Microsoft Word - 21-Bio_33155.doc
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Original Article
Biosci. J., Uberlândia, v. 32, n. 5, p. 1331-1340, Sept./Oct. 2016
VALIDATION OF THE NET RADIATION THROUGH SEBAL ALGORITHM
IN DIFFERENT CLASSES OF LAND USE AND OCCUPATION IN RIO DE
JANEIRO
VALIDAÇÃO DO SALDO DE RADIAÇÃO PELO ALGORITMO SEBAL EM
DIFERENTES CLASSES DE USO E OCUPAÇÃO NO RIO DE JANEIRO
Henos Carlos Knupler Jordão LISBOA¹; Iris Cristiane MAGISTRALI¹;
Rafael Coll DELGADO¹; José Francisco de OLIVEIRA-JÚNIOR¹; Givanildo de GOIS¹;
Paulo Eduardo TEODORO²
1. Instituto de Florestas, Universidade Federal Rural do Rio de Janeiro - UFRRJ, Seropédica, RJ, Brasil; 2. Departamento de Biologia
Geral, Universidade Federal de Viçosa - UFV, Viçosa, MG, Brasil. eduteodoro@hotmail.com
ABSTRACT: The aim of this study was (a) to assess and evaluate the net radiation (Rn) by SEBAL algorithm
and (b) to assess the net radiation (Rn) by the landscape’s spatial temporal dynamic using ISODATA algorithm, in entire
city of Rio de Janeiro. Has been calculated the Rn by using the TM sensor images and orbital platform Landsat 5 and by
Penman-Monteith method (FAO 56) with the conventional meteorological station data (EMC). The Rn values obtained
with the SEBAL algorithm to the EMC cut area were slightly smaller than those obtained by the Penman-Monteith method
(FAO 56), with VM = -36 (Wm-2) and EPE = 84.44 Wm-2. The Rn obtained by SEBAL has a high correlation with the
values obtained in the surface. The Rn values obtained with the algorithm for the land use and occupation classes in the
city of Rio de Janeiro were similar to those reported by other authors for the same classes.
KEYWORDS: Image processing. Remote sensing. Albedo.
INTRODUCTION
Net radiation (Rn) is the main source of
energy response for the heating of the soil and air
and the evaporation process (SILVA et al., 2005).
Its knowledge is important to for characterizing and
monitoring the climate and weather forecast,
identifying interactions of radiative fluxes
descending and ascending of short and long wave
that interact between environmental variables and
the surface, in addition to being relevant for
formulating public policies (DI PACE et al., 2008;
BIUDES et al., 2009; ANDRADE, 2009, SILVA et
al., 2011).
There are several conventional devices, such
as balance-radiometers, which measure the net
radiation in situ (GOIS et al., 2016a). However,
these devices only present good precision under
similar conditions and in small areas (DI PACE et
al., 2008). The use of remote sensing (RS) made
possible the spatial and temporal Rn monitoring over
large areas and with heterogeneous features at a low
cost (GOMES et al., 2009).
Among the most commonly used algorithms
in heat flux on the surface studies, it stands out
SEBAL (Surface Energy Balance Algorithm for
Land) proposed by Bastiaanssen et al. (1998a). This
algorithm has been used in numerous studies
(MORAN, 1994; BASTIAANSSEN et al.,1998a;
BASTIAANSSEN, 2000; GRANGER, 2000;
SILVA et al., 2011 and 2014; ANDRADE et al.,
2014). It can be applied in digital images from any
orbital sensor that perform radiance measurements
in the visible channels, near and thermal infrared,
such as: TM (Thematic Mapper) - Landsat 5
(BEZERRA et al., 2008; MENEZES et al., 2011),
NOAA-AVHRR (TIMMERMANS; MEIJERINK,
1999; BASTIAANSSEN; ALI, 2003),
MODIS/TERRA/AQUA (DI LONG et al., 2010;
SANTOS, 2011; OLIVEIRA, 2012) and
ASTER/TERRA (WANG et al., 2005).
