MODIS大气廓线产品在青海省空气温度遥感估算中的应用研究

doi:10.18306/dlkxjz.2021.08.011

地理科学进展 ›› 2021, Vol. 40 ›› Issue (8): 1386-1396.doi: 10.18306/dlkxjz.2021.08.011

MODIS大气廓线产品在青海省空气温度遥感估算中的应用研究

田圣戎¹^,²(), 朱文彬¹^,^*(), 周世健³

1.中国科学院地理科学与资源研究所,北京 100101
2.东华理工大学测绘工程学院,南昌 330013
3.南昌航空大学,南昌 330063

收稿日期:2020-11-06 修回日期:2021-04-20 出版日期:2021-08-28 发布日期:2021-10-28
通讯作者: *朱文彬(1987— ),男,山东青岛人,博士,副研究员,研究方向为水文水资源。E-mail:zhuwb@igsnrr.ac.cn
作者简介:田圣戎(1996— ),男,黑龙江齐齐哈尔人,硕士生,研究方向为摄影测量与遥感。E-mail:shengrongt@163.com
基金资助:
青海省重大科技专项(2019-SF-A4);青海省基础研究计划项目(2020-ZJ-715);青海三江源生态保护和建设二期工程科研和推广项目(2018-S-3);国家自然科学基金项目(42071032);中国科学院青年创新促进会资助项目(2020056)

Near surface air temperature estimation based on MODIS atmospheric profile product over Qinghai Province

TIAN Shengrong¹^,²(), ZHU Wenbin¹^,^*(), ZHOU Shijian³

1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. Faculty of Geomatics, East China University of Technology, Nanchang 330013, China
3. Nanchang Hangkong University, Nanchang 330063, China

Received:2020-11-06 Revised:2021-04-20 Online:2021-08-28 Published:2021-10-28
Supported by:
Key Science and Technology Project of Qinghai Province(2019-SF-A4);Basic Research Program of Qinghai Province(2020-ZJ-715);Scientific Research and Promotion Projects of the Second Phase Project of Ecological Protection and Construction of the Three Rivers Source in Qinghai Province(2018-S-3);National Natural Science Foundation of China(42071032);Youth Innovation Promotion Association of Chinese Academy of Sciences(2020056)

摘要/Abstract

摘要：

气温是反映生态环境的重要参数之一,准确估算气温的时空分布对于气候变化研究具有重要意义。论文基于2011—2019年青海省气温实测数据、MODIS产品和SRTM DEM数据,在像元尺度分别开展了晴天条件和有云条件下瞬时空气温度的遥感估算研究,并评价了不同气温估算方法的精度差异,进而通过多元回归模型生成研究区高精度月空气温度产品,对青海省气温的时空分布格局进行分析。研究结果表明,在未使用气温实测数据进行校准的情况下,通过将MOD07_L2大气廓线产品反演的空气温度与MOD06_L2地表温度平均的方法,能够显著提高气温的估算精度。晴天条件下相关系数(r)为0.93,均方根误差(RMSE)为4.71 ℃;有云条件下r为0.89,RMSE为5.16 ℃。在使用气温观测值进行校准的情况下,通过引入高程参数,多元回归模型月尺度空气温度估算的决定系数(R²)和RMSE总体分别保持在0.8以上和2.5 ℃以下。将上述回归模型应用到栅格尺度,从而生成整个青海省高精度卫星过境时刻的逐月气温产品,进而分析其时空分布格局。具体来说,青海省月最高气温出现在7月,全省平均气温为13.59 ℃,最低气温出现在1月,全省平均气温为-9.44 ℃;气温的空间分布主要受海拔控制,全省平均气温直减率为4 ℃/km。上述研究表明MODIS大气廓线产品在全天气气温估算方面具有独特优势,特别是在地面气温实测数据的支持下能够有效降低遥感估算的系统性误差,实现大尺度复杂地形条件下气温的高精度估算。

关键词: 气温估算, 多元回归模型, MODIS, 遥感, 青海省

Abstract:

