運用機器學習回歸方法改善臺灣地區從Landsat-8與Himawari-8融合影像中的高時空地表溫度

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雅瓜納(Tri Wandi Januar) 查詢紙本館藏

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環境科技博士學位學程

論文名稱

運用機器學習回歸方法改善臺灣地區從Landsat-8與Himawari-8融合影像中的高時空地表溫度
(A Machine Learning Regression Approach for Improving High-Spatiotemporal LST from Landsat and Himawari Fused Imagery in Taiwan)

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至系統瀏覽論文 (2028-7-31以後開放)

摘要(中)

摘要
本研究旨在通過利用蘭薩特和日本向日葵衛星影像的融合，提升臺灣地區高時空解析度的地表溫度（LST）估算。采用機器學習回歸方法以提高LST估算的準確性。本研究利用蘭薩特和日本向日葵的熱紅外（TIR）影像結合，估算每小時的時序LST數據。對發射率、高程和植被指數（如NDVI）進行特徵工程，創建結合特徵進行回歸分析。利用從特徵工程獲得的分類圖進行分層隨機抽樣，得到7000個樣本的數據集，將其分為80％的訓練數據和20％的測試數據。採用支持向量回歸（SVR）算法對融合的LST進行校正，顯著提高了準確性，特別是在山區地區。結果表明，蘭薩特和日本向日葵影像的融合，結合機器學習回歸，能夠有效提升臺灣地區高時空解析度的LST估算。本研究有助於了解地表溫度變化並為氣候模擬、環境監測和城市規劃等應用提供了機。

摘要(英)

Abstract
This research aims to enhance the estimation of high-spatiotemporal Land Surface Temperature (LST) in Taiwan by leveraging the fusion of Landsat and Himawari satellite imagery. A machine learning regression approach namely Support Vector Regression (SVR) is employed to improve the accuracy of the LST estimation. The study utilizes the combination of Landsat and Himawari thermal infrared (TIR) images to estimate hourly timeseries LST data. Feature engineering is performed on emissivity, elevation, and vegetation indices (such as NDVI) to create combined features for regression analysis. Stratified random sampling is employed using a classification map obtained from feature engineering, resulting in a dataset of 7,000 samples divided into 80% training and 20% testing data. The SVR algorithm is applied to correct the fused LST, resulting in a significant improvement in accuracy, especially in mountainous areas. The results demonstrate that the fusion of Landsat and Himawari imagery, combined with machine learning regression, can effectively enhance the estimation of high-spatiotemporal LST in Taiwan. This research contributes to the understanding of surface temperature variations and offers opportunities for applications in climate modeling, environmental monitoring, and urban planning.
.

關鍵字(中)

★ 時空圖像融合
★ 機器學習
★ 支持向量回歸
★ 地表溫度
★ Landsat-8
★ Himawari-8

關鍵字(英)

★ Spatiotemporal image fusion
★ machine learning
★ support vector regression
★ Land surface temperature
★ Landsat-8
★ Himawari-8

論文目次

Table of Contents
摘要……………………………………………………………..………………..iii
Abstract…………………………………………………………………………...iv
Table of Contents…………………………………………………………………..v
Chapter 1. Introduction…………………………………………………………….1
1.1. Motivation……………………………………………………………….1
1.2. Challenges and objectives………………………………………………..9
Chapter 2. Literature Review………………………………………..……………12
2.1. Spatiotemporal Image Fusion Methods…………………..…………….12
2.1.1. STARFM……………...…………………………………………14
2.1.2. ESTARFM……………………………………………………….17
2.1.3. STAEFM…………………………..…………………………….19
2.1.4. Land Surface Temperature Retrieval…………………………….26
Chapter 3. High-Spatiotemporal Image Fusion for LST Estimation over Taiwan: A Machine Learning Regression Approach for Improved Accuracy………………..28
3.1. Background……………………………………………………………...28
3.2. Methodology…………………………………………………………….34
3.2.1. Study Area and Datasets…………………………………………35
3.2.2. Generating Reference TIR Images……………………………….35
3.2.3. Support Vector Regression………………………………………40
3.3. Results…………………………………………………………………..47
3.3.1. Reference TIR Images…………………………………………...47
3.3.1. Predicted LST in Taiwan Scale…………..………………………48
3.3.1. Hourly LST Data…………………………………………………50
3.3.2. SVR for Improved Accuracy…………………………………….52
3.4. Discussion………………………………………………………………55
Chapter 4. Summary and Future Opportunities…………………………………...57
4.1. Summary………………………………………………………………..57
4.2. Future Opportunities……………………………………………………...58
References………………………………………………………………………..59