However, to improve the accuracy of
SEBAL algorithm are necessary parameterization of
several equations to better adjust it to the reality of
the study area (BASTIAANSSE et al., 1998a;
BASTIAANSSE et al., 1998b; ANDRADE et al.,
2014; MACHADO et al., 2014; SILVA et al.,
2014). This validation is performed by comparison
between the estimated and obtained data in the field
by onsite measurements of superficial energy flows
or using empirical methods, such as Penman-
Monteith method (FAO 56).
Based on the above, the study aims to
estimate and validate the net radiation through
SEBAL algorithm associated with the landscape’s
spatial temporal dynamics of the city of Rio de
Janeiro, state of Rio de Janeiro.
Received: 09/02/16
Accepted: 06/06/16
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MATERIAL AND METHODS
The study area was the city of Rio de
Janeiro (MRJ), located between latitudes 22° 45’
and 23° 50’ S, and longitudes 43° 05’ and 43° 50’
W (Figure 1). The region climate, according to
Köppen classification, is “Aw”, characterized by dry
and cold winters and humid and rainy summers. The
annual mean temperature is 23.9ºC and the
maximum and minimum are 27.3°C in summer and
21.1°C in winter, respectively, with rainfall around
1,258 mm.year-1 and mean number of 124 days with
rainfall (ZERI et al., 2011). MRJ presents vegetation
of Dense Ombrophilous Forest with predominance
of Oxisols with dystrophic and rarely eutrophic
features (GOIS et al., 2016b). Still occur litholic
soils in parts of the slopes (IBGE, 2012).
Figure 1. Geographic localization of the study area with mountain ranges.
Five images from the satellite sensor TM
Landsat 5 (Table 1) were selected systematically,
orbits 217 and point 76 of the series available in
Image Catalog in the site of National Institute for
Space Research (INPE) (INPE, 2014). The images
were based on the lower cloud cover on dates with
availability of the following weather variables:
mean air temperature (Tar, °C), relative air humidity
(UR, %) and insolation (n, hours) through
Conventional Weather Station (EMC), whose
coordinates are 22° 88’ S and 43°18’ W and
altitude of 11.10 m, located in MRJ (Code - 83743)
from National Institute for Meteorology (INMET)
(INMET, 2014).
The methodologies adopted by Silva et al.
(2014) and Freitas et al. (2012) were used in the
unsupervised classification, mapping of land use
and occupation in the MRJ with the "ISODATA"
classifier for the respective dates. We performed the
reclassification of classes in common, grouping
them into seven distinct classes, namely: water
(areas formed by continental waters and estuaries,
lagoons, rivers, canals, reservoirs and dams),
flooded area (areas occupied by marshes, shoals,
with the characteristic vegetation of these
environments), exposed soil (bare ground and
unpaved roads), urban area (built-up areas and
paved roads), vegetation (tree and shrub forest, in
advanced development and regeneration stages),
field (areas with undergrowth, graminoid, located on
plains or slopes and forest in early development and
regeneration stage) and agriculture (different types
of annual crops). Subsequently, we converted
similar pixels for acreage using ArcGIS software
version 10.2. We used in the study six spectral
bands 1, 2, 3, 4, 5 and 7, which has a maximum
resolution of 30 m, being that one pixel corresponds
to 0.09 ha.
Table 1. Technical information on the images used.
Year Satellite Sensor Date Orbit/Point
1986 Landsat-5 TM 01/28/1986 217/76
1990 Landsat-5 TM 02/24/1990 217/76
2003 Landsat-5 TM 12/29/2003 217/76
2006 Landsat-5 TM 08/31/2006 217/76
2010 Landsat-5 TM 02/15/2010 217/76
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In obtaining Rn (Table 2), the images were
processed in the ERDAS IMAGINE software
version 2014, using the platform Model Maker. We
used the standard of SEBAL algorithm
(BASTIAANSSEN et al., 1998; ALLEN et al.,
2002). On ERDAS IMAGINE 2014, the union of
the satellite bands, the radiometric calibration
(MARKHAM; BAKER, 1987; CHANDER;
MARKHAN, 2009), reflectivity, albedo at the top of
the atmosphere, albedo of surface, vegetation index,
emissivity of each pixel in the spectral domain of
the thermal band, emissivity of broadband, surface
temperature, long-wave radiation emitted by the
atmosphere and surface and descending short-wave
radiation emitted by the atmosphere were
calculated. ArcGIS version 10.2 was used for
making vector data, database and maps.