Near surface air temperature is one of the most essential variables in a wide range of disciplines such as hydrology, meteorology, and ecology. Accurate estimation of air temperature for continuous spatial and temporal coverage is also crucial to climate change studies. Considering the sparse distribution of meteorological stations in Qinghai Province of China, this study estimated its near surface air temperature (T_a) based on meteorological observations, MODIS products, and SRTM DEM data from 2011 to 2019. First, daily instantaneous T_a under clear and cloudy sky conditions was retrieved from MOD07_L2 and MOD06_L2 products without the support of meteorological observations. Three parameterization schemes were used in this process and their results were compared. Second, these daily T_a estimates with highest accuracy were combined with meteorological observations and SRTM DEM data to generate high-precision monthly air temperature through the multiple regression method. Finally, the spatial and temporal distribution of T_a in Qinghai Province was analyzed. The results show that the accuracy of daily instantaneous T_a estimates can be improved significantly through averaging the near surface air temperature retrieved from MOD07_L2 and land surface temperature retrieved from MOD06_L2 without the calibration of meteorological observations. Specifically, the correlation coefficient (r) and root mean square error (RMSE) under clear sky conditions was 0.93 and 4.71 ℃, respectively; and the r and RMSE under cloudy sky conditions was 0.89 and 5.16 ℃, respectively. The multiple regression model with the calibration of meteorological observations provided a good estimation of monthly air temperature. The cross-validation results indicate that the determination coefficient (R²) and RMSE were generally above 0.8 and below 2.5 ℃, respectively. The high accuracy of monthly T_a estimation made it possible for us to investigate the spatial and temporal distribution of T_a in Qinghai Province. To be specific, the maximum and minimum monthly air temperature occurred in July and January, which was 13.59 ℃ and -9.44 ℃, respectively. Its spatial distribution was dominated by elevation. The average adiabatic lapse rate over the whole province was 4 ℃/km, which agreed well with the ground-based meteorological observations. The analysis indicates that MODIS atmospheric profile product holds great potential for all-weather air temperature estimation. The introduction of ground-based observations can reduce significantly the systematic error of air temperature estimation. The combination of MODIS products and ground-based observations proves to be useful for accurate air temperature estimation over large areas with complex topography.

Key words: air temperature estimation, multiple regression model, MODIS, remote sensing, Qinghai Province

田圣戎, 朱文彬, 周世健. MODIS大气廓线产品在青海省空气温度遥感估算中的应用研究[J]. 地理科学进展, 2021, 40(8): 1386-1396.

TIAN Shengrong, ZHU Wenbin, ZHOU Shijian. Near surface air temperature estimation based on MODIS atmospheric profile product over Qinghai Province[J]. PROGRESS IN GEOGRAPHY, 2021, 40(8): 1386-1396.

图/表 8

图1

表1

晴天条件下瞬时气温的估算精度

方法	r	B/℃	MAE/℃	RMSE/℃	n
$T a 1$	0.88	-3.43	4.77	5.87	87908
$T a 2$	0.93	2.95	3.79	4.71
$T a 3$	0.83	-8.28	8.99	10.38

