高分辨红外辐射探测器地表温度数据在江苏地区1980—2009年间适用性评估

doi:10.18306/dlkxjz.2020.08.004

Abstract

Abstract:

Surface temperature plays a key role in physical and biological processes on Earth, and it is an important index for evaluating surface thermal environment. Understanding the temporal and spatial variations of surface temperature is of great significance for urban heat island monitoring and ecological quality evaluation. Current land surface temperature products derived from satellite remote sensing suffer from partial coverage or cloud-cover blockage problems, which have potential limitations on the study of climate and ecological environment. The recently developed NOAA satellites High-Resolution Infrared Radiation Sounder (HIRS) land surface temperature (LST) product is among the longest LST records. To examine the spatiotemporal distribution of LST in Jiangsu Province and data performance of HIRS LST at different temporal scales, we evaluated the HIRS LST and in situ measurement correlation coefficient (R), bias, and unbiased root mean square difference (ubRMSD) using the daily, annual, and seasonal mean values, together with the long-term linear trend during 30 years in the province. Great consistency between the two products is observed. Their correlation coefficients are higher than 0.98 for all stations, and those for daily anomalies range from 0.65 to 0.80 across the region. The bias and the ubRMSD indicate that the HIRS data have generally underestimated the LST across the northern and some southern areas, mainly because of its large underestimation of the occurrences of temperature higher than 32 ℃. Nevertheless, the HIRS LST has largely overestimated the occurrences of summer days with temperature ranging from 20-30 ℃. As for the intraannual variations, the HIRS LST shows highest correlation with in situ measurements in the spring, but the correlation is lowest in the winter. Trend test shows that both sets of data show significant increasing trends with similar patterns in the spring, autumn, and winter. However, the long-term trends are significantly overestimated across the region in the summer, and underestimated in other seasons in the HIRS LST data.

Key words: land surface temperature (LST), remote sensing, High-Resolution Infrared Radiation Sounder (HIRS), product evaluation, tendency analysis, Jiangsu Province

SHI Xiao, WANG Guojie, SUN Ming, LI Yvtao, WANG Boni, SHEN Jie. Evaluation of the long-term high-resolution infrared radiation sounder land surface temperature during 1980-2009 in Jiangsu Province, China[J].PROGRESS IN GEOGRAPHY, 2020, 39(8): 1283-1295.

