Sept., 2014
Journal of Resources and Ecology
J. Resour. Ecol. 2014 5 (3) 253-262
DOI:10.5814/j.issn.1674-764x.2014.03.008
www.jorae.cn
Vol.5 No.3
Article
Mapping Air Temperature in the Lancang River Basin Using the
Reconstructed MODIS LST Data
FAN Na1,2, XIE Gaodi1*, LI Wenhua1, ZHANG Yajing3, ZHANG Changshun1 and LI Na1,2
1 Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Center for Environmental Education & Communications of Ministry of Environmental Protection, Beijing 100029, China
Abstract: Air temperature is an important climatological variable and is usually measured in meteorological
stations. Accurate mapping of its spatial and temporal distribution is of great interest for various scientific disciplines,
but low station density and complexity of the terrain usually lead to significant errors and unrepresentative
spatial patterns over large areas. Fortunately the current studies have shown that the regression models can
help overcome the problem with the help of time series remote sensing data. However, noise induced by cloud
contamination and other atmospheric disturbances variability impedes the application of LST data. An improved
Savizky-Golay (SG) algorithm based on the LST background library is used in this paper to reconstruct MODIS LST
product. Data statistical analysis included 12 meteorological stations and 120 reconstructed MODIS LST images of
the period from 2001 to 2010. The coefficient of correlations (R 2) for 80% of the stations was higher than 0.5 (below
0.5 for only 2 stations) which illustrated that there is a considerably close agreement between monthly mean TA (air
temperature) and the reconstructed LST in the Lancang River basin. Comparing to the regression model for every
month with only LST data, the regression model with LST and NDVI had higher R 2 and RMSE. Finally, the LSTNDVI regression method was applied as an estimate model to produce distributed maps of air temperature with
month intervals and 1 km spatial in the Lancang River basin of 2010.
Key words: air temperature; land surface temperature; MODIS; spatio-temporal analysis; regression
1 Introduction
Air temperature is traditionally measured at the shelter
height 2 m above and one of the most important and frequent
records with high accuracy and temporal resolution in
meteorological stations, which is dependent on the regional
infrastructure for weather data collection (Jones et al. 1986;
Prihodko and Goward 1997; Stisen et al. 2007). It has been
widely used in a range of researches, within climate change
(Gielen et al. 2007; Wang et al. 2007), terrestrial hydrology
(Arnold et al. 1998; Neteler 2010), ecological system
modeling (Kätterer and Andrén 2009; Yang et al. 2009),
agricultural models for crop growth and yield simulation
models (Abraha and Savage 2008; Mo et al. 2005), and so on
(Tran et al. 2007). All these researches rely on the sensitivity
of air temperature to regional differences, such as land cover,
surface moisture, topographic conditions and so on.
Received: 2014-02-26 Accepted: 2014-05-06
Foundation: Ministry of Science and Technology of China (2008FY110300).
* Corresponding author: XIE Gaodi. Email: [email protected].
Ground meteorological stations provide important local
data of air temperature but have limited ability to describe
the spatial heterogeneity of air temperature over large areas
of the earth by the following reasons: (i) Air temperature
from meteorological station which is just in situ point data
cannot reflect the spatial variation of air temperature; (ii)
Even the different geographical interpolation methods, e.g.
inverse distance weighting, Kriging and spline methods
have been used, but they also may lead to the results with
significant errors and unrepresentative spatial patterns
that because spatial interpolation approaches only implies
spatial average situations. So geographical interpolation
methods can’t optimally represent all the environments and
the spatial information of climate (Hofstra et al. 2008); (iii)
The inadequate density of the station network, especially
most of weather stations are designed to be located near
cities for convenient operation and the complexity of the
254
terrain, may be successful in estimating temperatures
near weather stations, but can’t insure the interpolating
accuracy far away weather stations (Neteler 2010). In all,
meteorological measurements station are far from capturing
the range of climate variability in sparsely populated and
underdeveloped regions like parts of Lancang River.
As an alternative data source, the remote sensing can be
an important and valuable source of information, since it
can greatly improve the ability to get spatial estimates of
land surface temperature with a high spatial sampling rate at
regional and global spatial scales (Benali et al. 2012; Ke et
al. 2011; Yao and Zhang 2012). Daily time series data of the
Advanced Very High Resolution Radiometer (AVHRR) and
the Moderate Resolution Radiometer (MODIS) have been
successfully used, which can provide an unprecedented
global coverage of critical land surface parameters for
the past decades with a high spatial resolution, such as
surface temperature and vegetation indices, etc (Benali et
al. 2012; Raynolds et al. 2012). According to higher spatial
resolution and greater spectral resolution, MODIS data has
been frequently used in studies.
