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ECOSTRESS nonphotosyntheticvegetation vswir axis 4d4366d1

ecostress · other

ECOSTRESS nonphotosyntheticvegetation vswir axis 4d4366d1. v2.0 standardized NIRS package: 1 spectral source(s), 3 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecostress
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Private dataset. Full metadata and metrics are shown, but the bytes are not redistributed here — exporting the data requires a Dataverse token. The identity card carries no spectra, only descriptive statistics.
54
samples
2,151
wavelengths
1
sources
3
targets
27
metadata
other
family

Dataset property explorer

Mean profile risk0.45
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
ECOSTRESS nonphotosyntheticvegetation vswir axis 4d4366d1 property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureECOSTRESS nonphotosyntheticvegetation vswir axis 4d4366d1 profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 0.44PCA outliers: 0.59reference: 1.00repeatability: 0.00structure: 0.59ECOSTRESS nonph…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA0.59
Distance à la référence1.00
Répétabilité0.00
Baseline / forme0.44
Structure multi-régimes0.59
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.850.85Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.650.65Signature VERA25-likeSignature VERA25-like: 0.630.63Erreur calibration / référenc…Erreur calibration / référence blanche: 0.560.56Différence de sonde / géométr…Différence de sonde / géométrie: 0.510.51Fond différentFond différent: 0.500.50Dataset multi-régimesDataset multi-régimes: 0.490.49Spectre hors domaine valideSpectre hors domaine valide: 0.480.48
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.85forteSpike rate 1.00, RMS/SAM référence 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur interpolation / rééchantillonnageX0.65moyenneSpike rate 1.00, SNR normal/élevé 1.00, Noise RMS faible 1.00Artefacts numériques ou traitement spectral incorrect.
Signature VERA25-likeX0.63moyenneSpike rate 1.00, RMS/SAM référence 1.00, Jump rate 0.94Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Erreur calibration / référence blancheX0.56moyenneRMS/SAM référence 1.00, artefacts locaux 1.00, PCA Q 0.59Décalage systématique entre campagnes, instruments ou référence blanche.
Différence de sonde / géométrieX0.51moyenneRMS/SAM référence 1.00, PCA Q 0.59, Mahalanobis / T2 0.50Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Fond différentX0.50moyenneRMS/SAM référence 1.00, PCA Q 0.59, Mahalanobis / T2 0.50Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Dataset multi-régimesX0.49moyenneRMS/SAM référence 1.00, Structure PCA 0.59, PCA Q 0.59Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.
Spectre hors domaine valideX0.48moyenneRMS/SAM référence 1.00, Structure PCA 0.59, Mahalanobis / T2 0.50Variété, espèce, lot ou condition différente mais physiquement plausible.

