Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
EN  |  EL

Αναζήτηση

Πλοήγηση

Ο Χώρος μου

Comparison of multiple linear regression and artificial neural network models for downscaling TRMM precipitation products using MODIS data

Alexakis Dimitrios, Tsanis Giannis

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/FAFB97E1-60F6-4F7D-87B8-D7B3F465E579-
Αναγνωριστικόhttps://link.springer.com/article/10.1007%2Fs12665-016-5883-z-
Αναγνωριστικόhttps://doi.org/10.1007/s12665-016-5883-z-
Γλώσσαen-
Μέγεθος13 pagesen
ΤίτλοςComparison of multiple linear regression and artificial neural network models for downscaling TRMM precipitation products using MODIS dataen
ΔημιουργόςAlexakis Dimitriosen
ΔημιουργόςΑλεξακης Δημητριοςel
ΔημιουργόςTsanis Giannisen
ΔημιουργόςΤσανης Γιαννηςel
ΕκδότηςSpringer Nature [academic journals on nature.com]en
ΠερίληψηPrecipitation plays a significant role to energy exchange and material circulation in Earth’s surface system. According to numerous studies, traditional point measurements based on rain gauge stations are unable to reflect the spatial variation of precipitation effectively. On the other hand, satellite remote sensing could solve this limitation by directly providing spatial distribution of rainfall over large areas. During the last years, the Tropical Rainfall Measuring Mission (TRMM) has provided researchers with a large volume of rainfall data used for the validation of atmospheric and climate models. However, due to its coarse resolution (0.25°) the improvement of its resolution appears as a fundamental task. The main aim of this study is to compare two different integrated downscaling-calibration approaches namely multiple linear regression analysis and artificial neural networks for downscaling TRMM 3B42 precipitation data. The statistical relationship among TRMM precipitation data and different environmental parameters such as vegetation, albedo, drought index and topography were tested in the island of Crete, Greece. Free distributed satellite data of coarse resolution such as those of MODIS sensor were incorporated in the overall analysis. Multiple linear regression as well as artificial neural network models was developed and applied, and extensive statistical analysis was performed by downscaling the TRMM products. The downscaled precipitation estimates as well as the TRMM products were subsequently validated for their accuracy by using an independent precipitation dataset from a ground rain gauge network. The downscaling procedure succeeded to significant improvements of monthly precipitation estimation (100 % improvement in terms of spatial resolution) in terms of spatial analysis with means of satellite remote sensing.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2018-10-09-
Ημερομηνία Δημοσίευσης2016-
Θεματική ΚατηγορίαDownscalingen
Θεματική ΚατηγορίαMODISen
Θεματική ΚατηγορίαNeural networksen
Θεματική ΚατηγορίαPrecipitationen
Θεματική ΚατηγορίαTRMMen
Βιβλιογραφική ΑναφοράD. D. Alexakis and I. K. Tsanis, "Comparison of multiple linear regression and artificial neural network models for downscaling TRMM precipitation products using MODIS data," Environ. Earth Sci., vol. 75, no. 14, Jul. 2016. doi: 10.1007/s12665-016-5883-zen

Υπηρεσίες

Στατιστικά