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Estimation of all-weather land surface temperature with remote sensing： Progress and challenges

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：dlryouxiang@163.com
  • Introduction：丁利荣，研究方向为全天候地表温度反演与深度学习。E-mail： dlryouxiang@163.com
DING Lirong1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

    The Yangtze Delta Region Institute (Huzhou),University of Electronic Science and Technology of China, Huzhou 313001, China

  • Email：jzhou233@uestc.edu.cn
  • Introduction：周纪，研究方向为定量遥感。E-mail： jzhou233@uestc.edu.cn
ZHOU Ji12*,  
  • Affiliation：

    Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

    Shanghai Spaceflight Institute of TT and C and Telecommunication, Shanghai 201109, China

ZHANG Xiaodong34,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

WANG Shaofei1,  
  • Affiliation：

    School of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China

TANG Wenbin5,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

WANG Ziwei1,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

MA Jin1,  
  • Affiliation：

    Chongqing Landscape and Gardening Research Institute, Chongqing 401329

AI Lijiao6,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

LI Mingsong1,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

WANG Wei1

résumé

Land Surface Temperature (LST) is an important parameter for characterizing the surface–air exchange process, which plays an important role in climate change, ecological monitoring, hydrological simulation, and other studies. The traditional LST estimated from Thermal Infrared (TIR) remote sensing is mature in terms of retrieval methods, data production, and quality control. However, the TIR LST has considerable missing data under clouds because of the limitation that the TIR radiation from the ground surface cannot penetrate the clouds. In addition, Passive Microwave (PMW) remote sensing has disadvantages, such as strip gaps and coarse spatial resolution, because of the limitations of the physical mechanisms and imaging methods. Therefore, the all-weather LST unaffected by cloudiness must be obtained to support the subsequent studies. In the present study, we review and organize the basic principles and methods of the acquisition of all-weather LST. The methods are classified into two categories: (i) all-weather LST reconstruction from effective observation and (ii) multisource data integration.The comparative analysis indicates that multisource data integration can combine the advantages of TIR, PMW, and reanalysis data. Thus, it has the highest research value and potential for further research. Multisource data integration can be employed to obtain global long-time all-weather LST products characterized by spatial and temporal continuity. The LST retrieved from PMW remote sensing suffers from coarse spatial resolution and strip gaps. However, it is still an effective method of obtaining land surface information under clouds and an important input parameter for multisource data integration. The reconstructions of all-weather LST based on effective observation only apply to small areas with cloud cover in short periods. They are not practicable for long-term cloudy areas.From the analysis and conclusion, this study also collects and analyzes information about five currently released all-weather surface temperature products. The advantages and disadvantages of the existing products are also summarized. A global all-weather LST product with high quality and spatial resolution is urgently needed by the scientific community. After reviewing the all-weather LST products, we further summarize the applications of all-weather LST. Its applications are still in their infancy. Research on the applications of all-weather LST is relatively small in the current stage. However, all-weather LST has great potential for applications when its products further mature.Finally, further study directions and theoretical development of all-weather LST are discussed and prospected. First, with PMW LST as the basis for all-weather LST, two issues must be addressed: (i) filling the PMW LST strip gap to make the PMW surface temperature a complete spatial coverage; (ii) correcting thermal sampling depth to make that PMW LST obtain the same physical meaning as TIR LST. The reason is that the inconsistent observation caused by the varying thermal sampling depth is the actual reason for the inconsistent physical meaning of PMW and TIR observation information. Second, we should further strengthen the study on estimating all-weather LST from multisource data. The current study of multisource data integration is still in the preliminary stage, and no systematic and effective integration strategy has been developed. Third, the scientific community should enhance the production, publication, and application of all-weather LST products. Few all-weather LST products can be directly applied by users. Generating all-weather LST products with global spatial and temporal continuity and high spatial resolution should be the task of an all-weather LST study. Besides improving the data quality and reliability of all-weather LST, focusing on the operability and cost of the method in practical applications is necessary to make the all-weather LST usable data, thereby truly promoting the progress of the related studies.

mots-clés

remote sensing;all-weather land surface temperature;reconstruction;interpolation;multi-source data integration

