Agam, N., Kustas, W., Anderson, M., Li, F. and Colaizzi, P. 2008. Utility of Thermal Image Sharpening for Monitoring Field-Scale Evapotranspiration Over Rainfed and Irrigated Agricultural Regions. Geophysical Research Letters - GEOPHYS RES LETT, 35. doi:10.1029/2007GL032195
Agam, N., Kustas, W. P., Anderson, M. C., Li, F. and Colaizzi, P. D. J. G. R. L. 2007. Utility of thermal image sharpening for monitoring field‐scale evapotranspiration over rainfed and irrigated agricultural regions. 35(2). doi:10.1029/2007GL032195, 2008
Agam, N., Kustas, W. P., Anderson, M. C., Li, F. and Neale, C. M. J. R. S. o. E. 2007. A vegetation index based technique for spatial sharpening of thermal imagery. 107(4): 545-558.
Bahman abadi, B., Kaviani, A., daneshkar, p., and Nazari, R. 2018. Estimation of Actual Evapotranspiration Using Satellite Imageries and Single-Source and Two-Source Surface Energy Balance Algorithms in Qazvin Plain. Journal of Water Research in Agriculture. 32: 2(2): 227-246. doi:10.22092/jwra.2018.116961
Bian, Z., Roujean, J.-L., Lagouarde, J.-P., Cao, B., Li, H., Du, Y. and Liu, Q. 2020. A semi-empirical approach for modeling the vegetation thermal infrared directional anisotropy of canopies based on using vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing. 160: 136-148.
Chander, G., Markham, B. L. and Helder, D. L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment. 113(5): 893-903. doi:10.1016/j.rse.2009.01.007
Delgarm, S., Rahimpour, M., Bakhtiari, B. and Sayari, N. 2015. Comparison of actual evapotranspiration obtained from lysimeter data and SEBAL algorithm in Kerman plain by use of MODIS sensor images. Iranian Journal of Soil and Water Research. 46(2): 373-383. doi:10.22059/ijswr.2015.55941
Egdernezhad1, A., Masjedi 2, A. R., Shokouhfar 3, A. R. and Alavifazel 4, M. 2018. Evaluation of AquaCrop and WOFOST in simulating of corn yield under deficit irrigation. Journal of Plant production Sciences. 8(1): 69-82.
Gerhards, M., Schlerf, M., Mallick, K. and Udelhoven, T. 2019. Challenges and future perspectives of multi-/Hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sensing. 11(10): 1240.
https://doi.org/10.3390/rs11101240
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.
Hazaymeh, K. and Hassan, Q. K. 2015. Fusion of MODIS and Landsat-8 Surface Temperature Images: A New Approach. PLOS ONE. 10(3): e0117755. doi:10.1371/journal.pone.0117755
Hong, S.-H., Hendrickx, J. M. H. and Borchers, B. 2011. Down-scaling of SEBAL derived evapotranspiration maps from MODIS (250 m) to Landsat (30 m) scales. International Journal of Remote Sensing. 32(21): 6457-6477. doi:10.1080/01431161.2010.512929
Kustas, W. P., Norman, J. M., Anderson, M. C. and French, A. N. J. R. s. o. E. 2003. Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. 85(4): 429-440.
Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z. and Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment. 131: 14-37.
Lu, D. and Weng, Q. 2006. Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment. 104: 157-167. doi:10.1016/j.rse.2005.11.015
Mokhtari, A., Noory, H., Pourshakouri, F., Haghighatmehr, P., Afrasiabian, Y., Razavi, M. and Naeni, A. S. 2019. Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2. ISPRS Journal of Photogrammetry and Remote Sensing. 154: 231-245.
Nishida, K., Nemani, R. R., Glassy, J. M. and Running, S. W. 2003. Development of an evapotranspiration index from Aqua/MODIS for monitoring surface moisture status. IEEE Transactions on Geoscience and Remote Sensing. 41(2): 493-501.
Pan, X., Zhu, X., Yang, Y., Cao, C., Zhang, X. and Shan, L. 2018. Applicability of Downscaling Land Surface Temperature by Using Normalized Difference Sand Index. Scientific Reports. 8(1): 9530. doi:10.1038/s41598-018-27905-0
Sen, P. K. 2004. Diagnosing irrigation water resources with multi-sensor remote sensing and GIS techniques: Master of Science, Enschede, The Netherlands.
Willmott, C. J. 1982. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society. 63(11): 1309-1313.
Willmott, C. J. and Matsuura, K. 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research. 30(1): 79-82.
Xu, S., Zhao, Q., Yin, K., He, G., Zhang, Z., Wang, G. and Zhang, N. 2021. Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model. Remote Sensing. 13(6): 1186.
Zhang, Q., Chen, J. M., Ju, W., Wang, H., Qiu, F., Yang, F. and Feng, Y. 2017. Improving the ability of the photochemical reflectance index to track canopy light use efficiency through differentiating sunlit and shaded leaves. Remote Sensing of Environment. 194: 1-15.
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