Microsoft Word - 23-Bio_33990


1038 
Original Article 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

SYNOPTIC EVENTS ASSOCIATED WITH THE LAND SURFACE TEMPERATURE IN RIO DE 
JANEIRO 

 
EVENTOS SINÓPTICOS ASSOCIADOS DA TEMPERATURA SUPERFÍCIE DA TERRA NO RIO DE 

JANEIRO 

 
Rafael Coll DELGADO1; José Francisco de OLIVEIRA-JÚNIOR2; Givanildo GOIS3; 

 Rafael de Ávila RODRIGUES4; Paulo Eduardo TEODORO5* 
1. Universidade Federal Rural do Rio de Janeiro, Seropédica, RJ, Brasil; 2. Universidade Federal de Alagoas, Maceió, AL, 

Brasil. 3. Universidade Federal Fluminense, Volta Redonda, RJ, Brasil; 4. Universidade Federal de Goiás, Catalão, GO, 
Brasil. 5. Universidade Federal de Mato Grosso do Sul, Chapadão do Sul, MS, Brasil. eduteodoro@hotmail.com 

 
ABSTRACT: This article aimed to evaluate land surface temperature using MOD11A2 (Terra satellite) with 

spatial resolution of one kilometre, compares its findings with land surface temperature data gathered by conventional 
meteorological stations, and, finally, investigates relations between land surface temperature and synoptic systems events 
that occurred in Rio de Janeiro State between January until December of 2009. The highest surface temperatures recorded 
by the MOD11A2 product, derived from the MODIS sensor, were found in the Metropolitana, Baixadas Litorâneas, Norte 
Fluminense and Noroeste Fluminense regions of Rio de Janeiro State and were recorded during the summer, winter and 
spring seasons. Autumn was the only exception, and this was due to the influence of the coastal environment. The 
following synoptic systems interfered with the estimated surface temperature produced by the MOD11A2 product for Rio 
de Janeiro State in 2009: the Madden Julian Oscillation and South Atlantic Convergence Zone in the summer; and Frontal 
Systems, the South Atlantic Convergence Zone, the Madden Julian Oscillation and the Upper Tropospheric Cyclonic 
Vortex in spring. The land use and occupation types with the highest surface temperature are: forest, urban area and 
pasture land in the summer; forest, urban area, agriculture and pasture land in autumn; and urban area and agriculture 
during spring. The MOD11A2 product showed a drastic decrease of the surface temperature for all land types during 
winter, especially for forested land.   

 
KEYWORDS: Southeast region. Remote sensing. Thermal field. Meteorological satellite. 

 
INTRODUCTION 

 
Worldwide urbanization and population 

growth, and the ways in which they have changed 
the use and occupation of land, are thought to be 
probable causes of global warming. These processes 
do not only alter the landscape: anthropic activities 
also contribute to greenhouse gas emissions (GGE) 
which affect global warming (SATTERTHWAITE, 
2008). The urbanization process produces greater 
changes in land surface and atmospheric properties, 
such as the energy partitioning between urban and 
adjacent areas, thus creating a new urban climate 
(OKE et al., 1992). 

Changes to the land surface, such as 
urbanization, substitute natural surfaces for 
buildings, roads and streets, significantly increasing 
the level of soil waterproofing as well as altering its 
thermal properties (e.g. specific heat and thermal 
conductivity) and radioactivity (e.g albedo) (ANJOS 
et al., 2013). As a consequence, there have been an 
increasing number of floods as well as changes in 
radiation and energy surface balance. In the last few 
years, urbanized areas have seen an increase in 
concentrated heat (the phenomenon known as the 
urban heat island effect), linked to a rise in severe 
rainfall, and it is fast becoming a significant 

problem for affected populations and policy makers 
(RAMANATHAN; FENG, 2009).   

The process of urbanization alters the 
Surface Energy Balance (SEB), and does so as 
much for short as for long wavelength radiation 
emission. The thermal properties of the materials 
used in urban building and construction promote 
faster heat conduction than that of soil and 
vegetation in rural areas, thus contributing to 
increasing dissimilarity of temperature between 
urban and rural regions (FREITAS; DIAS, 2005). 
Surface temperature is directly influenced by 
climate variation, and precise evaluation is of great 
importance for research monitoring spatial 
dynamics such as urbanization processes, natural 
catastrophes or other landscape changes. The 
decrease of green areas creates changes in the local 
atmosphere, thus modifying patterns of temperature, 
rain, and wind direction and speed (KATO; 
YAMAGUCHI, 2005). 