References

  1. 1.
    Aires F, Prigent C, Rossow W B and Rothstein M. 2001. A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations. Journal of Geophysical Research: Atmospheres, 106(D14): 14887-14907
  2. 2.
    Anderson M C, Norman J M, Kustas W P, Houborg R, Starks P J and Agam N. 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, 112(12): 4227-4241
  3. 3.
    André C, Ottlé C, Royer A and Maignan F. 2015. Land surface temperature retrieval over circumpolar Arctic using SSM/I-SSMIS and MODIS data. Remote Sensing of Environment, 162: 1-10
  4. 4.
    Bai L L, Long D and Yan L. 2019. Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous Agricultural Land. Water Resources Research, 55(2): 1105-1128
  5. 5.
    Basist A, Grody N C, Peterson T C and Williams C N. 1998. Using the special sensor microwave/imager to monitor land surface temperatures, wetness, and snow cover. Journal of Applied Meteorology, 37(9): 888-911
  6. 6.
    Bechtel B. 2012. Robustness of annual cycle parameters to characterize the urban thermal landscapes. IEEE Geoscience and Remote Sensing Letters, 9(5): 876-880
  7. 7.
    Cammalleri C, Anderson M C, Gao F, Hain C R and Kustas W P. 2014. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agricultural and Forest Meteorology, 186: 1-11
  8. 8.
    Catherinot J, Prigent C, Maurer R, Papa F, Jiménez C, Aires F and Rossow W B. 2011. Evaluation of “all weather” microwave-derived land surface temperatures with in situ CEOP measurements. Journal of Geophysical Research: Atmospheres, 116(D23): D23105
  9. 9.
    Chen J, Brissette F P and Leconte R. 2011. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology, 401(3/4): 190-202
  10. 10.
    Crosson W L, Al-Hamdan M Z, Hemmings S N J and Wade G M. 2012. A daily merged MODIS Aqua–Terra land surface temperature data set for the conterminous United States. Remote Sensing of Environment, 119: 315-324
  11. 11.
    Dai F N. 2016. Retrieval and Validation of Land Surface Temperature from AMSR2 Data. Chengdu: University of Electronic Science and Technology of China.
  12. 12.
    Deardorff J W. 1978. Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. Journal of Geophysical Research: Oceans, 83(C4): 1889-1903
  13. 13.
    Dickinson R E. 1988. The force–restore model for surface temperatures and its generalizations. Journal of Climate, 1(11): 1086-1097
  14. 14.
    Dong L X, Yang H, Zhang P, Tang S H and Lu Q F. 2012. Retrieval of land surface temperature and dynamic monitoring of a high temperature weather process based on FY-3A/VIRR data. Journal of Applied Meteorological Science, 23(2): 214-222
  15. 15.
    Duan S B, Han X J, Huang C, Li Z L, Wu H, Qian Y G, Gao M F and Leng P. 2020. Land surface temperature retrieval from passive microwave satellite observations: state-of-the-art and future directions. Remote Sensing, 12(16): 2573
  16. 16.
    Duan S B, Li Z L and Leng P. 2017. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data. Remote Sensing of Environment, 195: 107-117
  17. 17.
    Duan S B, Li Z L, Leng P, Han X J and Chen Y Y. 2015. Generation of an all-weather land surface temperature product from MODIS and AMSR-E data//Proceedings of SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015. Guilin: SPIE: 980816 [DOI: 10.1117/12.2207848]
  18. 18.
    Duan S B, Li Z L, Wang N, Wu H and Tang B H. 2012. Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data. Remote Sensing of Environment, 124: 15-25
  19. 19.
    Fan X M, Liu H G, Liu G H and Li S B. 2014. Reconstruction of MODIS land-surface temperature in a flat terrain and fragmented landscape. International Journal of Remote Sensing, 35(23): 7857-7877
  20. 20.
    Fily M, Royer A, Goïta K and Prigent C. 2003. A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas. Remote Sensing of Environment, 85(3): 328-338
  21. 21.
    Ford T W and Quiring S M. 2019. Comparison of contemporary in situ, model, and satellite remote sensing soil moisture with a focus on drought monitoring. Water Resources Research, 55(2): 1565-1582
  22. 22.
