融合土地覆盖和土壤水分产品的近地表空气温度空间化方法

doi:10.12082/dqxxkx.2020.200078

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (10): 2023-2037.doi: 10.12082/dqxxkx.2020.200078

• 专栏：城乡生态环境综合监测 • 上一篇下一篇

融合土地覆盖和土壤水分产品的近地表空气温度空间化方法

高亮¹^,²(), 杜鑫¹, 李强子¹^,^*(), 王红岩¹, 张源¹, 王思远¹^,²

1.中国科学院空天信息创新研究院,北京 100101
2.中国科学院大学,北京 100049

收稿日期:2020-02-17 修回日期:2020-05-12 出版日期:2020-10-25 发布日期:2020-12-25
通讯作者: 李强子 E-mail:gaoliang@aircas.ac.cn;liqz@aircas.ac.cn
作者简介:高亮（1994— ）,男,甘肃定西人,硕士生,主要从事农业遥感和农业气象灾害方面的研究。E-mail:gaoliang@aircas.ac.cn
基金资助:
国家重点研发计划资助项目(2017YFD0300404-1);国家重点研发计划资助项目(2017YFD0300402)

A Near-surface Air Temperature Spatialization Method Integrating Landuse and Soil Moisture Products

GAO Liang¹^,²(), DU Xin¹, LI Qiangzi¹^,^*(), WANG Hongyan¹, ZHANG Yuan¹, WANG Siyuan¹^,²

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-02-17 Revised:2020-05-12 Online:2020-10-25 Published:2020-12-25
Contact: LI Qiangzi E-mail:gaoliang@aircas.ac.cn;liqz@aircas.ac.cn
Supported by:
National Key Research and Development Program of China(2017YFD0300404-1);National Key Research and Development Program of China(2017YFD0300402)

摘要/Abstract

摘要：

空气温度是评价人居环境的重要指标,与人类的生产生活息息相关;其观测对于水文、环境、生态和气候变化等方面的研究具有重要意义。传统的大范围空气温度观测数据一般通过气象站点获取,但由于气象观测站点空间分布离散稀疏的特点,所获取的数据不能精确描述空间连续的空气温度变化情况。因此,实现基于遥感数据的近地表空气温度精准估算具有重要的现实意义。本研究基于精细的地表覆盖类型、空间连续的土壤水分、地表温度（LST）数据,并结合其他辅助数据,构建了近地表空气温度空间化模型,并对近地表空气温度影响因子进行评估,发现地表覆盖类型对近地表空气温度的影响最大,土壤水分为最活跃的影响因素,经验证,模型精度较高,R²接近0.85,RMSE为0.5℃。本研究获取的精确空间连续的近地表空气温度信息,能够充分表达其空间异质性,为农业气象灾害灾变过程监测、农作物生长过程模拟、区域气候变化分析等研究提供良好的近地表空气温度数据支撑。

关键词: 近地表空气温度, 地表温度, 空气温度影响要素, 机器学习, 土地覆盖, 土壤水分, 空间化, 变量重要性分析

Abstract:

Air temperature is an important attribute for evaluating the living environment, and its studies and observations are closely related to human production and life. Air temperature observation data is of great significance for the study of hydrology, environment, ecology, and climate change. Traditional description of large-scale air temperature is generally obtained through meteorological stations. As affected by land surface condition and atmospheric state, the air temperature is spatially heterogeneous. However, due to the sparse spatial distribution of meteorological station sites, the data obtained from these meteorological stations cannot accurately describe the continuous spatial variation of air temperature across large areas. Hence, accurate inversion of near-surface air temperature based on remote sensing data is regarded as an effective and reasonably practicable solution. There are already some studies about obtaining spatially continuous near-surface air temperature using land surface temperature and other remote sensing data. In this study, we have used the remote sensing data, specifically the precise surface coverage type and spatially continuous soil moisture data, as the new input to improve the accuracy of temperature inversion. On this basis, we built a near-surface air temperature spatialization model using Land Surface Temperature (LST), land cover, soil moisture, land surface temperature, NDVI, DEM, aspect, and slope as the influencing factors. In order to fit the complex relationship between air temperature and its influencing factors, we chose four widely used machine learning algorithms and compared their accuracy to select the most reasonable model. At the same time, we also validated the results and evaluated the contribution of the influencing factors. Based on the results of the designed experiments, we found that precise surface cover type and spatially continuous soil moisture data played the most important role in near-surface air temperature spatialization model. The surface cover type has the greatest influence on the near-surface air temperature, and soil moisture is the most active influencing factor. The model validation results showed that the spatialization model has a relatively high accuracy, with an R²value close to 0.85, and a RMSE of 0.5℃. Comparing with traditional methods, the results of near-surface air temperature spatialization model in our study could express more refined spatial distribution pattern. The high precision near-surface air temperature inversion model proposed by our research is expected to provide effective data support to the study on the dynamic monitoring of agricultural meteorological disasters, simulation of crop growth processes, and analysis of regional climate change.