The polar-orbiting MODIS sensors produce daily Land
Surface Temperature (LST) products with global coverage
(Justice et al. 2002). Several researches have proposed
methods to estimate TA using LST product from MODIS:
Mostovoy et al. found that linear regression models had
high accuracy with low mean absolute error values ranging
from 0.97 to 2.07℃ in estimating air temperature (Mostovoy
et al. 2006). Benali et al. explored the possibilities and
accuracy of retrieving maximum (Tmax), minimum (Tmin) and
average air temperature (Tavg) from MODIS LST separately
for a 10 years period in Portugal (Benali et al. 2012). Zhu
et al. work have showed that the TVX method improve the
accuracy of daily maximum air temperature significantly
with RMSE=3.79℃, MAE=3.03℃, and r=0.83 (Zhu et al.
2013). Many researches demonstrated that a strong linear
correlation between MODIS LST and air temperature,
so undoubtedly regression models using remote sensing
data for estimating and mapping air temperature are with
relatively higher accuracy than the interpolating methods.
However, the problem is that the methods above have not
considered the quality of MODIS LST products.
Due to the contamination of clouds, fogs, and other
atmospheric factors, the LST product provided by NASA
may suffer from missing values and noises from various
sources, which can degrade the LST quality and hamper its
efficient applications. During the last decade, researchers
have proposed several algorithms for reconstructing
MODIS vegetation indices, such as the Maximum Value
Composite (MVC) algorithm, Best Index Slope Extraction,
Asymmetric Gaussian filter, and Savitzky Golay (SG) filter,
however few researches focus on solving the low quality
of LST product (Chen et al. 2004; Gu et al. 2009; Neteler
2010; Roerink et al. 2000). All the above methods for
reconstruct high-quality time-series MODIS NDVI data
Journal of Resources and Ecology Vol.5 No.3, 2014
based on mathematical theories can also be used to solve
the low quality of LST product with some adjustments.
As we all know, the prior knowledge plays an important
role in inversing parameters from remote sensing data.
Recently some new researches begin to use historical
datasets as background information. Fang et al. (2008)
have developed a temporal spatial data filtering algorithm
(TSF) which integrates both the multi-seasonal average
trend (background) and the seasonal observation to fill the
gaps and improve the quality of the MODIS standard LAI
product (Fang et al. 2008). However, this method will be
uneffective when the multi-year averaged values cannot be
calculated because of lacking high quality data.
Moreover, previous studies were mainly focused on daily
maximum and minimum air temperatures, but Monthly
mean TA is also greatly useful and needed for many
researches, such as climatic change, geography, vegetation
distribution, etc. Therefore, the main objectives of this
paper are: (i) to reconstruct monthly MODIS LST product
by explore a filtering algorithm using LST background
library even in the situation of lacking high quality data;
(ii) to explore systematically the relationship between air
temperature from meteorological stations and land surface
temperature from reconstructed MODIS LST product of
Lancang River basin; and (iii) to estimate and map TA by
using monthly linear regression methods between TA and
LST.
2 Study area
The Mekong River is an important international river, it
flowing through six countries, i.e., China, Myanmar, Laos,
Thailand, Combodia, and Viet Nam (Liu et al. 1998). The
section of Mekong River in China namely the Lancang
River, is one of China’s five major basins. The Lancang
River originates on the eastern Tibetan Plateau, near the
Deqin County with the highest elevation, it enters Yunnan
Province and plunges through deep gorges (He and Zhang
2005). The Lancang River (Fig. 1) flows about 2160 km in
distance and has 164 000 km2 of drainage area with a total
drop of about 4 500 m. The average flow at the exit is about
2 170 m3 s-1 (He et al. 2007).
The Lancang River has a boundary of longitude 94˚–
102˚E, latitude 21˚–34˚N. As a typical north-south river,
the basin extends 13º in latitude from north to south. It is
divided into three sections: the upper reach, the middle
reach and the lower reach. The Lancang River flows south
through Yunnan Province, hemmed in by mountains ranging
in height from 3000 to 4000 m in the north, 1500 to 2200 m
in the middle, and 500 to 1000 m in the south. Because of
diversity in the geomorphology and climate, the study area
almost includes all kinds of ecosystem except desert and
marine (Wang et al. 2007).
The Lancang River basin experiences five climate zones
and many kinds of geographical environment, which has
climatic gradients running from the upper reach to the
FAN Na, et al.: Mapping Air Temperature in the Lancang River Basin Using the Reconstructed MODIS LST Data
lower reach, and from lowlands to mountains (Qiu 1996).
Temperature increases from north to south, and decrease
with increasing elevation. The original region of the basin
with high altitude, low temperature and low rainfall is
cold climate, where the annual average temperature is
range from –3℃ to 3℃, and even the hottest month’s
average temperature is 6–12℃. The temperature of the
basin in Tibet increases from north to south, and shows a
clear vertical variation. It belongs to a plateau temperate
climate ,the annual average temperature is above 10℃.
The climate in the basin of northwest Yunnan is midstream.