    Fu P, Xie Y H, Weng Q H, Myint S, Meacham-Hensold K and Bernacchi C. 2019. A physical model-based method for retrieving urban land surface temperatures under cloudy conditions. Remote Sensing of Environment, 230: 111191
  23. 23.
    Galantowicz J F, Moncet J L, Liang P, Lipton A E, Uymin G, Prigent C and Grassotti C. 2011. Subsurface emission effects in AMSR-E measurements: implications for land surface microwave emissivity retrieval. Journal of Geophysical Research: Atmospheres, 116(D17): D17105
  24. 24.
    Gao H L, Fu R, Dickinson R E and Juárez R I N. 2008. A practical method for retrieving land surface temperature from AMSR-E over the amazon forest. IEEE Transactions on Geoscience and Remote Sensing, 46(1): 193-199
  25. 25.
    Ghafarian Malamiri H R, Rousta I, Olafsson H, Zare H and Zhang H. 2018. Gap-filling of MODIS time series Land Surface Temperature (LST) products using singular spectrum analysis (SSA). Atmosphere, 9(9): 334
  26. 26.
    Ghent D J, Corlett G K, Göttsche F M and Remedios J J. 2017. Global land surface temperature from the along-track scanning radiometers. Journal of Geophysical Research: Atmospheres, 122(22): 12167-12193
  27. 27.
    Gillespie A, Rokugawa S, Matsunaga T, Cothern J S, Hook S and Kahle A B. 1998. A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1113-1126
  28. 28.
    Gong T T, Lei H M, Yang D W, Jiao Y and Yang H B. 2017. Monitoring the variations of evapotranspiration due to land use/cover change in a semiarid shrubland. Hydrology and Earth System Sciences, 21(2): 863-877
  29. 29.
    Göttsche F M and Olesen F S. 2001. Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76(3): 337-348
  30. 30.
    Gu S Y, Qiu H and Zhang W J. 2004. Retrieval of land surface microwave emissivity by using satellite-borne AMSU data. Chinese Journal of Radio Science, 19(4): 452-457
  31. 31.
    He X L, Xu T R, Bateni S M, Ek M, Liu S M and Chen F. 2020. Mapping regional evapotranspiration in cloudy skies via variational assimilation of all-weather land surface temperature observations. Journal of Hydrology, 585: 124790
  32. 32.
    Holmes T R H, De Jeu R A M, Owe M and Dolman A J. 2009. Land surface temperature from Ka band (37 GHz) passive microwave observations. Journal of Geophysical Research: Atmospheres, 114(D4): D04113
  33. 33.
    Holmes T R H, Crow W T, Hain C, Anderson M C and Kustas W P. 2015. Diurnal temperature cycle as observed by thermal infrared and microwave radiometers. Remote Sensing of Environment, 158: 110-125
  34. 34.
    Holmes T R H, Crow W T, Tugrul Yilmaz M, Jackson T J and Basara J B. 2013. Enhancing model-based land surface temperature estimates using multiplatform microwave observations. Journal of Geophysical Research: Atmospheres, 118(2): 577-591
  35. 35.
    Hu L Q, Brunsell N A, Monaghan A J, Barlage M and Wilhelmi O V. 2014. How can we use MODIS land surface temperature to validate long-term urban model simulations? Journal of Geophysical Research: Atmospheres, 119(6): 3185-3201
  36. 36.
    Huang B, Wang J, Song H H, Fu D J and Wong K K. 2013. Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring. IEEE Geoscience and Remote Sensing Letters, 10(5): 1011-1015
  37. 37.
    Hulley G, Veraverbeke S and Hook S. 2014. Thermal-based techniques for land cover change detection using a new dynamic MODIS multispectral emissivity product (MOD21). Remote Sensing of Environment, 140: 755-765
  38. 38.
    Jiménez-Muñoz J.C., Sobrino J.A., 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of geophysical research: atmospheres 108.
  39. 39.
    Jin M L. 2000. Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 2. Cloudy-pixel treatment. Journal of Geophysical Research: Atmospheres, 105(D3): 4061-4076
  40. 40.
    Jin M L and Dickinson R E. 1999. Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 1. Without clouds. Journal of Geophysical Research: Atmospheres, 104(D2): 2105-2116
  41. 41.
    Jones L A, Kimball J S, McDonald K C, Chan S T K, Njoku E G and Oechel W C. 2007. Satellite microwave remote sensing of boreal and arctic soil temperatures from AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 45(7): 2004-2018
