The impact of climatic and non-climatic factors on land surface temperature in southwestern Romania

Land surface temperature is one of the most important parameters related to global warming. It depends mainly on soil type, discontinuous vegetation cover, or lack of precipitation. The main purpose of this paper is to investigate the relationship between high LST, synoptic conditions and air masses trajectories, vegetation cover, and soil type in one of the driest region in Romania. In order to calculate the land surface temperature and normalized difference vegetation index, five satellite images of LANDSAT missions 5 and 7, covering a period of 26 years (1986–2011), were selected, all of them collected in the month of June. The areas with low vegetation density were derived from normalized difference vegetation index, while soil types have been extracted from Corine Land Cover database. HYSPLIT application was employed to identify the air masses origin based on their backward trajectories for each of the five study cases. Pearson, logarithmic, and quadratic correlations were used to detect the relationships between land surface temperature and observed ground temperatures, as well as between land surface temperature and normalized difference vegetation index. The most important findings are: strong correlation between land surface temperature derived from satellite images and maximum ground temperature recorded in a weather station located in the area, as well as between areas with land surface temperature equal to or higher than 40.0 °C and those with lack of vegetation; the sandy soils are the most prone to high land surface temperature and lack of vegetation, followed by the chernozems and brown soils; extremely severe drought events may occur in the region.

[1]  Ramakrishna R. Nemani,et al.  A global framework for monitoring phenological responses to climate change , 2005 .

[2]  Sergio M. Vicente-Serrano,et al.  A multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index - SPEI , 2009 .

[3]  D. Radinovic,et al.  Measuring system of adverse weather phenomena , 2012 .

[4]  J. Welker,et al.  The influence of air mass source on the seasonal isotopic composition of precipitation, eastern USA , 2009 .

[5]  R. Draxler,et al.  NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System , 2015 .

[6]  Julio Lumbreras,et al.  Comparison of statistical clustering techniques for the classification of modelled atmospheric trajectories , 2010 .

[7]  José A. Sobrino,et al.  Land surface temperature retrieval from LANDSAT TM 5 , 2004 .

[8]  P. Hopke,et al.  Locating and quantifying PCB sources in Chicago: receptor modeling and field sampling. , 2003, Environmental science & technology.

[9]  L. Poissant Potential sources of atmospheric total gaseous mercury in the St. Lawrence River valley , 1999 .

[10]  Célia M. Gouveia,et al.  Modelling wildfire activity in Iberia with different atmospheric circulation weather types , 2016 .

[11]  Scott J. Goetz,et al.  Observed and predicted responses of plant growth to climate across Canada , 2005 .

[12]  P. Bogawski,et al.  Atmospheric conditions controlling extreme summertime evapotranspiration in Poland (central Europe) , 2016, Natural Hazards.

[13]  E. D. Martonne L'indice d'aridité , 1926 .

[14]  Julio Lumbreras,et al.  Analysis of long-range transport influences on urban PM10 using two-stage atmospheric trajectory clusters , 2007 .

[15]  Ioana Herbel,et al.  METHODS TO DETECT ATMOSPHERIC AND SURFACE HEAT ISLANDS IN URBAN AREAS , 2015 .

[16]  Dawen Yang,et al.  Impacts of climate change and vegetation dynamics on runoff in the mountainous region of the Haihe River basin in the past five decades , 2014 .

[17]  Mladjen Ćurić,et al.  Dependence between deficit and surplus of precipitation and forest fires , 2013 .

[18]  Jichao Yang,et al.  Response of waves and coastline evolution to climate variability off the Niger Delta coast during the past 110 years , 2016 .

[19]  Glenn Rolph,et al.  Real-time Environmental Applications and Display sYstem: READY , 2017, Environ. Model. Softw..

[20]  Jinghu Pan,et al.  Area Delineation and Spatial-Temporal Dynamics of Urban Heat Island in Lanzhou City, China Using Remote Sensing Imagery , 2016, Journal of the Indian Society of Remote Sensing.

[21]  A. Croitoru,et al.  Recent changes in reference evapotranspiration in Romania , 2013 .

[22]  Martha C. Anderson,et al.  A Multiscale Remote Sensing Model for Disaggregating Regional Fluxes to Micrometeorological Scales , 2004 .

[23]  Meng Meng,et al.  Impacts of changes in climate variability on regional vegetation in China: NDVI-based analysis from 1982 to 2000 , 2011, Ecological Research.

[24]  D. Radinović,et al.  Deficit and surplus of precipitation as a continuous function of time , 2009 .

[25]  Jianguo Wu,et al.  A hierarchical analysis of the relationship between urban impervious surfaces and land surface temperatures: spatial scale dependence, temporal variations, and bioclimatic modulation , 2016, Landscape Ecology.

[26]  Suwała Katarzyna The influence of atmospheric circulation on the occurrence of hail in the North German Lowlands , 2013, Theoretical and Applied Climatology.

[27]  M. Aslam,et al.  Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District☆ , 2015 .

[28]  Manfred Owe,et al.  On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces , 1993 .

[29]  Qihao Weng,et al.  Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. , 2009 .

[30]  B. Tychon,et al.  Modeling heat stress under different environmental conditions. , 2016, Journal of dairy science.

[31]  A. Croitoru,et al.  Changes in precipitation extremes in Romania , 2016 .

[32]  Thomas Blaschke,et al.  Examining Urban Heat Island Relations to Land Use and Air Pollution: Multiple Endmember Spectral Mixture Analysis for Thermal Remote Sensing , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  J. L. Barker,et al.  Landsat MSS and TM post-calibration dynamic ranges , 1986 .

[34]  Qijiao Xie,et al.  A multi-temporal Landsat TM data analysis of the impact of land use and land cover changes on the urban heat island effect , 2012 .

[35]  T. McKee,et al.  THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES , 1993 .

[36]  D. Artis,et al.  Survey of emissivity variability in thermography of urban areas , 1982 .

[37]  I. Tošić,et al.  Extreme daily precipitation in Belgrade and their links with the prevailing directions of the air trajectories , 2012, Theoretical and Applied Climatology.

[38]  M. Soltani,et al.  Synoptic and thermodynamic characteristics of 30 March–2 April 2009 heavy rainfall event in Iran , 2014, Meteorology and Atmospheric Physics.

[39]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[40]  J. Adame,et al.  Impact evaluation of potential volcanic plumes over Spain , 2015 .

[41]  Sinasi Kaya,et al.  Evaluation of spatio-temporal variability in Land Surface Temperature: A case study of Zonguldak, Turkey , 2015, Environmental Monitoring and Assessment.

[42]  C. Tucker,et al.  Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 , 2001 .

[43]  A. Chakraborty,et al.  Assessment of Agricultural Drought Using MODIS Derived Normalized Difference Water Index , 2011 .

[44]  S. Vicente‐Serrano,et al.  A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index , 2009 .

[45]  Bouchra R. Nasri,et al.  Atmospheric Predictors for Annual Maximum Precipitation in North Africa , 2016 .

[46]  D. Đorđević,et al.  Water-soluble main ions in precipitation over the southeastern Adriatic region: chemical composition and long-range transport , 2010, Environmental science and pollution research international.