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Soil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3190 - 3201 Titre : Soil moisture estimation with SVR and data augmentation based on alpha approximation method Type de document : Article/Communication Auteurs : Wei Xu, Auteur ; Zhaoxu Zhang, Auteur ; Qiming Qin, Auteur Année de publication : 2020 Article en page(s) : pp 3190 - 3201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] approximation [Termes IGN] erreur moyenne quadratique [Termes IGN] humidité du sol [Termes IGN] image ALOS [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] irrigation [Termes IGN] modèle de régression [Termes IGN] surveillance agricole Résumé : (auteur) Soil moisture content is an important parameter in hydrological, meteorological, and agricultural applications. Balenzano et al. proposed the alpha approximation method in 2011 for solving some complex issues during the retrieval of soil moisture over agricultural crops with synthetic aperture radar data. However, determining the constraints and solving the underdetermined system of equations in this method add new challenges. Considering the questions of constraints and underdetermined system of equations, the alpha approximation method is used to augment the measured data, and can avoid solving the underdetermined system of equations with constraints directly. Then, these data are applied in a support vector regression machine for soil moisture estimation. It is found that when an optimal model is determined, the method proposed in this article is superior to the direct use of the alpha approximation method, and the root-mean-squared error (RMSE) decreased from 0.0775 to 0.0339 and R 2 increased from 0.0467 to 0.6491. In addition, the method obtained a good result from a data set collected that included a different growing period of crops by changing the standardized method from StandardScaler to Scale , where the RMSE is 0.0501 and R 2 is 0.3204. This indicates the good generalization capability of this method. In conclusion, the proposed method solves the two questions effectively and provides a potential way for long-time or large-scale soil moisture monitoring with much less in situ measurements. Numéro de notice : A2020-235 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2950321 Date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2950321 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94981 [article]

Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery / Yuanheng Sun in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 826 - 840 Titre : Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery Type de document : Article/Communication Auteurs : Yuanheng Sun, Auteur ; Qiming Qin, Auteur ; Huazhong Ren, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 826 - 840 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande rouge [Termes IGN] canopée [Termes IGN] Chine [Termes IGN] image multibande [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] indice foliaire [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] teneur en chlorophylle des feuilles Résumé : (auteur) The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5–7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images. Numéro de notice : A2020-069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2940826 Date de publication en ligne : 27/09/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2940826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94615 [article]

Mining trajectory data and geotagged data in social media for road map inference: Mining social media for road map inference / Jun Li in Transactions in GIS, vol 19 n° 1 (February 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 1 (February 2015) . - pp 1 - 18 Titre : Mining trajectory data and geotagged data in social media for road map inference: Mining social media for road map inference Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Qiming Qin, Auteur ; Jiawei Han, Auteur ; Lu-An Tang, Auteur ; Kin Hou Lei, Auteur Année de publication : 2015 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données routières [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] exploration de données géographiques [Termes IGN] géobalise [Termes IGN] inférence [Termes IGN] mise à jour de base de données [Termes IGN] traitement du langage naturel Résumé : (auteur) As mapping is costly and labor-intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC-based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps. Numéro de notice : A2015--118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12072 Date de publication en ligne : 15/01/2014 En ligne : http://doi.wiley.com/10.1111/tgis.12072 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102453 [article]