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Multiscale assimilation of Sentinel and Landsat data for soil moisture and Leaf Area Index predictions using an ensemble-Kalman-filter-based assimilation approach in a heterogeneous ecosystem / Nicola Montaldo in Remote sensing, vol 14 n° 14 (July-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 14 (July-2 2022) . - n° 3458 Titre : Multiscale assimilation of Sentinel and Landsat data for soil moisture and Leaf Area Index predictions using an ensemble-Kalman-filter-based assimilation approach in a heterogeneous ecosystem Type de document : Article/Communication Auteurs : Nicola Montaldo, Auteur ; Andrea Gaspa, Auteur ; Roberto Corona, Auteur Année de publication : 2022 Article en page(s) : n° 3458 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] assimilation des données [Termes IGN] bassin méditerranéen [Termes IGN] écosystème [Termes IGN] filtre de Kalman [Termes IGN] humidité du sol [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Leaf Area Index [Termes IGN] modèle dynamique [Termes IGN] modèle hydrographique [Termes IGN] Sardaigne [Termes IGN] zone semi-aride Résumé : (auteur) Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic–land surface model is proposed. It is based on the ensemble Kalman filter (EnKF), and it is not limited to assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil–water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions. Numéro de notice : A2022-574 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14143458 En ligne : https://doi.org/10.3390/rs14143458 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101293 [article]

GNSSseg, a statistical method for the segmentation of daily GNSS IWV time series / Annarosa Quarello in Remote sensing, vol 14 n° 14 (July-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 14 (July-2 2022) . - n° 3379 Titre : GNSSseg, a statistical method for the segmentation of daily GNSS IWV time series Type de document : Article/Communication Auteurs : Annarosa Quarello , Auteur ; Olivier Bock , Auteur ; Emilie Lebarbier, Auteur Année de publication : 2022 Projets : VEGAN / Bock, Olivier Article en page(s) : n° 3379 Note générale : bibliographie This work was developed in the framework of the VEGA Project and supported by the CNRS Program LEFE/INSU. The contribution of the third author has been conducted as part of the Project Labex MME-DII (ANR11-LBX-0023-01) and within the FP2M Federation (CNRS FR 2036). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] coordonnées GPS [Termes IGN] données météorologiques [Termes IGN] erreur systématique [Termes IGN] programmation dynamique [Termes IGN] R (langage) [Termes IGN] segmentation [Termes IGN] série temporelle [Termes IGN] teneur intégrée en vapeur d'eau Résumé : (auteur) Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data, which have not yet been used in this context. This paper proposes a novel segmentation method called segfunc that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm, which is the most efficient exact algorithm to estimate the change point positions. The performance of the method is assessed through numerical simulation experiments. It is implemented in the R package GNSSseg, which is available on the CRAN. This paper presents the application of the method to a real data set from a global network of 120 GNSS stations. A hit rate of 32% is achieved with respect to available metadata. The final segmentation is made in a semi-automatic way, where the change points detected by three different penalty criteria are manually selected. In this case, the hit rate reaches 60% with respect to the metadata. Numéro de notice : A2022-575 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs14143379 Date de publication en ligne : 13/07/2022 En ligne : https://doi.org/10.3390/rs14143379 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101294 [article]