The annual average temperature here is 12–15℃, the
average temperature in hottest month is 24–28℃, and
even the average temperature in the coldest month reaches
5–10℃. The lower reach experiences subtropical or tropical
climates, where the average temperature, the hottest month
average temperature, the coldest month average temperature
are 15–22℃, 20–28℃, 5–20℃ respectively. It shows
greatly spatial heterogeneity in the Lancang River basin,
but the weather stations in this area are far apart and most
of them are located near cities or towns. The spatial method
95˚E
100˚E
Gansu
Qinghai
255
for air temperature is urgent, because it is a great challenge
to recognize the spatial pattern of air temperature in the
study area based on the weather stations.
3 Data
3.1 MODIS data
Terra-MODIS data are available from 3/2000 onward,
its products have a wide range of application, including
characterization of land use and land cover, change
detection of land use and land cover, hydrologic modeling,
agriculture monitoring and forecasting and so on (Amiri et
al. 2009; Son et al. 2012).
In the recent research, the MODIS product level V005
was used, which offers significantly improved data quality
compared to previous levels, especially for inland water
pixels, as well as other, product-specific improvements
(Wan 2008). The MODIS land surface temperature product
(MOD11A2) are 8-day Global 1 km SIN Grid VI data sets
using the generalized split window algorithm, which include
two kinds: LST_day and LST_night (The Terra satellite
platform over-passes at approximately 10:30 am and 1:30pm
local solar time) (Wan 2006). Here, only day land surface
temperature is used.
A total of 1840 images (in the title of h25v05, h26v05,
h26v06, h27v07, from 2001 to 2010) are downloaded from
the Land Processes Distributed Active Archive Center
(https://lpdaac.usgs.gov/lpdaac/products/modis).
Xizang
30˚N
Sichuan
30˚N
3.2 Meteorological data
Station
Table 1 Basic information of the meteorological stations.
Langcang River
Yunnan
Basin
Altitude (m)
High: 6882
Low: 87
N
0
95˚E
200
km
100˚E
Fig. 1 Map of the study area and distribution of the
meteorological stations.
25˚N
25˚N
The main tributaries
200 100
Daily air temperature data (2001 to 2010) for 12 weather
stations in the study area are subset from China land surface
meteorological dataset, which could be downloaded from
China Meteorological Information Center (http://cdc.cma.
gov.cn/index.jsp). These stations are located from about 559
m to 3801 m above sea level (Table 1 and Fig. 1). The daily
data were averaged as monthly composite data.
Code
56969
56959
56954
56964
56946
56751
56548
56444
56137
56128
56125
56018
Name
Mengla
Jinghong
Lancang
Simao
Gengma
Dali
Weixi
Deqin
Changdu
Leiwuqi
Nangqian
Zaduo
Latitude (°) Longitude (°) Altitude (m)
21.48
101.57
643
22.00
100.78
559
22.57
99.93
1040
22.78
100.97
1304
23.55
99.40
1100
25.70
100.18
1977
27.17
99.28
2396
28.48
98.92
3204
31.15
97.17
3267
31.22
96.60
3801
32.20
96.48
3630
32.90
95.30
4170
256
4 Methods
4.1 MODIS LST Map reconstruction method
The MODIS Reprojection Tool Software (MRT V4.0,
https://lpdaac.usgs.gov/lpdaac/tools/modis_reprojection_
tool; 15 May 2013) was used to reproject images from
the original Sinusoidal (SIN) to the Albert projection
(STDPR1:25.0, STDPR2:47.0, CenMer:105.0). MRT also
allows for mosaicing and geographical subsetting, and it
exports the resulting maps to standard GIS data formats
such as GeoTIFF. After these data preprocessing, then we
present an algorithm to reconstruct LST images (Fig. 2).
Compared with other reconstruction algorithm, our selfcontained LST reconstruction algorithm employed historical
LST datasets as the prior knowledge to restore the LST
profiles. This included four steps, the first two processes
were to eliminate low quality and unreliable pixels, the third
and fourth step were to fill the empty pixels:
4.1.1 Creating monthly composited data set
In order to eliminate the impact of the cloud, the monthly
LST data set was performed by MVC (maximum value
composite) methods using the 8-day data.
4.1.2 Removing the outliers based on histogram analysis
We found abnormal low data in the monthly LST dataset,
especially in summer as there were lots of undetected
clouds. To overcome this problem, an outlier detector was
applied to eliminate the remaining cloud-contaminated
pixels. The outlier-filtered was based on an image-based
histogram analysis that finds and remove pixels which show
unusually low LST values, when quartiles are considered
(Neteler 2005).
Low_boundary=1st_quartile−1.5(3rd_quartile
−1st_quartile)
(1)
4.1.3 Building the LST background library by using
historical LST datasets
This paper, we built the LST background library of the
Lancang River basin by using monthly LST dataset ranging
from 2001to 2010, which was used to provide the average
annual LST trend of the study area. We choose the temporal
weighted averaged method instead of the multi-year
averaged method to get a more reliable average LSI trend.
