이전|다음

지표면 온도 시공간 융합 모델 성능 비교: 지표 환경, 공간 규모 차이 및 데이터 상관성 영향 분석

  • 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

추상적인

시공간 융합은 현재 영상의 높은 공간 해상도와 높은 시간 해상도 두 가지 속성을 결합하는 가장 효과적인 방법으로, 정밀 원격 탐사 데이터 제품 생산에 중요한 의미를 가진다. 하지만 대다수의 시공간 융합 모델은 지표 반사율과 식생 지수를 기반으로 개발되었으며, 지표면 온도(LST, Land Surface Temperature)에는 상대적으로 적게 적용되고 있다. 또한, 기존의 지표면 온도 시공간 융합 모델 평가에서는 다양한 데이터 조합 및 데이터 상관관계가 모델 성능에 미치는 영향을 충분히 고려하지 않았다. 이에 본 논문은 네 가지 유형의 시공간 융합 모델(ESTARFM, STRUM, FSDAF, EDCSTFN)을 기반으로 각 모델의 지표면 온도 융합 결과를 비교하고, 지표 환경, 데이터 공간 규모 차이 및 데이터 상관관계가 융합 결과에 미치는 영향을 분석하였다. 결과는 다음과 같다: (1) EDCSTFN 모델이 LST 융합 정확도가 가장 높았으며, 그 다음이 FSDAF와 ESTARFM 모델이고, STRUM 모델의 정확도는 상대적으로 낮았다; ESTARFM과 EDCSTFN 모델의 융합 영상은 시각적 효과가 우수했으며, STRUM과 FSDAF 모델의 융합 영상은 상대적으로 부드러웠다. (2) 네 가지 모델 모두 단일 지상 구조 지역에서 융합 효과가 가장 이상적이었고, 그 다음은 뚜렷한 생리 변화 지역이며, 복잡한 지표 피복 유형 지역에서는 융합 효과가 좋지 않았다. EDCSTFN 모델은 다양한 지표 환경에서 높은 수준의 정확도를 유지했으며, 특히 복잡한 지표 피복 유형 지역에서 우수했으나, 단순한 지상 구조 지역에서는 ESTARFM 모델보다 성능이 떨어졌다. (3) 공간 규모 차이가 커질수록 ESTARFM, STRUM 및 FSDAF 모델의 융합 정확도는 규칙적으로 감소하는 반면, EDCSTFN 모델은 이런 감소 추세가 없었다. (4) 데이터 상관관계가 낮아질수록 네 가지 모델 모두 융합 정확도가 규칙적으로 감소하였으며, EDCSTFN 모델은 상대적으로 안정성과 강인성을 보였다.

키워드

시공간 융합, 지표면 온도, ESTARFM (향상된 공간 및 시간 적응 반사 융합 모델), STRUM (공간 및 시간 분해 모델), FSDAF (유연한 시공간 데이터 융합), EDCSTFN (향상된 심층 합성곱 신경망)

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

더 읽기

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