When using the data gathered by the 
Thematic Mapper (TM) to study the influence of 
landscape change in Cruzeiro do Sul, in the 
Brazilian State of Acre, Delgado et al. (2012) found 
an increase in the number of anthropised areas 
between 2005 and 2010. These researchers also 
established another important result: they noted that 

Received: 16/04/16 
Accepted: 05/12/16 



1039 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

areas transformed by human action had seen 
temperature increases, with values reaching 40°C 
and above, something which contributed to an 
increase in precipitation of 17.6 mm-1.year-1 (1971-
1990) and reached a maximum value of 30.5 mm-
1.year-1 between 1993 and 2002. 

Against that background, this article aimed 
to evaluate land surface temperature using 
MOD11A2 (Terra satellite) with spatial resolution 
of one kilometre, compares its findings with land 
surface temperature data gathered by conventional 
meteorological stations, and, finally, investigates 
relations between land surface temperature and 
synoptic systems events that occurred in Rio de 
Janeiro State between January until December of 
2009.   

 
MATERIAL AND METHODS 
 
Characterization and location of the research 
area 

This research examined Rio de Janeiro 
State, which is located in the Southeast region of 
Brazil, in the country’s east coast, and between the 
meridians 40° 57’ 59” W and 44° 53’ 18” W, and 
parallels 20° 45’ 54” S e 23° 21’ 57” S (Figure 1). 
Rio de Janeiro State shares borders with Espírito 
Santo State in the northeast, with Minas Gerais State 
in the north and northwest, with São Paulo State in 
the southeast, and with the Atlantic Ocean in the 
south and east. The Conventional Meteorology 
Stations belong to the National Institute of 
Meteorology (INMET) and are spatially distributed 
so as to represent the physiographic conditions of 
the State. Rio de Janeiro State has a territorial area 
of 43,780.172 km², and a population of 15,993.583 
in habitants. The state is geopolitically divided into 
92 municipalities, which are grouped together into 
eight different meso-regions (subdivisions of 
Brazilian States): Norte Fluminense, Noroeste 
Fluminense, Serrana, Centro Sul Fluminense, 
Baixadas Litorâneas, Metropolitana, Médio Paraíba 
and Costa Verde (IBGE, 2013).  

 

 
Figure 1. Hypsometric map (m) obtained from SRTM. Mesoregion classification and the INMET conventional 

meteorological stations used in 2009 in Rio de Janeiro State. 
 

Air temperature data series (2009) 
We used 2009 daily time series data 

gathered from seventeen stations for a specific day 
of the year (astronomic day of the year) to evaluate 
the evolution of the air temperature recorded by the 
INMET Conventional Meteorological Stations. The 
analysis of maximum and minimum air temperature 
was conducted between January until December 
2009. We then obtained the mean value representing 
the average time of the satellite passing over the 
state of Rio de Janeiro, which was at 10:30 Local 
Standard Time (LST). Images from the MOD11A2 

MODIS product, with spatial resolution of 1km, 
were acquired for the same period.   

 
Analysis of land surface temperature recorded 
by MOD11A2 and SRTM 

In order to conduct this study, we used 
twelve monthly images (January until December 
2009) gathered by the MOD11A2 MODIS product 
(Land Surface Temperature), onboard the Terra 
satellite. The product has 8-day data composed from 
the daily 1 Km resolution imagery These images 
were obtained from NASA (National Aeronautics 
and Space Administration), USGS (U.S. Geological 



1040 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

Survey) and EROS (Earth Resources Observation 
and Science Center) on the 8th September 2013. 
They can be found at the following web address: 
<glovis.usgs.gov>.  

The images are in the Hierarchical Data 
Format (HDF) and Sinusoidal projection. It was 
necessary to convert the HDF format into GEOTIFF 
and the Sinusoidal projection into UTM WGS 84 for 
the images to be processed by the ArcGis 10.2. To 
achieve this, we had to pre-process the images using 
the Modis Projection Tool (MRT) algorithm. The 
MRT is a software tool exclusively designed to 
work with MODIS images. When working with this 
software, we chose the ‘LST_Day_1km’ image, and 
also selected the geographic position and the 
UTMWGS84 projection parameter.  

The Tile (a subdivision of the available 
areas of MODIS Products) which includes Rio de 
Janeiro state was the H13V11 and H14V11, with a 
total of 24 images used to cover the period of 2009. 
The MOD11A2 product (Land Surface temperature 
– LST) is derived from the MODIS sensor, one of 
the best sensors for radiometric resolution in the 
collection 5 of the thermal band, with spatial 
resolution of 1 km. The sensor uses the LST 
algorithm, including the Day/Night LST algorithm 
(WAN, 2007), to produce data on land surface 
temperature. This algorithm was specifically 
developed for MODIS and produces daytime and 
night-time thermal images for the whole surface of 
the Earth. Daily periodicity is validated by the MAS 
(MODIS Airborne Simulator) images and by field 
measurements conducted between 1996 and 1998 
(WAN et al., 1998). The MOD11 product 
incorporates, in its algorithm emission data, viewing 
angle (developed to correct atmospheric effects), 
information on the surface reflectivity, emission, 
absorption and atmospheric dispersion, as well as 
information on solar radiation on the day. The A2 
product is a composition of eight days, produced 
after the daily data generated by the A1 product. We 
chose to use collection 5 as it has improved 
methodological capabilities when compared to 
collection 4 (WAN, 2007). 