Before calculating the trend, the empty pixels were filled
by using linear interpolation based on inverse distance
methods. Then, we got the continuous LST data for each
year. Because of low quality LST data would increase
fluctuation, so we defined the f (fluctuation coefficient)
index to measure the fluctuation of LST in a moving
temporal window, then set the ω (weight coefficient) index
according to the f index. The greater the f values, the lower
the weight when the average LST trend is calculated. The
mathematic form of p index, ω index can be expressed as
following.
Journal of Resources and Ecology Vol.5 No.3, 2014
MODIS 8-day
LST data
MODIS Monthly
LST data 2
IDW
lnterpolation
MVC
Rate
lnterpolation
Temporal
weighted
average
MODIS Monthly
LST data 1
MODIS Monthly
LST data 3
The outliers
detection based
on histogram
Monthly LST
background data
S-G
Filter
Reconstructed
LST data
Fig. 2 Flowchart of the algorithm.
1
∑ (LST (i , j , m, n) − LST (i , j , n) )
3
f (i , j , m, n ) =
12
2
(2)
m =1
N
ω(i,j,m,n)=
∑ f ( i , j , m, n ) − f ( i , j , m, n )
ω
(3)
∑ ω(i,j,m,n)×LST(i,j,m,n)
(4)
n =1
N
( N − 1) ∑ f ( i , j , m , n )
n =1
LSTback(i,j,m) =
N
n =1
where i and j present the location of a pixel; m means the
month of a year, m=1,2,…,12; n means the number of years
to establish the LST background, n=1,2,…,10, N=10; LST(i,
j, m, n) presents the LST value of pixel with location (i, j) at
the mth month of the nth year; LST (i , j , n ) is the average LST
value of pixels in the nth year; f (i,j,m,n) is the fluctuation
coefficient of pixel with location (i,j) at the mth month of the
nth year; ω(i, j, m, n) is the weight coefficient of pixel (i,j)
at the mth month of the nth year; and LSTback (i, j, m) means
the LST background value of pixel (i,j) at the mth month of
library.
4.1.4 Reconstructing a new LST time-series by S-G filter
Algorithm
Savitzky and Golay proposed a simplified least-squares-fit
convolution for smoothing (Savitzky and Golay 1964). The
algorithm can be understood as a weighted moving average
filter with weighting given as a polynomial of a certain
degree (Chen et al. 2004). This convolution is designed
to preserve higher moments within the data and to reduce
the bias introduced by the filter, so it has the advantage
of restoring LST data to reduce the negative offset effects
caused by the atmospheric factors such as clouds.
The mathematic form can be expressed as:
m
*
Yj =
∑CY
i
j +i
(5)
N
where Yj* is the resultant value; Y is the original data value;
Ci is the coefficient of the filter; m is the size of a moving
i=− m
257
FAN Na, et al.: Mapping Air Temperature in the Lancang River Basin Using the Reconstructed MODIS LST Data
window, and N is the total number of pixels in the moving
window.
The improved algorithm of this paper was based on LST
background library. Before the S-G smoothing, the rate of
changes of LST at time t from the LST background library
was used to fill the empty pixels, the mathematic form can
be expressed as follows:
a=LSTbask(i,j,t)−LSTbask(i,j,t−1)
(6)
The model with high R2 was chose to estimate the monthly
air temperature in 2010 for every month. Then we mapped
the spatial distribution of TA for every month and analyzed
the spatio-temporal pattern of TA.
5 Results
5.1 Reconstructed LST maps
Regression model design and assessment were based on
a statistical approach, so it’s necessary to analyze the
differences between LST and TA. Firstly, we performed the
spatio-temporal variation of monthly LST versus monthly
mean TA for every station for the period of 2001–2010.
Then the coefficient of correlations, the mean absolute error
(MAE) and the error to standard deviation (SED) were
explored to analysis the differences. Finally, establish the
mathematic form of liner regression to future analysis the
relationship between LST and TA.
A total of 1840 MODIS LST images were processed for
the basin of Lancang River, then we got 120 monthly
reconstructed LST maps from 2001 to 2010. In any case, the
rate of change can be used to reconstruct the LST map due
to the LST background library. This approach developed
reasonable results even in cases where 55% of the filtered
LST maps were void.
Fig. 3 showed an example of MODIS monthly LST
dataset by using the MVC method, LST map sfter histogram
filtered, reconstructed LST maps by using the improved S-G
filter and the differences between them. Fig. 3 indicated
that some anomalous pixels with nonsensical low values
in the MVC method LST map are effectively replaced by
the previously mentioned algorithm. The abnormal low
data mainly appear in the downstream of Lancang River,
which were caused by clouds and other atmospheric
processes. For these regions, the holes could be filled firstly
by new values which were calculated by the rate based on
the LST background library. Then through S-G filter we
reconstructed the LST maps in a year. Fig. 3 also showed
the differences between LST image after histogram filtered
and LST image reconstructed by improved S-G filter mainly
appeared in the downstream.
