An approach for global monitoring of surface water extent variations in reservoirs using MODIS data

By Ankush Khandelwal, Anuj Karpatne, M. Marlier, Jongyoun Kim, Dennis P. Lettenmaier, Vipin Kumar. 2017

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Gowtham Atluri
2. Anuj Karpatne
3. Vipin Kumar
4. Jared D. Willard
5. Charu C. Aggarwal
6. Xiaowei Jia
7. Pavel Berkhin
8. Alexander Strehl

Citations in the corpus

• Machine Learning for the Geosciences: Challenges and Opportunities (2017, unknown): “In a recent work, adaptive ensemble learning methods [48], [49], in conjunction with physics-based label refinement techniques [50], have been developed to address the challenge of heterogeneity and poor data quality for mapping the dynamics of surface water bodies using remote sensing data [51].”
• Spatio-Temporal Data Mining (2017, methodology): “…has been heavily studied in the remote sensing literature for a variety of problems [DeFries and Chan 2000; Jun and Ghosh 2011; Li et al. 2014b; Vatsavai 2008], e.g., the mapping of surface water dynamics using multi-spectral remote sensing data [Karpatne et al. 2016b; Khandelwal et al. 2017].”