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All issues Volume 246 (2018) MATEC Web Conf., 246 (2018) 02006 References
Open Access
Issue
MATEC Web Conf.
Volume 246, 2018
2018 International Symposium on Water System Operations (ISWSO 2018)
Article Number 02006
Number of page(s) 9
Section Parallel Session I: Water Resources System
DOI https://doi.org/10.1051/matecconf/201824602006
Published online 07 December 2018
  1. Q. Zhang, V. P Singh, P. Sun, X. Chen, X. C. Zhang, Z. X. Zhang, J. F. Li, Precipitation and stream-flow changes in China: changing patterns, causes and implications, J. Hydrol. 410, 204–21 (2011) [CrossRef] [Google Scholar]
  2. Y. T. Yang, Evapotranspiration over heterogeneous vegetated surfaces models and applications, Springer Theses Recognizing Outstanding Ph. D. Research, Tsinghua University, Beijing, China (2015) [Google Scholar]
  3. P. Bhantana, N. Lazarovitch, Evapotranspiration, crop coefficient and growth of two young pomegran-ate (Punica granatum L.) varieties under salt stress, Agr. Water Manage. 97(5), 715–722 (2010) [CrossRef] [Google Scholar]
  4. B. Zhang, S. Kang, F. Li, L. Zhang, Comparison of three evapotranspiration models to Bowen ratio-energy balance method for a vineyard in an arid desert region of northwest China, Agricultural and Forest Meteorology 149(10), 1629–1640 (2008) [CrossRef] [Google Scholar]
  5. D. Vickers, C. K. Thomas, J. C. Pettijohn, J. Martin, B. E. Law, Five years of carbon fluxes and inher-ent water-use efficiency at two semi-arid pine forests with different disturbance histories, Tellus Series Bchemical & Physical Meteorology, 64 (8), 978–988 (2011) [Google Scholar]
  6. BastiaanssenW. G. M., M. Menenti, R. A. Feddes, A. A. M Holtslag, A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation, J. Hydrol. 212–213, 198–212 (1998) [CrossRef] [Google Scholar]
  7. Z. Su, The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrology and Earth System Sciences 6, 85–99 (2002) [CrossRef] [Google Scholar]
  8. R. G. Allen, M. Tasumi, R. Trezza, Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model, J. Irrig. Drain. Eng. 133, 380–394 (2007) [CrossRef] [Google Scholar]
  9. G. B. Senay, S. Bohms, R. K. Singh, P. H. Gowda, N. M. Velpuri, H. Alemu, J. P. Verdin, Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach, J. Am. Water Resour. Assoc., 49, 577–591 (2013) [CrossRef] [Google Scholar]
  10. W. P. Kustas, J. M. Norman, A two-source approach for estimating turbulent fluxes using multiple an-gle thermal infrared observations, Water Resource Research 33, 1495–1508, (1997) [CrossRef] [Google Scholar]
  11. M. C. Anderson, J. M. Norman, G. R. Diak, W. P. Kustas, J. R. Mecikalski, A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing, Remote Sensing of Environment 60, 95–216, (1997) [CrossRef] [Google Scholar]
  12. J. D. Kalma, T. R. McVicar, M. F. McCabe, Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data, Surveys in Geophysics 29, 421–469 (2008) [CrossRef] [Google Scholar]
  13. S. Liang, K. Wang, X. Zhang, M. Wild, Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3(3), 225–240 (2010) [CrossRef] [Google Scholar]
  14. A. Kilic, R. G. Allen, R. Trezza, I. Ratcliffe, B. Kamble, C. W. Robison, D. Ozturk, Sensitivity of evapotranspiration retrievals from the 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 (2016) [CrossRef] [Google Scholar]
  15. P. H. Gowda, J. L. Chavez, P. D. Colaizzi, S. R. Evett, T. A. Howell, ET mapping for agri-cultural water management: Present status and challenges, Irrig. Sci. 26, 223–237 (2008) [CrossRef] [Google Scholar]
  16. A. P. Teresa, P. Isabel, C. Mario, C. Jose, L. S. Francisco, P. Paula, S. P. Luis, Evapotranspiration and crop coefficients for a super intensive olive orchard : An application of SIMDualKc and METRIC models using ground and satellite observations. Journal of Hydrology 519 (B), 2067–2080 (2014) [CrossRef] [Google Scholar]
  17. J. P. Burkhalter, T. C. Martin, R. G. Allen, J. Kjaersgaard, E. Wilson, R. Alvarado, J. S. Polly, J. S. Estimating crop water use via remote sensing techniques vs. conventional methods in the South Platte River basin, Colorado, Journal of the American Water Resources Association 49 (3), 498–517. (2013) [CrossRef] [Google Scholar]
