Retrieval of land surface temperature in the humid ... - IEEE Xplore

Retrieval of Land Surface Temperature in the Humid Tropics from MODIS Data by Modeling the Atmospheric Transmission and Thermal Emission Agnes Lim, S. C. Liew and L. K. Kwoh Centre for Remote Imaging Sensing and Processing (CRISP), National University of Singapore Blk SOC 1, Level 2, Lower Kent Ridge Road, Singapore 119260 Fax :( +65)7767717, Email :(crslima, crslsc, crsklk)@nus.edu.sg

Abstract –.In the humid tropics, humidity is high implying a high water vapour content in the atmosphere. This causes strong attenuation in the radiation measured in the thermal infrared as the main absorption in this region is due to water vapour. In this study Land Surface Temperature (LST) is retrieved from single band infrared (IR) band through the modeling of atmospheric transmission and thermal emission given only surface parameters. Comparison of the LST retrieved from single IR bands within the same dataset and cross comparison with the standard MODIS LST Product gives high correlation indicating the validity of the models adopted for the atmosphere. 1.

INTRODUCTION

Measurement of Land Surface Temperature (LST) from space is complicated by the effects of the atmosphere. The thermal radiation “seen” by the sensor is the sum of radiation from both the atmosphere and the surface. Radiation emitted by the surface is absorbed by the atmospheric gases such as water vapour, carbon dioxide and ozone. In addition, the atmosphere itself radiates thermal energy which is also detected by the sensor. It is practically impossible to obtain LST over a large area from ground base measurements. Space-borne measurements using satellites sensors in the thermal infrared bands are able to give estimates of surface temperature in the regional and global scale. Thermal infrared sensors onboard satellites measure the top-of-atmosphere (TOA) radiance and can be inverted to give TOA brightness temperature using the Planck’s function for blackbody emission. Sensors designed for LST retrieval work in that part of electromagnetic spectrum less affected by atmospheric effects. Though atmosphere appears to be transparent in these regions known as the “atmospheric windows”, its effects on the thermal radiation emitted by land surface are however still nonnegligible. In addition, surface emissivity is unknown if land surfaces are not black or grey bodies where emissivity can be obtained. Remote sensing via satellites gives a mean of deriving spatial and temporal values of surface temperature provided accurate atmospheric correction can be performed and surface emissivity can be accounted for. Several approaches have been developed for the retrieval of LST. Two types of methods commonly used to derive LST from space are the single infrared channel method and the split window method. The split window method was

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initially developed to determine sea surface temperature (SST). It relies on the differential absorption in adjacent infrared bands to correct for atmospheric effects. A generalized split window technique [1] was developed for LST that takes into consideration the viewing angle. In the case of the single channel method, explicit correction of the atmospheric influence is necessary. This method relies on the use of good radiative transfer model and atmospheric profiles which must be given by satellite soundings or conventional radiosonde. A major advantage of this method is the applicability to single channel IR satellite measurements. This method uses simulated satellite measurements that were calculated with the radiative transfer model MODTRAN for variable parameters of surface elevation, surface temperature and vertical profiles of temperature and humidity. From the simulations the relationship between these parameters is known and for a given set of ground height, measured brightness temperature, temperature profile and water vapour profile the LST can be estimated. [2] In this paper the single channel method is employed as the technique for atmospheric correction to obtain LST in the humid tropics where water vapor vapour content is high. Results obtained are compared between different MODIS thermal bands retrieved in a similar way as well as the MODIS Land Surface Temperature Products (MOD11). 2.

METHOD

Radiance Lλ emitted from the land surface is given by (1) where ε is emissivity which is wavelength dependent, t is the transmittance, B(λ,T) is the Planck Blackbody Function, τ is optical thickness, µ is the cosine of the view zenith angle, µs is the cosine of solar zenith angle and Fs is the solar flux. τ0

τ µ

L λ = ε(λ ) t (λ )B(λ, Ts ) + ∫ e B(λ, T)dτ + 0

τ0

(1 − ε(λ )) ∫ e 0

τ −τ − 0 µ

−

B(λ, T)dτ +

τ0 µs

(1 − ε(λ ))e e π

−

τ0 µ

(1)

Fs µ s

The detected radiance is the sum of radiance from the surface and the atmosphere. The first term in (1) is contributed by the land surface. The second term is due to atmospheric emission. The third term is due to the downwelling radiance emitted by the atmosphere and reflected upward by the surface, and the

last term is the contribution of the solar irradiance. In order to obtain LST, the atmospheric contributions needs to be removed. To determine the atmospheric contribution for different atmospheric conditions, the Moderate Resolution Atmospheric Radiance and Transmittance Model (MODTRAN) 3.0 [3] was used. Pressure profiles, temperature profiles and water vapour profiles were respectively fitted to an exponential, a linear and a linear-exponential curve to a height of 10km. A total of 48 different models of tropical atmosphere with varying water vapour content and surface temperature were used in the simulations to generate atmospheric transmittance under various conditions. A 3rd order polynomial was established for water vapour absorption as a function of total precipitable water. For gaseous absorption, a linear curve was used for the fitting. Simulations also take into account different zenith angles. 6 different angles varying between 0 to 75 degrees in steps of 15 degrees were used in the simulations to produce different path lengths. Angles residing between these steps were computed using linear interpolation. Given the surface air temperature and pressure, total water vapour and satellite viewing geometry, the atmospheric transmittance of the MODIS thermal bands and the thermal emission of the atmosphere can be computed from the model. The surface leaving radiance can then be retrieved. The three required atmospheric parameters (surface air temperature and pressure, total precipitable water) required were obtained from MODIS Atmospheric Profile Product (MOD07) [4] whereas satellite geometry is obtained from the geolocation product (MOD03). The atmospheric parameters were at a resolution of 5km, hence they are interpolated to 1km resolution to be compatible with the 1km thermal data. Radiance at MODIS bands 22 (3.959-3.989µm), 31 (10.78-11.28µm) and 32 (11.77-12.27µm) were atmospherically corrected and then converted to land surface temperature. Currently, the emissivity is assumed to be uniform throughout and taken to be 0.97, 0.98 and 0.99 for bands 22, 31 and 32 respectively. Better estimates can be used if available from other sources for areas that have high variability. Inter-band comparison as well as comparison between the retrieved LST and the MODIS LST product is performed to check the validity of the retrieved LST via modeling of the atmosphere. 3.