  42. 42.
    Jonsson P and Eklundh L. 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE transactions on Geoscience and Remote Sensing, 40(8): 1824-1832
  43. 43.
    Kalma J D, McVicar T R and McCabe M F. 2008. Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4): 421-469
  44. 44.
    Kang J, Tan J L, Jin R, Li X and Zhang Y. 2018. Reconstruction of MODIS land surface temperature products based on multi-temporal information. Remote Sensing, 10(7): 1112
  45. 45.
    Kilibarda M, Hengl T, Heuvelink G B, Gräler B, Pebesma E, Perčec Tadić M and Bajat B. 2014. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. Journal of Geophysical Research: Atmospheres, 119(5): 2294-2313
  46. 46.
    Kou X K, Jiang L M, Bo Y C, Yan S and Chai L N. 2016. Estimation of land surface temperature through blending MODIS and AMSR-E data with the Bayesian maximum entropy method. Remote Sensing, 8(2): 105
  47. 47.
    Kustas W P, Nieto H, Morillas L, Anderson M C, Alfieri J G, Hipps L E, Villagarcía L, Domingo F and Garcia M. 2016. Revisiting the paper “Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective”. Remote Sensing of Environment, 184: 645-653
  48. 48.
    Lei H M, Gong T T, Zhang Y C and Yang D W. 2018. Biological factors dominate the interannual variability of evapotranspiration in an irrigated cropland in the North China Plain. Agricultural and Forest Meteorology, 250-251, 262-276
  49. 49.
    Li A H, Bo Y C, Zhu Y X, Guo P, Bi J and He Y Q. 2013a. Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method. Remote Sensing of Environment, 135: 52-63
  50. 50.
    Li H, Yang Y K, Li R B, Wang H S, Cao B, Bian Z J, Hu T, Du Y M, Sun L and Liu Q H. 2019. Comparison of the MuSyQ and MODIS Collection 6 Land surface temperature products over barren surfaces in the Heihe River Basin, China. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 8081-8094
  51. 51.
    Li J, Wang T, Zhang S W and Jiang J S. 2004. Spatial resolution enhancement of microwave radiometer using BG algorithm. Journal of Remote Sensing, 8(5): 409-413
  52. 52.
    Li W B, Zhu Y J, Hong G and Zhao B L. 1998. The acquisition of land surface temperature in eastern China Using SSM/I Data. Progress in Natural Science, 8(3): 305-313
  53. 53.
    Li X M, Zhou Y Y, Asrar G R and Zhu Z Y. 2018. Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States. Remote Sensing of Environment, 206: 84-97
  54. 54.
    Li Z L, Tang B H, Wu H, Ren H Z, Yan G J, Wan Z M, Trigo I F and Sobrino J A. 2013b. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131: 14-37
  55. 55.
    Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Xiao Z Q, Yao Y J, Yuan W P, Zhang X T, Zhao X and Zhou J. 2021. The global Land surface satellite (GLASS) product suite. Bulletin of the American Meteorological Society, 102(2): E323-E337
  56. 56.
    Liang S L, Wang K C, Zhang X T and Wild M. 2010. Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 225-240
  57. 57.
    Liu Z H, Wu P H, Duan S B, Zhan W F, Ma X S and Wu Y L. 2017. Spatiotemporal reconstruction of land surface temperature derived from FengYun geostationary satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10): 4531-4543
  58. 58.
    Long D, Bai L L, Yan L, Zhang C J, Yang W T, Lei H M, Quan J L, Meng X Y and Shi C X. 2019. Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution. Remote Sensing of Environment, 233: 111364
  59. 59.
    Long D, Yan L, Bai L L, Zhang C J, Li X Y, Lei H M, Yang H B, Tian F Q, Zeng C, Meng X Y and Shi C X. 2020. Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach. Remote Sensing of Environment, 246: 111863
  60. 60.
    Lu L, Venus V, Skidmore A, Wang T J and Luo G P. 2011. Estimating land-surface temperature under clouds using MSG/SEVIRI observations. International Journal of Applied Earth Observation and Geoinformation, 13(2): 265-276
  61. 61.
    Lu L, Zhang T J, Wang T J and Zhou X M. 2018. Evaluation of Collection-6 MODIS land surface temperature product using multi-year ground measurements in an Arid Area of Northwest China. Remote Sensing, 10(11): 1852
