臺灣博碩士論文加值系統

土壤水分和植被是地表與大氣相互作用的關鍵參數。作為土壤蒸發和冠層蒸騰的總和，可以使用地表熱通量模型和地表溫度數據來估算蒸散。通常採用熱紅外（TIR）的衛星圖像來檢索遙感技術中的地表溫度數據。 Landsat-8 OLI和TIR傳感器已廣泛用於提供30米空間分辨率的地表溫度數據。然而，溫度，濕度，風速和土壤濕度等環境因素的快速變化會影響蒸散的動態。因此，蒸發蒸騰的估算除了高空間分辨率之外還需要高時間分辨率，用於日常，晝夜或甚至每小時分析。另一方面，Landsat-8的時間分辨率為16天。因此，更高空間分辨率的衛星傳感器具有較低的時間分辨率是一個挑戰，反之亦然。先前的研究通過開發空間和時間自適應反射融合模型（STARFM）解決了這種限制。此外，由於一些其他限制，STARFM算法被修改和擴展為增強的空間和時間自適應反射融合模型（ESTARFM）。 STARFM和ESTARFM通常用於融合可見圖像。對於相同的衛星傳感器，與可見圖像相比，TIR圖像的空間分辨率通常更粗糙。因此，對TIR圖像採用時空圖像融合方法成為一項挑戰。在這項研究中，通過融合Himawari-8和Landsat-8的TIR圖像，改進了最初的STARFM以合成具有10分鐘空間分辨率的Landsat等新TIR圖像。我們的目的是比較STARFM，ESTARFM和STARFM在融合Himawari-8和Landsat-8 TIR波段時的性能。最後，改進的STARFM顯示了具有30米空間分辨率的每小時地表溫度產品的有希望的結果，其適用於估計具有高空間和時間分辨率的實際蒸發蒸騰。

Soil moisture and vegetation are key parameters in land-atmosphere interactions. As the sum of soil evaporation and canopy transpiration, Evapotranspiration (ET) can be estimated using land surface heat flux models and land surface temperature (LST) data. Thermal infrared (TIR) satellite images are generally employed to retrieve LST data in remote sensing. Landsat-8 operational land imager (OLI) and thermal infrared sensors (TIRS) can provide LST data with 30 m spatial resolution that have been widely used. However, rapid changes in environmental factors such as temperature, humidity, wind speed and soil moisture will affect to dynamic of ET. Therefore, ET estimation needs high temporal resolution beside high spatial resolution for daily, diurnal or even hourly analysis. On the other hand, temporal resolution of Landsat-8 is 16 days. Thus, it has been a challenge that higher spatial resolution satellite sensors have lower temporal resolution, and vice versa. Previous study has solved such kind of limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM). In addition, due to some other limitations, the STARFM algorithm was modified and extended as enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Both STARFM and ESTARFM are commonly used for fusing visible images. For the same satellite sensors, TIR images are generally coarser in spatial resolution compared to visible images. Therefore, it becomes a challenge to employ the spatiotemporal image fusion methods for TIR images. In this study, the original STARFM was improved to synthesize Landsat-like TIR images with 10 minutes spatial resolution by fusing TIR images of Himawari-8 advanced Himawari imager (AHI) and Landsat-8 TIRS. We aim to compare the performances of STARFM, ESTARFM and an improvement of STARFM in blending Himawari-8 and Landsat-8 TIR bands. Finally, the improved STARFM shows the promising result of hourly LST products with 30 m spatial resolution that are applicable in estimating actual ET with high spatial and temporal resolutions.

摘要 i
ABSTRACT ii
ACKNOWLEDGEMENTS iii
Table of Contents iv
1. Introduction 1
1.1. Background 1
1.2. Challenge and Objectives 3
1.3. Thesis outline 4
2. Related works 5
3. Methodology 7
3.1. Study area and datasets 7
3.1.1. Preprocessing 8
3.2. Spatiotemporal image fusion methods 8
3.2.1. Spatial and temporal adaptive fusion model (STARFM) 9
3.2.2. Enhanced spatial and temporal adaptive fusion model (ESTARFM) 11
3.2.3. Improvement of STARFM for TIR bands 13
3.3. Land surface temperature 18
3.3.1. Land surface temperature retrieval 18
3.3.2. Error assessment for fused LSTs 19
3.4. Hourly evapotranspiration estimation 20
3.4.1. ET fraction 21
3.4.1. Reference ET 24
4. Results 28
4.1. Comparison of fused images 28
4.2. Error assessment of fused TIR images 30
4.3. Application of fused LST images in hourly ET estimation 32
4.3.1. Hourly actual evapotranspiration 32
3.4.1. Correlation between ET and meteorological parameters 35
5. Discussions 36
6. Conclusions and future work 38
References 39

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