Key words: Near-surface air temperature, Land surface temperature, Factors affecting air temperature, Machine learning, Land cover, soil moisture, spatialization, importance analysis

高亮, 杜鑫, 李强子, 王红岩, 张源, 王思远. 融合土地覆盖和土壤水分产品的近地表空气温度空间化方法[J]. 地球信息科学学报, 2020, 22(10): 2023-2037.DOI:10.12082/dqxxkx.2020.200078

GAO Liang, DU Xin, LI Qiangzi, WANG Hongyan, ZHANG Yuan, WANG Siyuan. A Near-surface Air Temperature Spatialization Method Integrating Landuse and Soil Moisture Products[J]. Journal of Geo-information Science, 2020, 22(10): 2023-2037.DOI:10.12082/dqxxkx.2020.200078

图/表 11

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参考文献 53

[1]	Prihodko L, Goward S N. Estimation of air temperature from remotely sensed surface observations[J]. Remote Sensing of Environment, 1997,60(3):335-346.
[2]	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(2):449-465.
[3]	崔晓临, 程贇, 张露, 等. 基于DEM修正的MODIS地表温度产品空间插值[J]. 地球信息科学学报, 2018,20(12):1768-1776.
	[ Cui X L, Cheng Y, Zhang L, et al. Spatial interpolation of MODIS land surface temperature products based on DEM correction[J]. Jounal of Geo-information Science, 2018,20(12):1768-1776. ]
[4]	Lu N, Liang S, Huang G, et al. Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature[J]. Remote Sensing of Environment, 2018,211:48-58.
[5]	Szymanowski M, Kryza M, Spallek W. Regression-based air temperature spatial prediction models: An example from Poland[J]. Meteorologische Zeitschrift, 2013,22(5):577-585.
[6]	Zhao D, Zhang W, Shi J X. A neural network algorithm to retrieve nearsurface air temperature from landsat ETM+ imagery over the Hanjiang River Basin, China; proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, F, 2007 [C]. IEEE.
[7]	Burrough P A, Mcdonnell R, Mcdonnell R A, et al. Principles of geographical information systems[M]. Oxford: Oxford University Press, 2015.
[8]	Li Z L, Tang B H, Wu H, et al. Satellite-derived land surface temperature: Current status and perspectives[J]. Remote Sensing of Environment, 2013,131:14-37.
[9]	郑文武, 曾永年. 地表温度的多源遥感数据反演算法对比分析[J]. 地球信息科学学报, 2011,13(6):840-847.
	[ Zheng W W, Zeng Y N. Comparative analysis of two land surface temperature retrieval algorithms based on multi-source remote sensing data[J]. Journal of Geo-information Science, 2011,13(6):840-847. ]
[10]	陈冰倩, 张友水, 程璟媛, 等. 福州市地表温度热点及时空变化分析[J]. 地球信息科学学报, 2019,21(5):710-719.
	[ Chen B Q, Zhang Y S, Cheng J Y, Zhao X. The hot spot and spatiotemporal changes of the land surface temperature in Fuzhou[J]. Journal of Geo-information Science, 2019,21(5):710-719. ]
[11]	李娅丽, 汪小钦, 陈芸芝, 等. 福建省地表温度与植被覆盖度的相关性分析[J]. 地球信息科学学报, 2019,21(3):445-454.
	[ Li Y L, Wang X Q, Chen Y Z, Wang M M. The correlation analysis of land surface temperature and fractional vegetation coverage in Fujian province[J]. Journal of Geo-information Science, 2019,21(3):445-454. ]
[12]	李召良, 段四波, 唐伯惠, 等. 热红外地表温度遥感反演方法研究进展[J]. 遥感学报, 2016,20(5):899-920.
