution01230.1003 – 0.1156: 30.1156 – 0.1309: 10.1309 – 0.1462: 00.1462 – 0.1615: 20.1615 – 0.1768: 30.1768 – 0.1921: 00.1921 – 0.2074: 10.2074 – 0.2227: 00.2227 – 0.238: 10.238 – 0.2533: 00.2533 – 0.2686: 10.2686 – 0.2838: 00.2838 – 0.2991: 00.2991 – 0.3144: 00.3144 – 0.3297: 00.3297 – 0.345: 00.345 – 0.3603: 00.3603 – 0.3756: 00.3756 – 0.3909: 10.3909 – 0.4062: 00.4062 – 0.4215: 00.4215 – 0.4368: 00.4368 – 0.4521: 00.4521 – 0.4674: 10.10.20.30.40.5
n / missing74 / 60
Mean ± SD0.2016 ± 0.107
Median0.1709
Range0.1003 – 0.4674
CV0.532
Skew / kurtosis1.6 / 2.2
Normal?no

PercCV_Cond

metadata · numeric
PercCV_Cond distribution0242.071 – 3.777: 13.777 – 5.484: 05.484 – 7.19: 07.19 – 8.897: 18.897 – 10.6: 010.6 – 12.31: 412.31 – 14.02: 014.02 – 15.72: 115.72 – 17.43: 017.43 – 19.14: 019.14 – 20.84: 120.84 – 22.55: 122.55 – 24.26: 024.26 – 25.96: 225.96 – 27.67: 127.67 – 29.38: 029.38 – 31.08: 031.08 – 32.79: 032.79 – 34.5: 034.5 – 36.2: 136.2 – 37.91: 037.91 – 39.62: 039.62 – 41.32: 041.32 – 43.03: 10204060
n / missing74 / 60
Mean ± SD19.18 ± 11.1
Median17.27
Range2.071 – 43.03
CV0.581
Skew / kurtosis0.65 / 0.14
Normal?yes

Calculated_VIS_Leaf_Abs

metadata · numeric
Calculated_VIS_Leaf_Abs distribution0120.8367 – 0.8415: 10.8415 – 0.8463: 10.8463 – 0.8511: 00.8511 – 0.856: 00.856 – 0.8608: 10.8608 – 0.8656: 00.8656 – 0.8704: 20.8704 – 0.8752: 00.8752 – 0.8801: 20.8801 – 0.8849: 00.8849 – 0.8897: 00.8897 – 0.8945: 00.8945 – 0.8993: 00.8993 – 0.9042: 20.9042 – 0.909: 00.909 – 0.9138: 00.9138 – 0.9186: 00.9186 – 0.9235: 00.9235 – 0.9283: 00.9283 – 0.9331: 10.9331 – 0.9379: 10.9379 – 0.9427: 10.9427 – 0.9476: 10.9476 – 0.9524: 10.800.850.900.951.00
n / missing74 / 60
Mean ± SD0.8952 ± 0.0395
Median0.889
Range0.8367 – 0.9524
CV0.0441
Skew / kurtosis0.11 / -1.5
Normal?yes

Mean_Fs

metadata · numeric
Mean_Fs distribution012604.7 – 630.3: 1630.3 – 655.9: 1655.9 – 681.5: 0681.5 – 707.1: 1707.1 – 732.7: 2732.7 – 758.2: 0758.2 – 783.8: 1783.8 – 809.4: 0809.4 – 835: 2835 – 860.6: 1860.6 – 886.2: 0886.2 – 911.8: 0911.8 – 937.3: 0937.3 – 962.9: 1962.9 – 988.5: 0988.5 – 1014: 01014 – 1040: 21040 – 1065: 11065 – 1091: 01091 – 1116: 01116 – 1142: 01142 – 1168: 01168 – 1193: 01193 – 1219: 11002005001,0002,0005,00010,000
n / missing74 / 60
Mean ± SD851.2 ± 179
Median818.7
Range604.7 – 1219
CV0.21
Skew / kurtosis0.58 / -0.41
Normal?yes

