scholarly journals Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG): Algorithm Framework Development

2021 ◽  
Author(s):  
Xingfeng Chen ◽  
Fengjie Zheng ◽  
Lili Wang ◽  
Limin Zhao ◽  
Jiaguo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Geostationary Satellite ◽  
Aerosol Retrieval ◽  
Algorithm Framework ◽  
Framework Development

Intercomparing the Aerosol Optical Depth Using the Geostationary Satellite Sensors (AHI, GOCI and MI) from Yonsei AErosol Retrieval (YAER) Algorithm

2018 ◽  
Vol 39 (2) ◽  
pp. 119-130 ◽  
Author(s):  
Hyunkwang Lim ◽  
Myungje Choi ◽  
Mijin Kim ◽  
Jhoon Kim ◽  
Sujung Go ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Geostationary Satellite ◽  
Aerosol Retrieval ◽  
Satellite Sensors

scholarly journals Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network

2019 ◽  
Vol 11 (3) ◽  
pp. 300 ◽  
Author(s):  
Penghai Wu ◽  
Zhixiang Yin ◽  
Hui Yang ◽  
Yanlan Wu ◽  
Xiaoshuang Ma
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Convolutional Neural Network ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Missing Values ◽  
Geostationary Satellite ◽  
Reconstruction Methods ◽  
Highly Nonlinear

Geostationary satellite land surface temperature (GLST) data are important for various dynamic environmental and natural resource applications for terrestrial ecosystems. Due to clouds, shadows, and other atmospheric conditions, the derived LSTs are often missing a large number of values. Reconstructing the missing values is essential for improving the usability of the geostationary satellite LST data. However, current reconstruction methods mainly aim to fill the values of a small number of invalid pixels with many valid pixels, which can provide useful land surface temperature values. When the missing data extent becomes large, the reconstruction effect will worsen because the relationship between different spatiotemporal geostationary satellite LSTs is complex and highly nonlinear. Inspired by the superiority of the deep convolutional neural network (CNN) in solving highly nonlinear and dynamic problems, a multiscale feature connection CNN model is proposed to fill missing LSTs with large missing regions. The proposed method has been tested on both FengYun-2G and Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager geostationary satellite LST datasets. The results of simulated and actual experiments show that the proposed method is accurate to within about 1 °C, with 70% missing data rates. This is feasible and effective for large regions of LST reconstruction tasks.

scholarly journals Neural Network for Aerosol Retrieval from Hyperspectral Imagery

2019 ◽  
Author(s):  
Steffen Mauceri ◽  
Bruce Kindel ◽  
Steven Massie ◽  
Peter Pilewskie
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Surface Albedo ◽  
Hyperspectral Image ◽  
A Priori ◽  
Image Data ◽  
Aerosol Retrieval ◽  
Hyperspectral Image Data ◽  
Priori Information ◽  
Better Than

Abstract. We retrieve aerosol optical thickness (AOT) independently for brown carbon-, dust and sulfate from hyperspectral image data. The model, a neural network, is trained on atmospheric radiative transfer calculations from MODTRAN 6.0 with varying aerosol- concentration and type, surface albedo, water vapor and viewing geometries. From a set of test radiative transfer calculations, we are able to retrieve AOT with a standard error of better than ±0.05. No a priori information of the surface albedo or atmospheric state is necessary for our model. We apply the model to AVIRIS-NG imagery from a recent campaign over India and demonstrate its performance under high and low aerosol loadings and different aerosol types.

Uncertainty Analysis of Neural-Network-Based Aerosol Retrieval

2012 ◽  
Vol 50 (2) ◽  
pp. 409-414 ◽  
Author(s):  
Kosta Ristovski ◽  
Slobodan Vucetic ◽  
Zoran Obradovic
Keyword(s):  
Neural Network ◽  
Uncertainty Analysis ◽  
Aerosol Retrieval
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