參考文獻

References
Alidoost, F., Sharifi, M.A., & Stein, A. (2015). Region- and pixel-based image fusion for disaggregation of actual evapotranspiration. International Journal of Image and Data Fusion. doi:10.1080/19479832.2015.1055834
Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop evapotranspiration (guidelines for computing crop water requirements). FAO Irrigation and Drainage Papers – 56. In. Rome: FAO – Food and Agriculture Organization of the Unites Nations.
Allen, R., Tasumi, M., Trezza, R., Waters R., & Bastiaansses, W. (2002). SEBAL (surface energy balance algorithms for land). Advance Training and Users Manual-Idaho Implementation, Version, vol. 1, pp. 97, 2002.
Anderson, M.C., Kustas, W.P., Norman, J.M., Hain, C.R., Mecikalski, J.R., Schultz, L., Dugo, M.P.G., Cammalleri, C., d’Urso, G., Pimstein, A., & Gao, F. (2011). Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrology and Earth System Science. 15, 223-239.
Artis, D.A. & Carnahan, W.A. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment. 12, 313-329.
Berg, A., Lintner B.R., Findell, K.L., Malyshev, S., Loikith, P.C., & Gentine, P. (2014). Impact of soil moisture-atmosphere interaction on surface temperature distribution. Journal of Climate. 27, 7976-7993, doi: 10.1175/JCLI-D-13-00591.1
Beg, A.A.F., Al-Sulttani, A.H., Ochtyra, A., Jarocińska, A., & Marcinkowska, A. (2018). Estimation of evapotranspiration using SEBAL algorithm and Landsat-8 data–a case study: Tatra Mountains Region. Journal of Geological Resource and Engineering. 6, 257-270, doi:10.17265/2328-2193/2016.06.002
Cammalleri, C., Anderson, M.C., Gao, F., Hain, C.R., & Kusts, W.P. (2013). A data fusion approach for mapping daily evapotranspiration at field scale. Water Resources Research. 49, 4672-4686.
Chirouze, J., Boulet, G., Jarlan, L., Fieuzal, R., Rodriguez, J.C., Ezzahar, J., Er-Raki, S., Bigeard, G., Merlin, O., Garatuza-Payan, J., Watts, C., & Chehbouni. (2014). Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate. Hydrology and Earth System Sciences. 18, 1165-1188.
Ershadi, A. (2014). Evapotranspiration: application, scaling and uncertainty. Doctoral dissertation, University of New South Wales, Sydney, Australia.
Farhanj, F. & Akhoondzadeh, M. (2017). Fusion of Landsat-8 thermal infrared and visible bands with multi-resolution analysis contourlet methods. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science. Vol XLII-4/W4.
Gao, F., Masek, J., Schwaller, M., & 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, 2207-2218.
Gebler, S., Franssen, H.j.H., Pütz, H., Post, H., Schmidt, M., & Vereecken, H. (2015). Actual evapotranspiration and precipitation measured by lysimeters: a comparison with eddy covariance and tipping bucket. Hydrology and Earth System Sciences. 19, 2145-2161.
Huang, C.Y., Ho, H.C., & Lin, T.H. (2018). Improving the image fusion procedure for high-spatiotemporal aerosol optical depth retrieval: a case study of urban area in Taiwan. Journal of Applied Remote Sensing. 12(4), doi: 10.1117/1.JRS.12.042605
Jurgens, C. (1997). The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data. International Journal of Remote Sensing. 18:17, 3583-3594, doi:10.1080/014311697216810
Ke, Y., Im, J., Park, S., & Gong, H. (2016). Downscaling of MODIS one-kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sensing. 8, 215, doi:10.3390/rs803021
Killic, A., Allen, R., Trezza, R., Ratcliffe, I., Kamble, B., Robison, C., & Ozturk, D. (2016). Sensitivity of evapotranspiration retrievals from METRIC processing algorithm to improved radiometric resolution of Landsat 8 thermal data and to calibration bias in Landsat-7 and 8 surface temperature. Remote Sensing of Environment. 185, 198-209.
Kumar, R., Shambhavi, S., Kumar, R., Singh, Y.K., & Rawat, K.S. (2013). Evapotranspiration mapping for agricultural water management: An overview. Journal of Applied and Natural Science. 5(2), 522-534.
