热红外遥感及其在农业旱情监测中的应用研究进展
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摘要
【目的】梳理目前热红外遥感研究进展,探讨热红外遥感技术在农业旱情监测方面的应用现状,为热红外遥感技术在土壤水分反演和旱情监测方面的应用探明发展道路。【方法】文章通过系统梳理和总结国内外热红外遥感及其在农业旱情监测中的应用研究进展,分析了目前研究存在的问题,并对未来热红外遥感在农业旱灾监测中的应用进行了展望。【结果】热红外遥感已经形成多源遥感数据和多时空分辨率数据并存的局面,地表温度遥感定量反演和地表蒸散发遥感估算已经取得较大进展,但热红外遥感数据产品系统生产仍然面临许多问题,尤其是时空尺度、云影响、像元间可比性和反演精度等问题,仍然需要开展深入的研究;目前农业旱情遥感监测已经取得了较大进展,国内外学者提出了多种不同的表征指数监测方法,但由于热红外遥感存在的许多固有问题,农业旱情遥感监测仍面临许多挑战,尤其是从热红外遥感前沿问题出发开展农业旱情遥感监测模型研究。【结论】在对地观测大数据时代背景下,热红外遥感及其在农业旱情遥感监测中的应用,迫切需要进一步深入研究全天候高精度地表温度遥感反演、高精度农田蒸散遥感定量估算、时空尺度转换与多尺度监测、旱情监测机理模型构建等前沿学术问题,同时,这些问题也是农业旱情遥感监测获得突破性进展的重要方向。热红外遥感理论与方法的发展,将有力地推动我国农业旱情监测精度的提高,为现代精准农业发展保驾护航。
[Purpose]The current research progress of thermal infrared remote sensing is summarized,the agricultural drought monitoring methods of thermal infrared remote sensing technology are illustrated and discussed to explore a development track for the thermal infrared remote sensing technology in soil moisture retrieval and agricultural drought monitoring. [Methods] In this paper,the problems and research progress of thermal infrared remote sensing and its application in agricultural drought monitoring is systematically analyzed,Furthermore,future development direction of the thermal infrared remote sensing in agricultural drought monitoring are discussed. [Result] Multi-source thermal infrared remote sensing data having a variety of different spatial and temporal resolution are produced. Great progress has been made in quantitative retrieval of land surface temperature and surface evaporation estimation,but the production of product system for thermal infrared remote sensing data still faces some problems,especially in comparability of pixel level,time and space scales,cloud effect and retrieval precision. Therefore,further research is still needed. Great progress has been made in remote sensing agricultural drought monitoring,a variety of characterization index monitoring methods were proposed,but due to many inherent problems of thermal infrared remote sensing,still face many challenges in agricultural drought monitoring by remote sensing,especially the agricultural drought monitoring model research from the angle of the thermal infrared remote sensing research frontier problems. [Conclusion]In the context of earth observation big data era,for the thermal infrared remote sensing and its application in agricultural drought monitoring is need further in-depth study of the frontier academic problems,such as high-accuracy quantitative retrieval of all-weather land surface temperature,farmland evapotranspiration estimation in high accuracy,the transform of different space-time scales,multi-scale drought monitoring and model building of monitoring mechanism,etc. At the same time these problems are also an important breakthrough direction for agricultural drought monitoring. The development of thermal infrared remote sensing theory and method will strongly promote the improvement of agricultural drought monitoring accuracy in China and support the development of modern precision in agriculture.
[Purpose]The current research progress of thermal infrared remote sensing is summarized,the agricultural drought monitoring methods of thermal infrared remote sensing technology are illustrated and discussed to explore a development track for the thermal infrared remote sensing technology in soil moisture retrieval and agricultural drought monitoring. [Methods] In this paper,the problems and research progress of thermal infrared remote sensing and its application in agricultural drought monitoring is systematically analyzed,Furthermore,future development direction of the thermal infrared remote sensing in agricultural drought monitoring are discussed. [Result] Multi-source thermal infrared remote sensing data having a variety of different spatial and temporal resolution are produced. Great progress has been made in quantitative retrieval of land surface temperature and surface evaporation estimation,but the production of product system for thermal infrared remote sensing data still faces some problems,especially in comparability of pixel level,time and space scales,cloud effect and retrieval precision. Therefore,further research is still needed. Great progress has been made in remote sensing agricultural drought monitoring,a variety of characterization index monitoring methods were proposed,but due to many inherent problems of thermal infrared remote sensing,still face many challenges in agricultural drought monitoring by remote sensing,especially the agricultural drought monitoring model research from the angle of the thermal infrared remote sensing research frontier problems. [Conclusion]In the context of earth observation big data era,for the thermal infrared remote sensing and its application in agricultural drought monitoring is need further in-depth study of the frontier academic problems,such as high-accuracy quantitative retrieval of all-weather land surface temperature,farmland evapotranspiration estimation in high accuracy,the transform of different space-time scales,multi-scale drought monitoring and model building of monitoring mechanism,etc. At the same time these problems are also an important breakthrough direction for agricultural drought monitoring. The development of thermal infrared remote sensing theory and method will strongly promote the improvement of agricultural drought monitoring accuracy in China and support the development of modern precision in agriculture.
