Satellite Aerosol Optical Depth Retrieval Based on Fully Connected Neural Network (FCNN) and a Combine Algorithm of Simplified Aerosol Retrieval Algorithm and Simplified and Robust Surface Reflectance Estimation (SREMARA)
Author:
Affiliation:
1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9973430/10141636.pdf?arnumber=10141636
Reference77 articles.
1. Global aerosol change in the last decade: An analysis based on MODIS data
2. A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data
3. Review of Deep Learning Algorithms and Architectures
4. Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective
5. Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia
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1. A deep learning-based combination method of spatio-temporal prediction for regional mining surface subsidence;Scientific Reports;2024-08-19
2. The Retrieval of Land Aerosol Optical Depth Based on Machine Learning Approach Over South Asia;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
3. Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China;Atmosphere;2024-04-30
4. Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction;IEEE Geoscience and Remote Sensing Letters;2024
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