Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery-Reference-Cited by-同舟云学术

Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery

Published:2024-07-16 Issue:8 Volume:196 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Rengma Nyenshu Seb,Yadav Manohar

Funder

Department of Science and Technology (DST), India

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10661-024-12856-z.pdf

Reference99 articles.

1. Abdullah, S., Barua, D., Abdullah, S. M. A., & Rabby, Y. W. (2022). Investigating the impact of land use/land cover change on present and future land surface temperature (LST) of Chittagong. Bangladesh. Earth Systems and Environment, 6(1), 221–235. https://doi.org/10.1007/s41748-021-00291-w

2. Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), 1–20. https://doi.org/10.1080/15481603.2019.1650447

3. Addas, A. (2023). Machine learning techniques to map the impact of urban heat island: Investigating the City of Jeddah. Land, 12(6), 1159.

4. Anbazhagan, S., & Paramasivam, C. R. (2016). Statistical correlation between land surface temperature (LST) and vegetation index (NDVI) using multi-temporal landsat TM data. International Journal of Advanced Earth Science and Engineering, 5(1), 333–346. https://doi.org/10.23953/cloud.ijaese.204

5. Basist, A., Grody, N. C., Peterson, T. C., & Williams, C. N. (1998). Using the special sensor microwave/imager to monitor land surface temperatures, wetness, and snow cover. Journal of Applied Meteorology and Climatology, 37(9), 888–911.