4.3 Mapping monthly mean TA in the Lancang River
basin using regression model
5.2 Statistical analysis of monthly LST vs. monthly mean
TA for every station from 2001 to 2010
Two regression equations were used in this paper and the
coefficient of determination was calculated for every month.
The station’s land surface temperature (LST) profiles were
created by extracting the reconstructed LST images for the
4.2 Spatio-temporal and statistical analysis of monthly
mean TA vs. the monthly reconstructed LST for
every station
96˚E
99˚E
102˚E
96˚E
99˚E
102˚E
93˚E
(a)
(b)
48
48
96˚E
99˚E
102˚E
93˚E
96˚E
99˚E
(c)
102˚E
(d)
43
35
0 125 250
96˚E
N
-275
500
km
99˚E
102˚E
0 125 250
96˚E
N
500
km
99˚E
102˚E
10
0 125 250
96˚E
24˚N
N
N
6
500
km
99˚E
102˚E
0 125 250
96˚E
0
500
km
99˚E
21˚N
21˚N
24˚N
27˚N
27˚N
30˚N
30˚N
33˚N
93˚E
33˚N
LST*(i,j,t)=LST(i,j,t)+a
(7)
*
where LST (i,j,t) is the invalid LST data with location (i,
j) at the tth month which need to be calculated, α is the rate
change between LSTback(i,j,t) and LSTback(i,j,t–1); LSTback(i,j,t)
is the LST data with location (i, j) at the tth month in LST
background library; LSTback(i,j,t–1) is the LST data with
location (i, j) at the (t–1)th month in LST background library.
102˚E
Fig. 3 2010-Jun LST by using the MVC method (a); 2010-Jun LST after histogram filtered (b); 2010-Jun LST reconstructed
by improved S-G filter (c); and difference between b and c (d).
258
Journal of Resources and Ecology Vol.5 No.3, 2014
TA
LST
Lancang
1 7 1 7 1 7 17 1 7 1 7 17 1 7 1 7 1 7
35
30
25
20
15
10
35
30
25
20
15
10
5
TA
LST
Simao
1 71 7 1 71 71 71 7 1 71 71 71 7
Month
Temperature (℃)
Temperature (℃)
Month
35
30
25
20
15
10
0
17 1 7 1 7 17 1 7 1 7 17 17 1 7 1 7
Month
TA
Gengma
LST
-5
0
5
Deqin
10 15 20
TS
30 y=0.73x+7.23
R2=0.62,P<0.001
25
20
15
Dali
10
9 12 15 18 21
TS
17 1 7 17 17 1 7 1 7 17 17 17 17
Month
TA
LST
Jinghong
y=0.60x+12.15
2
30 R =0.40,P<0.001
25
20
171717 1717 17171717 17
Month
TA
LST
Mengla
1 7 1 7 1 7 17 1 7 1 7 1 7 1 71 7 1 7
Month
LST
LST
40 y=0.79x+9.29
R2=0.57,P<0.001
30
20
10
Changdu
0
-5 0 5 10 15 20
TS
LST
35 y=0.38x+15.46
2
30 R =0.25,P<0.001
25
20
15
Weixi
10
5
10
15
20
TS
35 y=0.81x+10.85
R2=0.42,P<0.001
30
25
20
Gengma
15
13 15 18 20 23 25
TS
LST
LST
30 y=0.89x+11.94
R2=0.79,P<0.001
20
40 y=0.70x+19.40
R2=0.42,P<0.001
30
20
10
Nangqian
0
-10 -5 0 5 10 15 20
TS
35 y=0.67x+12.37
2
30 R =0.43,P<0.001
25
20
Jinghong
15
10 12 14 16 18 20 22 24
TS
LST
35
30
25
20
15
10
5
Temperature (℃)
Temperature (℃)
Month
30 y=0.77x+11.80
R2=0.66,P<0.001
20
10
0
Leiwuqi
-10
-10 -5 0 5 10 15
TS
LST
Temperature (℃)
17 17 17 17 1 7 1 7 17 1717 17
40
35
30
25
20
15
10
5
30 y=0.89x+11.94
R2=0.79,P<0.001
20
10
0
Zaduo
-10
-15 -10 -5 0 5 10 15
TS
10
(8)
i
Lancang
16 18 20 22 24 26 28
TS
35 y=0.40x+16.99
R2=0.24,P<0.001
30
25
20
Simao
15
12 14 16 18 20 22 24
TS
y=0.57x+12.534
30 R2=0.35,P<0.001
25
LST
Dali
Weixi
LST
i
LST
LST
Temperature (℃)
Temperature (℃)
TA
TA
n
∑ ABS(LST −TA )
n
LST
35
Changdu
TA
LST
30
25
20
15
10
5
0
-5 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7
Month
35
30
25
20
15
10
5
0
1
i =1
40
Nangqian
TA
LST
35
30
25
20
15
10
5
0
-5 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7
-10
Month
Month
30
25
20
15
10
5
MAE =
LST
30
Deqin
TA
LST
25
20
15
10
5
0
-5 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7
absolute error (MAE) and the error to standard deviation
(SED) were explored to analysis the differences between
LST serial and TA serial in each station (Table 2). The
mathematic form of MAE, SED can be expressed as:
LST
30
Leiwuqi
TA
LST
25
20
15
10
5
0
-5 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7
-10
Month
Temperature (℃)
30
Zaduo
TA
LST
25
20
15
10
5
0
-5 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7 1 7
-10
-15
Month
Temperature (℃)
Temperature (℃)
Temperature (℃)
Temperature (℃)
pixel in which the meteorological station was located based
on the GIS technology. The monthly mean TA for each
station was compared with the LST of the 12 stations from
2001 to 2010. Fig. 4 showed the result of temporal and
regression analysis between TA and LST. Close time series
variations were observed between the monthly LST and the
monthly mean TA. The correlation coefficient, the mean
20
Mengla
16 18 20 22 24 26 28
TS
Fig. 4 Temporal analysis and the results of regression between TA and LST for each station from the period of 2001 to 2010.