Spectral sources

nonphotosyntheticvegetation vswir

X · other · source instruments vary by sample
nonphotosyntheticvegetation vswir spectra02550751000123q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / none0.35none — median 5.394 (q25–q75 4.362–6.729)0.365none — median 5.36 (q25–q75 4.227–7.034)0.381none — median 5.506 (q25–q75 4.176–8.163)0.396none — median 6.148 (q25–q75 4.272–8.807)0.412none — median 7.295 (q25–q75 5.071–9.493)0.427none — median 7.848 (q25–q75 6.004–11.48)0.443none — median 8.883 (q25–q75 7.095–12.1)0.458none — median 9.692 (q25–q75 7.836–12.64)0.474none — median 10.29 (q25–q75 8.477–13.8)0.489none — median 10.71 (q25–q75 8.753–14.48)0.505none — median 11.77 (q25–q75 9.869–15.76)0.52none — median 13 (q25–q75 10.51–17.26)0.536none — median 14.19 (q25–q75 11.1–19.2)0.551none — median 15.6 (q25–q75 11.75–20.67)0.567none — median 16.27 (q25–q75 12.76–21.6)0.582none — median 17.25 (q25–q75 13.39–21.87)0.597none — median 18.17 (q25–q75 13.96–22.6)0.613none — median 18.68 (q25–q75 14.42–23.93)0.628none — median 20.01 (q25–q75 14.94–26.06)0.644none — median 21.01 (q25–q75 15.5–27.94)0.659none — median 22.84 (q25–q75 16–30.36)0.675none — median 22.91 (q25–q75 14.93–30.69)0.69none — median 23.97 (q25–q75 17.34–33.85)0.706none — median 29.4 (q25–q75 18.71–38.07)0.721none — median 32.13 (q25–q75 20.1–42.93)0.737none — median 34.47 (q25–q75 20.96–46.2)0.752none — median 36 (q25–q75 21.72–48.35)0.768none — median 37.58 (q25–q75 23.3–49.41)0.783none — median 39.09 (q25–q75 24.82–50.27)0.799none — median 40.78 (q25–q75 26.01–51.42)0.814none — median 41.87 (q25–q75 27.01–52.7)0.829none — median 42.71 (q25–q75 28.03–53.9)0.845none — median 43.6 (q25–q75 29.1–55.27)0.86none — median 44.41 (q25–q75 30.11–56.41)0.876none — median 44.92 (q25–q75 30.73–56.87)0.891none — median 45.36 (q25–q75 31.43–56.88)0.907none — median 45.85 (q25–q75 32.14–57.23)0.922none — median 46.26 (q25–q75 32.75–57.54)0.938none — median 46.69 (q25–q75 33.31–58.35)0.953none — median 47.04 (q25–q75 33.87–58.91)0.969none — median 46.38 (q25–q75 34.35–58.46)0.984none — median 47.52 (q25–q75 34.69–58.51)1none — median 48.28 (q25–q75 34.81–58.65)1.015none — median 49.39 (q25–q75 34.67–59.03)1.031none — median 50.36 (q25–q75 35.17–59.42)1.046none — median 50.95 (q25–q75 35.96–59.81)1.062none — median 51.94 (q25–q75 36.82–60.14)1.077none — median 52.9 (q25–q75 37.63–60.4)1.092none — median 53.81 (q25–q75 38.09–60.62)1.108none — median 53.95 (q25–q75 38.86–60.7)1.123none — median 52.94 (q25–q75 39.38–60.95)1.139none — median 53.45 (q25–q75 39.69–59.65)1.154none — median 51.2 (q25–q75 39.56–58.38)1.17none — median 51.37 (q25–q75 38.47–57.5)1.185none — median 51.44 (q25–q75 39.42–58.15)1.201none — median 51.65 (q25–q75 40.44–58.87)1.216none — median 52 (q25–q75 40.97–59.7)1.232none — median 52.62 (q25–q75 41.72–60.87)1.247none — median 53.13 (q25–q75 42.33–61.31)1.263none — median 53.65 (q25–q75 42.89–62.12)1.278none — median 54.49 (q25–q75 43.38–62.65)1.294none — median 55.4 (q25–q75 43.9–63.33)1.309none — median 55.34 (q25–q75 43.42–63.76)1.324none — median 54.86 (q25–q75 42.43–63.77)1.34none — median 54.69 (q25–q75 41.4–62.99)1.355none — median 53.16 (q25–q75 41.27–62.05)1.371none — median 51.55 (q25–q75 40.21–61.12)1.386none — median 49.87 (q25–q75 