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References 41

[1]	Seyfried M S, Flerchinger G N, Murdock M D, et al. Long-term soil temperature database, Reynolds Creek Experimental Watershed, Idaho, United States[J]. Water Resources Research, 2001,37:2843-2846. doi: 10.1029/2001WR000418
[2]	Brooks P D, McKnight D, Elder K. Carbon limitation of soil respiration under winter snowpacks: Potential feedbacks between growing season and winter carbon fluxes[J]. Global Change Biology, 2005,11:231-238.
[3]	Bai Y, Scott T A, Chen W, et al. Long-term variation in soil temperature of the Mojave Desert, southwestern USA[J]. Climate Research, 2011,46:43-50.
[4]	Wang Y, Chen W, Zhang J, et al. Relationship between soil temperature in may over Northwest China and the East Asian summer monsoon precipitation[J]. Acta Meteorologica Sinica, 2013,27:716-724.
[5]	Yang K, Zhang J. Spatiotemporal characteristics of soil temperature memory in China from observation[J]. Theoretical & Applied Climatology, 2015,126(3-4):1-11.
[6]	Xiao J, Yu F, Zhu W, et al. Comment on "The whole-soil carbon flux in response to warming" [J]. Science, 2018, 359: eaao0218. doi: 10.1126/science.aao0218. pmid: 29472453
[7]	胡泽银, 王世杰, 白晓永, 等. 贵州省地表温度的遥感反演评价及时空分异规律[J]. 生态学杂志, 2018,37(9):2794-2807.
	[ Hu Zeyin, Wang Shijie, Bai Xiaoyong, et al. Remote sensing retrieval and spatial-temporal differentiation of land surface temperature in Guizhou Province. Chinese Journal of Ecology, 2018,37(9):2794-2807. ]
[8]	林中立, 徐涵秋, 陈弘. 我国东部沿海三大城市群热岛变化及其与城市群发展的关系[J]. 环境科学研究, 2018,31(10):1695-1704.
	[ Lin Zhongli, Xu Hanqiu, Chen Hong. Urban heat island change and its relationship to the urbanization of three major urban agglomerations in China's eastern coastal region. Research of Environmental Sciences, 2018,31(10):1695-1704. ]
[9]	王建凯, 王开存, 王普才. 基于MODIS地表温度产品的北京城市热岛(冷岛)强度分析[J]. 遥感学报, 2007,11(3):330-339. doi: 10.11834/jrs.20070346
	[ Wang Jiankai, Wang Kaicun, Wang Pucai. Urban heat (or cool) island over Beijing from MODIS land surface temperature. Journal of Remote Sensing, 2007,11(3):330-339. ]
[10]	徐涵秋. 区域生态环境变化的遥感评价指数[J]. 中国环境科学, 2013,33(5):889-897.
	[ Xu Hanqiu. A remote sensing index for assessment of regional ecological changes. China Environmental Science, 2013,33(5):889-897. ]
[11]	赵亚芳. 环太湖地区城市热岛特征的遥感资料分析与数值模拟研究[D]. 南京: 南京大学, 2015.
	[ Zhao Yafang. Remote sensing data analysis and numerical simulation of the urban heat island effects in Lake Tai Basin. Nanjing, China: Nanjing University, 2015. ]
[12]	杨朝斌, 何兴元, 张树文, 等. 基于Landsat8的城市热岛效应与地表因子关系研究: 以长春市为例[J]. 干旱区资源与环境, 2016,30(12):110-115.
	[ Yang Chaobin, He Xingyuan, Zhang Shuwen, et al. Estimation of the relationship between urban heat island and land surface factors using Landsat 8 data: A case study in Changchun, China. Journal of Arid Land Resources and Environment, 2016,30(12):110-115. ]
[13]	丁海勇, 李往华. 基于TVX方法的南京市城区时空格局与地表温度的研究[J]. 长江流域资源与环境, 2018,27(4):735-744.
	[ Ding Haiyong, Li Wanghua. Analysis of land use land cover temporal-spatial distribution and land surface temperature in Nanjing City using TVX Method. Resources and Environment in the Yangtze Basin, 2018,27(4):735-744. ]
[14]	谢苗苗, 王仰麟, 付梅臣. 城市地表温度热岛影响因素研究进展[J]. 地理科学进展, 2011,30(1):35-41. doi: 10.11820/dlkxjz.2011.01.004
	[ Xie Miaomiao, Wang Yanglin, Fu Meichen. An overview and perspective about causative factors of surface urban heat island effects. Progress in Geography, 2011,30(1):35-41.]
[15]	李元征, 尹科, 周宏轩, 等. 基于遥感监测的城市热岛研究进展[J]. 地理科学进展, 2016,35(9):1062-1074. doi: 10.18306/dlkxjz.2016.09.002
	[ Li Yuanzheng, Ying Ke, Zhou Hongxuan, et al. Progress in urban heat island monitoring by remote sensing. Progress in Geography, 2016,35(9):1062-1074. ]
[16]	孟翔晨, 历华, 杜永明, 等. Landsat 8地表温度反演及验证: 以黑河流域为例[J]. 遥感学报, 2018,22(5):857-871.
	[ Meng Xiangchen, Li Hua, Du Yongming, et al. Retrieval and validation of the land surface temperature derived from Landsat 8 data: A case study of the Heihe River Basin. Journal of Remote Sensing, 2018,22(5):857-871. ]
[17]	Gutman G G. Vegetation indices from AVHRR: An update and future prospects[J]. Remote Sensing of Environment, 1991,35(2-3):121-136.
[18]	Parinussa R, Jeu R D, Schalie R V D, et al. A Quasi-Global approach to improve day-time satellite surface soil moisture anomalies through the land surface temperature input[J]. Climate, 2016,4:50. doi: 10.1002/2016GL071354.
[19]	魏然. 多源遥感地表温度数据时空融合研究及应用[D]. 武汉: 武汉大学, 2016.
	[ Wei Ran. Researching and application of multi-source remote sensing data fusing. Wuhan, China: Wuhan University, 2016. ]