Figure 1 Ground coverage of Test Data

A day pass on 8 January 2002 at 03:54 UTC and one night pass on 8 February 2002 at 15:45 UTC were used in this study. MODIS data was acquired and processed at the ground station at the Centre for Remote Imaging, Sensing and Processing (CRISP), Singapore. The MOD07 products for all four days were downloaded from the Land Processes Distributed Active Archived Centre. 4.

RESULTS

LST is theoretically independent of wavelength, hence the temperatures derived from atmospheric corrected radiance at different wavelengths for the same pass should exhibit a strong 1-1 correlation. Without atmospheric correction, radiance at different wavelengths are subjected to different degrees of absorption and atmospheric contamination. Figure 2 shows a plot of the brightness temperature at 4µm and 11µm corresponding to band 22 and 31 of MODIS bands. At 4µm, the radiation is less attenuated whereas at 11µm absorption by water vapour stronger and is further enhanced due to the water vapour continuum that stretches from 8-14µm. In the humid tropics, water vapour in the atmosphere is high causing more attenuation; making atmospheric correction necessary.

TEST AREA

The test data covers Thailand, Cambodia, Vietnam and Myanmar in Southeast Asia. Figure 1 shows the ground coverage of the data used.

Figure 2 Scattered Plot of brightness temperature at 4µm and 11µm.

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Figure 3, 4 and 5 show the scattered plots of the retrieved LST at bands 22, 31 and 32 (T22, T31 and T32) in various combinations for data acquired in the day and Figure 6, 7 and 8 for the data acquired at night. For data acquired in the day, high correlation factor greater than 0.9 was obtained between LST retrieved from different bands. On the other hand, the night data where a large portion of land was obscured by clouds shows a lower correlation factor indicating that contamination of clouds at sub pixel level affects the LST retrieved. The scattered points on the plots were also due to sub pixel clouds as these points lie on cloud edges

08 Jan 2002

Figure 5 Scattered Plot of T32/K against T31/K for day data

08 Jan 2002

08 Feb 2002

Figure 3 Scattered Plot of T31/K against T22/K for day data

Figure 6 Scattered Plot of T31/K against T22/K for night data

08 Jan 2002

08 Feb 2002

Figure 4 Scattered Plot of T32/K against T22/K for day data

Figure 7 Scattered Plot of T32/K against T22/K for night data

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08 Feb 2002

Figure 10 Scattered plot of LST retrieved by atmospheric modeling and MODIS LST Product for 8 February 2002

Figure 8 Scattered Plot of T32/K against T31/K for night data

Comparison with NASA MODIS LST Product was also made for both datasets. Figures 9 and 10 show the scattered plots for LST retrieved from single IR band and that from the standard MODIS LST product. Strong correlations (R2>0.9) was obtained between the retrieved LST and the MODIS LST product for both datasets. An average difference of 0.9K and 0.5K was observed between the retrieved LST and LST product for day and night data respectively. A visual check of the emissivities obtained from the LST product and that of the assumed value for band 31 and 32 were made and found to be very close to that of the assumption. Emissivities for band 31 and band 32 in the LST product were generally between 0.984-0.986 and 0.988-0.99 respectively.

5.

CONCLUSION

In this study, LST is recovered from the top of atmosphere radiance by modeling the atmospheric transmission and thermal emission. Simulations using radiative transfer code are carried for different atmospheric conditions and hence deriving the relations between the various atmospheric parameters such as surface sir temperature and water vapour content. Inter-band comparison of the LST retrieved separately from three MODIS bands using the single IR band technique and cross validation with MODIS standard LST product shows good agreement. The cross comparison further supports the validity of the models used as the two techniques for the retrieval of LST use different algorithms. The single band method described in this paper can be further adapted to retrieve the sub-pixel temperature distribution, for example, in determining the subpixel fire temperature and area. REFERENCE [1] Wan Zhengming and Dozier Jeff, 1996, “A Generalized Split Window Algorithm for Retrieving Land Surface Temperature from Space”, IEEE Trans. Geoscience and Remote Sensing, Vol. 34, No. 4, pp. 892-905 [2] Scheroedter M., Olesen F. and Fischer H., 2003, “Determination of Land Surface Temperature Distribution from a Single Channel IR Measurements and Effective Spatial Interpolation Method for the Use of TOVS, ECMWF and Radiosonde Profiles in the Atmospheric Correction Scheme”, Int. J. Remote Sensing, Vol. 24, No. 6, pp 1189-1196 [3] Berk A., Bernstein L. S. and Robertson D, C., “MODTRAN A Moderate Resolution Model for LOWTRAN 7”, Air Force Geophysics Laboratory Technical Report GL –TR-89-0122, Hanscon AFB, MA [4] S. Liang, H. Fang and M. Chen, 2001, “Atmospheric Correction of Landsat ETM+ Land Surface Imagery – Part I Methods”, IEEE Trans. Geoscience and Remote Sensing, Vol.39, No.11, pp 2490-2498

Figure 9 Scattered plot of LST retrieved by atmospheric modeling and MODIS LST Product for 8 January 2002

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