Combining optical remote sensing, McFLI discharge estimation, global hydrologic modelling, and data assimilation to improve daily discharge estimates across an entire large watershed

2020 ◽  
Author(s):  
Yuta Ishitsuka ◽  
Colin J. Gleason ◽  
Mark W. Hagemann ◽  
Edward Beighley ◽  
George H. Allen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Optical Remote Sensing ◽  
Hydrologic Modelling ◽  
Daily Discharge ◽  
Discharge Estimation

Daily discharge estimation at ungauged river sites using remote sensing

2012 ◽  
Vol 28 (3) ◽  
pp. 1043-1054 ◽  
Author(s):  
S. J. Birkinshaw ◽  
P. Moore ◽  
C.G. Kilsby ◽  
G. M. O'Donnell ◽  
A.J. Hardy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Daily Discharge ◽  
Discharge Estimation

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Design of global-contextual detection model for optical remote sensing targets

2020 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
ZHANG Rui-yan ◽  
◽  
JIANG Xiu-jie ◽  
AN Jun-she ◽  
CUI Tian-shu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Detection Model

Inverse Problems in Optical Remote Sensing of Coastal Waters

2006 ◽  
Author(s):  
Irina Dolina ◽  
Lev Dolin ◽  
Alexander Luchinin ◽  
Iosif Levin ◽  
Liza Levina
Keyword(s):  
Remote Sensing ◽  
Inverse Problems ◽  
Coastal Waters ◽  
Optical Remote Sensing
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