Mean_qP

metadata · numeric
Mean_qP distribution0120.3603 – 0.3748: 20.3748 – 0.3893: 10.3893 – 0.4038: 00.4038 – 0.4183: 00.4183 – 0.4328: 10.4328 – 0.4472: 10.4472 – 0.4617: 10.4617 – 0.4762: 10.4762 – 0.4907: 00.4907 – 0.5052: 00.5052 – 0.5197: 10.5197 – 0.5342: 00.5342 – 0.5486: 00.5486 – 0.5631: 10.5631 – 0.5776: 00.5776 – 0.5921: 00.5921 – 0.6066: 00.6066 – 0.6211: 00.6211 – 0.6356: 10.6356 – 0.65: 20.65 – 0.6645: 10.6645 – 0.679: 00.679 – 0.6935: 00.6935 – 0.708: 10.10.20.51
n / missing74 / 60
Mean ± SD0.5177 ± 0.119
Median0.4891
Range0.3603 – 0.708
CV0.23
Skew / kurtosis0.21 / -1.5
Normal?yes

Mean_Fv_Prime_over_Fm_Prime

metadata · numeric
Mean_Fv_Prime_over_Fm_Prime distribution0120.3582 – 0.3638: 10.3638 – 0.3694: 00.3694 – 0.375: 00.375 – 0.3806: 00.3806 – 0.3862: 10.3862 – 0.3918: 20.3918 – 0.3974: 00.3974 – 0.403: 10.403 – 0.4087: 10.4087 – 0.4143: 00.4143 – 0.4199: 00.4199 – 0.4255: 00.4255 – 0.4311: 00.4311 – 0.4367: 10.4367 – 0.4423: 00.4423 – 0.4479: 20.4479 – 0.4535: 00.4535 – 0.4591: 00.4591 – 0.4647: 00.4647 – 0.4703: 10.4703 – 0.4759: 20.4759 – 0.4815: 00.4815 – 0.4871: 00.4871 – 0.4928: 20.10.20.51
n / missing74 / 60
Mean ± SD0.4318 ± 0.0434
Median0.4387
Range0.3582 – 0.4928
CV0.1
Skew / kurtosis-0.1 / -1.3
Normal?yes

Mean_PhiPS2

metadata · numeric
Mean_PhiPS2 distribution01230.1415 – 0.1503: 20.1503 – 0.1591: 00.1591 – 0.1678: 00.1678 – 0.1766: 10.1766 – 0.1854: 20.1854 – 0.1941: 20.1941 – 0.2029: 00.2029 – 0.2116: 00.2116 – 0.2204: 10.2204 – 0.2292: 10.2292 – 0.2379: 00.2379 – 0.2467: 00.2467 – 0.2555: 00.2555 – 0.2642: 00.2642 – 0.273: 00.273 – 0.2818: 00.2818 – 0.2905: 00.2905 – 0.2993: 10.2993 – 0.3081: 30.3081 – 0.3168: 00.3168 – 0.3256: 00.3256 – 0.3344: 00.3344 – 0.3431: 00.3431 – 0.3519: 10.10.20.51
n / missing74 / 60
Mean ± SD0.2287 ± 0.0689
Median0.2046
Range0.1415 – 0.3519
CV0.301
Skew / kurtosis0.44 / -1.3
Normal?yes

Max_ETR

metadata · numeric
Max_ETR distribution0123136.5 – 146.1: 1146.1 – 155.7: 1155.7 – 165.4: 0165.4 – 175: 0175 – 184.6: 1184.6 – 194.3: 2194.3 – 203.9: 1203.9 – 213.5: 1213.5 – 223.2: 0223.2 – 232.8: 0232.8 – 242.4: 2242.4 – 252.1: 0252.1 – 261.7: 0261.7 – 271.3: 0271.3 – 281: 0281 – 290.6: 0290.6 – 300.2: 0300.2 – 309.9: 0309.9 – 319.5: 0319.5 – 329.1: 0329.1 – 338.7: 0338.7 – 348.4: 3348.4 – 358: 1358 – 367.6: 11002005001,000
n / missing74 / 60
Mean ± SD249 ± 83.7
Median221.7
Range136.5 – 367.6
CV0.336
Skew / kurtosis0.33 / -1.6
Normal?yes

