Abstract. Introduction

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1 Estimation of the Mean Annual Surface Temperature an Surface Frost Number Using the MODIS Lan Surface Temperature Proucts for Mapping Permafrost in China Youhua Ran, Xin Li, Rui Jin State Key Laboratory of Frozen Soils Engineering, Col an Ari Regions Environmental an Engineering Research Institute, Chinese Acaemy of Sciences, Lanzhou, Gansu, China Abstract In this paper, we propose a pragmatic scheme for estimation of MAST an surface frost number, base on the assumption that the average of maximum an minimum lan surface temperature (LST) represents the aily mean LST, using MODIS Aqua/Terra LST proucts. The avantage of this approach is that it allows the full use of every value at any time at any pixel of MODIS LST proucts to estimate the two important inices for mapping permafrost: mean annual surface temperature an surface frost number. This scheme is simple an easy to implement, an it oes not epen on other observations. The accuracy of the metho epens only on the accuracy of MODIS LST proucts. Eight years of atasets from 2003 to 2010 were generate. These atasets are valiate primary by comparison with in situ observations an analysis of the spatial pattern over China. Results show that the atasets have a high level of accuracy, although some uncertainty such as the complicate scale effect oes exist. These atasets can be use to support permafrost mapping in China. Keywors: permafrost, MAST, surface frost number, remote sensing, MODIS, China. Introuction Permafrost is a major element of the cryosphere an is extremely sensitive to climate warming an human activity (Li et al. 1996a, Jin et al. 2000, Haeberli & Hohmann 2008, Li et al. 2008, Wu et al. 2002). China s permafrost area ranks thir in the worl in terms of area an is the largest in terms of mile- an high-altitue permafrost areas, which are particularly sensitive to global climatic change. The Qinghai- Tibet Plateau (QTP) in particular plays a very important role in global change (Wu et al. 2002, Li et al. 2008). Significant permafrost egraation has occurre an is occurring in most permafrost regions in China, resulting in increase environmental fragility an relate hazars. All of these factors unerline the importance of permafrost mapping in China. In the past 50 years, many maps have been compile to investigate the istribution an thermal status of permafrost (LIGG/CAS 1988, Li & Cheng 1996, Qiu et al. 2000, CAREERI/CAS 2006). This has been one in an effort to stuy the response an feeback of permafrost to global change an human engineering activities an to ecrease an prevent permafrost hazars. All of these maps are base on a few in situ air temperature observations an a small amount of mean annual groun temperature (MAGT) investigation ata. The accuracy of these observations an ata largely epens on the mapper s empiricism. However, many investigators suggest that air temperature is not an optimal preictor of permafrost istribution because the very large effects of snow cover an other variables influencing permafrost occurrence are ignore. Instea, lan surface skin temperature (LST) shoul be use (Nelson 1987, Cheng, personal communications). The mean annual surface temperature (MAST) an surface frost number (Nelson 1987) are quantitative criteria in mapping permafrost. Satellite image ata have been successfully use to retrieve the LST since the avent of the Avance Very High Resolution 317 Raiometer (AVHRR) instrument on boar the NOAA series of satellites in the 1970s an the MODIS (Moerate-resolution Imaging Spectro raiometer) instrument, which was launche in 2000 as a payloa on the Terra satellite. In 2002, a secon MODIS instrument was launche on the Aqua satellite. MODIS improves upon the performance of AVHRR by proviing both higher spatial resolution an greater spectral resolution. The polar-orbiting MODIS sensors prouce aily lan surface temperature (LST) maps with global coverage. Base on remote sensing LST observations, many stuies have been mae to estimate the mean LST. A simplifie metho propose by Jin (1999, 2000) inclues two steps: the neighboring-pixel approach (NP) an surface air temperature ajustment. The scheme has a clear theoretical basis, but there are problems; for example, many parameters are ifficult to achieve, an spatial coverage is inaequate. Langer et al. (2010) an Westermann et al. (2011) examine the spatial an temporal variability of summer surface temperatures using a groun-base high-resolution thermal imaging system at a northern Siberia polygonal tunra site an at a high-arctic tunra site on Svalbar, Norway, respectively. Langer et al. (2010) point out that clouy conitions can lea to biase weekly temperature averages inferre from MODIS LST ata. They recommen a solution to improve the accuracy of average LST by combining clou cover information with a gap filling proceure. Westermann et al. (2011) conclue that a reliable gap filling proceure to moerate the impact of prolonge clouy perios woul be of high value for a future LST-base permafrost monitoring scheme. Hachem et al. (2009) propose a more practical metho to estimate the annual mean LST. This metho involves fitting a sinusoial moel over the aily LST of MODIS reaings to reprouce seasonal thermal variations near the groun for each 1-km 2 pixel. This metho is simple an practical. However, it oes take avantage the MODIS LST proucts that are available four times aily.

