Centre de documentation
scientifique

Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data / Long Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 43 - 54 Titre : Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data Type de document : Article/Communication Auteurs : Long Wang, Auteur ; Binbin He, Auteur ; Xiaojing Bai, Auteur ; Minfeng Xing, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Enhanced vegetation index [Termes IGN] étalonnage de modèle [Termes IGN] humidité du sol [Termes IGN] image Sentinel-SAR [Termes IGN] image Terra-MODIS [Termes IGN] indice foliaire [Termes IGN] Iowa (Etats-Unis) [Termes IGN] Leaf Area Index [Termes IGN] modèle de rétrodiffusion [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] série temporelle Résumé : (auteur) Soil moisture is an important state variable of the land surface ecosystem. In this paper, the water cloud model (WCM) and advanced integral equation model (AIEM) are coupled to retrieve soil moisture using time series Sentinel-1A data and moderate resolution imaging spectroradiometer (MODIS) data. Normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR), are cross-combined to initialize the calibrated model. The calibration results show the following: (1) Vegetation parameters have a great influence on model calibration; and (2) The combination of (NDVI, LAI) is recommended to calibrate the coupled model, the RMSE, R2 is 0.739 dB, and 0.716 for the observed and estimated backscattering coefficients. The soil moisture inversion results show that: (1) the accuracy of model calibration and soil moisture inversion are inconsistent; and (2) The normalized vegetation parameters, such as NDVI, EVI and FPAR, are suitable for WCM to describe vegetation characteristics, and NDVI is the optimum. When V2 is the NDVI, the average bias, MAE, RMSE, ubRMSE and R2 are –0.007 m3/m3, 0.074 m3/m3, 0.087 m³/m³, 0.087 m3/m3 and 0.750, respectively. Numéro de notice : A2019-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.43 Date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.43 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91965 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019011	SL	Revue	Centre de documentation	Revues en salle	Disponible

An extended approach for biomass estimation in a mixed vegetation area using ASAR and TM data / Minfeng Xing in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 5 (May 2014) . - pp 429 - 438 Titre : An extended approach for biomass estimation in a mixed vegetation area using ASAR and TM data Type de document : Article/Communication Auteurs : Minfeng Xing, Auteur ; Binbin He, Auteur ; Xingwen Quan, Auteur ; Xiaowen Li, Auteur Année de publication : 2014 Article en page(s) : pp 429 - 438 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse (combustible) [Termes IGN] image Envisat-ASAR [Termes IGN] image Landsat-TM [Termes IGN] télédétection en hyperfréquence [Termes IGN] végétation Résumé : (Auteur) The use of microwave remote sensing for estimating vegeta-tion biomass is limited in arid regions because of the het-erogeneous distribution of vegetation, variable scattering mechanisms from different vegetation components, and the strong influence from underlying ground surface. In order to minimize this problem, a synergistic method of optical and microwave remote sensing data for the retrieval of aboveg-round biomass (agb) based on the modified water cloud model (WCM) was developed in this paper. Vegetation cover-age which can be easily estimated from optical data as ad-ditional information was combined in this method. Dimidiate pixel model (dpm) and phenological subtraction methodology (psm) were used to estimate vegetation coverage and differen--tiate vegetation types in the sub-pixel domain, respectively. The percentage cover of unmixed vegetation was incorporated to minimize problems associated with heterogeneous vegeta-tion and sparse vegetation cover. Finally, the accuracy and sources of error in this novel AGB retrieval method were evalu-ated. The results showed that the predicted AGB correlated with the measured AGB (R2 = 0.8007; RMSE = 0.2808 kg/m2). Numéro de notice : A2014-241 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.80.5.429 En ligne : https://doi.org/10.14358/PERS.80.5.429 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33144 [article]