表1

图2

表2

图3

图4

图5

图6

参考文献 30

[1]	齐述华, 王军邦, 张庆员, 等. 利用MODIS遥感影像获取近地层气温的方法研究[J]. 遥感学报, 2005,9(5):570-575.
	[ Qi Shuhua, Wang Junbang, Zhang Qingyuan, et al. Study on the estimation of air temperature from MODIS data. Journal of Remote Sensing, 2005,9(5):570-575. ]
[2]	于文凭, 马明国. MODIS地表温度产品的验证研究: 以黑河流域为例[J]. 遥感技术与应用, 2011,26(6):705-712.
	[ Yu Wenping, Ma Mingguo. Validation of the MODIS land surface temperature products: A case study of the Heihe River Basin. Remote Sensing Technology and Application, 2011,26(6):705-712. ]
[3]	张丽文, 黄敬峰, 王秀珍. 气温遥感估算方法研究综述[J]. 自然资源学报, 2014,29(3):540-552.
	[ Zhang Liwen, Huang Jingfeng, Wang Xiuzhen. A review on air temperature estimation by satellite thermal infrared remote sensing. Journal of Natural Resources, 2014,29(3):540-552. ]
[4]	祝善友, 张桂欣. 近地表气温遥感反演研究进展[J]. 地球科学进展, 2011,26(7):724-730.
	[ Zhu Shanyou, Zhang Guixin. Progress in near surface air temperature retrieved by remote sensing technology. Advances in Earth Science, 2011,26(7):724-730. ]
[5]	王宾宾, 马耀明, 马伟强. 青藏高原那曲地区MODIS地表温度估算[J]. 遥感学报, 2012,16(6):1289-1309.
	[ Wang Binbin, Ma Yaoming, Ma Weiqiang. Estimation of land surface temperature retrieved from EOS/MODIS in Naqu area over Tibetan Plateau. Journal of Remote Sensing, 2012,16(6):1289-1309. ]
[6]	蔡明勇, 杨胜天, 曾红娟, 等. 基于多源空间信息的缺资料地区地表日均大气温度空间分布数据获取研究[J]. 干旱区地理, 2014,37(6):1240-1247.
	[ Cai Mingyong, Yang Shengtian, Zeng Hongjuan, et al. Muti-source spatial data based on daily average temperature simulation in data sparse regions. Arid Land Geography, 2014,37(6):1240-1247. ]
[7]	潘耀忠, 龚道溢, 邓磊, 等. 基于DEM的中国陆地多年平均温度插值方法[J]. 地理学报, 2004,59(3):366-374.
	[ Pan Yaozhong, Gong Daoyi, Deng Lei, et al. Smart distance searching-based and DEM-informed interpolation of surface air temperature in China. Acta Geographica Sinica, 2004,59(3):366-374. ]
[8]	Stahl K, Moore R D, Floyer J A, et al. Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density[J]. Agricultural and Forest Meteorology, 2006,139(3/4):224-236.
[9]	钱永兰, 吕厚荃, 张艳红. 基于ANUSPLIN软件的逐日气象要素插值方法应用与评估[J]. 气象与环境学报, 2010,26(2):7-15.
	[ Qian Yonglan, Lv Houquan, Zhang Yanhong. Application and assessment of spatial interpolation method on daily meteorological elements based on ANUSPLIN software. Journal of Meteorology and Environment, 2010,26(2):7-15. ]
[10]	Wloczyk C, Borg E, Richter R, et al. Estimation of instantaneous air temperature above vegetation and soil surfaces from Landsat 7 ETM+ data in northern Germany[J]. International Journal of Remote Sensing, 2011,32(24):9119-9136.
[11]	Pape R, Löffler J. Modelling spatio-temporal near-surface temperature variation in high mountain landscapes[J]. Ecological Modelling, 2004,178(3/4):483-501.
[12]	Sun Y J, Wang J F, Zhang R H, et al. Air temperature retrieval from remote sensing data based on thermodynamics[J]. Theoretical and Applied Climatology, 2005,80(1):37-48.
[13]	Nemani R R, Running S W. Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data[J]. Journal of Applied Meteorology, 1989,28(4):276-284.
[14]	Prihodko L, Goward S N. Estimation of air temperature from remotely sensed surface observations[J]. Remote Sensing of Environment, 1997,60:335-346.
[15]	江东, 王乃斌, 杨小唤, 等. 植被指数—地面温度特征空间的生态学内涵及其应用[J]. 地理科学进展, 2001,20(2):146-152.
	[ Jiang Dong, Wang Naibin, Yang Xiaohuan, et al. Ecological connotation and application of the vegetation index-surface temperature feature space. Progress in Geography, 2001,20(2):146-152. ]
[16]	Chen E, Allen L H Jr, Bartholic J F, et al. Comparison of winter-nocturnal geostationary satellite infrared-surface temperature with shelter-height temperature in Florida[J]. Remote Sensing of Environment, 1983,13:313-327.
[17]	Kawashima S, Ishida T, Minomura M, et al. Relations between surface temperature and air temperature on a local scale during winter nights[J]. Journal of Applied Meteorology, 2000,39(9):1570-1579.
[18]	Mendez A. Estimate ambient air temperature at regional level using remote sensing techniques[J]. Enschede, Netherlands: International Institute for Geo-Information Science and Earth Observation (ITC), 2004.
[19]	Wang W H, Liang S L, Meyers T. Validating MODIS land surface temperature products using long-term nighttime ground measurements[J]. Remote Sensing of Environment, 2008,112:623-635.
[20]	Zhu W B, Lü A F, Jia S F, et al. Retrievals of all-weather daytime air temperature from MODIS products[J]. Remote Sensing of Environment, 2017,189:152-163.
[21]	姚永慧, 张百平. 基于MODIS数据的青藏高原气温与增温效应估算[J]. 地理学报, 2013,68(1):95-107.
	[ Yao Yonghui, Zhang Baiping. MODIS-based estimation of air temperature and heating-up effect of the Tibetan Plateau. Acta Geographica Sinica, 2013,68(1):95-107. ]
[22]	Bisht G, Bras R L. Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study[J]. Remote Sensing of Environment, 2010,114(7):1522-1534.
[23]	Zhu W B, Lü A F, Jia S F. Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products[J]. Remote Sensing of Environment, 2013,130:62-73.
[24]	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.
[25]	覃志豪, Arnon Karnieli. 用NOAA-AVHRR热通道数据演算地表温度的劈窗算法[J]. 国土资源遥感, 2001,13(2):33-42.
	[ Qin Zhihao, Karnieli A. Split window algorithms for retrieving land surface temperature from NOAA-AVHRR data. Remote Sensing for Land and Resources, 2001,13(2):33-42. ]
[26]	柯灵红, 王正兴, 宋春桥, 等. 青藏高原东北部MODIS LST时间序列重建及与台站地温比较[J]. 地理科学进展, 2011,30(7):819-826.
	[ Ke Linghong, Wang Zhengxing, Song Chunqiao, et al. Reconstruction of MODIS LST time series and comparison with land surface temperature (T) among observation stations in the northeast Qinghai-Tibet Plateau. Progress in Geography, 2011,30(7):819-826. ]
[27]	Bisht G, Venturini V, Islam S, et al. Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days[J]. Remote Sensing of Environment, 2005,97:52-67.
[28]	Seddon A W R, Macias-Fauria M, Long P R, et al. Sensitivity of global terrestrial ecosystems to climate variability[J]. Nature, 2016,531:229-232.
[29]	Stisen S, Sandholt I, Nørgaard A, et al. Estimation of diurnal air temperature using MSG SEVIRI data in West Africa[J]. Remote Sensing of Environment, 2007,110:262-274.
[30]	Vancutsem C, Ceccato P, Dinku T, et al. Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa[J]. Remote Sensing of Environment, 2010,114:449-465.

