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         Geoplanning 
Vol 7, No 1, 2020, 37-46                                                                                                                                                            Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning  

doi: 10.14710/geoplanning.7.1.25-36 

Utilization of MODIS Surface Reflectance to Generate Air Temperature 
Information in East Java - Indonesia 

A. Faisola*, B. Budiyonoa, I. Indartob*, E. Novitab 

a PS THP, Fapertek, Papua University, Manokwari, Indonesia 
b PS Teknik Pertanian, FTP, University of Jember, East Java, Indonesia 

Abstract: Ambient air temperature is the main variable in the climatological and 
hydrological analysis. However, Indonesia's limited number of meteorological stations 
was becoming a problem to provide air temperature data for large areas.  This study 
aims to generate air temperature using the relationship of land surface temperature 
and vegetation index. A total of 6 climatological stations and 84 MODIS Images for 
three years (2015 to 2017) were used for the analysis.  Research methods include 
image georeferencing, band extraction from MODIS, derivation of NDVI, generating 
ambient air temperature, calibrating using the local meteorological station, and image 
interpretation. Results show that the MODIS Surface Reflectance product's accuracy in 
generating ambient air temperature in East Java at any period is 86,37%. So MODIS 
Surface Reflectance product can be used as an alternative solution to generate ambient 
air temperature.  
 
  
 

Copyright © 2020 GJGP-UNDIP  
This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

How to cite (APA 6th Style): 
Al Amin, M., Ilmiaty, R., & Marlina, A. (2020). Flood Hazard Mapping in Residential Area Using Hydrodynamic Model HEC-RAS 5.0. Geoplanning: 
Journal of Geomatics and Planning, 7(1), 25-36. doi: 10.14710/geoplanning.7.1.25-36 

 

1. INTRODUCTION 

Land air surface temperature or ambient-temperature is the main variable in the climatological and 
environmental-related analysis.  Usually, air temperature used as input for calculating evapotranspiration, 
crop water requirement, and water allocation for irrigation purposes, such as the work of Allen et al. 
(1998), Huntington & Allen (2009), Bachour (2013), Paparrizos et al. (2014), Faisol (2015), Faisol (2016), and 
Faisol et al. (2017). Conventionally, this data is obtained from a ground based climatological station. Based 
on The Meteorology, Climatology, and Geophysics Agency (BMKG), Indonesia's number of climatological 
stations in 2018 is 1.197 stations (The Meteorology Climatology and Geophysics Agency, 2018). However, 
most stations were observed manually, and some of them are no longer operated because the equipment 
was destroyed and/or no replacement tool. Therefore, measurement of land-air surface temperature by 
other methods promise one of the possible solutions to provide more data availability.  

With the advance of remote sensing technology, some sensors (Landsat, ASTER, MODIS, Sentinel) 
provide spectral bands to record heat energy reflected or emitted from earth surface features. For 
example, Landsat 8 is equipped with an OLI sensor that capable of recording land surface temperature 
maximum and minimum (USGS, 2016 and Laosuwan et al., 2017). ASTER provides a specifics channel to 
record heat energy refracted from the land surface (Jiménez-Muñoz & Sobrino, 2009). To generate air 
temperature information, the remote sensing data must have a visible electromagnetic spectrum (0.4 μm - 
0.7 m), near-infrared (0.7 μm - 1.3 m), and thermal infrared (8.0 μm - 14.0 m) (Faisol, 2015). Moderate 
Resolution Imaging Spectroradiometer (MODIS) is one satellite remote sensing data equipped with those 
electromagnetic spectra (National Aeronautic and Space Administration, 2018a) 

MODIS is the first interdisciplinary instrument that can be used to monitor land, ocean, and 
atmosphere. Its instrument operates on both the Terra and Aqua spacecraft with a viewing swath width of 

Article Info: 
Received: 9 May 2018 
in revised form: January 2019 
Accepted: January 2020 
Available Online:  7 July 2020 
 