A few data adjustments are necessary before 
applying the land surface temperature model. As 
they are pre-processed products, they are often 
available in a 16 bit format, so that in order to 
employ them they first need to be converted into 
their respective units. This was done by applying the 
ArcGIS 10.2 Spatial Analyst Tools – with a Raster 
Calculator in the Map Alebra toolset. Finally, in 
order to utilize the 16 bit MOD11A2 product, we 
also had to convert it to Kelvin (TK, K) (Equation 1).  

0.02*NDTK =               (1) 
ND is the Digital Number (MOD11A2) and 

0.02 is the constant used in converting to Kelvin.  
After converting the MOD11A2 product to 

temperature values in Kelvin, we used the ArcGIS 
10.2 software to work on the vector data, database, 
calculation of the surface temperature and map 
creation. We also used images generated by radar, 
which were obtained by the sensors aboard the 
Endeavour space shuttle, installed for the SRTM 
(Shuttle Radar Topography Mission) project. We 
used the ERDAS IMAGINE software to produce a 
mosaic of Rio de Janeiro State. The SRTM 
Elevation Digital Model, with 3 arc seconds (about 
90 m spatial resolution) is available free from the 
American government (MIRANDA, 2013).  

We used the ArcsGIS 10.2 Geographically 
Weighted Regression tool for a multiple linear 
regression analysis of the independent variables 
(latitude, longitude and altitude) to enable to 
evaluate the dependable variable (the land surface 
temperature of the MOD11A2 product). The 
analysis employed  the following equation:  

ii3i2i10iS
εAltβLongβLatββT ++++=                                     

(2) 
In the above equation, TSi (K) is the land 

surface temperature; Lati (degrees and tenths) is the 
latitude; Longi (degrees and tenths) is the longitude; 
Alti (m) is the altitude; β0, β1, β2, β3 are the 
regression coefficients; and εi is the random error, 
independently assumed and with normal 
distribution, zero mean and constant variation. 

We considered the negative sign of 
longitude and latitude to represent west from the 
Greenwich and South Hemisphere.  

 
Statistical methods 

We have used statistical methods available 
in the current literature to evaluate the performance 
of the methodology applied in our research. They 
are based on comparative analyses of the proposed 
methodology and the data gathered from the 
Automatic Meteorological Station. We applied the 
determination coefficient (r2), followed by the 
estimated mean standard error (SEM, ºC) (ALLEN 
et al., 1989), mean bias (MB ºC) and index of 
agreement (d) proposed by Willmott et al. (1985).  

 
Land use and occupation 

During the seasonal evaluation of the land 
surface temperature, we tried to verify the 
relationship between land temperature and the 
different forms of soil usage found in Rio de Janeiro 
State by using the data supplied by the 



1041 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

Environmental State Institute (Instituto Estadual do 
Ambiente – INEA) website  (web address:  
http://www.inea.rj.gov.br/basetematica_estadoambie
nte/). We selected four types of land use in the 
ArcGIS 10.2 software: forest, pasture, urban area 
and agriculture. For this purpose, we used the spatial 
analysis and data extraction tools. We then analysed 
the seasonal variations for the different classes and 
the information obtained for land surface 
temperature. Afterwards, we conducted an 
exploratory analysis of the data by using box-plots, 
which allowed us to visualise the location, 
dispersion, symmetry, outlier barriers as well as the 
outliers themselves, independently from the form of 
the distribution of the dataset. 

 
Description of the meteorological events between 
January until December 2009 

Based on the data gathered from the 
CLIMANÁLISE bulletins, we conducted a study of 
the main synoptic systems’ events that occurred in 
Rio de Janeiro State in 2009 (CPTEC, 2013). We 
then described the synoptic systems according to 
their influence on the variability of the land surface 
temperature, data which was gathered from the 
MOD11A2 during the days the Terra satellite 
passed over during the seasonal scale period. 

