Abstract
Month-to-month air temperature (Tair) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month Tair mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical Tair modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum Tair, but also for maximum Tair. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum Tair (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data.
Highlights
The surface air temperature is defined as the temperature measured by a thermometer exposed to the air in a place protected from direct solar radiation, normally located at about 1.5 m above surface level on land [1]
The good results of this study can be considered operational given the feasibility of the models employed, the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved compared to classical the climatic variable (Tair) modeling results
The results were within or above the accuracies reported in the literature considering the methodologies for Tair estimation based on remote sensing land surface temperatures (LST)
Summary
The surface air temperature (hereafter Tair) is defined as the temperature measured by a thermometer exposed to the air in a place protected from direct solar radiation, normally located at about 1.5 m above surface level on land [1]. The Tair is a key climatic and meteorological variable that makes it possible to quantify biophysical processes at surface level, such as energy flows, actual and potential evapotranspiration, water stress, and species distribution [2,3,4,5,6]. It is used in many environmental applications, including vector-borne disease bionomics [7], terrestrial hydrology [8], biosphere processes [9] and climate change [10]. There are many studies on climate interpolation methods using meteorological data that may or may not include geographical variables as predictors
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