Keywords:  
MODIS, air temperature, vegetation 
index 
 

*Corresponding Author: 
Arif Faisol 
Papua University, Manokwari, 
Indonesia 
Email: arif.unipa@gmail.com  
 

*Corresponding Author: 
Indarto 
PS TEP, FTP, Universitas Jember 
Email: indarto.ftp@unej.ac.id 

OPEN ACCESS 

http://ejournal.undip.ac.id/index.php/geoplanning
https://doi.org/10.14710/geoplanning.7.1.37-46
mailto:arif.unipa@gmail.com


 
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2,330 km and views the entire surface of the Earth every one to two days. Its detectors measure 36 spectral 
bands between 0.405 and 14.385 µm, and it acquires data at three spatial resolutions; 250m, 500m, and 
1.000m (National Aeronautic and Space Administration, 2018b). MODIS provides more than 150 science 
data products to support various studies in agriculture, climatology, marine, forestry, and others 
(Savtchenko et al., 2004). 

MODIS surface reflectance (MOD09) is one of MODIS science data product that computed from MODIS 
Level 1B land bands 1 (620-670 nm), 2 (841-876 nm), 3 (459-479), 4 (545-565 nm), 5 (1230-1250 nm), 6 
(1628-1652 nm), and 7 (2105-2155 nm). The product estimates the surface spectral reflectance for each 
band as it would have been measured at ground level as if there were no atmospheric scattering or 
absorption. It corrects for the effects of atmospheric gases and aerosols (National Aeronautic and Space 
Administration, 2018a). Besides, MODIS surface reflectance has been equipped with the thermal band that 
is band 31 (10,78 – 11,284 µm) and band 32 (11,77 – 12,27 µm) (Vermote et al., 2015). 

A lot of studies have been using MODIS to generate air temperature. Flores & Lillo (2010) using MODIS 
to estimate air temperature on a regional scale in Chile, Yao & Zhang (2012) estimating air temperature in 
The Southeastern Tibetan Plateau based on MODIS satellite image, Shen & Leptoukh (2011) using MODIS 
Land Surface Temperature to estimate surface air temperature in Eastern Eurasia, Zeng et al. (2015) using 
MODIS Land Surface Temperature Product to estimate daily air temperature in the US, and Noi et al. (2016) 
using MODIS Land Surface Temperature Product to estimating daily maximum and minimum air surface 
temperature in Vietnam. Those studies show that the accuracy of MODIS to generate air temperature up to 
92% compared with measured and air temperature records from meteorological stations. After seeing the 
gap, this study contributes to generate air temperature information in East Java, Indonesia. 

 

2. DATA AND METHODS 

2.1. Data 

The data that used in this research is MODIS surface reflectance (MOD09) image as many 84 scenes for 
three years (2015 to 2017), MODIS Geolocation (MOD03) that corresponding with MOD09 image, and daily 
air temperature data from 6 meteorological stations. MOD09 and MOD03 accessed from NASA website and 
daily temperature accessed from The Meteorology, Climatology and Geophysics Agency (BMKG) website.  

 

 

 

Figure 1. The Landuse Maps of East Java (Indonesian Geospatial Information Agency, 2018) 



 
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Figure 2. The Topography Maps of East Java (Shuttle Radar Topography Mission, 2018) 

 

2.2. Methods 

Generally, the main stages of data processing to generate air temperature information from MODIS 
Surface Reflectance is: 

1. Image georeferencing 

Image georeferencing is to georeferencing MODIS surface reflectance by associated with MODIS 
Geolocation. MODIS Geolocation is the MODIS data equipped with information about geodetic 
latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith 
and azimuth angles, and a land/sea mask for each 1 km sample. This information is included in the 
header to enable calculating the approximate location of the center of the detectors of any of the 36 
MODIS bands.  

2. MODIS bands extraction 

The purpose of this stage is to extract the MODIS bands that are used for generating air temperature. 
There is surface reflectance red bands (ρ band 1), surface reflectance near-infrared bands (ρ band 2), 
brightness temperature (band 31 and band 32), and solar zenith. This stage is done simultaneously 
with image georeferencing. 