 
RESULTS AND DISCUSSION  
 
Algorithm test 

The dispersion in the surface temperature 
estimates was high for every month of the year, as 

indicated by values for the determination coefficient 
(r2) of between 0.11 (October) to 0.52 (November) 
(Table 1). High dispersions in values suggest a 
significant contribution of non systematic errors 
(WILLMONTT, 1981). Highest dispersions in the 
estimates (higher than r2) were observed during the 
change between winter (August) and spring 
(September and October) with com r2 < 0.19, and 
the smallest dispersions (r2 > 0.49) during autumn 
(April and May) and in November. Even though the 
dispersion in the estimates was high, as indicated by 
the low r2, the standard error of the estimate was 
below 3.8K (October), representing less than 1.5% 
of the average value of the estimated surface 
temperature. In absolute terms, the months of 
January and February (summer months) and 
October (spring) had the highest value standard 
error of the estimate (> 3.1 K), whereas we recorded 
the lowest values (< 2.2 K) during the transition 
between autumn and winter (April and May) and in 
winter (June and July). The mean (Standard Error of 
the Estimate/average) of January and October 
presented the highest errors, 1.15 and 1.29% 
respectively, and the lowest errors (< 0.76%) 
occurred during the autumn months and in June. 
The average tendency was towards over estimation 
(Figure 3), with the exception of July, October and 
December, where there was an overestimation for 
values lower than 307K (December), 296 K (July) 
and 303 K (October) and underestimation for higher 
values. 

 
Table 1. Statistical analysis of the land surface temperature (K) and the MOD11A2 product data for the period 

of January to December 2009 in Rio de Janeiro State.  

Month EPE (K) VM a (K) b r2 d 

Jan 3.44 0.1937 16.28 0.957 0.28 0,49 
Feb 3.11 0.0554 -50.25 1.170 0.29 0,60 
Mar 2.83 0.1741 70.17 0.777 0.27 0,51 
Apr 1.73 0.0680 81.27 0.737 0.50 0,77 
May 1.83 0.1925 60.37 0.801 0.49 0,58 
Jun 2.24 0.0639 25.39 0.917 0.38 0,73 
Jul 1.75 0.0234 148.86 0.498 0.35 0,93 

Agu 2.48 0.1899 176.22 0.415 0.16 0,52 
Sep 2.70 0.1499 78.15 0.747 0.19 0,49 
Oct 3.83 0.1706 186.57 0.383 0.11 0,55 
Nov 2.33 0.3551 -107.26 1.377 0.52 0,38 
Dec 2.48 0.1816 163.17 0.468 0.27 0,64 

 
Values for the month of July showed high 

agreement between the estimates and the observed 
data, confirmed by the Willmott coefficient of 
agreement (d) of 0.93; followed by April with 0.77 



1042 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

and June with 0.73. We observed the lowest 
agreement values (< 0.49) during January and in 
spring (September and November). The lower levels 
of precision and agreement of the estimates in the 
spring and summer months are due to the highest 
measures of rainfall being recorded during this 
period in Rio de Janeiro State. Rainfall during these 
seasons is highly variable spatially and in terms of 
intensity due to the tropical convection system that 
intensifies the atmospheric systems related to the 
rain (e.g. the Frontal Systems and the South Atlantic 
Convergence Zone) and to the mesoscale connective 
systems (ANDRÉ et al., 2008). On the other hand, 
during the autumn and winter months, the total 
amount of rainfall is less than that in summer and 
spring and is mainly related to frontal systems, 
which are less spatially variable.  

Although the values for r² for January, 
February, May and June were lower than 0.38 and 
the standard error of the estimate was higher than 
1.8 K, the angular regression coefficient (b) for the 
observed data and estimated data was close to 1 
(0.92 in June and 1.17 in February). This result 
indicates the scale of the contribution of the 
proportional systematic error in the estimates 
(WILLMOTT, 1985), seen in the regression lines 
found approximately parallel to the line 1:1 (Figure 
2 – Summer and Winter). The autumn months 
(March and May) and the spring months (September 
and November) showed similar results, in other 
words, a significant contribution due to proportional 
systematic error. However, the difference between 
the ideal value was higher: 0.747<b<1.377. Thus, 
even though there had been a significant 
contribution due to proportional systematic error, 

the results showed that this contribution was less 
than that observed for January, February, May and 
June. The systematic error can be related to the 
algorithm (e.g. algorithm parameterization) applied 
to surface temperature, or to the systematic errors of 
the MOD11A2 MODIS product utilized for the 
research.  

In the multiple linear regression analysis, 
altitude was the most significant variable in the 
model, explaining 64% of the variability in land 
surface temperature in the State of Rio de Janeiro. 
When latitude and longitude were added, the final r2 
was of 0.71 (71%). Thus, latitude, longitude and 
altitude variables explained 71% of the spatial 
variability of annual land temperature 
measurements. In ANOVA p<0.05 the model was 
significant. Similar results using digital elevation 
models (SRTM) and air temperature were found by 
Lyra et al. (2011) and Oliveira et al. (2013). Figure 
2 showed that the algorithm applied to determine the 
land surface temperature, by using the images from 
MOD11A2 MODIS product, had the best mean 
statistical indexes for autumn and winter when 
compared to the data gathered by the Conventional 
Meteorological Stations in Rio de Janeiro State.  
Wienert and Kuttler (2005) showed that there is a 
close relationship between the intensity of UHI with 
latitude, where at low latitudes the average intensity 
observed is about 4°C, which increases toward high 
latitudes with average of about 6.1°C. The authors 
explain these variations by the increase of 
anthropogenic heat in high latitudes and also by the 
differences in surface energy balance between these 
regions.

