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Disaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models / Subit Chakrabarti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
[article]
Titre : Disaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models Type de document : Article/Communication Auteurs : Subit Chakrabarti, Auteur ; Jasmeet Judge, Auteur ; Tara Bongiovanni, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 4629 - 4641 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] cultures
[Termes IGN] désagrégation
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] humidité du sol
[Termes IGN] modèle de régressionRésumé : (Auteur) In this paper, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRMs) using high-resolution correlated information from auxiliary sources. It includes regularized clustering that assigns soft memberships to each pixel at a fine scale followed by a kernel regression that computes the value of the desired variable at all pixels. Coarse-scale remotely sensed SM was disaggregated from 10 to 1 km using land cover (LC), precipitation, land surface temperature, leaf area index, and in situ observations of SM. This algorithm was evaluated using multiscale synthetic observations in NC Florida for heterogeneous agricultural LCs. It was found that the rmse for 96% of the pixels was less than 0.02 m 3/m3. The clusters generated represented the data well and reduced the rmse by up to 40% during periods of high heterogeneity in LC and meteorological conditions. The Kullback-Leibler divergence (KLD) between the true SM and the disaggregated estimates is close to zero, for both vegetated and bare-soil LCs. The disaggregated estimates were compared with those generated by the principle of relevant information (PRI) method. The rmse for the PRI disaggregated estimates is higher than the rmse for the SRRM on each day of the season. The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, whereas the computational time needed was reduced by three times. The results indicate that the SRRM can be used for disaggregating SM with complex nonlinear correlations on a grid with high accuracy. Numéro de notice : A2016-888 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2547389 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2547389 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83068
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4629 - 4641[article]Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress / Tim Van Emmerik in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
[article]
Titre : Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress Type de document : Article/Communication Auteurs : Tim Van Emmerik, Auteur ; Susan C. Steele-Dunne, Auteur ; Jasmeet Judge, Auteur ; Nick Van De Giesen, Auteur Année de publication : 2015 Article en page(s) : pp 3855 - 3869 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] image radar
[Termes IGN] maïs (céréale)
[Termes IGN] teneur en eau de la végétation
[Termes IGN] végétationRésumé : (Auteur) Microwave backscatter from vegetated surfaces is influenced by vegetation structure and vegetation water content (VWC), which varies with meteorological conditions and moisture in the root zone. Radar backscatter observations are used for many vegetation and soil moisture monitoring applications under the assumption that VWC is constant on short timescales. This research aims to understand how backscatter over agricultural canopies changes in response to diurnal differences in VWC due to water stress. A standard water-cloud model and a two-layer water-cloud model for maize were used to simulate the influence of the observed variations in bulk/leaf/stalk VWC and soil moisture on the various contributions to total backscatter at a range of frequencies, polarizations, and incidence angles. The bulk VWC and leaf VWC were found to change up to 30% and 40%, respectively, on a diurnal basis during water stress and may have a significant effect on radar backscatter. Total backscatter time series are presented to illustrate the simulated diurnal difference in backscatter for different radar frequencies, polarizations, and incidence angles. Results show that backscatter is very sensitive to variations in VWC during water stress, particularly at large incidence angles and higher frequencies. The diurnal variation in total backscatter was dominated by variations in leaf water content, with simulated diurnal differences of up to 4 dB in X- through Ku-bands (8.6-35 GHz) . This study highlights a potential source of error in current vegetation and soil monitoring applications and provides insights into the potential use for radar to detect variations in VWC due to water stress. Numéro de notice : A2015-314 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2386142 En ligne : https://doi.org/10.1109/TGRS.2014.2386142 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76561
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3855 - 3869[article]Réservation
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