Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change
By Zhe Zhu, Curtis E. Woodcock. 2014
525 citations recorded by the source corpus.
Subfields: Atmospheric Science, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/64d08988a1fb69b1fd183d8e767ae5057f78ceab
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- David P. Roy
- M. Wulder
- Joanne C. White
- Txomin Hermosilla
- Zhe Zhu
- Nicholas C. Coops
- Curtis E. Woodcock
In Environmental Engineering
- Joanne C. White
- M. Wulder
- Nicholas C. Coops
- Zhe Zhu
- David P. Roy
- Txomin Hermosilla
- Curtis E. Woodcock
Citations in the corpus
- Land cover 2.0 (2018, methodology): “…as part of the land cover classi fi cation process (Hansen et al. 2008; Lindquist et al. 2008) or removed prior to classi fi cation using cloud masking algorithms that are labelled directly in the input data (Frey et al. 2008; Foga et al. 2017; Zhu and Woodcock 2014a; Zhu, Wang, and Woodcock 2015).”
- Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, methodology): “Univariate time series are widely used, and only a few of them use multiple spectral bands of Landsat time series (Fu and Weng, 2016; Goodwin et al., 2013; Goodwin and Collett, 2014; Zhu et al., 2016a; Zhu and Woodcock, 2014a, 2014b).”