 

 
Figure 2. The relationship between the air temperature (K) recorded by the conventional meteorological 

stations and the estimated temperature (K) produced by the MOD11A2 MODIS product aboard the 
Terra satellite for 2009 summer, autumn, winter and spring in Rio de Janeiro. 

 
Space-time distribution of surface temperature 

The highest surface temperatures obtained 
by the MOD11A2 product in the summer were 
within 307 to 310 K. These were observed in the 

Metropolitana, Baixada Litorânea, Norte 
Fluminense and Noroeste Fluminense regions of Rio 
de Janeiro, and coincided with areas of lesser 
altitude (<90 m), located at the windward side of the 



1043 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

main mountain ranges, and which were 
characterized by the influence of air temperature 
patterns. The lower surface temperatures were 
within 296 and 298 K and found in the mountain 
areas of the Costa Verde, Centro Sul Fluminense 
and Serrana regions. Thus, the higher (and lower) 
temperatures were conditioned by the higher (and 
lower) altitudes found in the state, indicating 
geographical influence on the distribution of surface 
temperature. Lyra et al. (2011) observed that, in a 
tropical region, altitude is the main geographical 
factor (latitude, longitude and altitude) affecting the 
distribution of air temperature. With a high 
temperature nucleus, the Metropolitana region was 
the only exception to these findings, reflecting the 
scale of the urbanised areas in this region, itself a 
consequence of the high level of industrialization. 
The findings of the MOD11A2 product confirmed 
that the Metropolitana region had higher surface 
temperatures. Again, this is because of the scale of 
the urbanized area when compared to the Baixada 
Litorânea, Norte Fluminense and Noroeste 
Fluminense regions and which, consequently, 
results in the increase of the albedo caused by this 
change in the use and coverage of land in the region. 
The increase in temperature, moreover, is also 
caused by the influence of urban heat island 
formation in the region (ZERI et al., 2011).    

The thermal amplitude decrease among the 
mesoregions of Rio de Janeiro was determined 
based on the following physiographical factors: 
proximity to the Atlantic Ocean and influence of the 
mountain ranges in relation to the flow patterns 
(leeward and windward) which interfere with the 
surface temperature. We observed, across the land 
surface, an increase in temperature during the 
summer period. However, due to their position, the 
windward regions of the mountain ranges (Serra do 
Mar and Serra da Mantiqueira) receive higher 
amounts of solar radiation than that of the leeward 
regions. This process was responsible for the 
formation of a horizontal gradient in the surface 
temperature. Finally, an important additional factor 
is that the mesoregions of the state have varied use 
and land occupation characteristics, including urban 
areas, pasture areas through to areas of native 
vegetation, which imply different regimes of surface 
temperature.   

Figure 3a shows the composition of the 
MODIS product during the days of flight of the 
Terra satellite in the summer. The Southeast region 
experienced storms and heavy rainfall in December, 
especially because of the Madden-Julian Oscillation 
(MJO), which was responsible for the occurrence of 
these events in Rio de Janeiro State, which was then 

followed by the South Atlantic convergence zone 
(SACZ) episode in the same month. While January 
thus saw two SACZ episodes in the region, the 
second one was weaker, which contributed to the 
formation of milder air temperatures along the 
Southeast coast region. February did not see Frontal 
Systems affecting the state, only the south part of 
the country, and that is due to the anomalous 
anticyclone activity in the east part of Brazil, which 
had the consequence of increasing the air 
temperature in this area. Climatologically speaking, 
we noted that January and February saw the 
intensification of the La Niña phenomenon, while 
December saw the beginning of the El Niño 
phenomenon. The summer season was characterised 
by a powerful drought and positive anomalies in air 
temperature, even though there had been variability 
in the systems creating and inhibiting rain in the 
Southeast region (CPTEC, 2013). 

In contrast to the summer season, autumn 
saw the highest surface temperatures recorded by 
the MOD11A2 product in all the regions of Rio de 
Janeiro State. The temperatures reached between 
299 and 300K to 307 and 309K. The exceptions 
were the Serrano and Costa Verde regions, which 
recorded surface temperatures between 293 and 
294K. These two regions have extended green areas 
and little urbanization, which contribute to a smaller 
variation of albedo and milder surface temperatures. 
Only the coastal region observed a strong horizontal 
gradient in surface temperature. The high 
temperatures recorded throughout Brazil are thought 
to be responsible for this exceptional difference 
between the seasons, with maximum and minimum 
temperatures above the recorded national historical 
average levels, especially in the Southeast region 
(CPTEC, 2013).  

