Früher|Nächster

A review of spatiotemporal fusion methods for remotely sensed land surface temperature

  • role： First author第一作者
  • Affiliation：

    School of Geography and information Engineering, China University of Geosciences (Wuhan), Wuhan 430070, China

  • Email：1201910759@cug.edu.cn
  • Introduction：李冉，研究方向为遥感数据重建和时空融合。E-mail：1201910759@cug.edu.cn
LI Ran1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and information Engineering, China University of Geosciences (Wuhan), Wuhan 430070, China

    Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China

  • Email：wangmm@cug.edu.cn
  • Introduction：王猛猛，研究方向为定量遥感反演和遥感信息智能提取研究。E-mail：wangmm@cug.edu.cn
WANG Mengmeng12*,  
  • Affiliation：

    School of Geography and information Engineering, China University of Geosciences (Wuhan), Wuhan 430070, China

ZHANG Zhengjia1,  
  • Affiliation：

    Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Belvaux 4422, Luxembourg

HU Tian3,  
  • Affiliation：

    School of Geography and information Engineering, China University of Geosciences (Wuhan), Wuhan 430070, China

LIU Xiuguo1

Resümee

Remotely sensed Land Surface Temperature (LST) from a single source rarely has high temporal and spatial resolutions due to the sensors’ optical characteristics. Spatiotemporal fusion uses data from multiple sources to retrieve LST with high temporal frequency and spatial detail, and the spatiotemporal contradiction is disentangled in the fusion process. According to an in-depth study of spatiotemporal fusion, LST exhibits unique features distinct from other land surface variables. However, the inherent mechanism and potential application of LST spatio-temporal fusion have yet to be compiled and extensively explored. Based on the intersection between LST and spatiotemporal fusion, this work collects, analyzes, and summarizes the state-of-the-art developments in LST spatio-temporal fusion. The research background, principles, methods, and applications of this field are systematically elaborated. In particular, the relations and differences with the ubiquitous spatiotemporal fusion technology are emphasized.In essence, spatiotemporal fusion methods extract exquisite temporal variation of pixels from the low spatial resolution images and obtain spatial correspondence from images at various scales to predict high spatial resolution images. The spatiotemporal fusion shows great promise over homogeneous and stable land surface, but has an unsatisfactory performance over heterogeneous landscapes with unstable thermal conditions. In comparison with Land Surface Reflectance (LSR), the spatiotemporal fusion for LST can be less sensitive to the land cover classification uncertainties because of its lower spatial resolution and lower diversity among different land types, but it is difficult to achieve using the general laws for accurate prediction due to the drastic temporal variation of LST.After spatiotemporal fusion was successfully implemented in LSR, several studies adapted it to LST with some improvements based on the thermal characteristics. In the existing five categories of spatiotemporal fusion models based on weight and learning, Bayesian and hybrid models have been applied to LST. Among these models, the weight models are more mature, robust, and effective, but they cannot easily capture the temporal change of LST. Furthermore, the improvement is relatively limited based on STARFM, ESTARFM, or other classical weight models. Learning models can realize a nonlinear prediction based on the structural similarity of training data when supported by reliable network architecture and abundant training. In particular, the deep learning models have more superior ability to depict and extract the LST with weak spectral characteristics, but suitable neural networks and model parameters must be selected and optimized. Although fusion studies based on the Bayesian framework (including maximum a posterior and Bayesian maximum entropy) are relatively rare, they have shown great potential for achieving unbiased and nonlinear predictions and low-quality requirements for the initial data as LST. The hybrid models can integrate the preponderances of the above-mentioned models and acquire more flexible, efficient, and accurate prediction results compared with a single fusion model, which could be the mainstream of the future spatiotemporal fusion model.Although the spatiotemporal fusion models are consistently developed, most of them only focus on generating fused products, with a lack of quantitative and qualitative analysis with respect to the practical applications of the fused LST products, such as agriculture and ecology. In this work, the applications in this field are divided into six aspects: land temperature, sea surface temperature, agroforestry, urban heat island, public health, and others, which cover the majority of remote sensing service fields. However, the breadth and depth of the application of the LST fusion products are less than those of LSR fusion products. The mutual development between theoretical research and application demand is urgently needed.The primary impediment to the application and dissemination of spatiotemporal fusion is the data itself, as evidenced by the diversity of multi-source data, the spatial continuity of image, and the sensitivity of temperature in time series. The angular effect, unstable inversion accuracy, and dramatical diurnal variation significantly constrain their potential applications. Considering these characteristics of LST and existing defects of the spatiotemporal fusion model, this work proposed the future work prospects, such as improving LST inversion accuracy, complementing the strengths of multi-source data, employing a deep learning model, enhancing algorithm flexibility, and constructing a spatiotemporal fusion integrated procedure. The implementation of these strategies will propel the development of theoretical research and operational application of LST with the spatiotemporal fusion technology.

Schlüsselwort

remote sensing;land surface temperature;spatio-temporal fusion;dense time series;spatial downscaling

References

  1. 1.
    Alsweiss S, Jelenak Z and Chang P. 2017. Remote sensing of sea surface temperature using AMSR-2 measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9): 3948-3954
  2. 2.
    Amorós-López J, Gómez-Chova L, Alonso L, Guanter L, Zurita-Milla R, Moreno J and Camps-Valls G. 2013. Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring. International Journal of Applied Earth Observation and Geoinformation, 23: 132-141
  3. 3.
    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
  4. 4.
    Bai J, Liu S M and Hu G. 2008. Inversion and verification of land surface temperature with remote sensing TM/ETM+ data. Transactions of the CSAE, 24(9): 148-154
  5. 5.
    Belgiu M and Stein A. 2019. Spatiotemporal image fusion in remote sensing. Remote Sensing, 11(7): 818
  6. 6.
    Bhattarai N, Quackenbush L J, Dougherty M and Marzen L J. 2015. A simple Landsat-MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution. International Journal of Remote Sensing, 36(1): 115-143
  7. 7.
    Boyte S P, Wylie B K, Rigge M B and Dahal D. 2018. Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for Central Great Basin rangelands, USA. Giscience and Remote Sensing, 55(3): 376-399
  8. 8.
    Cheng Q, Liu H Q, Shen H F, Wu P H and Zhang L P. 2017. A spatial and temporal nonlocal filter-based data fusion method. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4476-4488
  9. 9.
    Cline B L. 1970. New eyes for epidemiologists: aerial photography and other remote sensing techniques. American Journal of Epidemiology, 92(2): 85-89
  10. 10.
    DeLang M, Becker J, Chang K-L, Serre M, Cooper O, Schultz M, Schroeder S, Lu X, Zhang L, Deushi M, Josse B, Keller C A, Lamarque J-F, Lin M, Liu J, Marécal V, Strode S A, Sudo K, Tilmes S, Zhang L, Cleland S E, Collins E L, Brauer M, West J J. 2021. Mapping yearly fine resolution global surface ozone through the Bayesian maximum entropy data fusion of observations and model output for 1990—2017. Environmental Science & Technology, 55(8): 4389-4398.
  11. 11.
    Ding R J and Zhao C F. 2018. Study on the merging sea surface temperature data based on optimal interpolation and bayesian maximum entropy method. Journal of Ocean Technology, 37(2): 35-42
  12. 12.
    Dong W Q and Meng J H. 2018. Review of spatiotemporal fusion model of remote sensing data. Remote Sensing for Land and Resources, 30(2): 1-11
  13. 13.
    Fu D J, Chen B Z, Wang J, Zhu X L and Hilker T. 2013. An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model. Remote Sensing, 5(12): 6346-6360
  14. 14.
    Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218
  15. 15.
    Gao H, Zhu X, Guan Q, Yang X, Yao Y, Zeng W and Peng X. 2021. cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units. IEEE Transactions on Geoscience and Remote Sensing, 60:1-16.
  16. 16.
    Gevaert C M and García-Haro F J. 2015. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sensing of Environment, 156: 34-44
  17. 17.
    Gong C, Zhou C, Xiao H S, Wu X and Zhang G. 2018. Dynamic monitoring of forest land drought in Hunan based on multisource remote sensing data fusion. Journal of Central South University of Forestry and Technology, 38(10): 27-33
  18. 18.
    Guan X D, Liu G H, Huang C, Liu Q S, Wu C S, Jin Y and Li Y F. 2017. An object-based linear weight assignment fusion scheme to improve classification accuracy using Landsat and MODIS data at the decision level. IEEE Transactions on Geoscience and Remote Sensing, 55(12): 6989-7002
  19. 19.
    Guo D Z, Shi W Z, Hao M and Zhu X L. 2020. FSDAF 2.0: improving the performance of retrieving land cover changes and preserving spatial details. Remote Sensing of Environment, 248: 111973
  20. 20.
    Guo Y L, Li Y M, Zhu L, Liu G, Wang S and Du C G. 2015. An improved unmixing-based fusion method: potential application to remote monitoring of inland waters. Remote Sensing, 7(2): 1640-1666
  21. 21.
    Hall D L and Llinas J. 1997. An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1): 6-23
  22. 22.
    Han X J, Duan S B, Huang C and Li Z L. 2019. Cloudy land surface temperature retrieval from three-channel microwave data. International Journal of Remote Sensing, 40 (5-6):1793-1807.
  23. 23.
    Hazaymeh K and Hassan Q K. 2015. Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images. Journal of Applied Remote Sensing, 9(1): 1-14
  24. 24.
    Hilker T, Wulder M A, Coops N C, Linke J, McDermid G, Masek J G, Gao F and White J C. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113(8): 1613-1627
  25. 25.
    Houborg R, McCabe M F and Gao F. 2016. A spatio-temporal enhancement method for medium resolution LAI (STEM-LAI). International Journal of Applied Earth Observation and Geoinformation, 47: 15-29
  26. 26.
    Hu H L, Cheng Y H and Gong A D. 2005. Advances in the application of remotely sensed data to the study of urban heat island. Remote Sensing for Land and Resources, 17(3): 5-9, 13
  27. 27.
    Hu T, Li H, Cao B, van Dijk A I J M, Renzullo L J, Xu Z H, Zhou J, Du Y M and Liu Q H. 2019. Influence of emissivity angular variation on land surface temperature retrieved using the generalized split-window algorithm. International Journal of Applied Earth Observation and Geoinformation, 82:1-11
  28. 28.
    Hu X R. 2018. Spatial and Temporal Distribution Characteristics of Sea Surface Temperature Based on Ensemble Kalman Filtering Fusion in the Northwest Pacific Ocean. Shanghai Ocean University
  29. 29.
    Huang B and Song H H. 2012. Spatiotemporal reflectance fusion via sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 50(10): 3707-3716
  30. 30.
    Huang B, Wang J, Song H H, Fu D J and Wong K. 2013a. Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring. IEEE Geoscience and Remote Sensing Letters, 10(5): 1011-1015
  31. 31.
    Huang B, Zhang H K, Song H H, Wang J and Song C Q. 2013b. Unified fusion of remote-sensing imagery: generating simultaneously high-resolution synthetic spatial-temporal-spectral earth observations. Remote Sensing Letters, 4(6): 561-569
  32. 32.
    Huang B and Zhao Y Q. 2017. Research status and prospect of spatiotemporal fusion of multi-source satellite remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1492-1499
  33. 33.
    Hwang T, Song C H, Bolstad P V and Band L E. 2011. Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain. Remote Sensing of Environment, 115(10): 2499-2512
  34. 34.
    Jin M L, Dickinson R E and Zhang D. 2005. The footprint of urban areas on global climate as characterized by MODIS. Journal of Climate 18(10): 1551-1565
  35. 35.
    Karnieli A, Agam N, Pinker R T, Anderson M, Imhoff M L, Gutman G G, Panov N and Goldberg A. 2010. Use of NDVI and land surface temperature for drought assessment: merits and Limitations. Journal of Climate, 23(3): 618-633
  36. 36.
    Ke Y H, Im J, Park S and Gong H L. 2016. Downscaling of MODIS one kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sensing, 8(3): 215
  37. 37.
    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
  38. 38.
    Kwan C, Budavari B, Gao F and Zhu X L. 2018. A hybrid color mapping approach to fusing MODIS and Landsat images for forward prediction. Remote Sensing, 10(4): 520
  39. 39.
    Lan T, Shao G, Tang L, Xu Z, Zhu W and Liu L. 2021. Quantifying Spatiotemporal Changes in Human Activities Induced by COVID-19 Pandemic Using Daily Nighttime Light Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 2740-2753
  40. 40.
    Li A H, Bo Y C, Zhu Y X, Guo P, Bi J and He Y Q. 2013. Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method. Remote Sensing of Environment, 135: 52-63
  41. 41.
    Li J, Li Y F, He L, Chen J and Plaza A. 2020. Spatio-temporal fusion for remote sensing data: an overview and new benchmark. Science China-Information Sciences, 63 (4): 140301
  42. 42.
    Li X D, Ling F, Foody G M, Ge Y, Zhang Y H and Du Y. 2017a. Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sensing of Environment, 196: 293-311
  43. 43.
    Li Y, Huang C L, Hou J L, Gu J, Zhu G F and Li X. 2017b. Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River basin, Northwest China. Agricultural and Forest Meteorology, 244-245: 82-97
  44. 44.
    Liao C H, Wang J F, Pritchard I, Liu J G and Shang J L. 2017. A spatio-temporal data fusion model for generating NDVI time series in heterogeneous regions. Remote Sensing, 9(11): 1125
  45. 45.
    Liao L M, Song J L, Wang J D, Xiao Z Q and Wang J. 2016. Bayesian method for building frequent Landsat-like NDVI datasets by integrating MODIS and Landsat NDVI. Remote Sensing, 8(6): 452
  46. 46.
    Liu H and Weng Q H. 2012. Enhancing temporal resolution of satellite imagery for public health studies: a case study of West Nile virus outbreak in Los Angeles in 2007. Remote Sensing of Environment, 117: 57-71
  47. 47.
    Liu J B, Ma Y, Wu Y T and Chen F. 2016. Review of methods and applications of high spatiotemporal fusion of remote sensing data. Journal of Remote Sensing, 20(5): 1038-1049
  48. 48.
    Liu W J, Zeng Y N, Li S N and Huang W. 2020. Spectral unmixing based spatiotemporal downscaling fusion approach. International Journal of Applied Earth Observation and Geoinformation, 88: 102054
  49. 49.
    Liu X, Deng C W, Wang S G, Huang G B, Zhao B J and Lauren P. 2016. Fast and accurate spatiotemporal fusion based upon extreme learning machine. IEEE Geoscience and Remote Sensing Letters, 13(12): 2039-2043
  50. 50.
    Liu Z H. 2017. Spatiotemporal Variation Characteristics of Urban Heat Islands in Hefei City, Anhui Province of China Based on Spatio-Temporal Fusion. Anhui University (刘紫涵. 2017. 基于时空融合技术的合肥市热岛效应时空演变分析. 安徽大学)
  51. 51.
    Lu M, Chen J, Tang H J, Rao Y H, Yang P and Wu W B. 2016. Land cover change detection by integrating object-based data blending model of Landsat and MODIS. Remote Sensing of Environment, 184: 374-386
  52. 52.
    Luo Y N, Guan K Y and Peng J. 2018. STAIR: a generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product. Remote Sensing of Environment, 214: 87-99
  53. 53.
    Maselli F and Rembold F. 2002. Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments. International Journal of Remote Sensing, 23(12): 2475-2488
  54. 54.
    Mizuochi H, Hiyama T, Ohta T, Fujioka Y, Kambatuku J R, Iijima M and Nasahara K N. 2017. Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: an integrated use of AMSR series, MODIS, and Landsat. Remote Sensing of Environment, 199: 370-388
  55. 55.
    Moosavi V, Talebi A, Mokhtari M H, Shamsi S R F and Niazi Y. 2015. A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature. Remote Sensing of Environment, 169: 243-254
  56. 56.
    Moreno-Martínez Á, Izquierdo-Verdiguier E, Maneta M P, Camps-Valls G, Robinson N, Muñoz-Marí J, Sedano F, Clinton N and Running S W. 2020. Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247: 111901
  57. 57.
    Nirmala D E and Vaidehi V. 2015. Comparison of pixel-level and feature level image fusion methods//Proceedings of the 2015 2nd International Conference on Computing for Sustainable Global Development. New Delhi: IEEE: 743-748.
  58. 58.
    Peng S, Tang B H, Li Z L, Wu H and Tang R L. 2016. Study of the relationship between thermal infrared directional and hemispherical radiative temperatures. Journal of Geo-Information Science, 18(1): 106-116
  59. 59.
    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
  60. 60.
    Rao C V, Rao J M, Kumar A S and Dadhwal V K. 2014. Fast spatiotemporal data fusion: merging LISS III with AWiFS sensor data. International Journal of Remote Sensing, 35(24): 8323-8344
  61. 61.
    Rao J M, Rao C V, Kumar A S, Lakshmi B and Dadhwal V K. 2015a. Spatiotemporal data fusion using temporal high-pass modulation and edge primitives. IEEE Transactions on Geoscience and Remote Sensing, 53(11): 5853-5860
  62. 62.
    Rao Y H, Zhu X L, Chen J and Wang J M. 2015b. An improved method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and Landsat TM/ETM+ images. Remote Sensing, 7(6): 7865-7991
  63. 63.
    Roy D P, Ju J C, Lewis P, Schaaf C, Gao F, Hansen M and Lindquist E. 2008. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment, 112(6): 3112-3130
  64. 64.
    Sakellariou S, Cabral P, Caetano M, Pla F, Painho M, Christopoulou O, Sfougaris A, Dalezios N and Vasilakos C. 2020. Remotely sensed data fusion for spatiotemporal geostatistical analysis of forest fire hazard. Sensors 20(17):5014
  65. 65.
    Shen H F, Meng X C and Zhang L P. 2016. An integrated framework for the spatio-temporal-spectral fusion of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7135-7148
  66. 66.
    Shen H F, Wu P H, Liu Y L, Ai T H, Wang Y and Liu X P. 2013. A spatial and temporal reflectance fusion model considering sensor observation differences. International Journal of Remote Sensing, 34(12): 4367-4383
  67. 67.
    Shi C L, Wang X H, Zhang M, Liang X J, Niu L Z, Han H Q and Zhu X M. 2019. A comprehensive and automated fusion method: the enhanced flexible spatiotemporal data fusion model for monitoring dynamic changes of land surface. Applied Sciences, 9(18): 3693
  68. 68.
    Song H H and Huang B. 2013. Spatiotemporal satellite image fusion through one-pair image learning. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 1883-1896
  69. 69.
    Song H H, Liu Q S, Wang G J, Hang R L and Huang B. 2018. Spatiotemporal satellite image fusion using deep convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3): 821-829
  70. 70.
    Sun C H. 2015. Research and Verification of Generating High Spatial and Temporal Resolution Surface Temperature Based on Fusing Multi-Source Remote Sensing Data. Xi’an University of Science and Technology (孙晨红. 2015. 多源遥感数据融合生成高时空分辨率地表温度研究与验证. 西安科技大学)
  71. 71.
    Tan J C, NourEldeen N, Mao K B, Shi J C, Li Z L, Xu T R and Yuan Z J. 2019a. Deep learning convolutional neural network for the retrieval of land surface temperature from AMSR2 data in China. Sensors, 19(13): 2987
  72. 72.
    Tan Z Y, Di L P, Zhang M D, Guo L Y and Gao M L. 2019b. An enhanced deep convolutional model for spatiotemporal image fusion. Remote Sensing, 11(24): 2898
  73. 73.
    Tan Z Y, Yue P, Di L P and Tang J M. 2018. Deriving high spatiotemporal remote sensing images using deep convolutional network. Remote Sensing, 10(7): 1066
  74. 74.
    Tao X, Liang S L, Wang D D, He T and Huang C Q. 2018. Improving satellite estimates of the fraction of absorbed photosynthetically active radiation through data integration: methodology and validation. IEEE Transactions on Geoscience and Remote Sensing, 56(4): 2107-2118
  75. 75.
    Tong X-Y, Xia G-S, Lu Q, Shen H, Li S, You S and Zhang L. 2020. Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237:111322
  76. 76.
    Wang J and Huang B. 2017. A rigorously-weighted spatiotemporal fusion model with uncertainty analysis. Remote Sensing, 9(10): 990
  77. 77.
    Wang M M, Zhang Z J, Hu T and Liu X G. 2019. A practical single-channel algorithm for land surface temperature retrieval: application to landsat series data. Journal of Geophysical Research: Atmospheres, 124(1): 299-316
  78. 78.
    Wang P J, Gao F and Masek J G. 2014. Operational data fusion framework for building frequent Landsat-like imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(11): 7353-7365
  79. 79.
    Wang Q M and Atkinson P M. 2018. Spatio-temporal fusion for daily Sentinel-2 images. Remote Sensing of Environment, 204: 31-42
  80. 80.
    Wang Q M, Blackburn G A, Onojeghuo A O, Dash J, Zhou L Q, Zhang Y H and Atkinson P M. 2017a. Fusion of Landsat 8 OLI and Sentinel-2 MSI data. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3885-3899
  81. 81.
    Wang Q M, Zhang Y H, Onojeghuo A O, Zhu X L and Atkinson P M. 2017b. Enhancing spatio-temporal fusion of MODIS and Landsat data by incorporating 250 m MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9): 1-8
  82. 82.
    Wang X F and Wang X Y. 2020. Spatiotemporal fusion of remote sensing image based on deep learning. Journal of Sensors, 2020(6): 1-11.
  83. 83.
    Wei J B, Wang L Z, Liu P, Chen X D, Li W and Zomaya A Y. 2017a. Spatiotemporal fusion of MODIS and Landsat-7 reflectance images via compressed sensing. IEEE Transactions on Geoscience and Remote Sensing, 55(12): 7126-7139
  84. 84.
    Wei J B, Wang L Z, Liu P and Song W J. 2017b. Spatiotemporal fusion of remote sensing images with structural sparsity and semi-coupled dictionary learning. Remote Sensing, 9(1): 21
  85. 85.
    Wei R. 2016. Method and Application Research on Spatial and Temporal Fusion with Muiti-Source Remote Sensing of Land Surface Temperature Data. Wuhan University (魏然. 2016. 多源遥感地表温度数据时空融合研究及应用. 武汉大学)
  86. 86.
    Weng Q H. 2009. Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4): 335-344
  87. 87.
    Weng Q H, Fu P and Gao F. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment, 145: 55-67
  88. 88.
    Wu B, Huang B, Cao K and Zhuo G H. 2017. Improving spatiotemporal reflectance fusion using image inpainting and steering kernel regression techniques. International Journal of Remote Sensing, 38(3): 706-727
  89. 89.
    Wu B, Huang B and Zhang L P. 2015a. An error-bound-regularized sparse coding for spatiotemporal reflectance fusion. IEEE Transactions on Geoscience and Remote Sensing, 53(12): 6791-6803
  90. 90.
    Wu M Q, Huang W J, Niu Z and Wang C Y. 2015b. Generating daily synthetic landsat imagery by combining landsat and MODIS data. Sensors, 15(9): 24002-24025
  91. 91.
    Wu M Q, Li H, Huang W J, Niu Z and Wang C Y. 2015c. Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring. Environmental Science: Processes and Impacts, 17(8): 1396-1404
  92. 92.
    Wu M Q, Niu Z, Wang C Y, Wu C Y and Wang L. 2012. Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. Journal of Applied Remote Sensing, 6(1): 063507
  93. 93.
    Wu P H, Shen H F, Zhang L P and Goettsche F M. 2015d. 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
  94. 94.
    Xie D F, Zhang J S, Zhu X F, Pan Y Z, Liu H L, Yuan Z M Q and Yun Y. 2016. An improved STARFM with help of an unmixing-based method to generate high spatial and temporal resolution remote sensing data in complex heterogeneous regions. Sensors, 16(2): 207
  95. 95.
    Xu C Y, Qu J J, Hao X J, Cosh M H, Prueger J H, Zhu Z L and Gutenberg L. 2018. Downscaling of surface soil moisture retrieval by combining MODIS/Landsat and in situ measurements. Remote Sensing, 10(2): 210
  96. 96.
    Xu F, Hu C, Li J, Plaza A and Datcu M. 2020. Special focus on deep learning in remote sensing image processing. Science China Information Sciences, 63(4): 140300
  97. 97.
    Xu Y, Huang B, Xu Y Y, Cao K, Guo C L and Meng D Y. 2015. Spatial and temporal image fusion via regularized spatial unmixing. IEEE Geoscience and Remote Sensing Letters, 12(6): 1362-1366
  98. 98.
    Xue J, Leung Y and Fung T. 2017. A Bayesian data fusion approach to spatio-temporal fusion of remotely sensed images. Remote Sensing, 9(12): 1310
  99. 99.
    Yang G J, Sun C H and Li H. 2015. Verification of high-resolution land surface temperature by blending ASTER and MODIS data in Heihe River basin. Transactions of the Chinese Society of Agricultural Engineering, 31(6): 193-200
  100. 100.
    Yang M. 2017. Comparison and Application of Downscale Spatial and Temporal Fusion Method for Land Surface Temperature. Xi’an University of Science and Technology (杨敏. 2017. 地表温度降尺度时空融合方法对比及其应用. 西安科技大学)
  101. 101.
    Yang M, Yang G J, Chen X N, Zhang Y F and You J N. 2018. Generation of land surface temperature with high spatial and temporal resolution based on FSDAF method. Remote Sensing for Land and Resources, 30(1): 54-62
  102. 102.
    Zhai H, Huang F and Qi H. 2020. Generating high resolution LAI based on a modified FSDAF model. Remote Sensing, 12(1): 150
  103. 103.
    Zhan W F, Chen Y H, Zhou J, Wang J F, Liu W Y, Voogt J, Zhu X L, Quan J L and Li J. 2013. Disaggregation of remotely sensed land surface temperature: literature survey, taxonomy, issues, and caveats. Remote Sensing of Environment, 131: 119-139
  104. 104.
    Zhang L F, Peng M Y, Sun X J, Cen Y and Tong Q X. 2019. Progress and bibliometric analysis of remote sensing data fusion methods (1992—2018). Journal of Remote Sensing, 23(4): 603-619
  105. 105.
    Zhang L P and Shen H F. 2016. Progress and future of remote sensing data fusion. Journal of Remote Sensing, 20(5): 1050-1061
  106. 106.
    Zhang W, Li A N, Jin H A, Bian J H, Zhang Z J, Lei G B, Qin Z H and Huang C Q. 2013. An enhanced spatial and temporal data fusion model for fusing Landsat and MODIS surface reflectance to generate high temporal Landsat-like data. Remote Sensing, 5(10): 5346-5368
  107. 107.
    Zhang Y H, Foody G M, Ling F, Li X D, Ge Y, Du Y and Atkinson P M. 2018. Spatial-temporal fraction map fusion with multi-scale remotely sensed images. Remote Sensing of Environment, 213: 162-181
  108. 108.
    Zhang Y J. 2019. Research on Landsat Surface Temperture Data Reconstruction Strategy Based on Spatio-Temporal Fusion Technology. Taiyuan University of Technology
  109. 109.
    Zhao C Y, Gao X B, Emery W J, Wang Y and Li J. 2018a. An integrated spatio-spectral-temporal sparse representation method for fusing remote-sensing images with different resolutions. IEEE Transactions on Geoscience and Remote Sensing, 56(6): 3358-3370
  110. 110.
    Zhao G H, Zhang Y N, Tan J L, Li C and Ren Y R. 2020a. A data fusion modeling framework for retrieval of land surface temperature from Landsat-8 and MODIS data. Sensors, 20(15): 4337
  111. 111.
    Zhao H, Gallo O, Frosio I and Kautz J. 2017. Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, 3(1): 47-57
  112. 112.
    Zhao W, Duan S B, Li A N and Yin G F. 2019. A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote Sensing of Environment, 221: 635-649
  113. 113.
    Zhao W, Guo Z, Yue J, Zhang X and Luo L. 2015. On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery. International Journal of Remote Sensing, 36:3368-3379.
  114. 114.
    Zhao Y, Liu D and Wei X. 2020b. Monitoring cyanobacterial harmful algal blooms at high spatiotemporal resolution by fusing Landsat and MODIS imagery. Environmental Advances 2:100008.
  115. 115.
    Zhao Y Q, Huang B and Song H H. 2018b. A robust adaptive spatial and temporal image fusion model for complex land surface changes. Remote Sensing of Environment, 208: 42-62
  116. 116.
    Zhong D T and Zhou F Q. 2018. A prediction smooth method for blending Landsat and moderate resolution imagine spectroradiometer images. Remote Sensing, 10(9): 1371
  117. 117.
    Zhou D C, Xiao J F, Bonafoni S, Berger C, Deilami K, Zhou Y Y, Frolking S, Yao R, Qiao Z and Sobrino J A. 2019a. Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sensing, 11(1): 48
  118. 118.
    Zhou F C, Li Z L, Wu H, Duan S B, Song X N and Yan G J. 2019b. A remote sensing method for retrieving land surface emissivity and temperature in cloudy areas: a case study over South China. International Journal of Remote Sensing, 40(5/6): 1724-1735
  119. 119.
    Zhou F Q and Zhong D T. 2020. Kalman filter method for generating time-series synthetic landsat images and their uncertainty from Landsat and MODIS observations. Remote Sensing of Environment, 239: 111628
  120. 120.
    Zhu X L, Cai F Y, Tian J Q and Williams T K A. 2018. Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions. Remote Sensing, 10(4): 527
  121. 121.
    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
  122. 122.
    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
  123. 123.
    Zhukov B, Oertel D, Lanzl F and Reinhackel G. 1999. Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1212-1226
  124. 124.
    Zurita-Milla R, Clevers J G P W and Schaepman M E. 2008. Unmixing-based Landsat TM and MERIS FR data fusion. IEEE Geoscience and Remote Sensing Letters, 5(3): 453-457
  125. 125.
    Zurita-Milla R, Kaiser G, Clevers J G P W, Schneider W and Schaepman M E. 2009. Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics. Remote Sensing of Environment, 113(9): 1874-1885

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website