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Detrending climate data prior to climate–growth analyses in dendroecology: a common best practice? / Clémentine Ols in Dendrochronologia, vol inconnu (2023)  

Public [article]  inDendrochronologia > vol inconnu (2023) . - n° 126094 Titre : Detrending climate data prior to climate–growth analyses in dendroecology: a common best practice? Type de document : Article/Communication Auteurs : Clémentine Ols , Auteur ; Stefan Klesse, Auteur ; Martin P. Girardin, Auteur ; Margaret E.K. Evans, Auteur ; R. Justin DeRose, Auteur ; Valérie Trouet, Auteur Année de publication : 2023 Article en page(s) : n° 126094 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cerne [Termes IGN] changement climatique [Termes IGN] croissance végétale [Termes IGN] dendrochronologie [Termes IGN] série temporelle [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Tree growth varies closely with high–frequency climate variability. Since the 1930s detrending climate data prior to comparing them with tree growth data has been shown to better capture tree growth sensitivity to climate. However, in a context of increasingly pronounced trends in climate, this practice remains surprisingly rare in dendroecology. In a review of Dendrochronologia over the 2018-2021 period, we found that less than 20% of dendroecological studies detrended climate data prior to climate-growth analyses. With an illustrative study, we want to remind the dendroecology community that such a procedure is still, if not more than ever, rational and relevant. We investigated the effects of detrending climate data on climate–growth relationships across North America over the 1951–2000 period. We used a network of 2,536 tree individual ring-width series from the Canadian and Western US forest inventories. We compared correlations between tree growth and seasonal climate data (Tmin, Tmax, Prec) both raw and detrended. Detrending approaches included a linear regression, 30-yr and 100-yr cubic smoothing splines. Our results indicate that on average the detrending of climate data increased climate–growth correlations. In addition, we observed that strong trends in climate data translated to higher variability in inferred correlations based on raw vs. detrended climate data. We provide further evidence that our results hold true for the entire spectrum of dendroecological studies using either mean site chronologies and correlations coefficients, or individual tree time series within a mixed-effects model framework where regression coefficients are used more commonly. We show that even without a change in correlation, regression coefficients can change a lot and we tend to underestimate the true climate impact on growth in case of climate variables containing trends. This study demonstrates that treating climate and tree-ring time series “like-for-like” is a necessary procedure to reduce false negatives and positives in dendroecological studies. Concluding, we recommend using the same detrending for climate and tree growth data when tree-ring time series are detrended with splines or similar frequency-based filters. Numéro de notice : A2023-092 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.dendro.2023.126094 Date de publication en ligne : 05/05/2023 En ligne : https://doi.org/10.1016/j.dendro.2023.126094 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103182 [article]

A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT / Shengli Tao in Earth System Science Data, vol 15 n° 4 (2023)  

Public [article]  inEarth System Science Data > vol 15 n° 4 (2023) . - pp 1577 - 1596 Titre : A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Zurui Ao, Auteur ; Jean-Pierre Wigneron, Auteur ; Sassan Saatchi, Auteur ; Philippe Ciais, Auteur ; Jérôme Chave, Auteur ; Thuy Le Toan, Auteur ; Pierre-Louis Frison , Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1577 - 1596 Note générale : bibliographie Data description paper Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] bande Ku [Termes IGN] fusion de données [Termes IGN] image radar moirée [Termes IGN] régression [Termes IGN] série temporelle Résumé : (auteur) Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions. Numéro de notice : A2023-097 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-15-1577-2023 Date de publication en ligne : 12/04/2023 En ligne : https://doi.org/10.5194/essd-15-1577-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103215 [article]

Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks / Sina Mohammadi in ISPRS Journal of photogrammetry and remote sensing, vol 198 (April 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 198 (April 2023) . - pp 272 - 283 Titre : Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks Type de document : Article/Communication Auteurs : Sina Mohammadi, Auteur ; Mariana Belgiu, Auteur ; Alfred Stein, Auteur Année de publication : 2023 Article en page(s) : pp 272 - 283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage profond [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] cultures [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] série temporelle Résumé : (auteur) Deep learning methods have achieved promising results in crop mapping using satellite image time series. A challenge still remains on how to better learn discriminative feature representations to detect crop types when the model is applied to unseen data. To address this challenge and reveal the importance of proper supervision of deep neural networks in improving performance, we propose to supervise intermediate layers of a designed 3D Fully Convolutional Neural Network (FCN) by employing two middle supervision methods: Cross-entropy loss Middle Supervision (CE-MidS) and a novel middle supervision method, namely Supervised Contrastive loss Middle Supervision (SupCon-MidS). This method pulls together features belonging to the same class in embedding space, while pushing apart features from different classes. We demonstrate that SupCon-MidS enhances feature discrimination and clustering throughout the network, thereby improving the network performance. In addition, we employ two output supervision methods, namely F1 loss and Intersection Over Union (IOU) loss. Our experiments on identifying corn, soybean, and the class Other from Landsat image time series in the U.S. corn belt show that the best set-up of our method, namely IOU+SupCon-MidS, is able to outperform the state-of-the-art methods by scores of 3.5% and 0.5% on average when testing its accuracy across a different year (local test) and different regions (spatial test), respectively. Further, adding SupCon-MidS to the output supervision methods improves scores by 1.2% and 7.6% on average in local and spatial tests, respectively. We conclude that proper supervision of deep neural networks plays a significant role in improving crop mapping performance. The code and data are available at: https://github.com/Sina-Mohammadi/CropSupervision. Numéro de notice : A2023-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.03.007 Date de publication en ligne : 29/03/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103105 [article]

