Use of multi-platform, multi-temporal remote-sensing data for calibration of a distributed hydrological model: an application in the Arno basin, Italy

2006 ◽  
Vol 20 (13) ◽  
pp. 2693-2712 ◽  
Author(s):  
Lorenzo Campo ◽  
Francesca Caparrini ◽  
Fabio Castelli
Keyword(s):  
Remote Sensing ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Distributed Hydrological Model ◽  
Sensing Data ◽  
Multi Temporal

scholarly journals Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach

2017 ◽  
Author(s):  
Gorka Mendiguren ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Driving Mechanism ◽  
Distributed Hydrological Model ◽  
Eof Analysis ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two source energy balance model (TSEB) driven mainly by satellite remote sensing data. The main hypothesis of the study is that while both approaches are essentially estimates, the spatial patterns of the remote sensing based approach are explicitly driven by observed land surface temperature and therefore represent the most direct spatial pattern information of ET; enabling its use for distributed hydrological model evaluation. Ideally the hydrological model simulation and remote sensing based approach should present similar spatial patterns and driving mechanism of ET. However, the spatial comparison showed that the differences are significant and indicating insufficient spatial pattern performance of the hydrological model. The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in 6 domains that are calibrated independently from each other, as it is often the case for large scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of Leaf Area Index (LAI), root depth (RD) and Crop coefficient (Kc) for each land cover type. This zonal approach of model parametrization ignores the spatio-temporal complexity of the natural system. To overcome this limitation, the study features a modified version of the DK-Model in which LAI, RD, and KC are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatio-temporal variability and spatial consistency between the 6 domains. The effects of these changes are analyzed by using the empirical orthogonal functions (EOF) analysis to evaluate spatial patterns. The EOF-analysis shows that including remote sensing derived LAI, RD and KC in the distributed hydrological model adds spatial features found in the spatial pattern of remote sensing based ET.

A parametric model for classifying land cover and evaluating training data based on multi-temporal remote sensing data

2014 ◽  
Vol 97 ◽  
pp. 219-228 ◽  
Author(s):  
Hunter Glanz ◽  
Luis Carvalho ◽  
Damien Sulla-Menashe ◽  
Mark A. Friedl
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Parametric Model ◽  
Training Data ◽  
Sensing Data ◽  
Multi Temporal

Discharge estimation for medium-sized river using multi-temporal remote sensing data: a case study in Brazil

2020 ◽  
Vol 65 (14) ◽  
pp. 2402-2418
Author(s):  
Évelyn Márcia Pôssa ◽  
Philippe Maillard ◽  
Lília Maria de Oliveira
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Multi Temporal ◽  
Discharge Estimation

Multi-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana

2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ram Avtar ◽  
Ali P. Yunus ◽  
Osamu Saito ◽  
Ali Kharrazi ◽  
Pankaj Kumar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Terrestrial Ecosystem ◽  
Remote Sensing Data ◽  
Climate Variations ◽  
Ecosystem Responses ◽  
Sensing Data ◽  
Multi Temporal
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