FAN Na, et al.: Mapping Air Temperature in the Lancang River Basin Using the Reconstructed MODIS LST Data
EDS =
1
n
∑
(ei − MAE )
2
(9)
n i =1
where n presents the total number of month from 2001 to
2010, n=120; LSTi means the land surface temperature of the
ith month ; TAi means the air temperature of the ith month.
Correlations between monthly LST and monthly mean
TA for every station were shown in Table 2, the coefficient
of correlations (R2) for 80% of the stations was higher than
0.5 (below 0.5 for only 2 stations). The differences between
monthly LST and monthly mean TA (MAE) were quantified
for all meteorological stations, the relatively obvious
difference between LST and TA were found in the stations
located high latitude area. The index of MAE in Nangqian
station reached up to 17.8℃. However the standard
deviation of differences (SED) for 90% of the stations is
lower than 5℃ (only one station over 5℃).
In order to further investigate the relationship between
monthly LST and monthly mean TA, the liner regression
Table 2 Correlations (R 2) between monthly LST and monthly
mean TA for each station (Significant correlations: **, p =0.01).
Name
Zaduo
Nangqian
Leiwuqi
Changdu
Deqin
Weixi
Dali
Gengma
Lancang
Jinghong
Simao
Mengla
R2
0.886**
0.644**
0.814**
0.756**
0.829**
0.498**
0.785**
0.645**
0.374**
0.649**
0.652**
0.591**
MAE (℃)
11.72
17.80
10.90
7.62
9.69
8.00
3.10
7.18
4.93
3.04
6.09
2.96
EDS (℃)
3.45
5.84
3.97
4.57
3.34
4.83
2.66
4.01
4.15
2.67
2.85
2.92
259
analysis were shown in the right of Fig. 4. The mean
coefficient of determination (R 2) was 0.48, 40% of the
station’s R2 were higher than 0.5, 4 station’s R2 were lower
than 0.5 but higher than 0.4, only 3 station’s R2 were below
0.4. These results of linear regression analysis illustrate that
there is a considerably closed agreement between monthly
mean TA and the reconstructed LST in the basin of Lancang
River.
5.3 Estimation of monthly mean TA with MODIS
monthly mean LST
The advantage of LST data is that the study area is
completely covered (hence each LST map pixel time series
can be considered as a “virtual meteorological station”
for temperature data). The time series variations analyses
and the relationship statistic above demonstrate that it is
reasonable to estimate monthly mean TA from MODIS
reconstructed LST according to the linear regression
relationship between LST and TA.
The reconstructed LST and TA were derived from each
station for the period 2001–2010, and then linear regression
models of them were developed (Table 3). It could be
seen that the coefficient of determination (R2) varies from
0.25 (June) to 0.81 (January). It was found that trees and
grasses shadow solar radiation and thus change the moisture
conditions of the air and the soil, necessarily affecting land
surface temperature and air temperature, so the different
ecosystems and land cover would result in the variation of
LST and TA (Vancutsem et al. 2010). Then, the NDVI index
as another independent variable was used to illustrate the
linear regression model for estimating the air temperature.
The R2 of every month were significant by using LST-NDVI
method. It varied from 0.33 (July) to 0.91 (February), only 3
station’s R2 values were lower than 0.50, which were shown
in Table 3. This indicated that the linear regression model
with LST and NDVI variables could be more effective than
the model only with LST.
Table 3 Linear regression models of TA for every month and the coefficient of determination.
Month
Jan.
Feb.
Mar.
Apr.
May.
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Regression equation
TA=1.22*LST–12.30
TA=1.01*LST–12.06
TA=0.98*LST–12.18
TA=0.98*LST–11.54
TA=0.89*LST–7.14
TA=0.79*LST–1.15
TA=0.74*LST+1.78
TA=0.88*LST–1.27
TA=1.14*LST–6.93
TA=1.38*LST–12.36
TA=1.27*LST–11.34
TA=1.21*LST–8.99
Note: significant correlations: **, p=0.01.