  18. S. R. Evett, W. P. Kustas, P. H. Gowda, M. C. Anderson, J. H. Prueger, T. A. Howell, Overview of the Bushland Evapotranspiration and Agricultural Remote sensing EXperiment 2008 (BEAREX08): A field experiment evaluating methods for quantifying ET at multiple scales, Advances in Water Resources 50, 4–19 (2012) [CrossRef] [Google Scholar]
  19. M. C. Anderson, R. G. Allen, A. Morse, W. P. Kustas, Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources, Remote Sensing of Environment 122, 50–65 (2012) [CrossRef] [Google Scholar]
  20. K. A. Semmens, M. Anderson, W. P. Kustas, M. Velez, Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach, Remote Sensing of Environment 185(185): 155–170 (2015) [CrossRef] [Google Scholar]
  21. C. Hu, P. Tang, Automatic algorithm for relative radiometric normalization of data obtained from Landsat TM and HJ-1A/B charge-coupled device sensors, J. Appl. Remote Sens. 6, 063509 (2012) [CrossRef] [Google Scholar]
  22. X. Y. Ouyang, L. Jia, G. C. Hu, Retrieval of land surface temperature over the Heihe River Basin using HJ-1B thermal infrared data, Remote Sensing 7, 300–318 (2014) [CrossRef] [Google Scholar]
  23. R. Zhang, R. Sun, J. P. Du, T. L. Zhang, Y. Tang, H. W. Xu, S. T. Yang, W. G. Jiang, Estima-tions of net primary productivity and evapotranspiration based on HJ-1A/B data in Jinggangshan City, China, Journal of Mountain Science 10, 777–789 (2013) [CrossRef] [Google Scholar]
  24. W. Z. Zhao, B. Liu, The response of sap flow in shrubs to rainfall pulses in the desert region of China, Agricultural and Forest Meteorology 150, 1297–1306 (2010) [CrossRef] [Google Scholar]
  25. X. J. Wu, J. Zhou, H. J. Wang, Y. Li, B. Zhong, Evaluation of irrigation water use efficiency using remote sensing in the middle reach of the Heihe river, in the semi-arid Northwestern China, Hydrological Processes 29 (9), 2243–2257 (2014) [Google Scholar]
  26. Z. W. Xu, Y. F. Ma, S. M. Liu, W. J. Shi, J. M. Wang, Assessment of the energy balance closure under advective conditions and its impact using remote sensing data, Journal of applied meteorology and cli-matology 56, 127–140 (2017) [CrossRef] [Google Scholar]
  27. S. M. Liu, Z. W. Xu, W. Z. Wang, Z. Z. Jia, M. J. Zhu, J. W. Bai, J. M. Wang, A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem, Hydrology and Earth System Sciences 15 (4): 1291–1306 (2011) [CrossRef] [Google Scholar]
  28. B. Zhong, A. X. Yang, A. H. Nie, Y. J. Yao, H. Zhang, S. L. Wu, Q. H. Liu, Finer resolution land-cover mapping using multiple classifiers and multisource remote sensed data in the Heihe river Basin, IEEE Journal of Selected Topics in Applied Earth Observation s and Remote Seing 8 (10), 4973–4992 (2015) [CrossRef] [Google Scholar]
  29. N. Bhattarai, L. J. Quackenbush, J. Im, S. B. Shaw, A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models, Remote Sensing of Environment 196, 178–192 (2017) [CrossRef] [Google Scholar]
  30. R. G. Allen, R. Trezza, M. Tasumi, J. Kjaersgaard, J. Mapping Evapotranspiration at High Reso-lution Using Internalized Calibration: Application Manual for Landsat Satellite Imagery; Version 3.0., Kimberly, University of Idaho (2014) [Google Scholar]
  31. D. Long, V. P. Singh, A modified surface energy balance algorithm for land (M-SEBAL) based on a trapezoidal framework, Water Resources Research 48, W02528 (2012) [CrossRef] [Google Scholar]
  32. Y. Li, J. Zhou, H. J. Wang, D. Z. Li, Y. Z. Zhou, Q. G. Zhou, Integrating soil moisture retrieved from L-band microwave radiation into an energy balance model to improve evapotranspiration estimation on the irrigated oases of arid regions in northwest China, Agricultural and Forest Meteorology 214—215, 306–318 (2015) [CrossRef] [Google Scholar]
  33. C. L. Huang, Y. Li, J. Gu, L. Lu, X. Li, Improving Estimation of Evapotranspiration under Water-Limited Conditions Based on SEBS and MODIS Data in Arid Regions. Remote Sensing 7, 16795–16814 (2015) [CrossRef] [Google Scholar]
  34. I. Baker, L. Prihodko, A. Denning, M. Goulden, S. Miller, H. Da Rocha, Seasonal drought stress in the Amazon: reconciling models and observations. Journal of Geophysical Research Biogeosciences 113 (G1), 212–221 (2015) [Google Scholar]

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