Publications

Khandelwal, A; Karpatne, A; Marlier, ME; Kim, J; Lettenmaier, DP; Kumar, V (2017). An approach for global monitoring of surface water extent variations in reservoirs using MODIS data. REMOTE SENSING OF ENVIRONMENT, 202, 113-128.

Abstract
Freshwater resources are among the most basic requirements of human society. Nonetheless, global information about the space-time variations of the area of freshwater bodies, and the water stored in them, is surprisingly limited. We introduce a new approach that uses MODIS multispectral data to map the global areal extent and temporal variations of known reservoirs at 500 m spatial resolution at nominal eight-day intervals from 2000 to 2015. We evaluate the performance of the approach on 94 reservoirs by comparing the variations in surface area extents with satellite radar altimetry measurements. Furthermore, we present detailed case studies for five reservoirs on four continents to demonstrate the impact of different challenges on the performance of the approach. For three of these case studies, we also evaluate surface area estimates using Landsat-based surface extent reference maps. Altimetry based height measurements for the 94 reservoirs show higher correlation with surface area computed using our approach, compared to surface area computed using previous approaches. One of the main reasons for these improvements is a novel post-processing technique that makes use of imperfect labels produced by supervised classification approaches on multiple dates to estimate the elevation structure of locations and uses it to enhance the quality and completeness of imperfect labels. However, effective estimation of this elevation structure requires that the water body shows sufficient area variations. Hence, the post-processing approach will not be effective for water bodies that are mostly unchanged or are too small to have sufficient variation. The approach is still challenged in regions with frequent cloud cover, snow and ice coverage, or complicated geometries that will require remote sensing data at finer spatial resolution. The surface area estimates we describe here are publically available. (C) 2017 Published by Elsevier Inc.

DOI:
10.1016/j.rse.2017.05.039

ISSN:
0034-4257