Journal of Geo-information Science >
Component Temperature Estimation from Simulated Geostationary Meteorological Satellite Data Based on MAP Criterion and Markov Models
Received date: 2012-11-29
Revised date: 2013-02-02
Online published: 2013-06-17
Land surface temperature (LST) is one of the key parameters in the physics of land surface processes, and satellite observation is the major way to derive regional LST distribution. However, the pixel observed by satellite is usually a mixture of different land cover types and the temperature of each type seems more meaningful land useful than the mixed pixel temperature. Therefore, component temperature estimation is an important research field. To take use of the rich temporal information imbedded in the geostationary satellite data, a practical approach was proposed to derive the component temperature based on MAP criterion and Markov models in this study. The approach was on the basis of mixed pixel temperature theory and under the assumption that there were only two components in the mixed pixel (vegetation and soil) and the nearby pixels had the same temperature for each component. The method was applied to simulated geostationary satellite data, and the component temperature estimation results showed good agreement with the true values. The RMSEs of vegetation and soil temperature were within 2K for most cases. In addition, the sensitivity analysis of the method to the mixed pixel temperature estimation error, the vegetation fraction cover (FVC) estimation error and its range of the pixels considered in the estimation were conducted. The results indicated that the pixel temperature error and the FVC error showed little effect on the estimation results while the FVC range played an important role in the estimation because of its determinant function on the first guess of the component temperature.
DIAO Wei, LI Ai-Nong, LI Shao-Liang . Component Temperature Estimation from Simulated Geostationary Meteorological Satellite Data Based on MAP Criterion and Markov Models[J]. Journal of Geo-information Science, 2013 , 15(3) : 422 -430 . DOI: 10.3724/SP.J.1047.2013.00422
[1] Balick L K,Hutchinson B A. Directional thermal infraredexitance distributions from a leafless deciduous forest[J].IEEE Transactions on Geoscience and Remote Sensing,1997(24):693-698.
[2] Kimes D S,Kirchener J A. Directional radiometric measurementsof row-crop temperatures[J]. International Journalof Remote Sensing, 1983(4):299-311.
[3] Lagouarde J P, Kerr Y H,Brunet Y. An experimental studyof angular effects on surface temperature for various plantcanopies and bare soils[J]. Agricultural and Forest Meteorology,1995(77):167-190.
[4] 王锦地,李小文,孙晓敏,等.用热辐射方向性模型反演非同温像元组分温度[J].中国科学E辑,2000(30):54-60．
[5] Li Z L, Stoll M P, Zhang R H, et al. On the separate retrievalof soil and vegetation temperature from ATSR2 data[J].Science in China (Series D), 2001,44(2):97-111.
[6] Zhang R H, Su H B, Li Z L, et al. The potential informationin the temperature difference between shadow and sunlitof surfaces and a new way of retrieving the soil moisture[J]. Science in China (Series D), 2001,44(2):112-123
[7] Shi Y L. Thermal infrared inverse model for componenttemperatures of mixed pixels[J]. International Journal ofRemote Sensing, 2011,32(8):2297-2309
[8] 陈良富,庄家礼,徐希孺,等.非同温混合像元热辐射有效比辐射率概念及其验证[J].科学通报,2000,45(1):22-28.
[9] 徐希孺,范闻捷,陈良富.开放的复杂目标热辐射特性的矩阵表达式[J].中国科学D辑,2001,3l(12):1046-1051．
[10] 刘强,陈良富,柳钦火,等.作物冠层的热红外辐射传输模型[J].遥感学报,2003,7(3):161-167．
[11] Becker F,Li Z L. Temperature independent spectral indicesin thermal infrared bands[J]. Remote Sensing of Environment,1990(32):17-33.
[12] Palluconi F, Kahle A B, Hoover G, et al. The spectralemissivity of prairie and pasture grasses at Konza prairie[J]. Kansas, In Proc. Am. Meteorol, Soc. Symp. on theFirst ISLSCP Field Experiment (FIFE), Anaheim, CA,Am. Meteorol. Soc., Boston, MA.1990,7-9:77-78.
[13] Kallel A, Ottlé C,LeHégarat-Mascle S. Surface temperaturedownscaling from multi-resolution instruments basedon MAP criterion and Markov models[C] 3rd InternationalSymposium; Recent advances in Quantitative remotesensing, 26 September-1 October Torrent, Spain, 2010,50-55.
[14] Kallel A, Ottlé C, LeHégarat-Mascle S, et al. Surface temperaturedownscaling from multiresolution instrumentsbased on Markov models[J]. IEEE Transaction on Geoscienceand Remote Sensing, 2012, doi:10.1109/TGRS.2012.2207461
[15] Jiang G M, Li Z L,Nerry F. Land surface emissivity retrievalfrom combined mid-infrared and thermal infrareddata of MSG-SEVERI[J]. Remote Sensing of Environment,2006(105):326-340.
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