3. Derivation of Normalized Difference Vegetation Index (NDVI) 

NDVI is a measure of the degree of vegetation cover for an area. NDVI of MODIS imagery calculated 
with the following equation (Vermote & Vermeulen, 1999):    

 
[1] 

Where: 

NDVI = Normalized Difference Vegetation Index 

ρBand 1 = Surface reflectance band 1 (red) 

ρBand 2 = Surface reflectance band 2 (near-infrared) 

4. Generating air temperature 

The air temperature was generated using the relationship of land surface temperature and vegetation 
index with the following equation (Hong, 2008): 



 
Faisol et al. / Geoplanning: Journal of Geomatics and Planning, Vol 7, No 1, 2020, 37-46 
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40 | 
 

Ta = 0,9731 Ts + 7,5878. 𝑁𝐷𝑉𝐼 − 5,0638.cos 𝜃 − 2,7233 [2] 

Where: 

Ta  = Air temperature (oK) 

Ts  = Land surface temperature (oK) 

NDVI = Normalized Difference Vegetation Index 

𝜃  = Solar zenith angle (radians) 

Solar zenith angle (𝜃) is the angle between the zenith and the center of the Sun's disc and extracted 
from MODIS Geolocation. Land surface temperature (Ts) calculated with the following equation (Hong, 
2008):  

 
[3] 

Where: 

Ts  = Land surface temperature (oK) 

Tb  = Brightness temperature (oK) 

ε0  = Surface emissivity  

Brightness temperature extracted from MODIS surface reflectance. Surface emissivity (ε0) calculated 
with the following equation (Bastiaanssen et al., 2002):  

 [4] 

Where: 

ε0  = Surface emissivity  

NDVI = Normalized Difference Vegetation Index 

5. Calibrating using local meteorological station 

This process is to know the accuracy of air temperature generated from MODIS surface reflectance by 
comparing it with local meteorological station data. The methods used in calibrating is Root Mean 
Square Error (RMSE) with the following equation: 

 

[5] 

Where: 

RMSE = Root mean square error 

xi   = Air temperature data from meteorological stations (oC) 

yi   = Air temperature that generated from MODIS surface reflectance (oC) 

n  = Amount of data 

6. Image interpretation 

This stage aims to create air temperature distribution maps at any period. 

Flowchart to generating air temperature using MODIS Surface Reflectance product shown in Figure 3.  



 
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Figure 3. Flowchart to generate air temperature from MODIS Surface Reflectance (Authors, 2018) 

 

3. RESULTS AND DISCUSSION 

From MODIS surface reflectance interpretation, air temperature in the study area during 2015 – 2017 is 
17.37 oC – 37.09 oC. Generally, air temperature in residential and lowland areas higher than in agriculture 
and plateau area. Air temperature distribution in the study area shows in Figure 4 to Figure 6. The 
comparison between air temperature generated from MODIS surface reflectance and meteorological 
stations data recording is shown in Figure 7 to Figure 12.  

 

 

 

Figure 4. Air temperature distribution in East Java on 29 March 2015 (analysis, 2018) 



 
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Figure 5. Air temperature distribution in East Java on 12 June 2016 (analysis, 2018) 

 

 

 

Figure 6. Air temperature distribution in East Java on 24 May 2017 (analysis, 2018) 

 

 

 

Figure 7. Air temperature comparison between MODIS surface reflectance processing with Banyuwangi 
meteorological stations (analysis, 2018) 



 
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Figure 8. Air temperature comparison between MODIS surface reflectance processing with Malang 
meteorological stations (analysis, 2018)  

 

 

 

Figure 9. Air temperature comparison between MODIS surface reflectance processing with Pasuruan 
meteorological stations (analysis, 2018) 

 

 

 

Figure 10. Air temperature comparison between MODIS surface reflectance processing with Nganjuk 
meteorological stations (analysis, 2018) 