R2
0.81**
0.71**
0.62**
0.48**
0.33**
0.25**
0.26**
0.36**
0.6**
0.79**
0.8**
0.77**
Month
Jan.
Feb.
Mar.
Apr.
May.
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Regression equation
TA=0.87*LST+14.60*NDVI–13.72
TA=0.54*LST+25.49*NDVI–14.19
TA=0.49*LST+24.86*NDVI–11.62
TA=0.54*LST+21.48*NDVI –9.47
TA=0.61*LST+15.52*NDVI –7.27
TA=0.66*LST+8.97*NDVI –2.87
TA=0.72*LST+5.32*NDVI –0.96
TA=0.87*LST+5.80*NDVI –4.67
TA=1.03*LST+7.60*NDVI –9.12
TA=1.28*LST+5.22*NDVI –13.69
TA=1.04*LST+7.93*NDVI –11.63
TA=0.92*LST+12.41*NDVI –11.27
R2
0.89**
0.91**
0.87**
0.76**
0.57**
0.37**
0.33**
0.44**
0.71**
0.81**
0.85**
0.88**
260
Journal of Resources and Ecology Vol.5 No.3, 2014
Month MAE (℃) RMSE (℃)
Jan.
2.26
1.99
Feb.
2.14
1.66
Mar.
2.49
2.03
Apr.
3.21
2.46
May.
3.68
2.90
Jun.
3.81
2.83
Month MAE (℃) RMSE (℃)
Jul.
3.36
2.20
Aug.
3.16
2.23
Sep.
2.48
2.00
Oct.
2.56
2.17
Nov.
2.69
2.00
Dec.
2.34
2.06
So the linear regression equations of using LST and
NDVI as factors for every month were chose to estimate air
temperature (Table 3). The indexes of MAE, RMSE were
calculated to further determine the effectiveness of this
model. Tables 3 and 4 indicated a robust linear relationship
between TA, LST and NDVI. The MAE index varied from
3.81 (Jun.) to 2.14 (Feb.), however the RMSE index for
every month were lower than 2℃. Additionally, in summer
(Jul. to Sep.) the R2 index was lower and the RMSE index
was higher than other seasons, that because the quality LST
product in summer was usually low due to the clouds and
fogs. All of the analyses above indicated that the LST-NDVI
model performs properly in the estimation of monthly mean
air temperature in the study area.
5.4 Temporal and spatial variation of TA in Lancang
River
According to the LST-NDVI models for every month,
22˚N 24˚N 26˚N 28˚N 30˚N 32˚N
21˚N 23˚N 25˚N 27˚N 29˚N 31˚N 33˚N
94˚E 96˚E 98˚E 100˚E
Table 5 The statistical value for estimated monthly mean TA
in the study area.
Month
Jan.
Feb.
Mar.
Apr.
May.
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Min
–28.23
–19.26
–14.18
–10.25
–4.83
1.61
3.45
–0.36
–7.16
–17.76
–24.41
–24.64
Feb.
Mean
3.06
6.50
9.49
12.59
14.96
17.45
18.71
18.61
16.53
10.94
6.09
4.04
Mar.
94˚E 96˚E 98˚E 100˚E 94˚E 96˚E 98˚E 100˚E102˚E
Apr.
May
Jun.
32℃
32℃
32℃
32℃
32℃
32℃
-29℃
-29℃
-29℃
-29℃
-29℃
-29℃
Jul.
Aug.
s.d.
10.35
8.99
7.89
6.81
5.02
3.46
2.91
3.35
5.00
8.28
9.59
9.26
monthly mean TA in 2010 were calculated, then the maps
from the period of Jan to Dec were drew to analysis the
temporal and spatial variations (Fig. 5). Its tem-spatial
pattern could be generalized as follows: (i) a large degree
of variation of TA over space and time was observed, the
trend of TA was very close to regional climate and landform
pattern, and TA decreases from valleys to mountain, TA
decreases from south to north; (ii) From south to north,
the degree variations of TA in a year were different. Fig.5
illustrated that the variations in a year of upper reaches
were much higher, in winter most area was below 0℃ and
in summer the temperature was increased greatly. However,
94˚E 96˚E 98˚E 100˚E 94˚E 96˚E 98˚E 100˚E 94˚E 96˚E 98˚E 100˚E
Jan.
Max
19.64
21.88
24.06
25.63
25.99
27.31
31.02
31.20
28.75
28.34
25.54
20.08
Sep.
Oct.
Nov.
Dec.
32℃
32℃
32℃
32℃
32℃
32℃
-29℃
-29℃
-29℃
-29℃
-29℃
-29℃
22˚N 24˚N 26˚N 28˚N 30˚N 32˚N 21˚N 23˚N 25˚N 27˚N 29˚N 31˚N 33˚N
Table 4 The statistical error value for estimated monthly
mean TA in the study area.
