Shengzhe Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Shengzhe Chen has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Fengming Hui
School of Geospatial Engineering and Science, the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), and the Key Laboratory of Comprehensive Observation of Polar Environment, Ministry of Education, Sun Yat-sen University, Zhuhai, China
1 shared publication Jiping Liu
Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, USA
1 shared publication- Jiping Liu
Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY, USA
1 shared publication Xiao Cheng
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, China
1 shared publication- Xiao Cheng
Sun Yat-sen University
1 shared publication Yifan Ding
College of Global Change and Earth System Science, and State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China
1 shared publication
Latest Publications
- 5 January 2021
- Published by Springer Nature in Advances in Atmospheric Sciences
- Vol. 38 (2) , 168-186
Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data
- 5 January 2021
- Published by Springer Nature in Advances in Atmospheric Sciences
- Vol. 38 (2) , 168-186
Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data
- Open Access
- 25 August 2020
- Published by MDPI AG in Remote Sensing
- Vol. 12 (17) , 2746
Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network
The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season.
- 25 August 2020
- Published by MDPI AG in Remote Sensing
- Vol. 12 (17) , 2746
Open AccessRetrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network
The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season.