Zhang, X.; Huang, T.; Gulakhmadov, A.; Song, Y.; Gu, X.; Zeng, J.; Huang, S.; Nam, W.-H.; Chen, N.; Niyogi, D.
Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data. Remote Sens. 2022, 14, 3536.
https://doi.org/10.3390/rs14153536
AMA Style
Zhang X, Huang T, Gulakhmadov A, Song Y, Gu X, Zeng J, Huang S, Nam W-H, Chen N, Niyogi D.
Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data. Remote Sensing. 2022; 14(15):3536.
https://doi.org/10.3390/rs14153536
Chicago/Turabian Style
Zhang, Xiang, Tailai Huang, Aminjon Gulakhmadov, Yu Song, Xihui Gu, Jiangyuan Zeng, Shuzhe Huang, Won-Ho Nam, Nengcheng Chen, and Dev Niyogi.
2022. "Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data" Remote Sensing 14, no. 15: 3536.
https://doi.org/10.3390/rs14153536
APA Style
Zhang, X., Huang, T., Gulakhmadov, A., Song, Y., Gu, X., Zeng, J., Huang, S., Nam, W.-H., Chen, N., & Niyogi, D.
(2022). Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data. Remote Sensing, 14(15), 3536.
https://doi.org/10.3390/rs14153536