94˚E 96˚E 98˚E 100˚E102˚E95˚E 97˚E 99˚E 101˚E94˚E 96˚E 98˚E 100˚E102˚E95˚E 97˚E 99˚E 101˚E94˚E 96˚E 98˚E 100˚E102˚E 95˚E 97˚E 99˚E 101˚E
Fig. 5 Spatial distribution of monthly mean TA in the study region.
FAN Na, et al.: Mapping Air Temperature in the Lancang River Basin Using the Reconstructed MODIS LST Data
the deviation of TA in a year of lower reaches was not
obviously. (iii) Temporal differentiation in a year was
obviously, TA in the summer was higher than in the other
seasons. The coldest month in the study area was January
(in January the lowest temperature was –28.23℃, the
highest temperature was 19.64℃) and the warmest month
was July with the highest temperature 17.29℃ (in July the
lowest temperature was 3.45℃, the highest temperature
was 31.02℃)(Table 5); And (4) the standard deviation of
monthly mean TA for the warmest month was the smallest
(2.91℃), it for the warmest month was the biggest (10.35℃)
(Table 2). This illustrated that the spatial differentiation in
winter was much clearer than in summer.
6 Conclusion
To overcome influence of the missing values and noises
in the MODIS LST product, this paper has explored the
improved Savizky-Golay (SG) algorithm by using the timeweighted average algorithm to establish LST background
library in which can restore the monthly high quality of
LST profiles.
The relationship between the monthly mean TA and the
reconstructed LST of the 12 stations from 2001 to 2010
were statistical analyzed. The result has shown that there
is a close relationship between monthly mean TA and
the reconstructed LST with the average of the coefficient
correlations (R 2) is 0.68. That illustrated the results of
reconstructed method were effective and the reconstructed
LST product could be used to estimate and map of air
temperature over the Lancang River basin.
Due to its complex terrain as well as low station
density in the Lancang River, the typical method for
obtaining temperature maps from meteorological stations
has limits to capture the spatial and temporal distributes
of air temperature. In this paper, it has been shown that
a close correlation exist between monthly mean TA and
the restricted LST in the study area. We compared two
models for estimate the monthly air temperature in 2010,
the first method only used the reconstructed MODIS LST
to build the linear regression models, while the second
method calculated monthly mean air temperature using the
regression model from LST and NDVI. We found that all
these models displayed positive slopes, but the correlations
of every month in 2010 for the LST-NDVI method (R2 is
range from 0.33 to 0.91 and the average of R2 is 0.57 ) were
stronger than the he LST-NDVI method (R2 is range from
0.26 to 0.81 and the average of R2 is 0.7).
The LST-NDVI method was chose to estimate and map
monthly air temperature in 2010, which the RMSE of 12
months is range from 1.99℃ (May) to 2.90℃ (Jan.). Then
the analysis of air temperature spatial-temporal pattern in
the study area can be drawn. A large degree of variation of
TA over space and time had been observed in the Lancang
River basin. In spatial, TA decreases from valleys to
261
mountain and TA increases from north to south. In temporal,
the mean temperature TA in the summer was higher than in
the other seasons, but the spatial differentiation of a month
in winter was much clearer than in summer with the highest
Std dev (10.35℃) in Jan.
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澜沧江流域基于重建的MODIS地表温度数据的空气温度空间化制图
范 娜1,2，谢高地1，李文华1，张雅京3，张昌顺1，李 娜1
1 中国科学院地理科学与资源研究所，北京 100101；
2 中国科学院大学，北京 100049；
3 环境保护部宣传教育中心，北京 100029；
摘 要：空气温度是一个非常重要的气候变量，通常由气象台站观测获得。对其时空特征的精确估算是很多模型的基础，
但是由于台站分布密度的不均和研究区复杂的地形，往往使其空间化的结果较差。目前，随着遥感技术的发展，使用热红外遥
感数据估算的地表温度，结合地面观测数据，建立回归模型可以提高区域空气温度估算的精度。由于云和其它大气因素会影响
遥感反演的地表温度数据结果，因此本研究本文将2001-2010年的LST历史数据作为先验知识，用以建立LST背景库，并提出了
基于LST背景库的Savitzky-Golay(SG)滤波算法来实现LST时间序列数据的重建工作。将重建后的 LST 与研究区12个气象站空气
温度数据进行了时序分析和回归分析，结果表明在月尺度合成序列上LST-TA的一致性较好，且具有非常好的线性相关关系，
80%的台站的决定系数高于0.5。通过对比分析发现，加入植被指数（NDVI）的各月空气温度回归模型比直接用LST建立的回
归模型精度更高。因此，本研究使用LST-NDVI模型对澜沧江流域2010年12个月份的空气温度进行空间化制图，并分析了其年
内时空格局特征。
关键词: 空气温度；地表温度； MODIS；时序分析；回归分析