  62. 62.
    Lu X L, Liu R G, Liu J Y and Liang S L. 2007. Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogrammetric Engineering and Remote Sensing: Journal of the American Society of Photogrammetry, 73(10): 1129-1139
  63. 63.
    Ma J, Zhou J, Göttsche F M, Liang S L, Wang S F and Li M S. 2020. A global long-term (1981-2000) land surface temperature product for NOAA AVHRR. Earth System Science Data, 12(4): 3247-3268
  64. 64.
    Ma Y F, Liu S M, Song L S, Xu Z W, Liu Y L, Xu T R and Zhu Z L. 2018. Estimation of daily evapotranspiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data. Remote Sensing of Environment, 216: 715-734
  65. 65.
    Mao K B, Shi J C, Li Z L, Qin Z H and Jia Y Y. 2005. The land surface temperature and emissivity retrieved from The AMSR passive microwave data. Remote Sensing for Land and Resources, 17(3): 14-17
  66. 66.
    Mao K B, Shi J C, Li Z L, Qin Z H, Li M C and Xu B. 2007. A physical-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data. Science in China Series D: Earth Sciences, 50(7): 1115-1120
  67. 67.
    Martins J P A, Trigo I F, Ghilain N, Jimenez C, Göttsche F M, Ermida S L, Olesen F S, Gellens-Meulenberghs F and Arboleda A. 2019. An all-weather land surface temperature product based on MSG/SEVIRI observations. Remote Sensing, 11(24): 3044
  68. 68.
    McFarland M J, Miller R L and Neale C M U. 1990. Land surface temperature derived from the SSM/I passive microwave brightness temperatures. IEEE Transactions on Geoscience and Remote Sensing, 28: 839-845
  69. 69.
    Meng Q Y, Zhang L L, Sun Z H, Meng F, Wang L and Sun Y X. 2018. Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: a 12-year case study in Beijing, China. Remote Sensing of Environment, 204: 826-837
  70. 70.
    Metz M, Rocchini D and Neteler M. 2014. Surface temperatures at the continental scale: tracking changes with remote sensing at unprecedented detail. Remote Sensing, 6(5): 3822-3840
  71. 71.
    Morland J C, Grimes D I F and Hewison T J. 2001. Satellite observations of the microwave emissivity of a semi-arid land surface. Remote Sensing of Environment, 77(2): 149-164
  72. 72.
    Neteler M. 2010. Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote Sensing, 2(1): 333-351
  73. 73.
    Njoku E G and Li L. 1999. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz. IEEE Transactions on Geoscience and Remote Sensing, 37(1): 79-93
  74. 74.
    Østby T I, Schuler T V and Westermann S. 2014. Severe cloud contamination of MODIS Land Surface Temperatures over an Arctic ice cap, Svalbard. Remote Sensing of Environment, 142: 95-102
  75. 75.
    Owe M and Van De Griend A A. 2001. On the relationship between thermodynamic surface temperature and high-frequency (37 GHz) vertically polarized brightness temperature under semi-arid conditions. International Journal of Remote Sensing, 22(17): 3521-3532
  76. 76.
    Pan G D, Wang C, Zhang W G, Wang H M and Tian G L. 2003. Analysis of seasonal change of land cover characteristics with SSM/I Data in China. Journal of Remote Sensing, 7(6): 498-503
  77. 77.
    Parinussa R M, de Jeu R A M, Holmes T R H and Walker J P. 2008. Comparison of microwave and infrared land surface temperature products over the NAFE’06 research sites. IEEE Geoscience and Remote Sensing Letters, 5(4): 783-787
  78. 78.
    Prigent C, Jimenez C and Aires F. 2016. Toward “all weather,” long record, and real-time land surface temperature retrievals from microwave satellite observations. Journal of Geophysical Research: Atmospheres, 121(10): 5699-5717
  79. 79.
    Prigent C, Rossow W B, Matthews E and Marticorena B. 1999. Microwave radiometric signatures of different surface types in deserts. Journal of Geophysical Research: Atmospheres, 104(D10): 12147-12158
  80. 80.
    Qin Z H, Dall’Olmo G, Karnieli A and Berliner P. 2001. Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data. Journal of Geophysical Research: Atmospheres, 106(D19): 22655-22670
  81. 81.