[20]	杨敏, 杨贵军, 陈晓宁, 等. 基于FSDAF方法融合生成高时空分辨率地表温度[J]. 国土资源遥感, 2018,30(1):54-62.
	[ Yang Min, Yang Guijun, Chen Xiaoning, et al. Generation of land surface temperature with high spatial and temporal resolution based on FSDAF method. Remote Sensing for Land & Resources, 2018,30(1):54-62. ]
[21]	Coccia G, Siemann A L, Pan M, et al. Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of land surface temperature from HIRS[J]. Remote Sensing of Environment, 2015,170(1):290-305.
[22]	谢志清, 杜银, 曾燕, 等. 长江三角洲城市带扩展对区域温度变化的影响[J]. 地理学报, 2007,62(7):717-727.
	[ Xie Zhiqing, Du Yin, Zeng Yan, et al. Impact of urban expansion on regional temperature change in the Yangtze River Delta. Acta Geographica Sinica, 2007,62(7):717-727. ]
[23]	施建成, 杜阳, 杜今阳, 等. 微波遥感地表参数反演进展[J]. 中国科学: 地球科学, 2012,42(6):814-842.
	[ Shi Jiancheng, Du Yang, Du Jinyang, et al. Advances in surface parameter retrieving for microwave remote sensing. Scientia Sinica Terrae, 2012,42(6):814-842. ]
[24]	徐涵秋. 新型Landsat 8卫星影像的反射率和地表温度反演[J]. 地球物理学报, 2015,58(3):741-747.
	[ Xu Hanqiu. Retrieval of the reflectance and land surface temperature of the newly-launched Landsat 8 satellite. Chinese Journal of Geophysics, 2015,58(3):741-747. ]
[25]	江苏省气象局. 江苏省气候图集 [M]. 北京: 气象出版社, 2009.
	[ Jiangsu Meteorological Administration. Climatological altas of Jiangsu Province. Beijing, China: China Meteorological Press, 2009. ]
[26]	中国气象局. 地面气象观测规范 [M]. 北京: 气象出版社, 2003.
	[ China Meteorological Bureau. Specifications for surface meteorological observation. Beijing, China: China Meteorological Press, 2003. ]
[27]	李黄. 自动气象站实用手册 [M]. 北京: 气象出版社, 2007.
	[ Li Huang. Solutions for automatic weather station. Beijing, China: China Meteorological Press, 2007. ]
[28]	Ullah W, Wang G, Gao Z, et al. Comparisons of remote sensing and reanalysis soil moisture products over the Tibetan Plateau, China[J]. Cold Regions Science and Technology, 2018,146:110-121.
[29]	孙添, 王国杰, 娄丹, 等. 青藏高原区域多源土壤湿度数据的对比分析[J]. 江苏农业科学, 2018,46(10):285-290.
	[ Sun Tian, Wang Guojie, Lou Dan, et al. Contrastive analysis of multi-sensor soil moisture datasets of Tibetan Plateau. Jiangsu Agricultural Sciences, 2018,46(10):285-290. ]
[30]	Sen P K. Estimates of the regression coefficient based on Kendall's Tau[J]. Journal of the American Statistical Association, 1968,63:1379-1389.
[31]	Guan Y, Zhang X, Zheng F, et al. Trends and variability of daily temperature extremes during 1960-2012 in the Yangtze River Basin, China[J]. Global and Planetary Change, 2015,124:79-94.
[32]	娄丹. 卫星遥感土壤湿度的时空分布及其对大气降水反馈作用[D]. 南京: 南京信息工程大学, 2016.
	[ Lou Dan. Spatial and temporal distribution of satellite remote sensing soil moisture and its feedback to atmospheric precipitation. Nanjing, China: Nanjing University of Information Science and Technology, 2016. ]
[33]	Mann H B. Nonparametric tests against trend[J]. Econometrica, 1945,13:245-259.
[34]	Kendall M G, Gibbons J D. Rank correlation methods[J]. Biometrika, 1990,25(1):86-91.
[35]	Gocic M, Trajkovic S. Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia[J]. Global and Planetary Change, 2013,100:172-182.
[36]	Siemann A L, Coccia G, Pan M, et al. Development and analysis of a long term, global, terrestrial land surface temperature dataset based on HIRS satellite retrievals[J]. Journal of Climate, 2016,29(10): 160229122352006. doi: 10.2307/26385468.
[37]	Dorigo W, Wagner W, Albergel C, et al, ESA CCI soil moisture for improved earth system understanding: state-of-the art and future directions[J]. Remote Sensing of Environment, 2017,203:S0034425717303061. doi: 10.1016/j.rse.2017.07.001.
[38]	Zhang Y, Chen W, Smith S L, et al. Soil temperature in Canada during the twentieth century: Complex responses to atmospheric climate change[J]. Journal of Geophysical Research, 2005,110(D3). doi: 10.1029/2004jd004910.
[39]	García-Suárez A M, Butler C J. Soil temperatures at Armagh Observatory, Northern Ireland, from 1904 to 2002[J]. International Journal of Climatology, 2006,26:1075-1089.
[40]	Yeşilırmak E. Soil temperature trends in Büyük Menderes Basin, Turkey[J]. Meteorological Applications, 2014,21:859-866.
[41]	Araghi A, Mousavi-Baygi M, Adamowski J. Detecting soil temperature trends in Northeast Iran from 1993 to 2016[J]. Soil and Tillage Research, 2017,174:177-192. doi: 10.1016/j.still.2017.07.010

Evaluation of the long-term high-resolution infrared radiation sounder land surface temperature during 1980-2009 in Jiangsu Province, China

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