38.9–59.65)1.402none — median 45.11 (q25–q75 36.26–56.12)1.417none — median 39.69 (q25–q75 31.7–53.29)1.433none — median 38.08 (q25–q75 30.72–51.3)1.448none — median 37.45 (q25–q75 30.7–50.96)1.464none — median 37.84 (q25–q75 31.02–51.2)1.479none — median 38.49 (q25–q75 31.51–52.04)1.495none — median 39.25 (q25–q75 32.23–53.38)1.51none — median 39.97 (q25–q75 33.25–54.75)1.526none — median 40.68 (q25–q75 33.97–56.11)1.541none — median 41.11 (q25–q75 36.19–56.8)1.556none — median 41.55 (q25–q75 36.59–57.45)1.572none — median 41.97 (q25–q75 36.99–58.14)1.587none — median 42.5 (q25–q75 37.48–58.86)1.603none — median 43.34 (q25–q75 38.32–59.47)1.618none — median 44.14 (q25–q75 39.06–59.83)1.634none — median 44.67 (q25–q75 39.49–60.25)1.649none — median 45.01 (q25–q75 39.6–60.83)1.665none — median 45.19 (q25–q75 38.76–61.06)1.68none — median 45.08 (q25–q75 38.14–60.96)1.696none — median 43.95 (q25–q75 37.35–60.7)1.711none — median 43.13 (q25–q75 36.84–59.97)1.727none — median 42.82 (q25–q75 36.33–59.04)1.742none — median 42.27 (q25–q75 36.38–59.59)1.758none — median 42.04 (q25–q75 36.47–58.99)1.773none — median 42.54 (q25–q75 36.83–58.71)1.788none — median 42.95 (q25–q75 37.1–58.67)1.804none — median 43.34 (q25–q75 37.33–58.83)1.819none — median 43.78 (q25–q75 37.59–59.02)1.835none — median 43.95 (q25–q75 38.11–59.24)1.85none — median 44.52 (q25–q75 37.04–59.04)1.866none — median 44.54 (q25–q75 35.02–57.86)1.881none — median 42.14 (q25–q75 33.8–56.31)1.897none — median 38.01 (q25–q75 27.67–49.48)1.912none — median 33.91 (q25–q75 22.19–44.17)1.928none — median 33.21 (q25–q75 20.89–43.15)1.943none — median 34.71 (q25–q75 21.81–44.31)1.959none — median 36.44 (q25–q75 23.37–46.19)1.974none — median 37.97 (q25–q75 24.87–47.9)1.99none — median 39.15 (q25–q75 26.26–49.81)2.005none — median 39.47 (q25–q75 26.9–51.01)2.021none — median 38.45 (q25–q75 26.45–50.77)2.036none — median 36.38 (q25–q75 25.53–50.01)2.051none — median 35.03 (q25–q75 24.5–48.56)2.067none — median 34.44 (q25–q75 23.31–47.08)2.082none — median 34.23 (q25–q75 23.53–46.43)2.098none — median 33.89 (q25–q75 23.18–46.3)2.113none — median 33.67 (q25–q75 23.49–46.58)2.129none — median 33.57 (q25–q75 23.6–47.11)2.144none — median 33.48 (q25–q75 23.89–47.4)2.16none — median 34.15 (q25–q75 24.22–47.73)2.175none — median 34.63 (q25–q75 24.89–48.04)2.191none — median 34.97 (q25–q75 25.57–48.49)2.206none — median 35.24 (q25–q75 26.14–48.75)2.222none — median 35.15 (q25–q75 26.24–48.79)2.237none — median 33.85 (q25–q75 25.28–48.03)2.253none — median 31.44 (q25–q75 23.14–45.77)2.268none — median 29.8 (q25–q75 20.91–43.67)2.283none — median 29.45 (q25–q75 20.03–42.76)2.299none — median 29.36 (q25–q75 18.9–41.86)2.314none — median 28.56 (q25–q75 19–41.54)2.33none — median 28.44 (q25–q75 19.25–41.6)2.345none — median 28.29 (q25–q75 18.71–41.38)2.361none — median 28.64 (q25–q75 19.16–41.58)2.376none — median 28.85 (q25–q75 19.2–41.42)2.392none — median 28.99 (q25–q75 19.22–41.21)2.407none — median 28.86 (q25–q75 19.01–40.71)2.423none — median 28.04 (q25–q75 18.28–39.49)2.438none — median 26.92 (q25–q75 17.37–38.13)2.454none — median 26.27 (q25–q75 16.06–36.6)2.469none — median 25.8 (q25–q75 14.96–35.12)2.485none — median 25.21 (q25–q75 14.29–33.92)2.5none — median 25.45 (q25–q75 13.91–34.03)

