Using Machine learning method to estimate Air Temperature from MODIS over Berlin

Abstract:

Land Surface Temperature (LST) is defined as the temperature of the interface between the Earth’s surface and its atmosphere and thus it is a critical variable to understand land-atmosphere interactions and a key parameter in meteorological and hydrological studies, which is involved in energy fluxes. Air temperature (T_air) is one of the most important input variables in different spatially distributed hydrological, ecological models.

The estimation of near surface air temperature is useful for a wide range of applications. Some applications from traffic or energy management, require T_air data in high spatial and temporal resolution at two meters height above the ground (T_2m), sometimes in near-real-time. Thus, a parameterization based on boundary layer physical principles was developed that determines the air temperature from remote sensing data (MODIS). T_air is commonly obtained from synoptic measurements in weather stations. However, the derivation of near surface air temperature from the LST derived from satellite is far from straight forward. T_2m is not driven directly by the sun, but indirectly by LST, thus T_2m can be parameterized from the LST and other variables such as Albedo, NDVI, Water vapor and etc.

Most of the previous studies have focused on estimating T_2m based on simple and advanced statistical approaches, Temperature-Vegetation index and energy-balance approaches but the main objective of this research is to explore the relationships between T_2m and LST in Berlin by using Artificial intelligence method with the aim of studying key variables to allow us establishing suitable techniques to obtain T_air from satellite Products and ground data. Secondly, an attempt was explored to identify an individual mix of attributes that reveals a particular pattern to better understanding variation of T_2m during day and nighttime over the different area of Berlin. For this reason, a three layer Feedforward neural networks is considered with LMA algorithm. Considering the different relationships between T_2m and LST for different land types enable us to improve better parameterization for determination of the best non-linear relation between LST and T_2m over Berlin during day and nighttime. The results of the study will be presented and discussed.