	[ Li Z L, Duan S B, Tang B H, et al. Review of methods for land surface temperature derived from thermal infrared remotely sensed data[J]. Journal of Remote Sensing, 2016,20(5):899-920. ]
[13]	李召良, 唐伯惠, 唐荣林, 等. 地表温度热红外遥感反演理论与方法[J]. 科学观察, 2017,12(6):57-59.
	[ Li Z L, Tang B H, Tang R L, et al. Theory and method of surface infrared thermal remote sensing inversion[J]. Science Focus, 2017,12(6):57-59. ]
[14]	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, 2004,80(1):37-48.
[15]	白琳, 徐永明, 何苗, 等. 基于随机森林算法的近地表气温遥感反演研究[J]. 地球信息科学学报, 2017,19(3):390-397.
	[ Bai L, Xu YM, He M, et al. Remote Sensing Inversion of near surface air temperature based on random forest[J]. Journal of Geo-information Science, 2017,19(3):390-397. ]
[16]	Cresswell M P, Morse A P, Thomson M C, et al. Estimating surface air temperatures, from Meteosat land surface temperatures, using an empirical solar zenith angle model[J]. International Journal of Remote Sensing, 1999,20(6):1125-1132.
[17]	Florio E N, Lele S R, Chi C Y, et al. Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: A statistical approach[J]. International Journal of Remote Sensing, 2010,25(15):2979-2994.
[18]	徐伟燕, 孙睿, 金志凤, 等. 基于MODIS数据的近地表气温估算[J]. 气象与环境科学, 2015,38(1):1-6.
	[ Xu W Y, Sun R, Jin Z F, et al. Estimation of Near Surface Air Temperature Based on MODIS data[J]. Meteorological and Environmental Sciences, 2015,38(1):1-6. ]
[19]	曲培青, 施润和, 刘朝顺, 等. 基于MODIS地表参数产品和地理数据的近地层气温估算方法评价——以安徽省为例[J]. 国土资源遥感, 2011,91(4):78-82.
	[ Qu P Q, Shi R H, Liu C S, et al. The Evaluation of MODIS data and geographic data for estimating near surface air temperature[J]. Remote Sending For Land & Resources 2011,91(4):78-82. ]
[20]	帅晨, 沙晋明, 林金煌, 等. 不同下垫面遥感指数与地温关系的空间差异性研究[J]. 地球信息科学学报, 2018,20(11):1657-1666.
	[ Shuai C, Sha J M, Lin J H, et al. Spatial difference of the relationship between remote sensing index and land surface temperature under different underlying surfaces[J]. Journal of Geo-information Science, 2018,20(11):1657-1666. ]
[21]	李羽滢. 基于夏季微气候优化的成都市绿道景观设计研究[D]. 成都:西南交通大学, 2019.
	[ Li Y Y. Chengdu greenway landscape design based on summer microclimate optimization in hot and humid areas[D]. Chengdu: SouthWest Jiaotong University, 2019. ]
[22]	Cheng K S, Su Y F, Kuo F T, et al. Assessing the effect of landcover changes on air temperatu×re using remote sensing images: A pilot study in northern Taiwan[J]. Landscape and Urban Planning, 2008,85(2):85-96.
[23]	Flerchinger G N, Reba M L, Link T E, et al. Modeling temperature and humidity profiles within forest canopies[J]. Agricultural and Forest Meteorology, 2015,213:251-262.
[24]	Pape R, LöFFLER J. Modelling spatio-temporal near-surface temperature variation in high mountain landscapes[J]. Ecological Modelling, 2004,178(3-4):483-501.
[25]	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(2):262-274.
[26]	Shamir E, Georgakakos K P. MODIS Land Surface Temperature as an index of surface air temperature for operational snowpack estimation[J]. Remote Sensing of Environment, 2014,152:83-98.
