Next Article in Journal
Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands
Next Article in Special Issue
An Effective Similar-Pixel Reconstruction of the High-Frequency Cloud-Covered Areas of Southwest China
Previous Article in Journal
Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method
Previous Article in Special Issue
Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(3), 300; https://doi.org/10.3390/rs11030300

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

1,2,3
,
1,* , 1,4,* , 1,2,3
and
1,5
1
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2
Institute of Physical Science and Information Technology, Anhui University, Hefei 230601, China
3
Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China
4
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
5
Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China
*
Authors to whom correspondence should be addressed.
Received: 28 December 2018 / Revised: 25 January 2019 / Accepted: 31 January 2019 / Published: 1 February 2019
Full-Text   |   PDF [10500 KB, uploaded 12 February 2019]   |  

Abstract

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. View Full-Text
Keywords: geostationary satellite land surface temperature (GLST); convolutional neural networks (CNN); multiscale feature connection; large missing regions; reconstruction geostationary satellite land surface temperature (GLST); convolutional neural networks (CNN); multiscale feature connection; large missing regions; reconstruction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wu, P.; Yin, Z.; Yang, H.; Wu, Y.; Ma, X. Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network. Remote Sens. 2019, 11, 300.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top