Sampling

Wavelengths2,151
Axis range0.35–2.5 none
Mean spacing0.001 none
Griduniform
Observations57

Signal & quality

Value range2.66 – 99.9
Mean range7.11 – 55.4
Mean level38.08
Area81.89
PTP48.32
Noise RMS0.0024244
SNR1.6e+04
SNR dB8e+01 dB
Dynamic range48.3
Smoothness0.02467
Saturated0.0%
X-outliers18

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count3,284
Spike rate2.68%
Jump count1,150
Jump rate0.94%
Clip fraction0.00%

Shape & reference

Baseline slope10.735
Curvature RMS0.024867
D1 RMS0.089988
RMS to mean11.561
RMS p9522.965
SAM to mean0.17238
SAM p950.50708
Affine offset p9514.774
Affine gain p95 Δ0.40718
Affine residual p9515.281
Xcorr lag p9550

Outliers & repeatability

PCA Q p95/median4.7
Hotelling T2 p95/median4
Mahalanobis H p95/median2
Repeat groups3
RMS intra-ID0
SAM intra-ID3.9425e-08
CV intra-ID0

Dimensionality (PCA)

Effective rank2.1
PCs → 95% var2
PCs → 99% var4
Top-10 cum. var100.0%
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_reflectance38.0790.44moyenValeur 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_curve81.8910.44moyenValeur 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_peak48.320.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance389.590.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms0.00242440.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr157070.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min57.3110.00faibleZone 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_count3,2841.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate2.68%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count1,1500.94fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate0.938%0.94fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.00245%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope10.7350.44moyenDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.0248670.05faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.0899880.04faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio4.70550.59moyenSpectre atypiqueArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio3.96530.50moyenExtrême mais cohérentVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio1.99130.50moyenOutlier globalDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p9522.9651.00fortSpectre 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_p950.507081.00fortForme différenteFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDrepeatability.rms_intra_id00.00faibleStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDrepeatability.sam_intra_id3.9425e-080.00faibleStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDrepeatability.cv_intra_id00.00faibleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densitystructure.pca_score_density0.00480010.59moyenSous-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_p951.76720.38faiblePopulation 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_p950.585030.59moyenSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-2,000-1,00001,000-1,000-50005001,000PC1 -241.3 · PC2 -313.5PC1 133 · PC2 -564.5PC1 224.3 · PC2 -282.7PC1 -460.3 · PC2 -90.23PC1 -1940 · PC2 478.1PC1 -1784 · PC2 426.2PC1 -929.8 · PC2 205.8PC1 -972.2 · PC2 265.6PC1 271.2 · PC2 -388.4PC1 448.5 · PC2 -586.9PC1 6.628 · PC2 -569.9PC1 -214.9 · PC2 -189.3PC1 -908.6 · PC2 66.8PC1 -486.1 · PC2 -251.5PC1 -184.8 · PC2 -100.9PC1 -440.3 · PC2 -144.6PC1 503.9 · PC2 -423.3PC1 595.4 · PC2 -414.7PC1 -227.2 · PC2 -191.2PC1 -297 · PC2 -109.5PC1 353.2 · PC2 -517.8PC1 220.4 · PC2 -350.3PC1 -470.3 · PC2 -267.5PC1 -568.9 · PC2 -403.4PC1 176.4 · PC2 -444PC1 75.83 · PC2 -399.5PC1 -197.3 · PC2 -435.2PC1 818 · PC2 357.8PC1 936.8 · PC2 229PC1 783.2 · PC2 690PC1 629.4 · PC2 678.6PC1 818 · PC2 357.8PC1 936.8 · PC2 229PC1 783.2 · PC2 690PC1 271.8 · PC2 558PC1 272.2 · PC2 385.4PC1 -433.7 · PC2 -58.45PC1 -501.3 · PC2 -156PC1 225.1 · PC2 -430.4PC1 -435.1 · PC2 178.5PC1 235.5 · PC2 -371.5PC1 -671.1 · PC2 104.4PC1 -638.4 · PC2 193.8PC1 -129.2 · PC2 -18.4PC1 -464.8 · PC2 159.8PC1 284.5 · PC2 96.74PC1 261 · PC2 141.9PC1 249.7 · PC2 162.7PC1 535.4 · PC2 120.7PC1 530.5 · PC2 -173.2PC1 652.7 · PC2 -313.3PC1 109 · PC2 414.1PC1 267.4 · PC2 337.4PC1 278.7 · PC2 316.4PC1 215 · PC2 316.7PC1 2.791 · PC2 519.8PC1 491.6 · PC2 278.9PC1 (71.2%)PC2 (25.2%)57 scores
PCA explained variance0%25%50%75%100%PC1: 71.2% (cumulative 71.2%)1PC2: 25.2% (cumulative 96.4%)2PC3: 2.3% (cumulative 98.7%)3PC4: 0.5% (cumulative 99.2%)4PC5: 0.3% (cumulative 99.5%)5PC6: 0.2% (cumulative 99.7%)6PC7: 0.1% (cumulative 99.8%)7PC8: 0.1% (cumulative 99.9%)8PC9: 0.0% (cumulative 99.9%)9PC10: 0.0% (cumulative 100.0%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)