 
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Figure 11. Air temperature comparison between MODIS surface reflectance processing with Surabaya 
meteorological stations (analysis, 2018) 

 

 

 

Figure 12. Air temperature comparison between MODIS surface reflectance processing with Sidoarjo 
meteorological stations (analysis, 2018) 

 

Previous research has shown MODIS accuracy for generating air temperatures up to 92% compared to 

air temperature records and measurements from meteorological stations (Flores & Lillo, 2010; Noi et al., 

2016; Shen & Leptoukh, 2011; Yao & Zhang, 2012; Zeng et al., 2015). In this study, air temperature 

generated from MODIS surface reflectance is higher than data recording from meteorological stations with 

an average accuracy of 86.37%. Several factors cause the difference in air temperature generated from 

MODIS surface reflectance and meteorological stations data recording, e.g., (1) The air temperature 

generated from MODIS surface reflectance is the air temperature at imagery acquisition time. In contrast, 

air temperature from meteorological stations is the average temperature recording in the morning, 

daytime, and afternoon; (2) Air temperature generated from MODIS surface reflectance depends on 

weather conditions during recording, while air temperature from meteorological stations is based on 

weather conditions throughout the day; (3) MODIS acquisition time for East – Java is 09.10 am – 10.20 am, 

so it caused air temperature higher than data recording from meteorological stations.       

 

 



 
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4. CONCLUSION 

This study has successfully modeled air temperature with MODIS so that it provides the view that 
MODIS images can be used for air temperature in fast growing regions on a regional scale. Generally, 
MODIS surface reflectance can be used to generate air temperature. With 86.37% accuracy, MODIS surface 
reflectance can be used as an alternative solution to get air temperature data due to limited climatological 
stations.   

For detailing the analysis, future work needs to be done by using better satellite resolution. If it possible, 
the results of this study would be better by combining some of satellite imagery and data about air 
temperature. 

 

5. ACKNOWLEDGMENTS 

The authors would like thanks to RISTEKDIKTI – Ministry of Research, Technology and Higher Education 
of The Republic of Indonesia that financed this research by Penelitian Kerjasama Antar Perguruan Tinggi 
(PKPT) grant. 

 

6. REFERENCES 

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing 
crop water requirements. Fao, Rome, 300(9), D05109. 

Bachour, R. (2013). Evapotranspiration modeling and forecasting for efficient management of irrigation 
command areas. Utah State University. 

Bastiaanssen, W., Ralf, W., Richard, A., Masahiro, T., & Ricardo, T. (2002). SEBAL: Surface Energy Balance 
Algorithms for Land, version 1.0. The Idaho Department of Water Resource. University of Idaho, 
Kimberly, USA. 

Faisol, A. (2015). An Application of Remote Sensing to Analysis Crop Water Requirement. Gadjah Mada 
University. 

Faisol, A. (2016). An Application of Moderate Resolution Imaging Spektroradiometer to Optimize Water 
Allocation in Irrigation Area. Proceedings of AESAP, 136–139. Bogor, Indonesia: The 1st International 
Conference on the Role of Agricultural Engineering for Sustainable Agriculture Production. 

Faisol, A., Indarto, I., Novita, E., & Budiyono, B. (2017). Utilization of MODIS Surface Reflectance to 
Generate Air Temperature Information in East Java-Indonesia. Geoplanning: Journal of Geomatics and 
Planning, 7(1). 

Flores, F., & Lillo, M. (2010). Simple air temperature estimation method from MODIS satellite images on a 
regional scale. Chilean Journal of Agricultural Research, 70(3), 436–445. [Crossref] 

Hong, S. (2008). Mapping regional distributions of energy balance components using optical remotely 
sensed imagery. New Mexico Institute of Mining and Technology Socorro, New Mexico. 

