Passive Microwave Remote Sensing for Sea Ice Thickness Retrieval Using Neural Network and Genetic Algorithm

Abstract-Over the years, global warming has gained much attention from the global community. The fact that the sea ice plays an important role and has significant effects towards the global climate has prompted scientists to conduct various researches on the sea ice in the Polar Regions. One of the...

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Bibliographic Details
Main Authors: H J Yap, W K Lim, H T Ewe, H T Chuah
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 2009
Subjects:
Arctic
Climate change
Global warming
Sea ice
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1075.1794
http://www.piers.org/piersonline/pdf/Vol5No2Page191to195.pdf
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Summary:Abstract-Over the years, global warming has gained much attention from the global community. The fact that the sea ice plays an important role and has significant effects towards the global climate has prompted scientists to conduct various researches on the sea ice in the Polar Regions. One of the important parameters being studied is the sea ice thickness as it is a direct key indication towards the climate change. However, to conduct studies on the sea ice scientists are often facing with tough challenges due to the unfavorable harsh weather conditions and the remoteness of the Polar Regions. Thus, microwave remote sensing offers an attractive mean for the observation and monitoring of the changes of sea ice in the Polar Regions for the scientists. In this paper, we will be presenting 2 approaches using passive microwave remote sensing to retrieve sea ice thickness. The first approach involves the training and testing of the neural network (NN) by using data sets generated from the Radiative Transfer Theory with Dense Medium Phase and Amplitude Correction Theory (RT-DMPACT) forward scattering model. Once training is completed, the inversion for sea ice thickness could be done speedily. The second approach utilizes a genetic algorithm (GA) which would perform a search routine to identify possible solutions in sea ice thickness that would match the corresponding brightness temperatures profile of the sea ice. The results obtained from both approaches are presented and tested by using Special Scanning Microwave Imager (SSM/I) data with the aid of the sea ice measurements in the Arctic sea.

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