    Qu Y Q, Zhu Z L, Montzka C, Chai L N, Liu S M, Ge Y, Liu J, Lu Z, He X L, Zheng J and Han T. 2021. Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China. Journal of Hydrology, 592: 125616
  82. 82.
    Quan J L, Chen Y H, Zhan W F, Wang J F, Voogt J and Wang M J. 2014. Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a Gaussian volume model. Remote Sensing of Environment, 149: 33-46
  83. 83.
    Quan J L, Zhan W F, Ma T, Du Y Y, Guo Z and Qin B Y. 2018. An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes. Remote Sensing of Environment, 206: 403-423
  84. 84.
    Royer A and Poirier S. 2010. Surface temperature spatial and temporal variations in North America from homogenized satellite SMMR-SSM/I microwave measurements and reanalysis for 1979-2008. Journal of Geophysical Research: Atmospheres, 115(D8): D08110
  85. 85.
    Scharlemann J P W, Benz D, Hay S I, Purse B V, Tatem A J, Wint G R W and Rogers D J. 2008. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS One, 3(1): e1408
  86. 86.
    Schollaert Uz S, Ruane A C, Duncan B N, Tucker C J, Huffman G J, Mladenova I E, Osmanoglu B, Holmes T R H, McNally A, Peters-Lidard C, Bolten J D, Das N, Rodell M, McCartney S, Anderson M C and Doorn B. 2019. Earth observations and integrative models in support of food and water security. Remote Sensing in Earth Systems Sciences, 2(1): 18-38
  87. 87.
    Semmens K A, Anderson M C, Kustas W P, Gao F, Alfieri J G, McKee L, Prueger J H, Hain C R, Cammalleri C, Yang Y, Xia T, Sanchez L, Mar Alsina M and Vélez M. 2016. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sensing of Environment, 185: 155-170
  88. 88.
    Sheffield J, Wood E F, Pan M, Beck H, Coccia G, Serrat-Capdevila A and Verbist K. 2018. Satellite remote sensing for water resources management: potential for supporting sustainable development in data-poor regions. Water Resources Research, 54(12): 9724-9758
  89. 89.
    Shen H F, Huang L W, Zhang L P, Wu P H and Zeng C. 2016. Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: a 26-year case study of the city of Wuhan in China. Remote Sensing of Environment, 172: 109-125
  90. 90.
    Shwetha H R and Kumar D N. 2016. Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN. ISPRS Journal of Photogrammetry and Remote Sensing, 117: 40-55
  91. 91.
    Sobrino J A, Jiménez-Muñoz J C, Sòria G, Ruescas A B, Danne O, Brockmann C, Ghent D, Remedios J, North P, Merchant C, Berger M, Mathieu P P and Göttsche F M. 2016. Synergistic use of MERIS and AATSR as a proxy for estimating Land Surface Temperature from Sentinel-3 data. Remote Sensing of Environment, 179: 149-161
  92. 92.
    Song L S, Liu S M, Kustas W P, Nieto H, Sun L, Xu Z W, Skaggs T H, Yang Y, Ma M G, Xu T R, Tang X G and Li Q P. 2018. Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale. Remote Sensing of Environment, 219: 72-88
  93. 93.
    Sruthi S and Aslam M A M. 2015. Agricultural drought analysis using the NDVI and Land surface temperature data; a case study of raichur district. Aquatic Procedia, 4: 1258-1264
  94. 94.
    Stull R B. 1988. An Introduction to Boundary Layer Meteorology. Dordrecht: Springer
  95. 95.
    Sun L, Anderson M C, Gao F, Hain C, Alfieri J G, Sharifi A, McCarty G W, Yang Y, Yang Y, Kustas W P and McKee L, 2017a. Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach. Water Resources Research, 53(7): 5298-5319
  96. 96.
    Sun L, Chen Z X, Gao F, Anderson M, Song L S, Wang L M, Hu B and Yang Y, 2017b. Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data. Computers and Geosciences, 105: 10-20
  97. 97.
    Tagesson T, Horion S, Nieto H, Zaldo Fornies V, Mendiguren González G, Bulgin C E, Ghent D and Fensholt R. 2018. Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters. Remote Sensing of Environment, 206: 424-441
  98. 98.
    Tang W B, Xue D J, Long Z Y, Zhang X D and Zhou J. 2021. Near-real-time estimation of 1-km all-weather land surface temperature by integrating satellite passive microwave and thermal infrared observations. IEEE Geoscience and Remote Sensing Letters, 19: 7001305
  99. 99.
