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2025 , Vol. 48 >Issue 5: 765 - 777

DOI: https://doi.org/10.12118/j.issn.1000-6060.2024.087

气候与环境

基于实测数据的黑河流域多源地表温度产品误差分析

  • 李旭 ,
  • 江红南 ,
  • 许剑辉
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  • 1.新疆大学生态与环境学院，新疆 乌鲁木齐 830017
    2.新疆大学地理与遥感科学学院，新疆 乌鲁木齐 830017
    3.广东省科学院广州地理研究所，广东 广州 510070
李旭（1999-），男，硕士研究生，主要从事生态遥感研究. E-mail: lx625625@stu.xju.edu.cn
江红南（1980-），男，博士，副教授，主要从事干旱区环境遥感研究. E-mail: jiang_hn0609@sina.com

收稿日期: 2024-02-08

  修回日期: 2025-02-14

  网络出版日期: 2025-05-13

基金资助

新疆维吾尔自治区重点实验室开放课题(2023D04060)

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Error analysis of multi-source land surface temperature products in the Heihe River Basin based on in-situ data

  • LI Xu ,
  • JIANG Hongnan ,
  • XU Jianhui
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  • 1. College of Ecology and Environment, Xinjiang University, Urumqi 830017, Xinjiang, China
    2. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, Xinjiang, China
    3. Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, Guangdong, China

Received date: 2024-02-08

  Revised date: 2025-02-14

  Online published: 2025-05-13

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摘要

利用2017—2019年黑河流域7个地面观测站的地表温度观测数据，采用偏差（BIAS）、均方根误差（RMSE）、相关系数（CC）、标准偏差比（RSD）统计指标从不同时间尺度对风云三号C星可见光红外扫描辐射计（FY-3C VIRR）地表温度产品、Terra卫星中分辨率成像光谱仪（MOD11A1/MOD11C3）地表温度产品、欧洲中期天气预报中心开发的欧洲中期天气预报中心第五代陆面再分析数据集（ERA5-LAND）和中国气象局陆面数据同化系统（CLDAS-V2.0）4套地表温度产品的误差进行了分析。结果表明：（1） 在空间分布上，4种地表温度产品均呈现南高北低的空间分布，但FY-3C VIRR和MOD11A1地表温度产品呈现出更多空间细节。（2） FY-3C VIRR白天地表温度产品的BIAS和RMSE整体上相对较低，显示了较高的精度；MOD11A1白天地表温度产品具有最高的CC（0.957~0.987之间），但其误差较大，MOD11A1白天地表温度产品整体上都被高估。（3） MOD11A1夜间地表温度产品的精度整体优于FY-3C VIRR、ERA5-LAND和CLDAS-V2.0夜间地表温度产品，其中CLDAS-V2.0夜间地表温度产品的误差最大。（4） FY-3C VIRR、MOD11A1和ERA5-LAND夜间地表温度产品的精度优于白天地表温度产品；而CLDAS-V2.0白天地表温度产品的精度高于夜间地表温度产品。

关键词： 地表温度; FY-3C VIRR; MODIS; ERA5-LAND; CLDAS-V2.0; 黑河流域

本文引用格式

李旭 , 江红南 , 许剑辉 . 基于实测数据的黑河流域多源地表温度产品误差分析[J]. 干旱区地理, 2025 , 48(5) : 765 -777 . DOI: 10.12118/j.issn.1000-6060.2024.087

Abstract

This study used in-situ land surface temperature observation data (from 2017 to 2019) from seven stations in the Heihe River Basin, northern Gansu Province, China, to evaluate the errors of four land surface temperature products: the Fengyun-3C visible and infrared radiometer (FY-3C VIRR) land surface temperature product, the Terra moderate resolution imaging spectroradiometer (MOD11A1/MOD11C3) land surface temperature product, the European Center for medium-range weather forecasts fifth-generation land surface reanalysis dataset (ERA5-LAND), and the China Meteorological Administration land data assimilation system (CLDAS-V2.0). Bias (BIAS), root mean square error (RMSE), correlation coefficient (CC), and ratio of standard deviation (RSD), were employed as statistical metrics for analyzing errors across different temporal scales. The results indicated the following. (1) All four land surface temperature products exhibited a general spatial pattern of higher temperature in the south and lower temperature in the north. However, the FY-3C VIRR and MOD11A1 products exhibited finer spatial details. (2) The FY-3C VIRR daytime land surface temperature product demonstrated relatively lower BIAS and RMSE values, indicating higher accuracy. Further, the MOD11A1 daytime land surface temperature product yielded the highest CC values, ranging across 0.957-0.987. However, it also produced larger errors. This was attributed to the tendency of the MOD11A1 daytime product to overestimate temperatures. (3) The MOD11A1 nighttime land surface temperature product outperformed the FY-3C VIRR, ERA5-LAND, and CLDAS-V2.0 nighttime products in terms of accuracy. Among these, the CLDAS-V2.0 nighttime product exhibited the largest errors. (4) For the FY-3C VIRR, MOD11A1, and ERA5-LAND products, the nighttime land surface temperature accuracy surpassed those of their respective daytime products. Conversely, the CLDAS-V2.0 daytime land surface temperature product exhibited higher accuracy than its nighttime counterparts.

