National annual average surface temperature and freezing index by remote sensing (2008)- A Big Earth Data Platform for Three Poles

The 2008 national remote sensing annual average surface temperature and freezing index is a 5 km instantaneous surface temperature data product based on MODIS Aqua/Terra four times a day by Ran Youhua et al. (2015). A new method for estimating the annual average surface temperature and freezing index has been developed. The method uses the average daily mean surface temperature observed by LST in morning and afternoon to obtain the daily mean surface temperature. The core of the method is how to recover the missing data of LST products. The method has two characteristics: (1) Spatial interpolation is carried out on the daily surface temperature variation observed by remote sensing, and the spatial continuous daily surface temperature variation obtained by interpolation is utilized, so that satellite observation data which is only once a day is applied; (2) A new time series filtering method for missing data is used, that is, the penalty least squares regression method based on discrete cosine transform.

Verification shows that the accuracy of annual mean surface temperature and freezing index is only related to the accuracy of original MODIS LST, i.e. the accuracy of MODIS LST products is maintained. It can be used for frozen soil mapping and related resources and environment applications.

File naming and required software

File format: ESRI GRID format
Data reading method: data can be opened and viewed by ArcGIS software

Data Citations Data citation guideline What's data citation?

Cite as:

Ran, Y., Li, X. (2017). National annual average surface temperature and freezing index by remote sensing (2008). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.270556. CSTR: 18406.11.Meteoro.tpdc.270556. (Download the reference： RIS | Bibtex )

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)