Temporal spectrum of spatial correlations between GNSS station position time series / Yujiao Niu in Journal of geodesy, vol 97 n° 2 (February 2023)  

Public [article]  inJournal of geodesy > vol 97 n° 2 (February 2023) . - n° 12 Titre : Temporal spectrum of spatial correlations between GNSS station position time series Type de document : Article/Communication Auteurs : Yujiao Niu, Auteur ; Paul Rebischung , Auteur ; Min Li, Auteur ; Na Wei, Auteur ; Chuang Shi, Auteur ; Zuheir Altamimi , Auteur Année de publication : 2023 Article en page(s) : n° 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle [Termes IGN] bruit blanc [Termes IGN] corrélation automatique de points homologues [Termes IGN] filtrage du bruit [Termes IGN] série temporelle [Termes IGN] station GNSS [Termes IGN] transformation de Fourier [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) The background noise in Global Navigation Satellite Systems (GNSS) station position time series is known to be both temporally and spatially correlated. Its temporal correlations are well modeled and routinely taken into account when deriving parameters of interest like station velocities. On the other hand, a general model of the spatial correlations in GNSS time series is lacking, and they are usually ignored, although their consideration could benefit several purposes such as offset detection, velocity estimation or spatial filtering. In order to improve the realism of current spatio-temporal correlation models, we investigate in this study how the spatial correlations of GNSS time series vary with the temporal frequency. A frequency-dependent measure of the spatial correlations is therefore introduced and applied to station position time series from the latest reprocessing campaign of the International GNSS Service (IGS), as well as to Precise Point Positioning time series provided by the Nevada Geodetic Laboratory (NGL). Different spatial correlation regimes are thus evidenced at different temporal frequencies. The different levels of spatial correlations between IGS and NGL datasets furthermore suggest that some part of the spatially correlated background noise in GNSS time series consists of GNSS errors rather than aperiodic Earth surface deformation signal. Numéro de notice : A2023-226 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-023-01703-7 Date de publication en ligne : 06/02/2023 En ligne : https://doi.org/10.1007/s00190-023-01703-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102746 [article]

The ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science / Médéric Gravelle in Earth System Science Data, vol 15 n° 1 (2023)  

Public [article]  inEarth System Science Data > vol 15 n° 1 (2023) . - pp 497 - 509 Titre : The ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science Type de document : Article/Communication Auteurs : Médéric Gravelle, Auteur ; Guy Wöppelmann , Auteur ; Kevin Gobron, Auteur ; Zuheir Altamimi , Auteur ; Mikaël Guichard, Auteur ; Thomas Herring, Auteur ; Paul Rebischung , Auteur Année de publication : 2023 Article en page(s) : pp 497 - 509 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] déformation verticale de la croute terrestre [Termes IGN] données marégraphiques [Termes IGN] littoral [Termes IGN] série temporelle [Termes IGN] système d'observation du niveau des eaux littorales SONEL [Termes IGN] vitesse de déplacement Résumé : (auteur) A new reanalysis of GNSS data at or near tide gauges worldwide was produced by the university of La Rochelle (ULR) group within the 3rd International GNSS Service (IGS) reprocessing campaign (repro3). The new solution, called ULR-repro3, complies with the IGS standards adopted for repro3, implementing advances in data modelling and corrections since the previous reanalysis campaign, and extending the average record length by about 7 years. The results presented here focus on the main products of interest for sea level science, that is, the station position time series and associated velocities on the vertical component at tide gauges. These products are useful to estimate accurate vertical land motion at the coast and supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas. To provide realistic velocity uncertainty estimates, the noise content in the position time series was investigated considering the impact of non-tidal atmospheric loading. Overall, the ULR-repro3 position time series show reduced white noise and power-law amplitudes and station velocity uncertainties compared to the previous reanalysis. The products are available via SONEL (https://doi.org/10.26166/sonel_ulr7a; Gravelle et al., 2022). Numéro de notice : A2023-079 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-15-497-2023 Date de publication en ligne : 01/02/2023 En ligne : https://doi.org/10.5194/essd-15-497-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102521 [article]

CDPS: Constrained DTW-Preserving Shapelets / Hussein El Amouri (2023)  

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Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data / Thuong V. Tran in GIScience and remote sensing, vol 60 n° 1 (2023)  

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Establishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP / Pengfei Xia in GPS solutions, vol 27 n° 1 (January 2023)  

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Mobile mapping mesh change detection and update / Teng Wu (2023)  

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Structured learning of geospatial data / Loïc Landrieu (2023)  

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Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) / Rolando Carbonari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

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Bayesian inference on the initiation phase of the 2014 Iquique, Chile, earthquake / Cédric Twardzik in Earth and planetary science letters, vol 600 (15 December 2022)  

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Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery / Thomas A. Lake in Remote sensing in ecology and conservation, vol 8 n° 6 (December 2022)  

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Multi-frequency simulation of ionospheric scintillation using a phase-screen model / Fernando D. Nunes in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)  

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Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)  

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