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纸质出版日期： 2019-7 ，
录用日期： 2018-2-19
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孟翔晨, 刘昊, 程洁. 2019. 基于FY-2F数据的中国区域地表温度日变化模型评价及特征研究. 遥感学报, 23(4): 570–581
Meng X C, Liu H and Cheng J. 2019. Evaluation and characteristic research in diurnal surface temperature cycle in China using FY-2F data. Journal of Remote Sensing, 23(4): 570–581
地表温度日变化模型作为非常重要的输入参数在气象、水文、生态等领域研究中具有重要意义。风云二号(FY-2F)静止气象卫星的地表温度产品的时间分辨率为1小时，这为拟合精确的地表温度日变化(DSTC)模型提供了可能。本文首先利用194个气象站点对应的2014年的FY-2F地表温度产品评价了GOT01、VAN06、JNG06、INA08、GOT09和GEM_V这6种地表温度日变化模型在中国区的模拟精度，对不同时间窗口和不同地表覆盖类型拟合精度的差异进行了分析；其次，选用JNG06模型探究了中国区域地表温度随经纬度、季节和地表覆盖类型的日变化规律。研究结果表明：在不同时间窗口内，GOT09模型获得了全局最优的拟合精度，均方根误差为0.89 K；JNG06和GEM_V模型精度次之，均方根误差分别为0.92 K和0.94 K；GOT01、INA08和VAN06模型精度最差；各模型在城市和建筑区、农用地和自然植被以及常绿阔叶林这3类地表覆盖类型的拟合精度最好，其均方根误差在0.89—0.92 K，在其余地表覆盖类型的拟合精度在1.0 K以上。JNG06模型模拟的地表温度在4种典型的地表类型随纬度的变化规律较为明显，地表温度在1月份随纬度变化较为剧烈，在7月份整体波动较为平缓。综上所述，使用FY-2F地表温度产品建立的DSTC模型在中国区域具有较高的精度，模拟的地表温度随着纬度变化的规律较为明显。使用本文模型既可以纠正现有模型又可获取归一化地表温度产品，同时可以检验和标定陆面模式地表温度模拟结果。
Diurnal Surface Temperature Cycle (DSTC) model is an important input parameter in the field of meteorology
hydrology
and ecology. In the past 20 years
various DSTC models under clear-sky conditions have been developed on the basis of Spinning Enhanced Visible and Infrared Imager (SEVIRI) and Geostationary Operational Environment Satellite (GOES) satellites. However
only a few related studies have focused on China due to its complex topography
geomorphology
and climatic conditions. This situation restricts the application and development of DSTC models in the region. Although the DSTC model is mature
its evaluation is often based on the data of a certain point or day
which lacks verification at a large range of time and spatial scales. Moreover
the free variable setting of the DSTC model cannot meet the need at large time and spatial scales. Thus
the variable setting of the DSTC model must be expanded. In this study
six DSTC models were evaluated on the basis of FY-2F land surface temperature product at the monthly average and large space scales for China. In addition
JNG06 model was used to analyze the diurnal variation characteristics of Land Surface Temperature (LST) with the changes in seasonality
latitude and longitude
and land cover types. Semi-empirical model is simple and convenient and has a wide range of applications. Physical model is close to the actual physical condition of the surface. Therefore
five semi-empirical models
namely
GOT01
VAN06
JNG06
INA08
and GOT09
and the GEM-V physical model were used for DSTC simulation and analysis. On the basis of the geographical location of 194 meteorological stations
the corresponding FY-2F LST data were extracted
and the average monthly and hourly LST data after quality control were set as the model initial value. To evaluate the fitting accuracy of DSTC model in China
the model evaluation was divided into two parts: 1) the fitting of DSTC model was divided into five time periods
and the precision of DSTC model was analyzed; and 2) the fitting accuracy of each model under different land cover types was statistically explored on the basis of the land cover types of 194 stations. JNG06 model was also used to analyze the diurnal variation characteristics of LST with the changes in seasonality
latitude and longitude
and land cover types. Analysis of the average root mean square error (RMSE) of five time windows showed that GOT09 model obtained the global optimal fitting accuracy with an RMSE of 0.89 K
followed by JNG06 and GEM_V models with RMSEs of 0.92 and 0.94 K
respectively. GOT01
INA08
and VAN06 models obtained the worst accuracy. Each model had the best fitting precision in urban and built-up
cropland/natural vegetation mosaic
and evergreen broadleaf forest with an RMSE between 0.89 and 0.92 K. The fitting precision in mixed forest and cropland followed with an RMSE of around 1.0 K. Each model had the worst fitting precision in barren or sparse vegetation and savannas with an RMSE of above 1.3 K. From the results
we can conclude that the accuracies of GOT01
VAN06
and INA08 are poor in all land cover types and time windows
and the remaining models are relatively robust with close accuracy. The land surface temperature simulated by JNG06 model in four land cover types vary with the changes in latitude and longitude. The rule of LST varies with the change in latitude and but is unaffected by the change in longitude. The DSTC model can be used as the input parameter of climate and hydrological models. The model can also be used as reference for future studies on the DSTC model and its applications.