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 3

material_name

target · categorical
material_name classesPinus coulteri barkPinus coulteri bark: 33Quercus sp. litterQuercus sp. litter: 33Abies concolor dry needlesAbies concolor dry needles: 33Pinus coulteri dry needlesPinus coulteri dry needles: 33Pinus lambertiana dry needlesPinus lambertiana dry needles: 33Pinus ponderosa dry needlesPinus ponderosa dry needles: 33Abies concolor barkAbies concolor bark: 22Acer rubrumAcer rubrum: 22Betula papyriferaBetula papyrifera: 22Calocedrus decurrens barkCalocedrus decurrens bark: 22+10 more+10 more: 2020
n / missing54 / 0
Classes28
Balance (entropy)0.98
Imbalance ratio3
Top classPinus coulteri bark (3)

type

target · categorical
type classesnon photosynthetic vegetationnon photosynthetic vegetation: 5151vegetationvegetation: 33
n / missing54 / 0
Classes2
Balance (entropy)0.31
Imbalance ratio17
Top classnon photosynthetic vegetation (51)

class_label

target · categorical
class_label classesbarkbark: 1818needlesneedles: 1212leavesleaves: 1111branchesbranches: 99ShrubShrub: 33flowersflowers: 11
n / missing54 / 0
Classes6
Balance (entropy)0.87
Imbalance ratio18
Top classbark (18)

Metadata 7

ecostress_resource_id

metadata · categorical
n / missing54 / 0
Classes54
Balance (entropy)1
Imbalance ratio1
Top classnonphotosyntheticvegetation.bark.abies.concolor.vswir.vh311.ucsb.asd.spectrum (1)

material_type

metadata · categorical
material_type classesnon photosynthetic vegetationnon photosynthetic vegetation: 5151vegetationvegetation: 33
n / missing54 / 0
Classes2
Balance (entropy)0.31
Imbalance ratio17
Top classnon photosynthetic vegetation (51)

location

metadata · categorical
location classes37.04403333, -119.30225, WGS8437.04403333, -119.30225, WGS84: 1919USA, Massachusetts, Harvard F…USA, Massachusetts, Harvard Forest: 101034.413836, -119.880173, WGS8434.413836, -119.880173, WGS84: 8834.51457, -119.79877, WGS8434.51457, -119.79877, WGS84: 6634.4925, -119.7904, WGS8434.4925, -119.7904, WGS84: 4437.0443, -119.3026, WGS8437.0443, -119.3026, WGS84: 4434.698, -120.0477, WGS8434.698, -120.0477, WGS84: 33
n / missing54 / 0
Classes7
Balance (entropy)0.9
Imbalance ratio6
Top class37.04403333, -119.30225, WGS84 (19)

date

metadata · categorical
date classes5/10/20145/10/2014: 19193/18/20153/18/2015: 18187/8/20137/8/2013: 101011/2/201311/2/2013: 77
n / missing54 / 0
Classes4
Balance (entropy)0.95
Imbalance ratio3
Top class5/10/2014 (19)

species

metadata · categorical
species classesbarkbark: 1818needlesneedles: 1212leavesleaves: 1111branchesbranches: 99ShrubShrub: 33flowersflowers: 11
n / missing54 / 0
Classes6
Balance (entropy)0.87
Imbalance ratio18
Top classbark (18)