Max_Fs

metadata · numeric
Max_Fs distribution0123613.4 – 641.6: 1641.6 – 669.7: 1669.7 – 697.9: 0697.9 – 726: 0726 – 754.2: 3754.2 – 782.4: 1782.4 – 810.5: 1810.5 – 838.7: 0838.7 – 866.8: 1866.8 – 895: 0895 – 923.1: 0923.1 – 951.3: 0951.3 – 979.5: 1979.5 – 1008: 11008 – 1036: 11036 – 1064: 01064 – 1092: 01092 – 1120: 01120 – 1148: 11148 – 1177: 11177 – 1205: 01205 – 1233: 01233 – 1261: 01261 – 1289: 11002005001,0002,0005,00010,000
n / missing74 / 60
Mean ± SD892.6 ± 205
Median833.8
Range613.4 – 1289
CV0.23
Skew / kurtosis0.55 / -0.73
Normal?yes

Max_qP

metadata · numeric
Max_qP distribution0120.3733 – 0.3887: 10.3887 – 0.404: 00.404 – 0.4194: 00.4194 – 0.4348: 20.4348 – 0.4502: 10.4502 – 0.4656: 00.4656 – 0.481: 00.481 – 0.4964: 00.4964 – 0.5118: 20.5118 – 0.5272: 10.5272 – 0.5426: 10.5426 – 0.558: 00.558 – 0.5734: 10.5734 – 0.5888: 00.5888 – 0.6042: 00.6042 – 0.6196: 00.6196 – 0.635: 00.635 – 0.6504: 00.6504 – 0.6658: 00.6658 – 0.6812: 20.6812 – 0.6965: 20.6965 – 0.7119: 00.7119 – 0.7273: 00.7273 – 0.7427: 10.10.20.51
n / missing74 / 60
Mean ± SD0.5544 ± 0.118
Median0.5263
Range0.3733 – 0.7427
CV0.213
Skew / kurtosis0.18 / -1.3
Normal?yes

Max_Fv_Prime_over_Fm_Prime

metadata · numeric
Max_Fv_Prime_over_Fm_Prime distribution0120.3989 – 0.4058: 10.4058 – 0.4127: 10.4127 – 0.4196: 10.4196 – 0.4265: 00.4265 – 0.4334: 00.4334 – 0.4402: 10.4402 – 0.4471: 00.4471 – 0.454: 00.454 – 0.4609: 00.4609 – 0.4678: 00.4678 – 0.4747: 10.4747 – 0.4816: 20.4816 – 0.4884: 00.4884 – 0.4953: 10.4953 – 0.5022: 00.5022 – 0.5091: 00.5091 – 0.516: 00.516 – 0.5229: 00.5229 – 0.5297: 10.5297 – 0.5366: 00.5366 – 0.5435: 20.5435 – 0.5504: 00.5504 – 0.5573: 20.5573 – 0.5642: 10.10.20.51
n / missing74 / 60
Mean ± SD0.49 ± 0.0584
Median0.4863
Range0.3989 – 0.5642
CV0.119
Skew / kurtosis-0.29 / -1.4
Normal?yes

