Modeling the distribution of land surface temperature for Bystrytsia river basin using Landsat 8 data

A I Kovalchuk,I P Kovalchuk,O S Mkrtchian

doi:10.15421/111869

Abstract

Development of accurate and practicable methods of land surface temperatures (LST) mapping has benefits for a range of scientific and practical applications. The paperconsiders mapping of LST for the Bystrytsia river basin located in Western Ukraine using Landsat 8 imagerywith two thermal infrared bands, which capture emissivity values closely related to land surface temperature (LST).Three multispectral images referring to different seasons (autumn, winter and summer) were used in the study. The method of LST estimation consists of several successive steps. After preprocessing (clipping, masking, and re-projecting), the images were converted from digital numbers to top of atmosphere spectral radiance,and then – to brightness temperature.However, the brightness temperature differs from LST due to emissivity of land surface being different from that of ideal blackbody.The emissivity can vary significantly with vegetation, surface moisture and surface roughness, and can be approximately estimated from land surface reflectivity at red and near-infrared spectral ranges. Estimated values of LST were compared with measurements of Ivano-Frankivsk state weather station, showing rather good compliance for all the three scenes.Obtained estimates of LST show some regularities of its spatial distribution, which also vary significantly from season to season.All the three scenes show conspicuous vertical gradient in LST; summer and autumn scenes are also characterized by significant local variability in LST due to different land cover types (e.g., urban development, forests, different agricultural lands), whereas in winter, differences in LST for mountainous slopes of different aspects appear to be more pronounced. Graphs of LST change with elevation have a parabolic form: sharper decrease of LST is typical for lower elevations, while the vertical LST gradient decreases above 700–1000 m a.s.l.

Highlights

Thermal regime of an area isan important constituent of its natural conditions, possessing large ecological significance and influencing a set of hydrological and geomorphological processes
The most reliable source of climatic data are weather stations with sufficiently long uninterrupted observation series. The network of such weather stations in many countries is rather patchy and tenuous, as in the case ofUkraine where weather stations located in Carpathian region cannot cover the respective variability of climatic conditions Another possibility is the study of temperature regime using automatic ground sensors; this methods allows studying only inside small local areas or along relatively short profiles, in a limited number of observation points corresponding to the number of available sensors
The purpose of this study is to research into the possibility of using remotely sensed imagery to analyze and map the spatial distribution of Land surface temperatures (LST), on an example of Bystrytsia river basin located in western part of Ukraine

Summary

Introduction

Thermal regime of an area isan important constituent of its natural conditions, possessing large ecological significance and influencing a set of hydrological and geomorphological processes. Spatial interpolation can be carried out using geostatistical methods, based on the statistical analysis of the spatial variability of temperature fields; using regression models that characterize relationships between the temperature and its spatially distributed predictors (e.g., elevation field and morphometric terrain characteristics); or the combination of regression and geostatistical approaches The latter approach is the most general and can take form e.g. of building the regression model first and interpolating the residuals of regression by means of geostatistical method (Mkrtchian, Shuber, 2009).the prediction accuracy of this approach can be low when the network of weather stations is sparse,leading to poor model predictions for the places too remote from samples (weather stations) in geographic or feature spaces (e.g. for highlands represented with few weather stations)

Objectives

Methods

Results

Conclusion