[27]	Wan Z, Hook S, Hulley G. MOD11A1 MODIS/terra land surface temperature/emissivity daily L3 global 1 km SIN grid V006[M]. Washington DC: NASA EOSDIS LP DAAC, 2015: 10-50.
[28]	Koike T, Nakamura Y, Kaihotsu I, et al. Development of an advanced microwave scanning radiometer (AMSR-E) algorithm for soil moisture and vegetation water content[J]. Proceedings of Hydraulic Engineering, 2004(2), 48:217-222.
[29]	胡媛媛, 仲雷, 马耀明, 等. 青藏高原典型下垫面地表能量通量的模型估算与验证[J]. 高原气象, 2018,37(6):1499-1510.
	[ Hu Y Y, Zhong L, Ma Y M, et al. Model esimation and validation of the surface energy fluxes at typical underlying surfaces over the Qinghai-Tibetan Plateau[J]. Plateau Meteorology, 2018,37(6):1499-1510. ]
[30]	张舒婷, 段四波, 幸泽峰, 等. 地表组分温度遥感反演算法研究进展[J]. 中国农业信息, 2019,31(1):11-23.
	[ Zhang S T, Duan S B, Xin Z F, et al. Progress in land surface component temperature retrieval algorithms from remote sensing data[J]. China Agricultural Informatics, 2019,31(1):11-23. ]
[31]	Gong P, Wang J, Yu L, et al. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data[J]. International Journal of Remote Sensing, 2013,34(7):2607-2654.
[32]	Van D E, Griend A A, OWE M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces[J]. International Journal of Remote Sensing, 2007,14(6):1119-1131.
[33]	Didan K. MOD13A2 MODIS/terra vegetation indices 16-Day L3 global 1km SIN grid V006[M]. Washington DC : NASA EOSDIS LP DAAC. 2015: 10-45.
[34]	Danielson J J, Gesch D B. Global multi-resolution terrain elevation data 2010 (GMTED2010)[M]. US Department of the Interior, US Geological Survey, 2011.
[35]	Corbari C, Sobrino J A, Mancini M, et al. Land surface temperature representativeness in a heterogeneous area through a distributed energy-water balance model and remote sensing data[J]. Hydrology and Earth System Sciences, 2010,14(10):2141-2151.
[36]	王倩倩, 覃志豪, 王斐. 基于多源遥感数据反演地表温度的单窗算法[J]. 地理与地理信息科学, 2012,8(3):24-26, 62, 113.
	[ Wang Q Q, Qin Z H, Wang F. Improved case-based reasoning based cellular automaton for simulating land cover change[J]. Geography and Geo-information Science, 2012,8(3):24-26, 62, 113. ]
[37]	Chuan Y Z, Zhong R N, Guo D C. Methods for modelling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian mountains, China[J]. Ecological Modelling, 2005,189(1-2):209-220. doi: 10.1016/j.ecolmodel.2005.03.016
[38]	王奕森, 夏树涛. 集成学习之随机森林算法综述[J]. 信息通信技术, 2018,12(1):49-55.
	[ Wang Y S, Xia S T. A survey of Random Forests Algorithms[J]. Information and Communications Technologies, 2018,12(1):49-55. ]
[39]	杨浩浩. 几种机器学习算法及其集成模型在回归问题中的应用与比较[D]. 兰州:兰州大学, 2018.
	[ Yang H H. The application and comparison of several machine learning algorithms and their integrated models in regression problems[D]. Lanzhou: Lan Zhou University, 2018. ]
[40]	Elith J, Leathwich J R, Hastle T. A working guide to boosted regression trees[J]. J Anim Ecol, 2008,77(4):802-813. pmid: 18397250