Max_PhiPS2

metadata · numeric
Max_PhiPS2 distribution0120.1516 – 0.1627: 10.1627 – 0.1739: 00.1739 – 0.185: 10.185 – 0.1961: 00.1961 – 0.2073: 10.2073 – 0.2184: 20.2184 – 0.2296: 10.2296 – 0.2407: 00.2407 – 0.2519: 10.2519 – 0.263: 00.263 – 0.2742: 20.2742 – 0.2853: 00.2853 – 0.2964: 00.2964 – 0.3076: 00.3076 – 0.3187: 00.3187 – 0.3299: 00.3299 – 0.341: 00.341 – 0.3522: 00.3522 – 0.3633: 10.3633 – 0.3745: 20.3745 – 0.3856: 10.3856 – 0.3967: 00.3967 – 0.4079: 00.4079 – 0.419: 10.10.20.51
n / missing74 / 60
Mean ± SD0.2765 ± 0.0865
Median0.258
Range0.1516 – 0.419
CV0.313
Skew / kurtosis0.32 / -1.4
Normal?yes

Meas_Photo_at_CO2R_400

metadata · numeric
Meas_Photo_at_CO2R_400 distribution01210.27 – 11.07: 211.07 – 11.86: 011.86 – 12.65: 012.65 – 13.45: 113.45 – 14.24: 114.24 – 15.03: 215.03 – 15.82: 015.82 – 16.62: 016.62 – 17.41: 217.41 – 18.2: 018.2 – 19: 119 – 19.79: 019.79 – 20.58: 120.58 – 21.38: 021.38 – 22.17: 022.17 – 22.96: 022.96 – 23.76: 123.76 – 24.55: 024.55 – 25.34: 025.34 – 26.13: 026.13 – 26.93: 126.93 – 27.72: 027.72 – 28.51: 128.51 – 29.31: 1102050100
n / missing74 / 60
Mean ± SD18.28 ± 6.26
Median17.28
Range10.27 – 29.31
CV0.343
Skew / kurtosis0.53 / -0.88
Normal?yes

Meas_CiCa_at_CO2R_400

metadata · numeric
Meas_CiCa_at_CO2R_400 distribution0120 – 0.03068: 10.03068 – 0.06136: 00.06136 – 0.09203: 00.09203 – 0.1227: 00.1227 – 0.1534: 00.1534 – 0.1841: 00.1841 – 0.2147: 00.2147 – 0.2454: 00.2454 – 0.2761: 00.2761 – 0.3068: 00.3068 – 0.3375: 00.3375 – 0.3681: 00.3681 – 0.3988: 10.3988 – 0.4295: 10.4295 – 0.4602: 20.4602 – 0.4909: 00.4909 – 0.5215: 20.5215 – 0.5522: 10.5522 – 0.5829: 10.5829 – 0.6136: 20.6136 – 0.6442: 00.6442 – 0.6749: 00.6749 – 0.7056: 20.7056 – 0.7363: 10.00.20.40.60.8
n / missing74 / 60
Mean ± SD0.5089 ± 0.181
Median0.5312
Range0 – 0.7363
CV0.356
Skew / kurtosis-1.7 / 4.3
Normal?no

Mod_Photo_at_Ci_388

metadata · numeric
Mod_Photo_at_Ci_388 distribution01213.89 – 15.8: 115.8 – 17.7: 117.7 – 19.61: 019.61 – 21.51: 021.51 – 23.42: 023.42 – 25.33: 025.33 – 27.23: 227.23 – 29.14: 129.14 – 31.04: 231.04 – 32.95: 032.95 – 34.85: 034.85 – 36.76: 136.76 – 38.66: 138.66 – 40.57: 040.57 – 42.48: 042.48 – 44.38: 144.38 – 46.29: 046.29 – 48.19: 048.19 – 50.1: 250.1 – 52: 152 – 53.91: 053.91 – 55.81: 055.81 – 57.72: 057.72 – 59.63: 1102050100
n / missing74 / 60
Mean ± SD35.59 ± 13.6
Median32.5
Range13.89 – 59.63
CV0.383
Skew / kurtosis0.14 / -0.81
Normal?yes