2 318 Tenth International Conference on Permafrost This paper proposes a pragmatic scheme for estimating MAST an surface frost number using MODIS Aqua/Terra LST proucts, i.e., four times aily LST observations. Metho Assumption The assumption that the arithmetic average of maximum an minimum LST can represent the aily mean LST is well-foune (Liu et al. 2006). Thus the MODIS Terra/ Aqua aytime an nighttime LST proucts (MOD11C1 an MYD11C1 version 041) with 0.05 spatial resolution were acquire from NASA Distribute Active Archive Center (DAAC) an use to estimate the aily mean LST. The reason for the use of MODIS Aqua is that Aqua overpass time is later than Terra (13PM vs. 10AM for aytime, 1AM vs. 10PM for nighttime) an therefore has a better chance of capturing the aytime maximum an nighttime minimum temperature (Kogan et al. 2010). Figure 1 shows that the MODIS Terra/Aqua aytime an nighttime LST proucts have a high egree of correlation with aily maximum an minimum in situ LST. The higher temporal resolution in situ LST is obtaine from the WATER (Watershe Allie Telemetry Experimental Research) experiment (Li et al. 2009) in The in situ LST observations from the Linze grasslan station are use to assess the representation of MODIS Terra/Aqua aytime an nighttime LST proucts for aily maximum an minimum LST. The lan cover types at the station are iverse in this area, with a istribution of wetlan, grasslan, salinize meaow, an farmlan. Workflow Base on the assumption given above, we present a scheme to estimate the annual mean LST. The workflow of this scheme is shown in Figure 2. This scheme inclues four steps. Step 1: Select aytime an nighttime LST. For one ay, the aily mean LST is calculate using equation 1. At least once, aytime an nighttime observation was selecte from both sensors. T = ( T + Tn) / 2 (1) aily Step 2: Ientify the LST amplitue an interpolate it over the region. To estimate the aily mean LST for the pixels that i not have a pair of LST observations (typically owing to clou cover), the aily LST amplitue is interpolate accoring to the near LST pair pixels. Experiment shows that the LST amplitue is more spatially homogeneous than the LST itself. This provies the possibility for interpolating LST amplitue for the entire region of China. The amplitue is calculate using equation 2. Base on this step, any one observation is sufficient to estimate the aily average surface temperature. We believe that observation is stronger than no observation in this process. D = T max -T min (2) Figure 1. The representation of MODIS Aqua/Terra aytime an nighttime LST proucts for maximum an minimum LST. (Top: in col season; Bottom: in warm season.) Figure 2. The workflow to estimate the annual mean LST using MODIS Aqua/Terra aytime an nighttime LST proucts.

3 Ran, Li, & Jin 319 Step 3: Calculate the aily mean LST for pixels that i not contain a pair generally ue to clou cover. When the aily LST amplitue is known, we can calculate the aily mean LST for pixels that i not contain an LST pair using equation 3. T aily ' T = T t t D + D / 2 / 2 t = 1or3 t = 2or4 (3) where t represents the time for one ay. 1 an 3 show the aytime LST, 2 an 4 show the nighttime LST; enotes one ay in a certain year; aily is the aily mean LST, an T represents the LST. Step 4: Fitting the seasonal cycle of LST. The aily mean LST is expane spatially as much as possible by steps 2 an 3. For step 4, a continuous ouble sine function moel reproucing the seasonal cycle of LST in each pixel was aopte by fitting a sinusoial interpolation between the aily average LSTs for each pixel year by year as follows: Figure 3. The MAST over China in 2004 (Units: K). T a = a1 sin( b1 x + c1 ) + a2 sin( b2 x + c2 ) (4) where x is the Julian ay an Ta represents the MAST. The coefficients are erive from fitting expane spatially aily mean LST. Step 5: Accoring to the ouble sine function, the annual average LST an surface frost number are prouce. Here the surface frost number (Nelson 1987) was use. F = DDF DDF + DDT (5) Figure 4. Surface frost number in where DDF an DDT are surface freezing an thawing inices, respectively, expresse in egree ays, an F inicates surface frost number. The freezing an thawing inices (TC ays) are erive from the ouble sine function moel, i.e., equation 4 an integrating this curve below an above the 0 C axis. Result an Valiation Annual average LST an surface frost number atasets from were generate by implementing the workflow escribe above. An example result is shown in Figures 3 an 4. Figure 3 shows the estimate MAST over China an the pattern of temperature istribution. Temperatures in the Northeast an the QTP are coler than in other regions. The temperature in the northern region of the QTP is coler than in the south. This gives a clear outline of permafrost istribution in China. Figure 4 also shows that the estimate surface frost number reveals the pattern of permafrost istribution in China. In situ aily lan surface temperature ata from 664 sites were obtaine from the Chinese Meteorological Aministration (CMA) an are use to valiate the estimate annual average LST an surface frost number, as shown in Figures 5 an 6. The correlation coefficient (R) of the linear relationship between in situ annual average LST an estimate annual average LST in 2004 is 0.88, an the stanar eviation (SD) is 3.14 (p < ). For the surface frost number, R is 0.95, an the SD is (both p < ). This strong linear relationship inicates that the estimate MAST an surface frost number have a high level of accuracy. However, the permafrost bounary is very sensitive to MAST an the surface frost number, an is complicate by the coarse spatial resolution of the ata which makes valiation ifficult. Summary an Future Prospects A pragmatic scheme for estimating the MAST an surface frost number using MODIS Aqua/Terra LST proucts is presente. The avantage of this approach is the use of every value at any time at any pixel of MODIS LST proucts in estimating the two important inices for mapping permafrost: mean annual surface temperature an surface frost number. The scheme is simple an easy to implement, an it oes not epen on other observations. The accuracy of the metho epens only on the accuracy of MODIS LST proucts. Eight years of atasets from were generate using this scheme to provie mean annual surface temperature an surface frost number to map permafrost in China. These

