Remote Sensing Satellite Data Preparation for Simulating and Forecasting River Discharge

Simulating and forecasting river discharge using satellite information is one of the most economical ways of measuring discharge, especially in remote areas where in situ gauges are too expensive to install, maintain, and operate. However, satellite signals are affected by climatic factors, such as...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 56; číslo 6; s. 3432 - 3441
Hlavní autoři: Zaji, Amir Hossein, Bonakdari, Hossein, Gharabaghi, Bahram
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Atmospheric particulates
Brightness
Calibration
Case studies
Computer simulation
Data
Data analysis
Detection
Discharges (electric)
Dust storms
Earth
Forecasting
Forest fires
Gauges
Methods
Microwave measurement
Microwave theory and techniques
Mist
Outliers (statistics)
pattern classification
Procedures
Radiometers
Remote sensing
River discharge
River flow
River forecasting
Rivers
satellite applications
Satellite broadcasting
Satellite observation
Satellites
signal analysis
Smoke
Storms
surface discharges
Thermal emission
Water discharge
White River
ISSN:0196-2892, 1558-0644
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Popis
Shrnutí:Simulating and forecasting river discharge using satellite information is one of the most economical ways of measuring discharge, especially in remote areas where in situ gauges are too expensive to install, maintain, and operate. However, satellite signals are affected by climatic factors, such as clouds, mist, dust storms, and smoke from large forest fires. Hence, a reliable method of preparing and treating space-based signals is necessary. In this paper, a method is introduced to remove inaccurate and out-of-range signals having a low correlation with the in situ discharge measurements, using a combination of data classification and outlier detection procedures. To forecast future river discharge using space-based signals, the signal data set should not contain any gaps. Therefore, we introduced a procedure for the missing signals to be estimated by a model calibrated using the measured discharge. This procedure is illustrated using a case study for the White River, near Boston, MA, USA, that involved using a ddata set of three years' daily signals of the passive microwave information from the Advanced Microwave Scanning Radiometer for Earth Observing System satellite, which are obtained from the difference between thermal emission of wet and dry land surfaces, and the in situ discharge measurements of the river. The best model was selected from 6200 available models using the Pareto front. The optimum model detected 339 samples as outliers and eliminated them from the data set. Subsequently, the signals were calibrated with the in situ information, and the results indicated the superior accuracy in simulating White River discharge using satellite information.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2018.2799901