Mod_Photo_at_Ci_388_times_CiCa

metadata · numeric
Mod_Photo_at_Ci_388_times_CiCa distribution0123-7.599 – -5.842: 1-5.842 – -4.084: 0-4.084 – -2.326: 0-2.326 – -0.5687: 0-0.5687 – 1.189: 01.189 – 2.947: 02.947 – 4.704: 04.704 – 6.462: 06.462 – 8.22: 08.22 – 9.977: 19.977 – 11.73: 211.73 – 13.49: 013.49 – 15.25: 215.25 – 17.01: 117.01 – 18.77: 318.77 – 20.52: 020.52 – 22.28: 022.28 – 24.04: 224.04 – 25.8: 025.8 – 27.55: 027.55 – 29.31: 129.31 – 31.07: 031.07 – 32.83: 032.83 – 34.58: 1-2002040
n / missing74 / 60
Mean ± SD16.4 ± 9.88
Median16.8
Range-7.599 – 34.58
CV0.603
Skew / kurtosis-0.6 / 2.2
Normal?yes

Est_Photo_stomata_limit

metadata · numeric
Est_Photo_stomata_limit distribution0123.662 – 5.057: 15.057 – 6.452: 06.452 – 7.847: 17.847 – 9.242: 19.242 – 10.64: 110.64 – 12.03: 012.03 – 13.43: 013.43 – 14.82: 114.82 – 16.22: 216.22 – 17.61: 017.61 – 19.01: 019.01 – 20.4: 020.4 – 21.8: 221.8 – 23.19: 023.19 – 24.59: 224.59 – 25.98: 025.98 – 27.38: 027.38 – 28.77: 028.77 – 30.17: 030.17 – 31.56: 131.56 – 32.96: 032.96 – 34.35: 034.35 – 35.75: 035.75 – 37.14: 2010203040
n / missing74 / 60
Mean ± SD19.19 ± 10.4
Median18.04
Range3.662 – 37.14
CV0.542
Skew / kurtosis0.41 / -0.68
Normal?yes

Est_Ci_colimit

metadata · numeric
Est_Ci_colimit distribution012351 – 84: 384 – 117: 1117 – 150: 1150 – 183: 0183 – 216: 1216 – 249: 3249 – 282: 3282 – 315: 0315 – 348: 0348 – 381: 0381 – 414: 0414 – 447: 0447 – 480: 0480 – 513: 0513 – 546: 1546 – 579: 0579 – 612: 0612 – 645: 0645 – 678: 0678 – 711: 0711 – 744: 0744 – 777: 0777 – 810: 0810 – 843: 102004006008001,000
n / missing74 / 60
Mean ± SD244.2 ± 211
Median225.5
Range51 – 843
CV0.865
Skew / kurtosis2 / 4.7
Normal?no

Fitted_Vcmax

metadata · numeric
Fitted_Vcmax distribution012359.45 – 68.49: 168.49 – 77.53: 177.53 – 86.57: 086.57 – 95.6: 195.6 – 104.6: 1104.6 – 113.7: 0113.7 – 122.7: 3122.7 – 131.8: 1131.8 – 140.8: 0140.8 – 149.8: 0149.8 – 158.9: 1158.9 – 167.9: 0167.9 – 177: 1177 – 186: 0186 – 195: 0195 – 204.1: 0204.1 – 213.1: 0213.1 – 222.1: 1222.1 – 231.2: 1231.2 – 240.2: 0240.2 – 249.3: 1249.3 – 258.3: 0258.3 – 267.3: 0267.3 – 276.4: 10100200300
n / missing74 / 60
Mean ± SD149.7 ± 66.8
Median123.9
Range59.45 – 276.4
CV0.446
Skew / kurtosis0.56 / -0.76
Normal?yes

