Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

In order to enhance the retrieval accuracy of cloud top height (CTH) from MODIS data, neural network models were employed based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Three types of methods were established using MODIS inputs: cloud parameters, calibrated radiance, and a...

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Bibliographic Details
Main Authors: Yinhe Cheng, Hongjian He, Qiangyu Xue, Jiaxuan Yang, Wei Zhong, Xinyu Zhu, Xiangyu Peng
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Sensors
Subjects:
CTH
neural networks
MODIS
CALIPSO
remote sensing retrieval
Online Access:https://www.mdpi.com/1424-8220/24/2/541

Internet

https://www.mdpi.com/1424-8220/24/2/541

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