    Tomlinson C J, Chapman L, Thornes J E and Baker C. 2011. Remote sensing land surface temperature for meteorology and climatology: a review. Meteorological Applications, 18(3): 296-306
  100. 100.
    Trigo I F, Monteiro I T, Olesen F and Kabsch E. 2008. An assessment of remotely sensed land surface temperature. Journal of Geophysical Research: Atmospheres, 113(D17): D17108
  101. 101.
    Udahemuka G, Van den Bergh F, Van Wyk B J and Van Wyk M A. 2008. Robust fitting of diurnal brightness temperature cycles: pattern recognition special edition. South African Computer Journal, 2008(40): 31-36
  102. 102.
    Voogt J A and Oke T R. 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3): 370-384
  103. 103.
    Wan Z M. 2008. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sensing of Environment, 112(1): 59-74
  104. 104.
    Wan Z M. 2014. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment, 140: 36-45
  105. 105.
    Wan Z M and Dozier J. 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 892-905
  106. 106.
    Wang K C and Liang S L. 2009. Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface Longwave radiation observations at SURFRAD sites. Remote Sensing of Environment, 113(7): 1556-1565
  107. 107.
    Wang S F, Zhou J, Lei T J, Wu H, Zhang X D, Ma J and Zhong H L. 2020. Estimating land surface temperature from satellite passive microwave observations with the traditional neural network, deep belief network, and convolutional neural network. Remote Sensing, 12(17): 2691
  108. 108.
    Weiss D J, Atkinson P M, Bhatt S, Mappin B, Hay S I and Gething P W. 2014. An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 98: 106-118
  109. 109.
    Weng Q H and Fu P. 2014. Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data. Remote Sensing of Environment, 140: 267-278
  110. 110.
    Wentz F J, Gentemann C, Smith D and Chelton D. 2000. Satellite measurements of sea surface temperature through clouds. Science, 288(5467): 847-850
  111. 111.
    Wilson A M and Jetz W. 2016. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biology, 14(3): e1002415
  112. 112.
    Wu P H, Shen H F, Zhang L P and Göttsche F M. 2015. Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature. Remote Sensing of Environment, 156: 169-181
  113. 113.
    Wu P H, Yin Z X, Zeng C, Duan S B, Göttsche F M, Ma X S, Li X H, Yang H and Shen H. 2021. Spatially continuous and high-resolution land surface temperature product generation: a review of reconstruction and spatiotemporal fusion techniques. IEEE Geoscience and Remote Sensing Magazine, 9(3): 112-137
  114. 114.
    Xia H P, Chen Y H, Li Y and Quan J L. 2019. Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures. Remote Sensing of Environment, 224: 259-274
  115. 115.
    Xu S and Cheng J. 2021. A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254: 112256
  116. 116.
    Xu S, Cheng J and Zhang Q. 2019. Reconstructing all-weather land surface temperature using the Bayesian Maximum entropy method over the Tibetan Plateau and Heihe River Basin. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3307-3316
  117. 117.
    Yang G, Sun W W, Shen H F, Meng X C and Li J L. 2019. An integrated method for reconstructing daily MODIS land surface temperature data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(3): 1026-1040
  118. 118.
    Yoo C, Im J, Cho D, Yokoya N, Xia J S and Bechtel B. 2020. Estimation of all-weather 1 km MODIS Land surface temperature for humid summer days. Remote Sensing, 12(9): 1398
  119. 119.
    Yu Y Y, Privette J L and Pinheiro A C. 2008. Evaluation of split-window land surface temperature algorithms for generating climate data records. IEEE Transactions on Geoscience and Remote Sensing, 46(1): 179-192
  120. 120.
    Zeng C, Shen H F, Zhong M L, Zhang L P and Wu P H. 2015. Reconstructing MODIS LST based on multitemporal classification and robust regression. IEEE Geoscience and Remote Sensing Letters, 12(3): 512-516
  121. 121.
    Zhan W F, Zhou J, Ju W M, Li M C, Sandholt I, Voogt J and Yu C. 2014. Remotely sensed soil temperatures beneath snow-free skin-surface using thermal observations from tandem polar-orbiting satellites: an analytical three-time-scale model. Remote Sensing of Environment, 143: 1-14