Fitted_Jmax

metadata · numeric
Fitted_Jmax distribution0246120.3 – 127.2: 2127.2 – 134: 0134 – 140.9: 1140.9 – 147.7: 0147.7 – 154.6: 1154.6 – 161.5: 1161.5 – 168.3: 2168.3 – 175.2: 0175.2 – 182: 2182 – 188.9: 0188.9 – 195.7: 0195.7 – 202.6: 0202.6 – 209.4: 0209.4 – 216.3: 0216.3 – 223.2: 0223.2 – 230: 0230 – 236.9: 0236.9 – 243.7: 0243.7 – 250.6: 0250.6 – 257.4: 0257.4 – 264.3: 0264.3 – 271.2: 0271.2 – 278: 0278 – 284.9: 51002005001,000
n / missing74 / 60
Mean ± SD198.6 ± 65.7
Median170.3
Range120.3 – 284.9
CV0.331
Skew / kurtosis0.44 / -1.7
Normal?no

Fitted_Rd

metadata · numeric
Fitted_Rd distribution0123-0.9303 – -0.7131: 1-0.7131 – -0.4959: 0-0.4959 – -0.2786: 0-0.2786 – -0.06138: 0-0.06138 – 0.1559: 10.1559 – 0.3731: 00.3731 – 0.5903: 10.5903 – 0.8076: 00.8076 – 1.025: 11.025 – 1.242: 01.242 – 1.459: 11.459 – 1.677: 21.677 – 1.894: 21.894 – 2.111: 02.111 – 2.328: 12.328 – 2.545: 02.545 – 2.763: 32.763 – 2.98: 02.98 – 3.197: 03.197 – 3.414: 03.414 – 3.632: 03.632 – 3.849: 03.849 – 4.066: 04.066 – 4.283: 1-20246
n / missing74 / 60
Mean ± SD1.646 ± 1.28
Median1.747
Range-0.9303 – 4.283
CV0.776
Skew / kurtosis-0.09 / 0.94
Normal?yes

RMSE_DEoptim

metadata · numeric
RMSE_DEoptim distribution01230.1 – 0.1837: 10.1837 – 0.2673: 00.2673 – 0.3509: 10.3509 – 0.4346: 00.4346 – 0.5182: 30.5182 – 0.6018: 10.6018 – 0.6855: 10.6855 – 0.7691: 10.7691 – 0.8527: 10.8527 – 0.9364: 00.9364 – 1.02: 11.02 – 1.104: 01.104 – 1.187: 01.187 – 1.271: 11.271 – 1.355: 11.355 – 1.438: 01.438 – 1.522: 11.522 – 1.605: 01.605 – 1.689: 01.689 – 1.773: 01.773 – 1.856: 01.856 – 1.94: 01.94 – 2.024: 02.024 – 2.107: 10123
n / missing74 / 60
Mean ± SD0.8309 ± 0.541
Median0.6803
Range0.1 – 2.107
CV0.651
Skew / kurtosis1 / 0.97
Normal?yes

RMSE_photo

metadata · numeric
RMSE_photo distribution0242.481 – 2.683: 42.683 – 2.885: 42.885 – 3.087: 03.087 – 3.289: 03.289 – 3.491: 13.491 – 3.693: 03.693 – 3.895: 13.895 – 4.097: 04.097 – 4.299: 04.299 – 4.501: 04.501 – 4.703: 24.703 – 4.905: 04.905 – 5.107: 05.107 – 5.309: 05.309 – 5.511: 15.511 – 5.713: 05.713 – 5.915: 05.915 – 6.117: 06.117 – 6.319: 06.319 – 6.521: 06.521 – 6.723: 06.723 – 6.925: 06.925 – 7.127: 07.127 – 7.329: 112510
n / missing74 / 60
Mean ± SD3.615 ± 1.43
Median2.838
Range2.481 – 7.329
CV0.395
Skew / kurtosis1.6 / 2.3
Normal?no

raw_label

metadata · categorical
raw_label classes0.0095671420.009567142: 220.0085989870.008598987: 220.0250844810.025084481: 220.0083184440.008318444: 220.0093824090.009382409: 220.0125656910.012565691: 220.006915750.00691575: 220.0243899190.024389919: 220.0120411390.012041139: 110.004943490.00494349: 11+10 more+10 more: 1010
n / missing74 / 0
Classes66
Balance (entropy)0.99
Imbalance ratio2
Top class0.009567142 (2)