引文
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[3]田国良,柳钦火,陈良富.热红外遥感(第二版)2014.北京:电子工业出版社,2014.
[4]李瑞,刘涛,武威,等.农业图像监测技术的应用与展望.中国农机化学报,2016,(12):168~173.
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[19]Wan Z M and Li Z L.A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data.IEEE Transactions on Geoscience and Remote Sensing,1997,35(4):980~996.
[20]Caselles V,Coll C and Valor E.Land surface emissivity and temperature determination in the whole HAPEX-Sahel area from AVHRR data.International Journal of Remote Sensing,1997,18(5):1009~1027.
[21]Becker F,Li Z L.Temperature-independent spectral indices in thermal infrared bands.Remote Sensing of Environment,1990,32(1):17~33.
[22]徐希孺,陈良富,庄家礼.基于多角度热红外遥感的混合像元组分温度演化反演方法.中国科学(D辑),2001(1):81~88.
[23]李召良,Stoll,仁华,等.利用ATSR数据分解土壤和植被温度的研究.中国科学:技术科学,2000,30(s1):27~38.
[24]Sobrino J A and Cuenca J.Angular variation of thermal infrared emissivity for some natural surfaces from experimental measurements.Applied Optics,1999,38(18):3931~3936.
[25]Borel C C.Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data.International Journal of Remote Sensing,2008,29(17/18):5029~5045.
[26]Wang N,Wu H,Nerry F,et al.Temperature and Emissivity Retrievals From Hyperspectral Thermal Infrared Data Using Linear Spectral Emissivity Constraint.IEEE Transactions on Geoscience&Remote Sensing,2011,49(4):1291~1303.
[27]高彦春,龙笛.遥感蒸散发模型研究进展.遥感学报,2008,12(3):515~528.
[28]郑有飞,陈鹏,吴荣军,等.地表蒸散的遥感估算模型及其在农业干旱监测中的应用.生态学杂志,2011,30(4):837~844.
[29]Bastiaanssen W G M,Pelgrum H,Wang J,et al.A remote sensing surface energy balance algorithm for land(SEBAL).:Part 2:Validation.Journal of Hydrology,1998,212(1-4):213~229.
[30]Bastiaanssen W.SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin Turkey.Journal of hydrology,2000,229,1:87~100.
[31]Su Z.The Surface Energy Balance System(SEBS)for estimation of turbulent heat fluxes.Hydrology and Earth System Sciences Discussions,2002,6,1:85~100.
[32]Shuttleworth W J,Wallace J S.Evaporation from sparse crops-an energy combination theory.Quarterly Journal of the Royal Meteorological Society,1985,111(469):839~855.
[33]Shuttleworth W J,Gurney R J.The theoretical relationship between foliage temperature and canopy resistance in sparse crops.Quarterly Journal of the Royal Meteorological Society,1990,116(492):497~519.
[34]隋洪智,田国良,李付琴.农田蒸散双层模型及其在干旱遥感监测中的应用.遥感学报,1997,1(3):220~224.
[35]Norman J M,Kustas W P,Humes K S.Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature.Agricultural&Forest Meteorology,1995,77(3):263~293.
[36]Anderson MC,Kustas WP,Norman JM.Up scaling flux observations from local to continental scales using thermal remote sensing.Agronomy Journal,2007,99:240~254.
[37]Mecikalski JR,Diak GR,Anderson MC,et al.Estimating fluxes on continental scales using remotely sensed data in an atmospheric-land exchange model.Journal of Applied Meteorology,1999,38:1352~1369.
[38]Watson K,Pohn H A,Offield T W.Thermal Inertia Mapping From Nimbus Satellite Data.Proceedings of The International Symposium on Remote Sensing of Environment,1972,14(3):321.
[39]Watson K,Pohn H A.Thermal inertia mapping from satellites discrimination of geologic units in Oman.Journal of Research of U.S.Geological Survey,1974,2(2):147~158.
[40]Rosema A,Bijleveld J H.TELL-US test of an algorithm for the determination of soil moisture and evaporation from remotely sensed surface temperature.E.A.R.S.,Kanaalweg 1,Delft,The Netherlands,1977.
[41]Price J C.The Potential of Remote Sensed Thermal Infrared Data to Infer Surface Soil Moisture and Evaporation.Water Resources Research,1980,16(4):787~795.
[42]Price J C.On the Analysis of Thermal Infrared Imagery.The Limited Utility of Apparent Thermal Inertia.Remote Sensing of Environment,1985,18:59~73.
[43]Pratt D A.A Calibration Procedure for Fourier Series Thermal Inertia Models.Photogram metric Engineering and Remote Sensing,1980,46(4):529~538.
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