Abstract
Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. We present a novel method to fully reconstruct MODIS daily LST products for central Europe at 1 km resolution and globally, at 3 arc-min. We combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The reconstructed MODIS LST for central Europe was calibrated to air temperature data through linear models that yielded R2 values around 0.8 and RMSE of 0.5 K. This new method proves to scale well for both local and global reconstruction. We show examples for the identification of extreme events to demonstrate the ability of these new LST products to capture and represent spatial and temporal details. A time series of global monthly average, minimum and maximum LST data and long-term averages is freely available for download.
Highlights
Remote sensing based time series are becoming increasingly available, and this tendency will continue to grow because of new Earth observation satellites being launched, but because of the availability of new methods to harmonize their data [1,2] and reconstruct incomplete records [3,4,5,6,7] along with the growing demand of different sectors for the monitoring of environment, analysis of trends and patterns, and forecasting
We used the MODerate resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products MOD11A1/MYD11A1 collection 6 acquired from the Land Processes Distributed Active Archive Center (LP DAAC) data pool starting with the year 2003, the first year fully covered by both products
The results consist of two parts: (1) an analysis of the MODIS LST products used here and of the LST reconstruction method; and (2) the identification of extreme events as an exemplary use case
Summary
Remote sensing based time series are becoming increasingly available, and this tendency will continue to grow because of new Earth observation satellites being launched, but because of the availability of new methods to harmonize their data [1,2] and reconstruct incomplete records [3,4,5,6,7] along with the growing demand of different sectors for the monitoring of environment, analysis of trends and patterns, and forecasting In this scenario, air temperature is as an essential climatic and ecological driver, one of the most important variables in climate research and global change. The obtained precision depends on the quality, representativeness and spatial distribution of the input network(s) of stations [12,13,14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