reference_value

metadata · numeric
reference_value distribution05100.003656 – 0.005141: 50.005141 – 0.006626: 80.006626 – 0.008111: 40.008111 – 0.009595: 90.009595 – 0.01108: 40.01108 – 0.01257: 30.01257 – 0.01405: 70.01405 – 0.01553: 50.01553 – 0.01702: 10.01702 – 0.0185: 10.0185 – 0.01999: 20.01999 – 0.02147: 10.02147 – 0.02296: 10.02296 – 0.02444: 30.02444 – 0.02593: 40.02593 – 0.02741: 20.02741 – 0.0289: 40.0289 – 0.03038: 10.03038 – 0.03187: 20.03187 – 0.03335: 20.03335 – 0.03484: 20.03484 – 0.03632: 00.03632 – 0.03781: 20.03781 – 0.03929: 10.000.010.020.030.04
n / missing74 / 0
Mean ± SD0.01636 ± 0.00991
Median0.0138
Range0.003656 – 0.03929
CV0.606
Skew / kurtosis0.61 / -0.88
Normal?no
Constant metadata 30
  • SpectralRep1
  • Acquisition_MethodLC
  • Measurement_QuantityReflectance
  • Leaf_or_Canopyleaf
  • Target_TypeVegetation
  • Spectra_UnitsRatio
  • Wavelength_Unitsnanometers
  • Spectral_Resolution1nm
  • CHN_Year_Analyzed2,014
  • CHN_QC1
  • GE_Rep1
  • Leaf_Area_sq_cm2
  • datasetEcoSIS
  • dataset_sluguw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
  • task_typeregression
  • trait_headerLMA_gDW_cm2
  • target_original_nameLMA_gDW_cm2
  • measurement_hintUW-BNL_NASA_HyspIRI_Airborne_Campaign_Compiled_Leaf_and_Canopy_Spectra
  • n_points_original2,151
  • dimensions1D
  • axis_notewavelengths_as_provided_by_EcoSIS_resource
  • official_data_urlhttps://data.ecosis.org/dataset/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
  • official_package_urlhttps://ecosis.org/package/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
  • licenseCreative Commons Attribution
  • license_urlhttp://www.opendefinition.org/licenses/cc-by
  • rights_statusexplicit_open
  • usage_scopepublic_reuse_possible
  • public_release_allowedTrue
  • parent_standardized_dataset_idecosis_uw_bnl_nasa_hyspiri_airborne_campaign_leaf_and_canopy_s_reflectance_nirs
  • notesTask-specific EcoSIS target not present in active parent Y table, wavelengths kept as provided by EcoSIS resource, no project interpolation.

2 variable(s) omitted (no recorded values).

Alignment

Alignment levelsample
Sample id availableyes
Samples74
Observations (total)97
Reps per samplemin 1 · mean 1.311 · max 2

Provenance & citation

ContributorUW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data
Origin · url [open]https://data.ecosis.org/dataset/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
Origin · url [open]https://data.ecosis.org/api/3/action/package_show?id=uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
Origin · url [open]https://ecosis.org/package/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)

Governance & integrity

Tierpublic
LicenseCC-BY-4.0
Permitted useResearch and benchmarking.
Access policyOpen per source license.
RedistributionEcoSIS CKAN API package_show verified open license: Creative Commons Attribution.
Content version1.0.0
Schema / protocol2.0
Content hashcde65afb49d381f7…
Processing hash7f8cf0bcba95dc82…
Metadata hashb4e40e8c4e94cfed…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

ds = get("leaf_lma_gdw_cm2_uw_bnl_ecosis")            # DOI-pinned, checksum-verified, cached
X, y = ds.x(), ds.y()
print(X.shape, y.shape)
card.jsoncroissant.jsonIdentity metadata only — the dataset bytes live at the origin / DOI.