[41]	Cao L J. Support vector machines experts for time series forecasting[J]. Neurocomputing, 2003,51:321-339.
[42]	郭云开, 刘雨玲, 张晓炯, 等. 利用辐射传输模型和随机森林回归反演LAI[J]. 测绘工程, 2019,28(6):17-21,29.
	[ Guo Y K, Liu Y L, Zhang X T, et al. LAI inversion using radiation transfer model and random forest regression[J]. Engineering of Surveying and Mapping, 2019,28(6):17-21,29. ]
[43]	Awad M, Khannna R. Efficient learning machines[M]. Berlin: Springer, 2015: 67-80.
[44]	Yeh C Y, Huang C W, Lee S J. A multiple-kernel support vector regression approach for stock market price forecasting[J]. Expert Systems with Applications, 2011,38(3):2177-2186.
[45]	陈天昕. 基于AdaBoost与SVM集成算法的高炉炉温状态解析[D]. 南昌:江西财经大学, 2019.
	[ Chen T X. Analysis of blast furnace temperature state based on AdaBoost and SVM integrated algorithms[D]. Nanchang: Jiangxi University of Finance and Economics, 2019. ]
[46]	张曼, 刘旭华, 何雄奎, 等. 岭回归在近红外光谱定量分析及最优波长选择中的应用研究[J]. 光谱学与光谱分析, 2010,30(5):1214-1217.
	[ Zhang M, Liu X H, He X K, et al. Study on the application of ridge regression to near-infrared Sspectroscopy quantitative analysis and optimum wavelength selection[J]. Spectroscopy and Spectral Analysis, 2010,30(5):1214-1217. ]
[47]	Van Niel T G, Mcvicar T R, Datt B. On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification[J]. Remote Sensing of Environment, 2005,98(4):468-480.
[48]	李军龙, 张剑, 张丛, 等. 气象要素空间插值方法的比较分析[J]. 草业科学, 2006,23(8):6-11.
	[ Li J L, Zhang J, Zhang C, et al. Comparative analysis of spatial interpolation methods of meteorological elements[J]. Pratacultural Science, 2006,23(8):6-11. ]
[49]	马秀霞, 黄领梅, 沈冰. 陕西省月平均气温空间插值方法研究[J]. 水资源与水工程学报, 2017,28(5):100-105.
	[ Ma X X, Huang L M, Shen B. Study on spatial interpolation method of monthly mean temperature in Shaanxi Province[J]. Journal of Water Resources and Water Engineering, 2017,28(5):100-105. ]
[50]	杨永川, 杨轲, 王志浩, 等. 空间插值法在热环境流动观测中的应用[J]. 中南大学学报(自然科学版), 2012,43(9):3741-3748.
	[ Yang Y C, Yang K, Wang Z H, et al. Application of spatial interpolation in moving observations of thermal environment[J]. Journal of Central South University (Science and Technology), 2012,43(9):3741-3748. ]
[51]	Louppe G. Understanding random forests: From theory to practice[J]. Eprint Arxiv, 2014.10.13140/2.1.1570.5928
[52]	Jiang R, Tang W, Wu X, et al. A random forest approach to the detection of epistatic interactions in case-control studies[J]. BMC Bioinformatics, 2009,10(1):S65.
[53]	GRöMPING U. Variable importance assessment in regression: linear regression versus random forest[J]. The American Statistician, 2009,63(4):308-319.

融合土地覆盖和土壤水分产品的近地表空气温度空间化方法

A Near-surface Air Temperature Spatialization Method Integrating Landuse and Soil Moisture Products

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[13]	刘艳霞, 冯莉, 田慧慧, 阳少奇. 中国气候舒适度时空分布特征分析[J]. 地球信息科学学报, 2020, 22(12): 2338-2347.
[14]	杨丹, 周亚男, 杨先增, 郜丽静, 冯莉. LSTM支持下时序Sentinel-1A数据的太白山区植被制图[J]. 地球信息科学学报, 2020, 22(12): 2445-2455.
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