Land Surface Temperature and Emissivity Retrieved from AMSR Passive Micro-Wave Data Kebiao Mao*1,3,5 Jiancheng Shi1,4 Zhaoliang Li2 Zhihao Qin3,5 Yuanyuan Jia2 1. State LAB of Remote sensing science, Institute of Remote Sensing Applications, Chinese Academy of Science, P.O. Box 9718, Datong Load Strict, Beijing 100101, China. 2 Institute of Geographical Science and Natural Resources Research, CAS, 100101, China 3 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China. 4. Institute for Computational Earth System Science, University of California, Santa Barbara, CA 93106, USA. 5. International Institute for Earth System, Nanjing University, Nanjing, 210093, China. Tel (86/10) 64854300-8039, Fax (86/10) 64889643,*
[email protected] Abstract—A regression analysis between brightness of all AMSR bands and MODIS land surface temperature product provided by NASA indicated good correlation, so retrieving land surface temperature from AMSR passive data is available without ground truth data (soil moisture and land surface type). The analysis results (North-Africa, North-East China, Tibet) indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately, the land surface at least are classified into two groups. For non-snow covered land surface, The average land surface temperature error is about2- 3℃ relative to the MODIS LST product. For snow covered land surface, The average land surface temperature error is about 3-4℃ relative to the MODIS LST product. On the other hand, the emissivity of passive microwave is very important parameter for retrieving soil moisture. We compute the emissivity through land surface temperature retrieved by statistical regression method and make some analysis. Keywords-LST, emissivity, AMSR data
I.
INTRODUCTION
AMSR and MODIS are two EOS (Earth Observing System) sensor instruments in Aqua satellite. AMSR is passive microwave radiometer, which observes atmospheric, land, oceanic, and cryospheric parameters, including precipitation, sea surface temperatures, ice concentrations, snow water equivalent, surface wetness, wind speed, atmospheric cloud water, and water vapor. MODIS owns 36 bands and are designed for retrievals of SST, LST and atmospheric properties. The two instruments can make up for each other in their own predominance. The MODIS owns high resolution but MODIS are influenced great by cloud. On the contrary, the passive AMSR is influenced little by cloud. Passive microwave brightness temperature can be used to retrieve land surface temperature. Therefore, AMSR data has more advantages in this aspect. So how to utilize the multiple instrument characteristics is an important methodology in remote sensing.
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II.
THEORETICAL BASIS FOR THE PASSIVE MICROWAVE REMOTE SENSING OF LST AND EMISSIVITY
The theoretical basis for passive microwave of LST retrieval is based on the thermal radiance of the ground and its transfer from the ground through the atmosphere to the remote sensor. Generally speaking, the ground is not a blackbody. Thus ground emissivity has to be considered for computing the passive microwave radiance emitted by the ground. Atmosphere has important effects on the received radiance at remote sensor level. Considering all these impacts, the general radiance transfer equation for passive microwave remote sensing of LST can be formulated as follows:
B f (T f ) = τ f (θ )ε f B f (Ts ) + τ f (θ )(1 − ε f ) B f (Ta↓ ) + B f (Ta↑ )
(1)
where Ts is the LST, Ta is average atmosphere temperature, Tf is the brightness temperature in frequency f, τf(θ) is the atmospheric transmittance in frequency f at viewing direction θ (zenith angle from nadir), and εf is the ground emissivity. Bf(Ts) is the ground radiance, and Bf(Ta↑)and Bf(Ta↓) are the downwelling and upwelling path radiances, respectively. Every term of radiance transfer equation owes the Planck function. Planck function is a bridge between spectral radiance emitted by a blackbody at a given frequency and temperature. The expression is as following:
B f (Ti ) =
2hf 3 c(e hf / kT − 1)
(2)
Where Bf(Ti) is the spectral radiance of the blackbody, the unit is Wm-2sr-1Hz-1, h is Planck constant with h= 6.63*10-34J, f is frequency and unit is Hz, k is Boltzman constant with k=1.380658*10-23J,Ti is temperature in Kelvin, and c is the light speed with c=2.992458*108ms-1 ， Because the wavelength of passive wave is longer than VIR and TIR, according to Ralleigh-Jeans approximation, the expression (2) can be simplified as follows:
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Bf(Ts)=
2kT
λ
2
(3)
Thus, the equation (1) can be written as follows:
T f = τ f ε fTs + τ f (1 − ε f )Ta↓ + Ta↑
（4）
Seen from equation (4), the LST can be retrieved through building the linear relationship between brightness temperatures. On the other hand, because the influence of the atmosphere is very little for passive microwave, the εf can be approximately obtained by equation (5) if LST is known.
εf = III.