遥感FY-2FMCD12C1地表温度日变化模型气象站点
remote sensingFY-2FMCD12C1LST (Land Surface Temperature)diurnal surface temperature cyclemeteorology stations
Aires F, Prigent C and Rossow W B. 2004. Temporal interpolation of global surface skin temperature diurnal cycle over land under clear and cloudy conditions. Journal of Geophysical Research: Atmospheres, 109(D4): D04313
陈颖, 段四波, 冷佩, 陈媛媛, 韩晓静. 2016. 极轨卫星热红外地表温度日变化模拟. 遥感信息, 31(6): 7–14
Chen Y, Duan S B, Leng P, Chen Y Y and Han X J. 2016. Modeling of diurnal cycle of land surface temperature based on polar orbiting satellite thermal infrared data. Remote Sensing Information, 31(6): 7–14
Cheng J, Liang S L, Wang J D and Li X W. 2010. A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1588–1597
Coops N C, Duro D C, Wulder M A and Han T. 2007. Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location and elevation information. International Journal of Remote Sensing, 28(10): 2391–2396
Crosson W L, Al-Hamdan M Z, Hemmings S N J and Wade G M. 2012. A daily merged MODIS Aqua-Terra land surface temperature data set for the conterminous United States. Remote Sensing of Environment, 119: 315–324
Dickinson R E, Henderson-Sellers A and Kennedy P J. 1993. Biosphere-atmosphere Transfer Scheme (BATS) Version 1E As Coupled to the NCAR Community Climate Model. NCAR Technical Note NCAR/TN-387+STR. NCAR [DOI: 10.5065/D67W6959]
Duan S B, Li Z L, Tang B H, Wu H and Tang R L. 2014. Generation of a time-consistent land surface temperature product from MODIS data. Remote Sensing of Environment, 140: 339–349
Duan S B, Li Z L, Wang N, Wu H and Tang B H. 2012. Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data. Remote Sensing of Environment, 124: 15–25
Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X M. 2010. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(5): 168–182
Göttsche F M and Olesen F S. 2001. Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76(3): 337–348
Göttsche F M and Olesen F S. 2009. Modelling the effect of optical thickness on diurnal cycles of land surface temperature. Remote Sensing of Environment, 113(11): 2306–2316
Huang F, Zhan W F, Duan S B, Ju W M and Quan J L. 2014. A generic framework for modeling diurnal land surface temperatures with remotely sensed thermal observations under clear sky. Remote Sensing of Environment, 150: 140–151
Ignatov A and Gutman G. 1999. Monthly mean diurnal cycles in surface temperatures over land for global climate studies. Journal of Climate, 12(7): 1900–1910
Inamdar A K, French A, Hook S, Vaughan G and Luckett W. 2008. Land surface temperature retrieval at high spatial and temporal resolutions over the southwestern United States. Journal of Geophysical Research: Atmospheres, 113(D7): D07107
Jiang G M, Li Z L and Nerry F. 2006. Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI. Remote Sensing of Environment, 105(4): 326–340
Jin M L. 2000. Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 2. Cloudy‐pixel treatment. Journal of Geophysical Research: Atmospheres, 105(D3): 4061–4076
Jin M L and Dickinson R E. 1999. Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 1. Without clouds. Journal of Geophysical Research, 104(D2): 2105–2116