Li, S. & Jiang, G.M. (2018). Land surface temperature retrieval from Landsat-8 data with the generalized split-window algorithm. IEEE. 6, 18149-18162, doi::10.1109/ACCESS.2018.2818741
Meng, X.H., Evans, J.P., & McCabe, M.F. (2014). The impact of observed vegetation changes on land-atmosphere feedbacks during drought. Journal of Hydrometeorology. 15, 759-776, doi: 10.1175/JHM-D-13-0130.1
Rosas, J., Houborg, R., & McCabe, M.F. (2017). Sensitivity of Landsat 8 surface temperature estimates to atmospheric profile data: a study using MODTRAN in dryland irrigated systems. Remote Sensing. 9, 988, doi:10.3390/rs9100988
Rouse, J.W., Jr., Haas, R.H., Schell, J.A., Deering, D.W., & Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC type III final report: Greenbelt, Maryland, NASA, 371 p.
Rwasoka, D.T., Gumindoga, W., & Gwenzi, J. (2011). Estimation of actual evapotranspiration using the surface energy balance system (SEBS) algorithm in the Upper Manyame catchment in Zimbabwe. Physics and Chemistry of the Earth. 36, 736-746.
Sattari, F., Hashim, M., & Pour, A.B. (2018). Thermal sharpening of land surface temperature maps based on the impervious surface index with the TsHARP method to ASTER satellite data: A case study from the metropolitan Kuala Lumpur, Malaysia. Measurement. 125, 262-278.
Schneider, S.H. (1989). The greenhouse effect: science and policy. Science, vol, 243.
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., Alsina, M.M., & Velez, M. (2015). Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sensing of Environment. 0034-4257.
Senay, G.B., Bohms, S., Singh, R.K., Gowda, P.H., Velpuri, N.M., Alemu, H., & Verdin, J.P. (2013). Operational evapotranspiration mapping using remote sensing and weather datasets: a new parameterization for the SSEB approach. Journal of the American Water Resources Association. 49, 577-591.
Shoko, C., Dube, T., Sibanda, & M., Adelabu, S. (2015). Applying the surface energy balance system (SEBS) remote sensing model to estimate spatial variations in evapotranspiration in Southern Zimbabwe. Transactions of the Royal Society of South Africa. 70, 47-55.
Sobrino, J.A., Jiménez-Muñoz, J.C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment. 90, 434-440.
Su, Z. (2002). The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6(1), 85-99.
Sun, Z., Wei, B., Su, W., Shen, W., Wang, C., You, D., & Liu, Z. (2011). Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China. Mathematical and Computer Modelling. 64, 1086-1092.
Trezza, R., Allen, R.G., & Tasumi, M. (2013). Estimation of actual evapotranspiration along the Middle Rio Grande of New Mexico using MODIS and Landsat imagery with the METRIC model. Remote Sensing. 5, 5397-5423, doi:10.3390/rs5105397
U.S. Geological Survey. (2019). Landsat 8 (L8) data users handbook.
Wang, F., Qin, Z., Li, W., Song, C., Karnieli, A., & Zhao, S. (2014). An efficient approach for pixel decomposition to increase the spatial resolution of land surface temperature images from MODIS thermal infrared band data. Sensors. 15, 304-330.
Yang, Y., Anderson, M.C., Gao, F., Hain, C.R., Semmens, K.A., Kustas, W.P., Noormets, A., Wyne, R.H., Thomas, V.A., & Sun, G. (2017). Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion. Hydrology and Earth System Science. 21, 1017-103.
Zheng, H., Wang, Q., Zhu, X., Li, Y., & Yu, G. (2014). Hysteresis responses of evapotranspiration to meteorological factors at a diel timescale: patterns and causes. Plos One. v.9(6), e98857.
Zhu, X.L., Chen, J., Gao, F., Chen, X.H., & Masek, J.G. (2010). An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment. 114, 2610-2623.
Zitouna-Chebbi, R., Prévot, L., Chakhar, A., Abdallah, M.M.B., & Jacob, F. (2018). Observing actual evapotranspiration from flux tower eddy covariance measurements within a hilly watershed: case study of the Kamech site, Cap Bon Peninsula, Tunisia. J. Atmosphere. 9, 68, doi:10.3390/atmos9020068

指導教授

林唐煌(Tang-Huang Lin)

審核日期

2023-8-17

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