天气条件	r	B/℃	MAE/℃	RMSE/℃	n
有云天	0.89	2.86	4.16	5.16	80979
全天气	0.92	2.90	3.97	4.93	168887

MODIS大气廓线产品在青海省空气温度遥感估算中的应用研究

Near surface air temperature estimation based on MODIS atmospheric profile product over Qinghai Province

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 30

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	黄华兵, 王先伟, 柳林. 城市暴雨内涝综述：特征、机理、数据与方法[J]. 地理科学进展, 2021, 40(6): 1048-1059.
[2]	刘泽星, 陈洋波. 基于RS和GIS的城市化进程中河道时空演变分析——以深圳市布吉河为例[J]. 地理科学进展, 2021, 40(4): 693-702.
[3]	蒲旭凡, 丁永建, 侯丽陶, 张小文, 张世强. 基于GF-1的青藏高原东南部积雪分布及MODIS积雪产品适用性研究[J]. 地理科学进展, 2021, 40(3): 477-486.
[4]	史潇, 王国杰, 孙明, 李玉涛, 王博妮, 沈婕. 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[J]. 地理科学进展, 2020, 39(8): 1283-1295.
[5]	廖小罕. 地理科学发展与新技术应用[J]. 地理科学进展, 2020, 39(5): 709-715.
[6]	胡栩, 聂勇, 徐霞, 蒋盛, 张镱锂. 塔里木盆地南缘和田地区土地利用变化的遥感研究[J]. 地理科学进展, 2020, 39(4): 577-590.
[7]	史卓琳, 黄昌. 河流水情要素遥感研究进展[J]. 地理科学进展, 2020, 39(4): 670-684.
[8]	唐寅, 王中根, 王婉清, 黄火键, 袁勇. 适用于遥感影像的水生态空间多功能分类体系研究[J]. 地理科学进展, 2020, 39(3): 454-460.
[9]	姜凯斯,刘正佳,李裕瑞,王永生,王昱. 黄土丘陵沟壑区典型村域土地利用变化及对区域乡村转型发展的启示[J]. 地理科学进展, 2019, 38(9): 1305-1315.
[10]	段洪涛,罗菊花,曹志刚,薛坤,肖启涛,刘东. 流域水环境遥感研究进展与思考[J]. 地理科学进展, 2019, 38(8): 1182-1195.
[11]	马明国,汤旭光,韩旭军,时伟宇,宋立生,黄静. 西南岩溶地区碳循环观测与模拟研究进展和展望[J]. 地理科学进展, 2019, 38(8): 1196-1205.
[12]	陈颖彪, 郑子豪, 吴志峰, 千庆兰. 夜间灯光遥感数据应用综述和展望[J]. 地理科学进展, 2019, 38(2): 205-223.
[13]	吴其慧, 李畅游, 孙标, 史小红, 赵胜男, 韩知明. 1986—2017年呼伦湖湖冰物候特征变化[J]. 地理科学进展, 2019, 38(12): 1933-1943.
[14]	张洪源, 吴艳红, 刘衍君, 郭立男. 近20年青海湖水量变化遥感分析[J]. 地理科学进展, 2018, 37(6): 823-832.
[15]	赵天杰. 被动微波反演土壤水分的L波段新发展及未来展望[J]. 地理科学进展, 2018, 37(2): 198-213.