Figure 3b shows the composition of the 
MODIS product when the Terra satellite passed 
over the state in autumn. No Frontal Systems were 
acting in the Southeast region in March, only two 
SACZ episodes. These two SACZ events 
contributed to a higher accumulation of precipitation 
in the Brazilian Southeast region, and particularly in 
the north part of Rio de Janeiro State. April had 
rainfall below the average for the period and this is 
because the center of the Subtropical Anticyclone of 
the South Atlantic (SASA) moved to the central and 
coastal parts of the Southeast region. The Bolivian 
High synoptic system was also contributed to the 
decrease in rainfall.  

 
 
 
 



1044 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

 
 
 

 
 
 

     

 
Figure 3. Surface temperature (K) recorded by the MOD11A2 MODIS product aboard the Terra satellite for 

2009 summer (A), autumn (B), winter (C) and spring (D) in Rio de Janeiro. 
 

Due to the low activity of the Frontal 
Systems and the presence of the Upper 
Tropospheric Cyclonic Vortex, the Southeast region 
of Brazil continued to see scarce rainfall in May 
(CPTEC, 2013), with the Upper Tropospheric 
Cyclonic Vortex characteristically provoking 
subsidence movement in its peripheral area. These 
synoptic systems contributed to the increase in the 
superficial thermal field of Rio de Janeiro State. 
Finally, during the autumn period, the small Frontal 
Systems activity, the SASA and the Upper 
Tropospheric Cyclonic Vortex, all contributed to 
increases in the surface temperature in the greater 
part of the mesoregions of Rio de Janeiro State. 

The highest surface temperatures recorded 
in the winter by the MOD11A2 product were within 
301 and 303 K, especially in the Metropolitana, 
Baixada Litorânea, Norte Fluminense and Noroeste 
Fluminense regions of Rio de Janeiro; the highest 
values were in the Norte Fluminense and Noroeste 
Fluminense regions. The lowest surface 
temperatures recorded were between 289 and 290 K 
in the Costa Verde and Serrana regions. Centro Sul 

Fluminense and Metropolitana regions were the 
exceptions with high surface temperatures. The 
leeward and windward flow of the mountain ranges, 
as well as the solar radiation, both impacted on 
surface temperature during this season. Similarly, to 
the autumn season, the winter saw only the coastal 
region developing a strong horizontal gradient in 
surface temperature, a development which started in 
the Metropolitan Region (Figure 3c).    

According to established patterns, part of 
the land surface should cool down during the winter 
months. However, the conditions for the meaningful 
increase of land surface temperature seen in the 
mesoregions of Rio de Janeiro State are related to a 
hot and dry air mass remaining over the Central and 
Southeast regions of Brazil during June, which, as 
consequence, contributed to the increase of 
temperatures in these regions. The Southeast region 
still saw rainfall in July due to the Subtropical Jet 
(SJ), favoring the intensification of the Frontal 
Systems, however, this only happened in two cases. 
In August the drought period followed the normal 



1045 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

pattern for the Central and Southeast regions 
(CPTEC, 2013).     

The MOD11A2 product recorded the 
highest surface temperatures in spring, between 307 
to 310 K and 311 to 314 K, a pattern different to 
that seen in all the previous seasons observed. 
Again, discrepancies were seen in the 
Metropolitana, Baixada Litorânea, Norte 
Fluminense, and Noroeste Fluminense regions of 
Rio de Janeiro, and, when compared to the summer 
period, spring showed variability in the superficial 
thermal field and further increases in surface 
temperatures. The lowest surface temperatures were 
between 298 and 300 K in the Costa Verde and 
Serrana regions. The exceptions were Centro Sul 
Fluminense and Médio Paraíba regions, with high 
and low surface temperatures between 291 and 291 
K. In contrast to the other seasons, there was no 
formation of strong horizontal gradients in surface 
temperature along the coastal region (Figure 3d).    

Spring was marked by increases in the 
warmth of parts of the land surface. In September, 
the Frontal Systems moved along the interior and 
coastal areas of the Southeast region of Brazil, 
generating conditions favorable the increase of rain 
in the central-south part of Rio de Janeiro State. The 
Frontal Systems incursion, followed by a trough in 
the medium and high troposphere, all helped to form 
areas of instability. These areas of instability 
contribute to the increase of the total accumulation 
of rainfall, especially in the north region of Rio de 
Janeiro State. Madden-Julian Oscillation, again, 
brought rainfall in the second half of October. 
During this month the convection system was 
mainly associated with SACZ and with the joint 
incursion of Frontal Systems. It should be pointed 
out, however, that spring is the season with the 
highest numbers of Frontal Systems in Rio de 
Janeiro State (ZERI et al., 2011). Finally, November 

did not see the occurrence of rain in the Southeast 
region of the country, as there was no SACZ, and 
only the Bolivian High and Upper Tropospheric 
Cyclonic Vortex influenced rain and air temperature 
patterns across this period (CPTEC, 2013).  

