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Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 9 [15/06/2019] . - pp 1022-1041 Titre : Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; A. Choudhary, Auteur ; D. K. Gupta, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1022-1041 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] couvert végétal [Termes IGN] échantillonnage d'image [Termes IGN] humidité du sol [Termes IGN] image radar moirée [Termes IGN] image Radarsat [Termes IGN] image Sentinel-SAR [Termes IGN] modèle de régression [Termes IGN] polarisation [Termes IGN] réseau neuronal artificiel [Termes IGN] Uttar Pradesh (Inde ; état) Résumé : (auteur) In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R2 (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R2 (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R2 (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models. Numéro de notice : A2019-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1464601 Date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1464601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93876 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2019091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)  

Public [article]  inGeocarto international > vol 33 n° 9 (September 2018) . - pp 942 - 956 Titre : Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; R. Prasad, Auteur ; D. K. Gupta, Auteur ; V. N. Mishra, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 942 - 956 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande C [Termes IGN] blé (céréale) [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] croissance végétale [Termes IGN] cultures [Termes IGN] données polarimétriques [Termes IGN] estimation statistique [Termes IGN] hiver [Termes IGN] image Sentinel-SAR [Termes IGN] Leaf Area Index [Termes IGN] régression [Termes IGN] régression linéaire [Termes IGN] réseau neuronal artificiel [Termes IGN] séparateur à vaste marge [Termes IGN] teneur en eau de la végétation Résumé : (Auteur) In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m2 area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R2 = 0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R2 = 0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms. Numéro de notice : A2018-337 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1316781 Date de publication en ligne : 18/04/2017 En ligne : https://doi.org/10.1080/10106049.2017.1316781 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90551 [article]

Operation analysis of a reservoir in GIS environment using remote sensing inputs / M.K. Goel in International Journal of Remote Sensing IJRS, vol 28 n° 1-2 (January 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n° 1-2 (January 2007) . - pp 335 - 352 Titre : Operation analysis of a reservoir in GIS environment using remote sensing inputs Type de document : Article/Communication Auteurs : M.K. Goel, Auteur ; P. Kumar, Auteur ; Sanjay K. Jain, Auteur ; R.S. Tiwaris, Auteur Année de publication : 2007 Article en page(s) : pp 335 - 352 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] allocation [Termes IGN] analyse des besoins [Termes IGN] eau pluviale [Termes IGN] gestion de l'eau [Termes IGN] image IRS-LISS [Termes IGN] Inde [Termes IGN] inférence [Termes IGN] irrigation [Termes IGN] simulation hydrodynamique [Termes IGN] système d'information géographique [Termes IGN] système expert Résumé : (Auteur) Reservoir management involves allocating available water among multiple uses and users, minimizing the risks of water shortages and flooding and optimizing the beneficial use of water. Irrigation demands from a reservoir, which are generally computed by using the design cropping pattern and average rainfall conditions, may vary over the years depending on the actual cropping pattern and meteorological conditions. This study demonstrates the utility of remote sensing inputs and geographic information system (GIS) environment for determining realistic irrigation demands from a reservoir. Remote sensing data are used to map the actual cropping pattern in the command area while the GIS is used for integrating the field-level irrigation demands up to the canal system head. Ten daily irrigation demands in the command area of Samrat Ashok Sagar Reservoir in Madhya Pradesh, India, have been estimated and the reservoir operation policy has been derived. Through the simulation analysis with 29 years of inflow data, rule curves have been derived for the operation of reservoir so that water deficit (if any) can be distributed in time much in advance and severe crop failure can be avoided. Inferences drawn from the analysis can guide the system operator in using the available water in a scientific and judicious manner. Numéro de notice : A2007-036 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600735616 En ligne : https://doi.org/10.1080/01431160600735616 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82959 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07011	RAB	Revue	Centre de documentation	En réserve L003	Disponible