Tf Ts
(5)
THE METHOD FOR RETRIEVING LST FROM AMSRE
The research for retrieving LST from passive microwave is little. McFarland et al.[1]make some research for passive microwave SSM/I data to retrieve LST and obtained some useful conclusions. The higher frequency can improved the accuracy of retrieval LST because it owns lower dielectric constant for water and a shallower emitting depth than lower frequency without the atmospheric scattering and re-emission. For microwave passive AMSR data, the frequency in 36.5V (GHz) is very suitable to retrieve LST. But single analysis indicates that the 89V is more suitable to retrieve LST for a single channel. On the other hand, the surface water has some influence because the high dielectric constant of water decreases the emissivity at 19 GHz, depress’ the brightness temperatures and increase the polarization difference. Thus, the term 19GHz can compensate for the influence of surface water. The difference between 36.5 GHz and 23.8GHz can be utilized to correct for the influence of atmospheric water vapor on the emission. Similarly, the 18.9V are used to correct for the influence of hydrometeors (cloud and rain droplets). For AMSR data, the LST retrieval equation can be depicted as following: Ts=A0+A1*T36.5V-A2*T23.8V–A3*T18.7H +A4*T89V (6) where Ts is land surface temperature, A0, A1, A2, A3 and A4 is coefficient，T36.5V , T23.8V , T18.7H and T89V is the brightness temperature. The unit of Ts , Ti is temperature in Kelvin. Of course, we can also change equation（6） into equation (7) in order to explain the physical basis. Ts=C0+C1*T36.5V+C2*(T36.5V -T23.5V) +C3*(T36.5V- T18.7H ) （7） +C4*T89V The T36.5V is the primary channel to retrieve LST, T36.5V T23.5V is utilized to attenuate the influence of atmospheric water vapor, T36.5V- T18.7H can compensate for the influence of surface water, T89V can decrease the average influence of atmosphere. Because it is very difficult to obtain the in situ ground truth measurement of LST matching the pixel scale (25×25km at nadir) AMSR data at the satellite pass to develop and validate algorithm. Generally speaking, LST varies from point to point on the ground, and ground measurement is generally point measurement. It is a problem to obtain the measured LST matching the pixel of AMSR data. On the other hand, precisely
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locating the pixel of the measured ground in AMSR data especially night images is also a problem, so the algorithm for retrieving LST is very few. McFarland et al. [1] overcome many difficulties and made some research, but just for local regions because the limitation of condition. AMSR and MODIS are two EOS (Earth Observing System) instruments in Aqua satellite, so the product of MODIS LST provides chance for us to develop algorithm. However, Wan et al. [2][3] overcome many of these difficulties and manage to get a data set between 2000~2001 to evaluate the MODIS LST product. The conclusion indicates that the accuracy of MODIS LST product is under 1K. So how to utilize the multiple instrument characteristics is an important methodology in remote sensing. We make the MODIS LST product as in situ ground truth measurement to regress with the lightness temperature of AMSR data which overcomes many difficulties including situ ground truth measurements, locating and matching in this research. We select three typical regions as research site: northeast of China (forest); north Africa（desert）; Tibet（ snow and frozen soils）. The information about sample data is as table 1. For the north-east China, the regression equation is as equation (8). Ts=59.1569+0.70184T36.5V+0.61157T23.8V–0.44454T18.7H （8） +0.03842T89V We got equation (9) for North- Afican. Ts=43.82921-0.63007T36.5V+0.40209T23.8V+0.1242T18.7H （9） +0.99705T89V We got equation (10) for Tibet. Ts=286.91667+0.35021T36.5V-0.57778T23.8V +0.01084T18.7H +0.16642T89V （10） The simulation average accuracy is about 2.62°C, 2.15°C and 2.69°C for north-east China, north African and Tibet respectively relative to MODIS LST product. We utilize some other sample (see table 2) data to validate the algorithm. For non-snow covered land surface, The average land surface temperature error is about2- 3℃ relative to the MODIS LST product. For snow covered land surface, The average land surface temperature error is about 3-4℃ relative to the MODIS LST product. TABLE I.
TRAINED SAMPLE INFORMATION IN NORTH-EAST OF CHINA, AFRICAN AND TIBET
Location
Time
Number of Pixels
Temperature of Rang(K)
Accuracy(°C)
North-East of China
2003-6-8
5134
275-320
2.62
African
2003-8-23
16287
280-310
2.15
Tibet
2003-1-5
2747
266-279
2.69
The analysis results indicate that the radiation mechanism is different for snow covered and none-snow covered. The main reason is that the radiation energy comes from different layer.
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TABLE II. VALIDATION SAMPLE INFORMATION IN NORTH-EAST OF CHINA, AFRICAN AND TIBET Number of Pixels
Temperature of Rang(K)
Accuracy(°C)
Location
Time
2003-6-4
1614
286-321
2.96
North-East of China
2003-6-5
1060
282-304
2.95
African
Tibet
IV.
2003-6-6
108
286-293
2.68
2003-8-24
18940
279-300
2.10
2003-8-25
13678
287-303
1.84
2003-8-26
8483
286-305
2.43
2003-1-4
296
263-279
3.96
2003-2-1
425
264-279
3.63
On the other hand, the emissivity of passive microwave data is an important parameter for retrieving soil moisture. Another important meaning for retrieval LST is that we can obtain the emissivity through LST. Due to the little influence of cloud and atmosphere for passive microwave data, we can approximately compute emissivity by εf=Tf /Ts. Figure 2 is the distribution of emissivity at 89V. V.
APPLICATION OF THE RETRIEVING METHOD
We apply the above algorithm to the passive microwave AMSR data. The image is taken on August 2003，which is in summer season. The LST retrieval results likes figure1. The average LST is about 27°C, the max LST is about 34°C. Seen from figure 1, the distribution of LST likes block, the reason of which is the low of resoluton of AMSR image（ a pixel is 25×25KM ） . The phenomena can not be found in high resolution image (such as TM, ASTER), which is a predominance to indicate the large region of thermal scene for AMSR data.