During the summer, agricultural land had 
the smallest surface temperature maximum value 
(313 K), while forest, urban and pasture land types 
had higher values (315 K). The use and occupation 
of land as seen by agriculture and urban areas had 
the highest quartiles, 299.3 K and 299 K 
respectively, whilst the forest land type showed the 
smallest mean values in the exploratory data 
analysis, with 303 K. For values that are above the 
average in the third quartile analysis, the agriculture 
and forest land types had the smallest values, 307.8 
K and 308.5 K respectively. There is a direct 
relationship between increases in the air temperature 
and the photoperiod in the summer season, as seen 
by the increase of energy output for these land 
types. In autumn, the smallest mean found was that 
of the forest land type. Generally speaking, those 
land types with the highest temperatures are the 
anthropised areas (urban and agricultural areas) 
followed by pasture land.  

In the exploratory data analysis forest land 
has the smallest magnitudes, followed by pasture 
land (Figure 4). The areas classified as dense green, 
with good amounts of shade and water over the 
ground or at the tree canopy, helped to decrease the 
surface temperature, presenting lower temperatures 
when compared with other land types. Forest and 
pasture land had the lowest average (302 K) during 
spring. Urban areas and agriculture land had the 
highest average values, 302 K and 303 K 
respectively. Both land types saw maximum and 
minimum values varying from 291 K to 314 K. 
Agriculture land was the highest in the third quartile 
with values above the 308 K average.

  

 
Figure 4. Box-plot of the categories of land use for summer (a), autumn (b), winter (c) and spring (d) in 2009 

in Rio de Janeiro.  
 



1046 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

CONCLUSIONS 
 
The highest surface temperatures recorded 

by the MOD11A2 product, derived from the 
MODIS sensor, were found in the Metropolitana, 
Baixadas Litorâneas, Norte Fluminense and 
Noroeste Fluminense regions of Rio de Janeiro State 
and were recorded during the summer, winter and 
spring seasons. Autumn was the only exception, and 
this was due to the influence of the coastal 
environment.  

The following synoptic systems interfered 
with the estimated surface temperature produced by 
the MOD11A2 product for Rio de Janeiro State in 

2009: the Madden Julian Oscillation and South 
Atlantic Convergence Zone in the summer; and 
Frontal Systems, the South Atlantic Convergence 
Zone, the Madden Julian Oscillation and the Upper 
Tropospheric Cyclonic Vortex in spring.  

The land use and occupation types with the 
highest surface temperature are: forest, urban area 
and pasture land in the summer; forest, urban area, 
agriculture and pasture land in autumn; and urban 
area and agriculture during spring. The MOD11A2 
product showed a drastic decrease of the surface 
temperature for all land types during winter, 
especially for forested land.   

 
 

RESUMO: Este artigo teve como objetivo avaliar a temperatura da superfície do solo usando o satélite  
MOD11A2 com resolução espacial de um quilômetro, comparar seus resultados com dados de temperatura da superfície 
terrestre adquiridas por estações meteorológicas convencionais, e, por fim, investigar as relações entre a temperatura da 
superfície da terra e eventos sistemas sinóticos que ocorreram no Estado do Rio de Janeiro entre janeiro até dezembro de 
2009. As temperaturas mais elevadas da superfície obtidas pelo produto MOD11A2, derivado do sensor MODIS, foram 
encontrados nas regiões Metropolitana, Baixadas Litorâneas, Norte Fluminense e Noroeste Fluminense do Estado do Rio 
de Janeiro e foram registradas durante as estações de verão, inverno e primavera. O outono foi a única exceção devido à 
influência do ambiente costeiro. Os seguintes sistemas sinóticos interferiram na temperatura da superfície estimada pelo 
produto MOD11A2 para Estado do Rio de Janeiro em 2009: a Madden Julian Oscillation e Zona de Convergência do 
Atlântico Sul no verão; Sistemas frontais, a Zona de Convergência do Atlântico Sul, o Madden Julian Oscillation e o Alto 
Ciclone Vortéx Troposférico na primavera. Os tipos de uso e ocupação do solo com a temperatura da superfície mais alta 
são: floresta, área urbana e terras de pastagem no verão; floresta, área urbana, agricultura e pastagens no outono; área 
urbana e na agricultura durante a primavera. O produto MOD11A2 mostrou diminuição drástica da temperatura da 
superfície para todos os tipos de solo durante o inverno, especialmente para terrenos florestais. 

 
PALAVRAS-CHAVE: Região sudeste. Sensoriamento remoto. Campo térmico. Satélites meteorológicos. 

 
 
REFERENCES 
 
ALLEN, R. G.; JENSEN, M. E.; BORNAN, R. D. Operational estimates of reference evapotranspiration. 
Agronomy Journal, Madson, v. 81, p. 650-662, 1989. 
 
