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:: Volume 14, Issue 2 (12-2024) ::
JGST 2024, 14(2): 89-103 Back to browse issues page
Evaluation of atmospheric effects on the performance of neural network and regression tree in FCOVER modelling
Ali Shamsoddini * , Afagh Zeydani
Abstract:   (171 Views)
The surface reflectance captured by satellites is exposed to the atmosphere gases and aerosols, and its value changes due to colliding with those particles. The change of surface reflectance can also effects on other uses of this quantity. Atmospheric correction using different correction methods and algorithms could lead to different results. In addition, different values of atmospheric parameters such as water vapor, visibility, aerosol optical depth and CO2 which are extracted from ground measurements, or from products of other sensors, or based on similar studies and the users’s guess can achieve different results. Furthermore, even small changes in reflectance can lead to significant uncertainty in parameter retrieval or other remote sensing applications. The main purpose of this study is to evaluate the effect of atmospheric parameters on the accuracy of parameter retrieval from reflectance. For this regard, after implementing the FLAASH atmospheric correction on the Landsat-8 image by changing the values of water vapor and visibility, FCOVER was modeled by Neural network and Regression tree algorithms. Afterwards, effects of the uncertainty related to each atmospheric parameter is evaluated by paired sample T-test. Results indicated changes of water vapor and visibility influences FCOVER retrieval, and causes more than 5 percent error. Also, the neural network and regression tree have shown relatively similar and suitable performance for FCOVER modelling despite the uncertainty in the input parameters of FLAASH model.
Article number: 6
Keywords: Atmospheric correction, Surface reflection, Parameter retrieval
Full-Text [PDF 754 kb]   (112 Downloads)    
Type of Study: Research | Subject: Photo&RS
Received: 2024/03/4
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Shamsoddini A, Zeydani A. Evaluation of atmospheric effects on the performance of neural network and regression tree in FCOVER modelling. JGST 2024; 14 (2) : 6
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Volume 14, Issue 2 (12-2024) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology