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地表温度時空間融合モデル性能比較：地表環境、空間スケール差異およびデータ相関の影響分析

  • role： First author第一作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

  • Email：cxy15390627@cug.edu.cn
  • Introduction：陈欣远，研究方向为遥感数据重建与时空融合。E-mail： cxy15390627@cug.edu.cn
CHEN Xinyuan,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

SHE Chuchu,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, 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。E-mail：wangmm@cug.edu.cn
WANG Mengmeng*,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

ZHANG Zhengjia,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

LI Ran,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

LI Yangyang,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

LIU Xiuguo

サマリー

時空間融合は、現在画像の高空間分解能と高時間分解能という二つの特性を組み合わせる最も効果的な方法であり、精緻なリモートセンシングデータ製品の生産に重要な意義を持つ。しかし、ほとんどの時空間融合モデルは地表反射率や植生指数を基に開発されており、地表面温度（LST、Land Surface Temperature）にはあまり適用されていない。また、既存の地表温度時空間融合モデルの評価では、異なるデータ組み合わせやデータ相関がモデルの性能に与える影響が十分に考慮されていない。本論文は4種類の時空間融合モデル（ESTARFM、STRUM、FSDAF、EDCSTFN）を基に、各モデルの地表温度融合結果を比較し、地表環境、データの空間スケールの違いおよびデータ相関が融合結果に及ぼす影響を分析した。結果は以下の通りである。 (1) EDCSTFNモデルはLST融合精度が最も高く、次いでFSDAFおよびESTARFMモデルであり、STRUMモデルの精度は比較的劣る。ESTARFMおよびEDCSTFNモデルの融合画像は視覚的効果が良好であり、STRUMおよびFSDAFモデルの融合画像は比較的滑らかであった。 (2) 4モデルともに地物構造が単一の領域で融合効果が最も理想的であり、次にフェノロジー変化が顕著な領域であり、複雑な地表被覆タイプの領域では融合効果が良くなかった。EDCSTFNモデルは異なる地表環境下でも高い精度を維持し、特に複雑な地表被覆タイプの領域で優れていたが、単純な地物構造の領域ではESTARFMモデルに劣った。 (3) 空間スケールの差が大きくなるにつれて、ESTARFM、STRUMおよびFSDAFモデルの融合精度は規則的に低下したが、EDCSTFNモデルには低下傾向がなかった。 (4) データ相関が低下するにつれて、4モデルすべての融合精度は規則的に低下し、その中でEDCSTFNモデルは比較的安定性とロバスト性を示した。

キーワード

時空間融合、地表温度、ESTARFM（拡張空間・時間適応反射率融合モデル）、STRUM（空間・時間アンミキシングモデル）、FSDAF（柔軟な時空間データ融合）、EDCSTFN（拡張深層畳み込みネットワーク）

References

  1. 1.
    Bateni S M and Entekhabi D. 2012. Relative efficiency of land surface energy balance components. Water Resources Research, 48(4): W04510
  2. 2.
    Chen B, Huang B and Xu B. 2017. Multi-source remotely sensed data fusion for improving land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 124: 27-39
  3. 3.
    Chen G S, Lu H L, Zou W T, Li L H, Emam M, Chen X B, Jing W P, Wang J and Li C. 2023. Spatiotemporal fusion for spectral remote sensing: a statistical analysis and review. Journal of King Saud University-Computer and Information Sciences, 35(3): 259-273
  4. 4.
    Chen J M and Liu J. 2020. Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sensing of Environment, 237: 111594
  5. 5.
    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
  6. 6.
    Ding L R, Zhou J, Li Z L, Zhu X M, Ma J, Wang Z W, Wang W and Tang W B. 2023. Near-real-time estimation of hourly all-weather land surface temperature by fusing reanalysis data and geostationary satellite thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing, 61: 5003918
  7. 7.
    Duan S B, Li Z L, Gao C X, Zhao W, Wu H, Qian Y G, Leng P and Gao M F. 2020. Influence of adjacency effect on high-spatial-resolution thermal infrared imagery: implication for radiative transfer simulation and land surface temperature retrieval. Remote Sensing of Environment, 245: 111852
  8. 8.
    Gao F, Anderson M C, Zhang X Y, Yang Z W, Alfieri J G, Kustas W P, Mueller R, Johnson D M and Prueger J H. 2017. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sensing of Environment, 188: 9-25
  9. 9.
    Gao F, Hilker T, Zhu X L, Anderson M, Masek J, Wang P J and Yang Y. 2015. Fusing Landsat and MODIS data for vegetation monitoring. IEEE Geoscience and Remote Sensing Magazine, 3(3): 47-60
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Huang B and Song H H. 2012. Spatiotemporal reflectance fusion via sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 50(10): 3707-3716
  14. 14.
    Huang B, Zhang H K, Song H H, Wang J and Song C Q. 2013. Unified fusion of remote-sensing imagery: generating simultaneously high-resolution synthetic spatial-temporal-spectral earth observations. Remote Sensing Letters, 4(6): 561-569
  15. 15.
    Lei C Y, Meng X C and Shao F. 2021. Spatio-temporal fusion quality evaluation based on “Point”-“Line”-“Plane” aspects. National Remote Sensing Bulletin, 25(3): 791-802
  16. 16.
    Li R, Wang M M, Zhang Z J, Hu T and Liu X G. 2022. A review of spatiotemporal fusion methods for remotely sensed land surface temperature. National Remote Sensing Bulletin, 26(12): 2433-2450
  17. 17.
    Li R B, Li H, Hu T, Bian Z J, Liu F J, Cao B, Du Y M, Sun L and Liu Q H. 2023. Land surface temperature retrieval from sentinel-3A SLSTR data: comparison among split-window, dual-window, three-channel, and dual-angle algorithms. IEEE Transactions on Geoscience and Remote Sensing, 61: 5003114
  18. 18.
    Li S L, Wang J L, Li D C, Ran Z X and Yang B. 2021. Evaluation of Landsat 8-like land surface temperature by fusing Landsat 8 and MODIS land surface temperature product. Processes, 9(12): 2262
  19. 19.
    Li X H, Wang L Y, Cheng Q, Wu P H, Gan W X and Fang L N. 2019. Cloud removal in remote sensing images using nonnegative matrix factorization and error correction. ISPRS Journal of Photogrammetry and Remote Sensing, 148: 103-113
  20. 20.
    Li Y, Huang C L, Hou J L, Gu J, Zhu G F and Li X. 2017. 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
  21. 21.
    Li Y T, Wu H, Li Z L, Duan S B and Ni L. 2020. Evaluation of spatiotemporal fusion models in land surface temperature using polar-orbiting and geostationary satellite data//IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa: IEEE: 236-239
  22. 22.
    Liu M L, Ke Y H, Yin Q, Chen X W and Im J. 2019. Comparison of five spatio-temporal satellite image fusion models over landscapes with various spatial heterogeneity and temporal variation. Remote Sensing, 11(22): 2612
  23. 23.
    Liu S M, Li X, Xu Z W, Che T, Xiao Q, Ma M G, Liu Q H, Jin R, Guo J W, Wang L X, Wang W Z, Qi Y, Li H Y, Xu T R, Ran Y H, Hu X L, Shi S J, Zhu Z L, Tan J L, Zhang Y and Ren Z G. 2018. The Heihe Integrated Observatory Network: a basin-scale land surface processes observatory in China. Vadose Zone Journal, 17(1): 1-21
  24. 24.
    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
  25. 25.
    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
  26. 26.
    Mao K B, Ma Y, Tan X L, Shen X Y, Liu G, Li Z L, Chen J M and Xia L. 2017. Global surface temperature change analysis based on MODIS data in recent twelve years. Advances in Space Research, 59(2): 503-512
  27. 27.
    Meng L H, Liu H J, Zhang X L, Ren C Y, Ustin S, Qiu Z C, Xu M Y and Guo D. 2019. Assessment of the effectiveness of spatiotemporal fusion of multi-source satellite images for cotton yield estimation. Computers and Electronics in Agriculture, 162: 44-52
  28. 28.
    Meng L H, Liu H J, Zhang X L, Xu M Y, Guo D and Pan Y. 2018. Cotton yield estimation model based on fusion image from MODIS and Landsat data//2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Hangzhou: IEEE: 1-5
  29. 29.
    Peng S S, Piao S L, Zeng Z Z, Ciais P, Zhou L M, Li L Z X, Myneni R B, Yin Y and Zeng H. 2014. Afforestation in China cools local land surface temperature. Proceedings of the National Academy of Sciences of the United States of America, 111(8): 2915-2919
  30. 30.
    Silva B N, Khan M and Han K. 2018. Load balancing integrated least slack time-based appliance scheduling for smart home energy management. Sensors, 18(3): 685
  31. 31.
    Sobrino J A, del Frate F, Drusch M, Jiménez-Muñoz J C, Manunta P and Regan A. 2016. Review of thermal infrared applications and requirements for future high-resolution sensors. IEEE Transactions on Geoscience and Remote Sensing, 54(5): 2963-2972
  32. 32.
    Song L S, Ding Z H, Kustas W P, Xu Y H, Zhao G L, Liu S M, Ma M G, Xue K J, Bai Y and Xu Z W. 2022. Applications of a thermal-based two-source energy balance model coupled to surface soil moisture. Remote Sensing of Environment, 271: 112923
  33. 33.
    Tan Z Y, Di L P, Zhang M D, Guo L Y and Gao M L. 2019. An enhanced deep convolutional model for spatiotemporal image fusion. Remote Sensing, 11(24): 2898
  34. 34.
    Tang Y J, Wang Q M and Atkinson P M. 2023. Filling then spatio-temporal fusion for all-sky MODIS land surface temperature generation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 1350-1364
  35. 35.
    Tang Y J, Wang Q M, Tong X H and Atkinson P M. 2024. Generation of 100-m, hourly land surface temperature based on spatio-temporal fusion. IEEE Transactions on Geoscience and Remote Sensing, 62: 5001716
  36. 36.
    Wang M M, He C, Zhang Z J, Hu T, Duan S B, Mallick K, Li H and Liu X G. 2023. Evaluation of three land surface temperature products from Landsat series using in situ measurements. IEEE Transactions on Geoscience and Remote Sensing, 61: 5000119
  37. 37.
    Wang Q M and Atkinson P M. 2018. Spatio-temporal fusion for daily Sentinel-2 images. Remote Sensing of Environment, 204: 31-42
  38. 38.
    Wang Q M, Tang Y J, Tong X H and Atkinson P M. 2024. Filling gaps in cloudy Landsat LST product by spatial-temporal fusion of multi-scale data. Remote Sensing of Environment, 306: 114142
  39. 39.
    Wang T, Tang R L, Li Z L, Tang B H, Wu H, Jiang Y Z and Liu M. 2018. A comparison of two spatio-temporal data fusion schemes to increase the spatial resolution of mapping actual evapotranspiration//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE: 7023-7026
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    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
  44. 44.
    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 F. 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
  45. 45.
    Xian G, Shi H, Zhou Q, Auch R, Gallo K, Wu Z T and Kolian M. 2022. Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data. Remote Sensing of Environment, 269: 112803
  46. 46.
    Xiao J, Aggarwal A K, Duc N H, Arya A, Rage U K and Avtar R. 2023. A review of remote sensing image spatiotemporal fusion: challenges, applications and recent trends. Remote Sensing Applications: Society and Environment, 32: 101005
  47. 47.
    Xie D H, Gao F and Li L Y. 2018. Assessment of predictive ability of STARFM based on different Modis-Landsat image pair date//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE: 7247-7250
  48. 48.
    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
  49. 49.
    Yin T G, Inglada J and Osman J. 2012. Time series image fusion: application and improvement of STARFM for land cover map and production//Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE: 378-381
  50. 50.
    Zhang B H, Zhang L, Xie D, Yin X L, Liu C J and Liu G. 2015. Application of synthetic NDVI time series blended from Landsat and MODIS data for grassland biomass estimation. Remote Sensing, 8(1): 10
  51. 51.
    Zhang M, Lin H, Long X R and Cai Y T. 2021. Analyzing the spatiotemporal pattern and driving factors of wetland vegetation changes using 2000-2019 time-series Landsat data. Science of the Total Environment, 780: 146615
  52. 52.
    Zhou D C, Xiao J F, Bonafoni S, Berger C, Deilami K, Zhou Y Y, Frolking S, Yao R, Qiao Z and Sobrino A J. 2018. Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sensing, 11(1): 48
  53. 53.
    Zhou J X, Chen J, Chen X H, Zhu X L, Qiu Y A, Song H H, Rao Y H, Zhang C S, Cao X and Cui X H. 2021. Sensitivity of six typical spatiotemporal fusion methods to different influential factors: a comparative study for a normalized difference vegetation index time series reconstruction. Remote Sensing of Environment, 252: 112130
  54. 54.
    Zhou Y J, Liu T X, Batelaan O, Duan L M, Wang Y X, Li X and Li M Y. 2023. Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland. Ecological Indicators, 146: 109892
  55. 55.
    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
  56. 56.
    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
  57. 57.
    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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