ANDRÉ, R. G. B.; MARQUES, V. S.; PINHEIRO, F. M. A.; FERRAUDO, A. S. Identificação de regiões 
pluviometricamente homogêneas no Estado do Rio de Janeiro, utilizando-se valores mensais. Revista 
Brasileira de Meteorologia, São José dos Campos, v. 23, p. 501-509, 2008. 
 
ANJOS, A.W.; DELGADO, R. C.; OLIVEIRA-JÚNIOR, J. F.; GOIS, G.; MORAES, N. O. Temperatura da 
superfície continental associada aos eventos meteorológicos na cidade do Rio de Janeiro, RJ. Enciclopédia 
Biosfera, Goiânia, v. 9, n. 17, p. 3692-3707, 2013.  
 
CPTEC – Centro de Previsão do Tempo e Estudos Climáticos. Boletim Climanálise. Disponível em: 
http://climanalise.cptec.inpe.br/~rclimanl/boletim/. Acesso em: 14 Ago 2013. 
 
DELGADO, R.C.; SOUZA, L. P.; SILVA, I. W. R.; PESSÔA, C. S.; GOMES, FA. Influência da mudança da 
paisagem amazônica no aumento da precipitação em Cruzeiro do Sul, AC. Enciclopédia Biosfera, Goiânia, v. 
8, p. 665-674, 2012. 
 
FREITAS, E. D.; DIAS, P. L. S. Alguns Efeitos de Áreas Urbanas na Geração de uma Ilha de Calor. Revista 
Brasileira de Meteorologia, São José dos Campos, v. 20, p. 335-366, 2005. 



1047 
Synoptic events associated…  DELGADO, R. C.  et al. 

Biosci. J., Uberlândia, v. 33, n. 5, p. 1038-1047, Sept./Oct. 2017 

 
IBGE – Instituto Brasileiro de Geografia e Estatística. Censo Demográfico 2010. Disponível em:< 
http://www.ibge.gov.br>. Acesso em mar.2013. 
 
INEA – Instituto Estadual do Ambiente. Base Temática. Disponível em: < 
http://www.inea.rj.gov.br/basetematica_estadoambiente/>. Acesso em jun. 2013. 
 
KATO, S.; YAMAGUCHI, Y. Analysis of urban heat-island effect using ASTER and ETM+ Data: Separation 
of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sensing of 
Environment, Salt Lake City, v. 99, p. 44-54, 2005. https://doi.org/10.1016/j.rse.2005.04.026 
 
LYRA, G. B.; SANTOS, M. J.; SOUZA, J. L.; LYRA, G. B.; SANTOS, M. A. Espacialização da temperatura 
do ar anual no estado de Alagoas com diferentes modelos digitais de elevação e resoluções espaciais. Ciência 
Florestal, Santa Maria, v. 21, p. 275-287, 2011. https://doi.org/10.5902/198050983231 
 
MIRANDA, E. E. Brasil em Relevo. Campinas: Embrapa Monitoramento por Satélite, 2005. Disponível em: 
<http://www.relevobr.cnpm.embrapa.br>. Acesso em: 02 set. 2013. 
 
OLIVEIRA, J. B.; ARRAES, F. D. D.; VIANA, P. C. Methodology for the spatialisation of a reference 
evapotranspiration from SRTM data. Revista Ciência Agronômica, Fortaleza, v. 44, p. 445-454, 2013. 
https://doi.org/10.1590/S1806-66902013000300005 
 
SATTERTHWAITE, D. Cities’ contribution to global warming: notes on the allocation of greenhouse gas 
emissions. Environment and Urbanization, Tokyo, v. 20, p. 539–549, 2008. 
https://doi.org/10.1177/0956247808096127 
 
WAN, Z.; FENG, Y. Z.; ZHANG, Y.; KING, M. D. Land-surface temperature and emissivity retrieval from 
MODIS Airborne Simulator (MAS) data, Summaries of the Seventh JPL. Airborne Earth Science Workshop, 
Airborne, v. 3, p. 57-66, 1998.  
 
WAN, Z. MODIS Land Surface Temperature Products Users' Guide Collection-5. ICESS, University of 
California, Santa Barbara, 2007. 
 
WILLMOTT, C. J.; ACKLESON, S. G.; DAVIS, J. J.; FEDDEMA, J. J.; KLINK, K. M.; LEGATES, D. R.; 
O’DONNELL, J.; ROWE, C. M. Statistics for the evaluation and comparison of models. Journal of 
Geography Research, Malbourne, v. 90, p. 8995-9005, 1985.  
 
ZERI, M.; OLIVEIRA-JÚNIOR, J. F.; LYRA, G. B. Spatiotemporal analysis of particulate matter, sulfur 
dioxide and carbon monoxide concentrations over the city of Rio de Janeiro, Brazil. Meteorology and 
Atmospheric Physics, Viena, v. 113, p. 1-14, 2013.