Calculation of the long-wave radiation
emitted by the atmosphere (RL↓, W. m-2) and short-
wave radiation emitted by the atmosphere in the
direction of each pixel (RS↓, W. m-2), which make
up the Rn, were performed in Excel 2013
spreadsheet.
Table 2. Parameters used in the equations for calculating the net radiation (Rn).
01/28/1986 02/24/1990 12/29/2003 08/31/2006 02/15/2010
Z 38.85 43.20 32.71 44.44 35.43
dr 0.98 0.98 0.98 1.01 0.99
E 51.15 46.79 57.29 45.56 54.57
Tair (K) 301.05 301.10 303.40 295.05 307.15
Legend: Z = Solar Zenital Angle; dr = Relative distance Earth-Sun (UA); E = Sun Elevation and Tar (K) = Air temperature.
With the Rn obtained from SEBAL algorithm and
the calculated from Penman-Monteith method (FAO-56)
with the data coming from EMC of MRJ, was evaluated
statistical performance through the following parameters:
mean relative error (E%), standard deviation (S),
coefficient of variation (CV, %), mean bias (MB) and
standard error of estimate (SEE).
RESULTS AND DISCUSSION
In Table 3, we observed that the mean
values of Rn obtained from SEBAL, in relation to
the FAO-56 method, underestimated the values of
Rn. Negative MB together with SEE (84.44 Wm
-2).
However, we emphasize lower variation (CV =
7.59%) for the estimated values of Rn obtained from
SEBAL. The E% between the observed and
estimated values of Rn was less than 7%, which is in
accordance with Daughtry et al. (1990), wherein E%
between measurements and estimates of Rn with SR
are less than 7%. In a study developed in Ceara, at
experimental field of Embrapa, Santos et al. (2010)
obtained difference between the observed and
estimated Rn of approximately 23%, being
considered satisfactory. Relative errors obtained did
not represent a validation of SEBAL. However,
FAO-56 method, used for validating the results may
have errors due to failures occurred by lack of
maintenance of the EMC instruments and failures in
time series without filling the gaps and the
appropriate homogenization of data (OLIVEIRA
JÚNIOR et al., 2015).
Table 3. Statistical analysis of the net radiation values (Rn, Wm-2) obtained by Penman-Monteith method
(FAO-56) and SEBAL algorithm.
DATE FAO56 (Wm-2) SEBAL (Wm-2) E (%) SEE (Wm-2) MB (Wm-2)
01/28/1986 704.42 599.29
4.05 84.44 -36.98
02/24/1990 596.80 569.94
12/29/2003 716.83 659.25
08/31/2006 450.41 534.66
02/15/2010 692.59 613.03
S 112.17 41.64
CV% 17.74 7.59
S: standard deviation; CV: coefficient of variation; E: mean relative error; SEE: standard error of estimate; MB: mean bias.
In Figure 2, proximity between the observed
and estimated Rn curves was checked. The curve
obtained with the SEBAL is well correlated with the
Rn curve from FAO-56 method (r = 0.90), it shows
low error of algorithm estimate. In a study in the
city of Santa Rita do Passa Quatro, São Paulo,
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Giongo et al. (2010) obtained correlation higher
than 0.95, for Rn registered in the USR towers
(sugarcane) and PDG (Cerrado) and the estimated
by SEBAL, in the area corresponding to each tower.
Tasumi et al. (2008), when estimating Rn of 49 sites
from United States (USA), observed an average
correlation of 95% between measured and estimated
values by SEBAL. Di Pace et al. (2008) also
obtained good Rn estimates measured in Brazilian
Northeast (BN).
Figure 2. Tendency of observed and estimated values of the net radiation in different years through TM -
Landsat 5 images.