Huntington, J. L., & Allen, R. G. (2009). Evapotranspiration and Net Irrigation Water Requirements for 
Nevada. World Environmental and Water Resources Congress 2009: Great Rivers, 1–15. [Crossref] 

Indonesian Geospatial Information Agency. (2018). Retrieved March 10, 2018, from 
http://tanahair.indonesia.go.id/portal-web  

Jiménez-Muñoz, J. C., & Sobrino, J. A. (2009). A single-channel algorithm for land-surface temperature 
retrieval from ASTER data. IEEE Geoscience and Remote Sensing Letters, 7(1), 176–179. [Crossref] 

Laosuwan, T., Gomasathit, T., & Rotjanakusol, T. (2017). Application of remote sensing for temperature 
monitoring: The technique for land surface temperature analysis. Journal of Ecological Engineering, 
18(3). [Crossref] 

National Aeronautic and Space Administration. (2018a). Retrieved March 30, 2018, from 
https://modis.gsfc.nasa.gov/data/dataprod/dataproducts.php?MOD_NUMBER=09  

National Aeronautic and Space Administration. (2018b). Retrieved March 9, 2018, from 
https://lpdaac.usgs.gov/ 

 
 

https://doi.org/10.4067/s0718-58392010000300011
https://doi.org/10.1061/41036(342)420
https://doi.org/10.1109/lgrs.2009.2029534
https://doi.org/10.12911/22998993/69358


 
Faisol et al. / Geoplanning: Journal of Geomatics and Planning, Vol 7, No 1, 2020, 37-46 
doi: 10.14710/geoplanning.7.1.25-36 

46 | 
 

Noi, P. T., Kappas, M., & Degener, J. (2016). Estimating daily maximum and minimum land air surface 
temperature using MODIS land surface temperature data and ground truth data in Northern Vietnam. 
Remote Sensing, 8(12), 1002. [Crossref] 

Paparrizos, S., Maris, F., & Matzarakis, A. (2014). Estimation and comparison of potential 
evapotranspiration based on daily and monthly data from sperchios valley in central Greece. Global 
NEST Journal, 16(1), 204–217. [Crossref] 

Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., & Nickless, D. (2004). Terra 
and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34(4), 710–
714. [Crossref] 

Shen, S., & Leptoukh, G. G. (2011). Estimation of surface air temperature over central and eastern Eurasia 
from MODIS land surface temperature. Environmental Research Letters, 6(4), 45206. [Crossref] 

Shuttle Radar Topography Mission. (2018). No Title. Retrieved March 5, 2018, from 
https://earthexplorer.usgs.gov/  

The Meteorology Climatology and Geophysics Agency. (2018). Retrieved March 5, 2018, from 
http://www.bmkg.go.id/ 

U.S. Geological Survey. (2016). Data Users Handbook version 2.0. Earth Resources Observation and 
Sciences, Greenbelt, Maryland. 

Vermote, E. F., Kotchenova, S. Y., & Ray, J. P. (2015). MODIS Surface Reflectance User’s Guide, version 1.4. 
USA: NASA. 

Vermote, E. F., & Vermeulen, A. (1999). Atmospheric correction algorithm: spectral reflectances (MOD09). 
ATBD Version, 4, 1–107. 

Yao, Y., & Zhang, B. (2012). MODIS-based air temperature estimation in the southeastern Tibetan Plateau 
and neighboring areas. Journal of Geographical Sciences, 22(1), 152–166. [Crossref] 

Zeng, L., Wardlow, B. D., Tadesse, T., Shan, J., Hayes, M. J., Li, D., & Xiang, D. (2015). Estimation of daily air 
temperature based on MODIS land surface temperature products over the corn belt in the US. Remote 
Sensing, 7(1), 951–970. [Crossref] 

 
 
 
 
 
 

https://doi.org/10.3390/rs8121002
https://doi.org/10.30955/gnj.001221
https://doi.org/10.1016/j.asr.2004.03.012
https://doi.org/10.1088/1748-9326/6/4/045206
https://doi.org/10.1007/s11442-012-0918-1
https://doi.org/10.3390/rs70100951