Key words： land surface temperature; FY-3C VIRR; MODIS; ERA5-LAND; CLDAS-V2.0; Heihe River Basin

参考文献

[1] He X L, Xu T R, Bateni S M, et al. Mapping regional evapotranspiration in cloudy skies via variational assimilation of all-weather land surface temperature observations[J]. Journal of Hydrology, 2020, 585: 124790, doi: 10.1016/j.jhydrol.2020.124790.
[2] 吴万民, 刘涛, 陈鑫. 西北干旱半干旱区NDVI季节性变化及其影响因素[J]. 干旱区研究, 2023, 40(12): 1969-1981.
  [Wu Wanmin, Liu Tao, Chen Xin. Seasonal changes of NDVI in the arid and semi-arid regions of northwest China and its influencing factors[J]. Arid Zone Research, 2023, 40(12): 1969-1981. ]
[3] 王岱, 崔洋, 王素艳, 等. 1961—2020年宁夏干旱事件年代际变化及风险评估[J]. 干旱区地理, 2024, 47(5): 785-797.
  [Wang Dai, Cui Yang, Wang Suyan, et al. Interdecadal changes and risk assessment of drought events in Ningxia from 1961 to 2020[J]. Arid Land Geography, 2024, 47(5): 785-797. ]
[4] Sekertekin A, Zadbagher E. Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area[J]. Ecological Indicators, 2021, 122: 107230, doi: 10.1016/j.ecolind.2020.107230.
[5] Wan Z M, Dozier J. A generalized split-window algorithm for retrieving land-surface temperature from space[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4): 892-905.
[6] Qin Z H, Karnieli A, Berliner P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region[J]. International Journal of Remote Sensing, 2001, 22(18): 3719-3746.
[7] Gillespie A, Rokugawa S, Matsunaga T, et al. A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(4): 1113-1126.
[8] Mu?oz-Sabater J, Dutra E, Agustí-Panareda A, et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications[J]. Earth System Science Data, 2021, 13(9): 4349-4383.
[9] Shi C X, Xie Z H, Qian H, et al. China land soil moisture EnKF data assimilation based on satellite remote sensing data[J]. Science China Earth Sciences, 2011, 54: 1430-1440.
[10] Han S, Shi C X, Xu B, et al. Development and progress of high resolution CMA land surface data assimilation system (HRCLDAS)[J]. Advances in Meteorological Science and Technology, 2018, 8(102-108): 116, doi: 10.13140/RG.2.2.31814.42562.
[11] Meng X C, Cheng J, Guo H, et al. Accuracy evaluation of the Landsat 9 land surface temperature product[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 8694-8703.
[12] Botje D, Dewan A, Chakraborty T C. Comparing coarse-resolution land surface temperature products over western Australia[J]. Remote Sensing, 2022, 14(10): 2296, doi: 10.3390/rs14102296.
[13] Wan Z M, Li Z L. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4): 980-996.
[14] Li X, Cheng G D, Liu S M, et al. Heihe Watershed allied telemetry experimental research (HiWATER): Scientific objectives and experimental design[J]. Bulletin of the American Meteorological Society, 2013, 94(8): 1145-1160.
[15] Che T, Li X, Liu S M, et al. Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China[J]. Earth System Science Data, 2019, 11(3): 1483-1499.
[16] Liu S M, Li X, Xu Z W, et al. The Heihe integrated observatory network: A basin-scale land surface processes observatory in China[J]. Vadose Zone Journal, 2018, 17(1): 1-21.
[17] Liu S M, Xu Z W, Wang W Z, et al. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem[J]. Hydrology and Earth System Sciences, 2011, 15(4): 1291-1306.
[18] Cheng J, Liang S L, Yao Y J, et al. Estimating the optimal broadband emissivity spectral range for calculating surface longwave net radiation[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 10(2): 401-405.
[19] Lu L, Zhang T J, Wang T J, et al. Evaluation of collection-6 MODIS land surface temperature product using multi-year ground measurements in an arid area of northwest China[J]. Remote Sensing, 2018, 10(11): 1852, doi: 10.3390/rs10111852.
[20] Jiang J X, Li H, Liu Q H, et al. Evaluation of land surface temperature retrieval from FY-3B/VIRR data in an arid area of northwestern China[J]. Remote Sensing, 2015, 7(6): 7080-7104.
[21] 纪王迪, 黄晓军, 包微, 等. 关中地区人类活动强度与地表温度的时空关联特征及其驱动作用[J]. 干旱区地理, 2024, 47(6): 967-979.
  [Ji Wangdi, Huang Xiaojun, Bao Wei, et al. Spatiotemporal correlation characteristics and driving forces of human activity intensity and surface temperature in the Guanzhong area[J]. Arid Land Geography, 2024, 47(6): 967-979. ]
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