sample_description

metadata · categorical
sample_description classesSamples were collected as par…Samples were collected as part of the HyspIRI Airborne Campaign proposal titled: HyspIRI discrimination of plant species and functional types along a strong environmental temperature gradient. The same materials were processed in the Nicolet and then measured using the ASD.: 2525Sample is bark with lichen an…Sample is bark with lichen and moss. Samples were collected as part of NSF Macrosystem Biology proposal titled: Collaborative Research: Thermal controls on ecosystem metabolism and function: scaling from leaves to canopies to regions. Samples were collected and overnighted to JPL facilities for processing. The same leaves were processed in the Nicolet and then measured using the ASD.: 88Samples were collected as par…Samples were collected as part of the HyspIRI Airborne Campaign. 48 individual plants were sampled in three times in 2013 - spring summer and fall. The name of the sample includes a 1 2 or 3 which references a different individual of the species. Samples were taken to JPL and processed within 48 hours of collection. The same leaves were processed in the Nicolet and then measured using the ASD.: 77Samples were collected as par…Samples were collected as part of the HyspIRI Airborne Campaign proposal titled: HyspIRI discrimination of plant species and functional types along a strong environmental temperature gradient. The same materials were processed in the Nicolet and then measured using the ASD. Sample was taken from an alive plant.: 33Samples were collected as par…Samples were collected as part of the HyspIRI Airborne Campaign proposal titled: HyspIRI discrimination of plant species and functional types along a strong environmental temperature gradient. The same materials were processed in the Nicolet and then measured using the ASD. Samples were alive, but dying and were yellow.: 22Samples were collected as par…Samples were collected as part of NSF Macrosystem Biology proposal titled: Collaborative Research: Thermal controls on ecosystem metabolism and function: scaling from leaves to canopies to regions. Samples were collected and overnighted to JPL facilities for processing. The same leaves were processed in the Nicolet and then measured using the ASD.: 22Samples were collected as par…Samples were collected as part of the HyspIRI Airborne Campaign proposal titled: HyspIRI discrimination of plant species and functional types along a strong environmental temperature gradient. The same materials were processed in the Nicolet and then measured using the ASD. Samples were previous year's litter, mostly decomposed grasses.: 22Samples were collected as par…Samples were collected as part of the HyspIRI Airborne Campaign proposal titled: HyspIRI discrimination of plant species and functional types along a strong environmental temperature gradient. The same materials were processed in the Nicolet and then measured using the ASD. Samples were taken from dead trees.: 22Samples were collected as par…Samples were collected as part of the HyspIRI Airborne Campaign proposal titled: HyspIRI discrimination of plant species and functional types along a strong environmental temperature gradient. The same materials were processed in the Nicolet and then measured using the ASD. Sample was from recently dead plant.: 11Samples were collected as par…Samples were collected as part of the HyspIRI Airborne Campaign proposal titled: HyspIRI discrimination of plant species and functional types along a strong environmental temperature gradient. The same materials were processed in the Nicolet and then measured using the ASD. Samples were current year's litter, grasses.: 11+1 more+1 more: 11
n / missing54 / 0
Classes11
Balance (entropy)0.74
Imbalance ratio25
Top classSamples were collected as part of the HyspIRI Airborne Campaign proposal titled: HyspIRI discrimination of plant species and functional types along a strong environmental temperature gradient. The same materials were processed in the Nicolet and then measured using the ASD. (25)

notes

metadata · categorical
n / missing54 / 37
Classes17
Balance (entropy)1
Imbalance ratio1
Top classnonphotosyntheticvegetation.bark.acer.rubrum.vswir.acru-1-81.ucsb.asd.ancillary.txt (1)
Constant metadata 13
  • categorynonphotosyntheticvegetation
  • instrumentucsb.asd
  • acquisition_modeBidirectional reflectance
  • signal_typeReflectance (percentage)
  • axis_unitWavelength (micrometers)
  • axis_min0.35
  • axis_max2.5
  • n_points_original2,151
  • publication_doi10.1016/j.rse.2019.05.015
  • citationMeerdink et al. 2019, Baldridge et al. 2009
  • licenseCopyright California Institute of Technology / JPL, all rights reserved
  • rights_statusmanual_review_needed
  • usage_scopeprivate_use_only

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

Alignment

Alignment levelobservation
Sample id availableyes
Samples54
Observations (total)57
Reps per samplemin 1 · mean 1.056 · max 2

Provenance & citation

ContributorECOSTRESS Spectral Library
Origin · url [open]https://speclib.jpl.nasa.gov/download
Origin · url [open]https://speclib.jpl.nasa.gov/
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)
Publication10.1016/j.rse.2019.05.015 — The ECOSTRESS spectral library version 1.0
Publication10.1016/j.rse.2008.11.007 — The ASTER Spectral Library Version 2.0

Governance & integrity

Tierprivate
LicenseLicenseRef-not-cleared
Permitted useResearch and benchmarking; private use only.
Access policyManual download / private-use-only per source.
RedistributionOfficial ECOSTRESS page requests citation and states copyright/all rights reserved; converted matrices are private/internal until redistribution rights are clarified.
Content version1.0.0
Schema / protocol2.0
Content hashfc3c300017dd8d4b…
Processing hashc9a49f9a51b37a85…
Metadata hashf19909c128554760…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

# private dataset — export requires a Dataverse token
ds = get("ecostress_nonphotosyntheticvegetation_vswir_2151points_57rows", token="…")
X, y = ds.x(), ds.y()
print(X.shape, y.shape)

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