Jin M L and Treadon R.E 2003. Correcting the orbit drift effect on AVHRR land surface skin temperature measurements. International Journal of Remote Sensing, 24(22): 4543–4558
Kahle A B. 1977. A simple thermal model of the Earth’s surface for geologic mapping by remote sensing. Journal of Geophysical Research, 82(11): 1673–1680
Li Z L, Tang B H, Wu H, Ren H Z, Yan G J, Wan Z M, Trigo I F and Sobrino J A. 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131: 14–37
梁顺林, 程洁, 贾坤, 江波, 刘强, 刘素红, 肖志强, 谢先红, 姚云军, 袁文平, 张晓通, 赵祥. 2016. 陆表定量遥感反演方法的发展新动态. 遥感学报, 20(5): 875–898
Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H, Yao Y J, Yuan W P, Zhang X T and Zhao X. 2016. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875–898
刘昊, 程洁. 2016. 中国区域地表温度日变化模型的构建及规律探究. 北京: 北京师范大学: 21
Liu H and Cheng J. 2016. Construction of Diurnal Surface Temperature Cycle Model and Probe about the Regular Pattern of the Model in China. Beijing: Beijing Normal University: 21
刘紫涵, 吴鹏海, 吴艳兰, 沈焕锋, 曾超. 2017. 风云静止卫星地表温度产品空值数据稳健修复. 遥感学报, 21(1): 40–51
Liu Z H, Wu P H, Wu Y L, Shen H F and Zeng C. 2017. Robust reconstruction of missing data in Feng Yun geostationary satellite land surface temperature products. Journal of Remote Sensing, 21(1): 40–51
Norman J M and Becker F. 1995. Terminology in thermal infrared remote sensing of natural surfaces. Remote Sensing Reviews, 12(3/4): 159–173
Parton W J and Logan J A. 1981. A model for diurnal variation in soil and air temperature. Agricultural meteorology, 23: 205–216
Price J C. 1977. Thermal inertia mapping: a new view of the earth. Journal of Geophysical Research, 82(18): 2582–2590
Schädlich S, Göttsche F M and Olesen F S. 2001. Influence of land surface parameters and atmosphere on METEOSAT brightness temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment, 75(1): 39–46
Sun D L and Pinker R T. 2005. Implementation of GOES-based land surface temperature diurnal cycle to AVHRR. International Journal of Remote Sensing, 26(18): 3975–3984
Van Den Bergh F, Van Wyk M A and Van Wyk B J. 2006. Comparison of data-driven and model-driven approaches to brightness temperature diurnal cycle interpolation//Proceedings of the 17th Annual Symposium of the Pattern Recognition Association of South Africa. Parys, South Africa: [s.n.]
熊艳艳, 吴先球. 2010. 粗大误差四种判别准则的比较和应用. 大学物理实验, 23(1): 66–68
Xiong Y Y and Wu X Q. 2010. The generalizing application of four judging criterions for gross errors. Physical Experiment of College, 23(1): 66–68
薛兴盛, 吴艳兰. 2017. 面向风云静止卫星地表温度产品的缺失数据修复方法对比. 安徽农业大学学报, 44(2): 308–315
Xue X S and Wu Y L. 2017. A comparison of missing data reconstruction methods for Feng Yun geostationary satellite land surface temperature products. Journal of Anhui Agricultural University, 44(2): 308–315
Zhan W F, Chen Y H, Voogt J, Zhou J, Wang J F, Liu W Y and Ma W. 2012a. Interpolating diurnal surface temperatures of an urban facet using sporadic thermal observations. Building and Environment, 57: 239–252
朱琳清, 周纪, 刘绍民, 李国全. 2017. 航空遥感地表温度时间归一化. 遥感学报, 21(2): 193–205
Zhu L Q, Zhou J, Liu S M and Li G Q. 2017. Temporal normalization research of airborne land surface temperature. Journal of Remote Sensing, 21(2): 193–205
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