The DOI matches its Crossref record; 155 of the 155 references it cites were found in a registry or an index.
Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities
Open accessAbstract
Abstract Drought is a common hydrometeorological phenomenon and a pervasive global hazard. As our climate changes, it is likely that drought events will become more intense and frequent. Effective drought monitoring is therefore critical, both to the research community in developing an understanding of drought, and to those responsible for drought management and mitigation. Over the past 50 years remote sensing has shifted the field away from reliance on traditional site-based measurements and enabled observations and estimates of key drought-related variables over larger spatial and temporal scales than was previously possible. This has proven especially important in data poor regions with limited in-situ monitoring stations. Available remotely sensed data products now represent almost all aspects of drought propagation and have contributed to our understanding of the phenomena. In this review we chart the rise of remote sensing for drought monitoring, examining key milestones and technologies for assessing meteorological, agricultural and hydrological drought events. We reflect on challenges the research community has faced to date, such as limitations associated with data record length and spatial, temporal and spectral resolution. This review then looks ahead to the future in terms of new technologies, such as the ESA Sentinel satellites, analytical platforms and approaches, such as Google EarthEngine, and the utility of existing data in new drought monitoring applications. We look forward to the continuation of 50 years of progress to provide effective, innovative and efficient drought monitoring solutions utilising remote sensing technology.
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Citations
Cites
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.06.031 · Cited by 14,719
Red and photographic infrared linear combinations for monitoring vegetation
Tucker, C. J. (1979) · Remote Sensing of Environment · doi:10.1016/0034-4257(79)90013-0 · Cited by 11,754
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
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A soil-adjusted vegetation index (SAVI)
Huete, A. R. (1988) · Remote Sensing of Environment · doi:10.1016/0034-4257(88)90106-x · Cited by 7,948
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NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space
Gao, B.-C. (1996) · Remote Sensing of Environment · doi:10.1016/s0034-4257(96)00067-3 · Cited by 6,951
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Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
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The Soil Moisture Active Passive (SMAP) Mission
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., et al. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2043918 · Cited by 3,754
Understanding: the Drought Phenomenon: The Role of Definitions
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Cited by
A first assessment of the impact of the extreme 2018 summer drought on Central European forests
Schuldt, B., Buras, A., Arend, M., Vitasse, Y., Beierkuhnlein, C., Damm, A., Gharun, M., Grams, T. E. E., Hauck, M., Hajek, P., et al. (2020) · Basic and Applied Ecology · doi:10.1016/j.baae.2020.04.003 · Cited by 1,021
Interpretable and explainable AI (XAI) model for spatial drought prediction
Dikshit, A., & Pradhan, B. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2021.149797 · Cited by 272
Metadata
- Authors
- West, H., Quinn, N., & Horswell, M.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 232
- Pages
- 111291
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2955786086
- Cited by
- 538
- References
- 155
- FWCI
- 22.31
- Type
- journal article
- Language
- en
- Open access
- green
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