Land use and occupation (Figure 3A) and Rn
values (Figure 3B) for the period studied, where the
light areas indicate smaller Rn values. There was a
predominance of vegetation in the main mountain
ranges (Pedra Branca, Tijuca and Gericinó) existing
in MRJ (GOULART et al., 2015), followed by high
Rn values in the evaluated dates. According to
Machado et al. (2014) and Caula et al., (2016),
vegetation areas have lower albedo values,
reflecting a smaller percentage of RS↓, followed by
a lower loss by RL↓ due to having lower
temperatures, favoring the energy availability in the
form of net radiation. Lowland regions of MRJ are
predominantly occupied with urban area and,
consequently, there is little vegetation area
(GOULART et al., 2015). Concomitantly, these
areas showed low Rn values (Figure 3B). Similarly,
in the Alto Rio Negaro basin, located at Planalto
Norte Catarinense and Primeiro Planalto
Paranaense, Uda et al. (2013) found smaller Rn
values observed in areas with lower biomass density
(with Soil Adjusted Vegetation Index - SAVI
positive and close to zero) and higher albedo. These
authors found higher losses of RL↓ in urban areas,
due to higher surface temperature, low biomass
density and higher albedo, which favors higher
radiation loss to the atmosphere and its lower
storage.
Increased urbanization related to reduction
of vegetation can cause serious impacts to the
studied area. Andrade and Corrêa (2014) found that
the reduction in vegetation cover cause changes in
the soil flow. This is due to greater exposure of this
radiation, increasing the air flow and decreasing the
Rn to surface, consequently evaporation process.
Thus, cloud formation and the hydrological regime
in the region are affected, which makes the local
atmosphere becomes warmer and with less moisture
content.
Andrade and Corrêa (2014) found a
significant variation of Rn values between
vegetation and urban areas in the city of Santarém,
PA, Brazil. However, Rn s not influenced only by
the total incident solar radiation, but also by
topography and type of surface coverage (DI
PEACE, 2008). In addition, Delgado et al. (2013)
observed that there is a characteristic thermal
variation between the land use and occupation
classes, and therefore, it is possible to classify each
type of coverage according to the thermal behavior,
being the class with the highest warming formed by
areas with human impact, followed by the pasture
class.
In Table 4 are presented the values obtained
with the extraction of the Rn calculated by SEBAL
from polygons of use and soil cover classes for
MRJ. Among all the classes, the water showed the
highest mean values of Rn, from 566.49 Wm
-2 to
711.96 Wm-2. Silva et al. (2005) found Rn of 751.3
Wm-2 in Sobradinho lake, and mapping the net
radiation from Alto Rio Negro basin, Uda et al.
(2013) obtained mean Rn of 610 Wm
-2 for the same
class.
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Figure 3. Maps of land use and occupation (A) and net radiation (Rn, Wm-2) (B) of the city of Rio de Janeiro,
RJ, in years 1986, 1990, 2003, 2006 and 2010.
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Table 4. Mean value of net radiation (Wm-2) for different land use and occupation classes in the city of Rio de
Janeiro, RJ.
Date
Water
Flooded
area
Vegetation Field Agriculture
Urban
area
Exposed
soil
01/28/1986 609,87 586.97 572.96 551.76 548.01 522.71 417.92
02/24/1990 603,13 582.04 563.36 534.99 525.67 504.03 517.00
12/29/2003 711,96 668.83 647.32 644.16 606.05 591.82 535.62
08/31/2006 566,49 573.42 544.86 531.75 498.25 488.65 424.98
02/15/2010 640,60 612.58 598.43 601.83 562.85 531.96 440.50
The class flooded soil, vegetation and field
show the highest Rn values (Figure 5). For flooded
area, we obtained Rn of 573.42 Wm
-2 in August
2006, with the higher Rn in December, 2003 (668.83
Wm-2). Machado et al. (2014) found Rn between 600
Wm-2 In August 2006, and 750 Wm-2 in January
2011, in mangrove area. Vegetation area showed Rn
between 544.86 Wm-2 and 647 Wm-2 and the field
class showed Rn between 531.75 Wm-2 to 644.16
Wm-2. These net radiations are similar to those
obtained in other studies, in which Oliveira (2009)
obtained values higher than 650 Wm-2, respectively,
for vegetation areas with higher density and water
bodies in Hydrographical Basin of Moxotó River in
semi-arid northeast region, Uda et al. (2013) found
mean Rn of 526 Wm
-2 for native forest.