4 320 Tenth International Conference on Permafrost References Figure 5. The correlation of estimate MAST an observation MAST in Figure 6. The correlation of estimate surface frost number an observation surface frost number in atasets were valiate primarily by comparison with in situ observations an analysis of the spatial istribution pattern over China. Results show that the atasets have a high level of accuracy, although some uncertainty, such as the complication of scale effects, oes exist. However, the permafrost bounary is very sensitive to MAST an surface frost number. The accuracy of this propose metho has much room for improvement. Snow cover shoul be consiere in mapping permafrost processes in the near future, especially in regions that maintain a snow cover. Remote sensing observations can be assimilate into a lan surface moel that woul output lan surface temperature with more accuracy an a higher spatial/temporal resolution. Acknowlegments This work is supporte by the Founation for Excellent Youth Scholars of CAREERI, CAS. We thank the eitor an the anonymous reviewers for their extremely helpful suggestions in revising the article. Col an Ari Regions Environmental an Engineering Research Institute (CAREERI), Chinese Acaemy of Sciences (CAS) Map of the glaciers, frozen groun an esert in China. 1:4,000,000. SinoMaps Press, Beijing (in Chinese). Haeberli, W. & Hohmann, R Climate, glaciers an permafrost in the Swiss Alps 2050: scenarios, consequences an recommenations. In NICOP 2008: Proceeings Ninth International Conference on Permafrost. Eite by D.L. Kane an K.M. Hinkel, K.M. Institute of Northern Engineering, University of Alaska Fairbanks, 1: Hachem, S., Allar, M., & Duguay, C Using the MODIS Lan Surface Temperature Prouct for Mapping Permafrost: An Application to Northern Quebec an Labraor, Canaa. Permafrost an Periglacial Processes 20(4): Jin, M.L. &. Dickinson, R.E Interpolation of surface raiative temperature measure from polar orbiting satellites to a iurnal cycle - 1. Without clous. Journal of Geophysical Research-Atmospheres 104(D2): Jin, M.L Interpolation of surface raiative temperature measure from polar orbiting satellites to a iurnal cycle 2. Clouy-pixel treatment. Journal of Geophysical Research-Atmospheres 105(D3): Jin, H.J., Li, S.X., Cheng, G.D., Wang, S.L., & Li, X Permafrost an climatic change in China. Global an Planetary Change 26 (4): Kogan, Felix; Powell, Alfre; Feorov, Oleg (e.) Use of Satellite an In-Situ Data to Improve Sustainability Springer Netherlans. 2010, 362 pp. Langer, M., Westermann, S., & Boike, J Spatial an temporal variations of summer surface temperatures of wet polygonal tunra in Siberia - implications for MODIS LST base permafrost monitoring. Remote Sensing of Environment 114(9): Lanzhou Institute of Glaciology an Geocryology, Chinese Acaemy of Sciences (LIGG/CAS) Map of Snow, Ice an Frozen Groun in China (1: 4,000,000). Cartographic Publishing House, Beijing, China. Li, S., Cheng, G., & Guo, D. 1996a. Numerical simulation on the changes of permafrost on the Qinghai Tibet Plateau uner a persisting warming climate. Science in China Series D: Earth Sciences 26(4): Li, S. & Cheng, G.D. (es). 1996b. Map of permafrost on the Qinghai-Tibet Plateau (1:3,000,000). Gansu Culture Press, Lanzhou (in Chinese). Li, X., Cheng, G.D., Jin, H.J., Kang, E.S., Che, T., Jin, R., Wu, L.Z., Nan, Z.T., Wang, J., & Shen, Y.P Cryospheric Change in China. Global an Planetary Change 62(3-4):

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