  122. 122.
    Zhang A Z and Jia G S. 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sensing of Environment, 134: 12-23
  123. 123.
    Zhang C J, Long D, Zhang Y C, Anderson M C, Kustas W P and Yang Y. 2021a. A decadal (2008-2017) daily evapotranspiration data set of 1 km spatial resolution and spatial completeness across the North China Plain using TSEB and data fusion. Remote Sensing of Environment, 262: 112519
  124. 124.
    Zhang G L, Xiao X M, Dong J W, Kou W L, Jin C, Qin Y W, Zhou Y T, Wang J, Menarguez M A and Biradar C. 2015. Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data. ISPRS Journal of Photogrammetry and Remote Sensing, 106: 157-171
  125. 125.
    Zhang L F, Jiao W Z, Zhang H M, Huang C P and Tong Q X. 2017. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sensing of Environment, 190: 96-106
  126. 126.
    Zhang X D. 2020. Estimation of All-weather Land Surface Temperature from Multi-source Satellite Remote Sensing Observations. Chengdu: University of Electronic Science and Technology of China (张晓东. 2020. 多源遥感协同下的全天候地表温度估算研究. 成都: 电子科技大学) [DOI: 10.27005/d.cnki.gdzku.2020.004684]
  127. 127.
    Zhang X D, Zhou J, Göttsche F M, Zhan W F, Liu S M and Cao R Y. 2019. A method based on temporal component decomposition for estimating 1-km all-weather land surface temperature by merging satellite thermal infrared and passive microwave observations. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4670-4691
  128. 128.
    Zhang X D, Zhou J, Liang S L, Chai L N, Wang D D and Liu J. 2020. Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 321-344
  129. 129.
    Zhang X D, Zhou J, Liang S L and Wang D D. 2021b. A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature. Remote Sensing of Environment, 260: 112437
  130. 130.
    Zhao T J, Zhang L X, Shi J C and Jiang L M. 2011. A physically based statistical methodology for surface soil moisture retrieval in the Tibet Plateau using microwave vegetation indices. Journal of Geophysical Research: Atmospheres, 116(D8) [DOI: 10.1029/2010JD015229]
  131. 131.
    Zhao W and Duan S B. 2020. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data. Remote Sensing of Environment, 247: 111931
  132. 132.
    Zhou F C, Song X N and Li Z L. 2014. Progress of land surface temperature retrieval based on passive microwave remote sensing. Remote Sensing for Land and Resources, 26(1): 1-7
  133. 133.
    Zhou J, Chen Y H, Zhang X and Zhan W F. 2013. Modelling the diurnal variations of urban heat islands with multi-source satellite data. International Journal of Remote Sensing, 34(21): 7568-7588
  134. 134.
    Zhou J, Dai F N, Zhang X D, Zhao S J and Li M S. 2015. Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass. International Journal of Applied Earth Observation and Geoinformation, 34: 35-50
  135. 135.
    Zhou J, Liang S L, Cheng J, Wang Y J and Ma J. 2019. The GLASS land surface temperature product. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 493-507
  136. 136.
    Zhou J, Zhang X D, Zhan W F, Gottsche F M, Liu S M, Olesen F S, Hu W X and Dai F N. 2017a. A thermal sampling depth correction method for land surface temperature estimation from satellite passive microwave observation over Barren Land. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4743-4756
  137. 137.
    Zhou L M, Tian Y H, Baidya Roy S, Thorncroft C, Bosart L F and Hu Y L. 2012. Impacts of wind farms on land surface temperature. Nature Climate Change, 2(7): 539-543
  138. 138.
    Zhou W, Peng B and Shi J C. 2017b. Reconstructing spatial–temporal continuous MODIS land surface temperature using the DINEOF method. Journal of Applied Remote Sensing, 11(4): 046016
  139. 139.
    Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2623
  140. 140.
    Zhu X L, Helmer E H, Gao F, Liu D S, Chen J and Lefsky M A. 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172: 165-177

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