Net radiation obtained for agriculture, urban
area and exposed soil were similar to those found by
other authors. In area cultivated with sugarcane crop
in four different biomes in the State of São Paulo,
Silva (2009) obtained 570.9 Wm-2 and 309.9 Wm-2
in February 22, 2005 and July 16, 2005, Uda et al.
(2013) obtained Rn of 444 Wm
-2 for agricultural area
in Alto Rio Negaro basin Rn computed in urban
class were similar to that obtained by Moreira et al.
(2011), who observed Rn lower than 632 Wm
-2 in
urban areas and higher than those obtained by Uda
et al. (2013), who obtained Rn 404 Wm
-2 and 429
Wm-2.
The obtained exposed soil areas are
according to the reported in the literature, wherein
Gusmão (2012) obtained Rn values of 425 W.m
-2
and 500 W.m-2. Values around 420 W m-2 were
found in exposed soil by Silva et al. (2005). On the
coast of Pernambuco, Machado et al. (2014)
obtained Rn values ranging from 450 Wm
-2 in 2006
and600 W m-2 in 2011 in exposed soil areas. These
smaller Rn values are due to higher albedo values
and surface temperature checked in exposed soil
areas that consequently reduce the net radiation
(MACHADO et al., 2014; OLIVEIRA JÚNIOR et
al., 2015).
In Figure 4 are showed Rn obtained for the
land use and occupation classes of MRJ. There is a
relational pattern between Rn obtained for each class
used, on the respective dates. Classes of lower
albedo as water, flooded area and vegetation showed
the highest Rn values, unlike the higher albedo
classes as urban area and exposed soil, which
showed the lowest Rn values. According to Uda et
al. (2013) and Santana et al. (2016), urban area and
exposed soil classes have characteristics opposite to
water bodies, this corresponds to areas with greater
loss of energy by reflection and emission.
Figure 4. Net radiation obtained for each land use and occupation class.
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The greatest Rn values of 12/29/2003 may
be a result of this day have been the hottest among
the studied dates, with temperature of 30.25°C.
Lower Rn observed in urban and exposed soil area
also reflected in higher surface temperature. Areas
with the highest soil exposure to radiation present
lower Rn, and consequently higher air temperature
values near the surface (ANDRADE; CORRÊA,
2014).
CONCLUSIONS
SEBAL algorithm is perfectly applied to the
city of Rio de Janeiro based on low spatial
resolution images for estimating the net radiation.
The calculation of the energy balance via
SEBAL algorithm reaches quantitatively and
spatially differentiated using ways of the net
radiation for different types of land cover in
different periods and weather conditions.
ACKNOWLEDGEMENTS
To Federal Rural University of Rio de
Janeiro (UFRRJ) and CNPq by the Scientific
Initiation scholarship to the first author, to CAPES
for financial support to last author e and use
availability of the laboratory of Environmental
Remote Sensing and Applied Climatology
(LSRACA) for research activities.
RESUMO: Os objetivos do estudo foram (i) estimar e validar o saldo de radiação (Rn), por meio do algoritmo
SEBAL e (ii) avaliar o Rn, através da dinâmica espaço-temporal da paisagem, baseado no algoritmo ISODATA, em todo o
município do Rio de Janeiro (MRJ), Rio de Janeiro. Foi calculado o Rn baseado em imagens do sensor TM e plataforma
orbital Landsat 5 e, através do método Penman-Monteith (FAO 56) com dados de entrada obtidos da Estação
Meteorológica Convencional (EMC). Os valores de Rn obtidos com Algoritmo SEBAL para o recorte da área da EMC
foram ligeiramente inferiores aos obtidos pelo método Penman-Monteith (FAO 56), apresentando VM = -36 (W.m-2) e
EPE = 84,44 W.m-2. O Rn obtido pelo SEBAL, apresentou alta correlação com os valores obtidos em superfície. Os valores
de Rn obtidos com o algoritmo para as classes de uso e ocupação do solo do município do Rio de Janeiro foram
semelhantes aos encontrados por outros autores para as mesmas classes.
PALAVRAS-CHAVE: Processamento de imagens. Sensoriamento remoto. Albedo.
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