The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
A review of remote sensing based actual evapotranspiration estimation
Abstract
Evapotranspiration is a major component of the global water cycle and provides a critical nexus between terrestrial water, carbon and surface energy exchanges. Evapotranspiration is inherently difficult to measure and predict especially at large spatial scales. Remote sensing provides a cost‐effective method to estimate evapotranspiration at regional to global scales. In the past three decades a large number of studies on remote sensing based evapotranspiration estimation have emerged. This review summarizes the basic theories underpinning current remote sensing based evapotranspiration estimation methods. It also lays out the development history of these methods and compares their advantages and limitations. Several key directions for further study are identified and discussed, including identification of uncertainty sources in remote sensing evapotranspiration models, merging of different remote sensing methods, application of data assimilation and fusion techniques in producing robust evapotranspiration estimates, and utilization of multi‐source remote sensing data and latest sensor technologies. Further advances in the remote sensing of evapotranspiration will enhance capabilities for monitoring of the global water and energy cycles, including water availability and ecosystem responses and feedbacks to climate change and human impacts. WIREs Water 2016, 3:834–853. doi: 10.1002/wat2.1168 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods
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Cites
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Natural evaporation from open water, bare soil and grass
Penman, H. L. (1948) · Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences · doi:10.1098/rspa.1948.0037 · Cited by 7,307
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., & Stocker, E. F. (2007) · Journal of Hydrometeorology · doi:10.1175/jhm560.1 · Cited by 6,767
On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters
PRIESTLEY, C. H. B., & TAYLOR, R. J. (1972) · Monthly Weather Review · doi:10.1175/1520-0493(1972)100<0081:otaosh>2.3.co;2 · Cited by 6,574
Jarvis, P. G. (1976) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1976.0035 · Cited by 3,260
A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation
Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998) · Journal of Hydrology · doi:10.1016/s0022-1694(98)00253-4 · Cited by 3,118
Improvements to a MODIS global terrestrial evapotranspiration algorithm
Mu, Q., Zhao, M., & Running, S. W. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.02.019 · Cited by 2,816
Self-citationEvaporation into the Atmosphere
Brutsaert, W. (1982) · Springer eBooks · doi:10.1007/978-94-017-1497-6 · Cited by 2,556
Ball, J. T., Woodrow, I. E., & Berry, J. A. (1987) · Progress in Photosynthesis Research · doi:10.1007/978-94-017-0519-6_48 · Cited by 2,496
Recent decline in the global land evapotranspiration trend due to limited moisture supply
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., de Jeu, R., et al. (2010) · Nature · doi:10.1038/nature09396 · Cited by 2,280
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Cited by
Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., et al. (2020) · Hydrology and Earth System Sciences · doi:10.5194/hess-24-1485-2020 · Cited by 365
Self-citationPhysics‐Constrained Machine Learning of Evapotranspiration
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., & Qiu, G. Y. (2019) · Geophysical Research Letters · doi:10.1029/2019gl085291 · Cited by 315
Metadata
- Authors
- Zhang, K., Kimball, J. S., & Running, S. W.
- Venue
- WIREs Water
- Publisher
- Wiley
- Published
- 2016-07
- Volume
- 3
- Issue
- 6
- Pages
- 834-853
- ISSN
- 2049-1948 (ISSN-L)
- OpenAlex
- W2595433512
- Cited by
- 676
- References
- 81
- FWCI
- 19.63
- Type
- journal article
- Language
- en
- Open access
- closed
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