Basin-scale and seasonal evaluation of automated threshold methods for surface water detection. Issue 7 (3rd July 2021)

• Record Type: Journal Article
• Authors: Storie, Joni; Storie, C. D.; Henry, C.J.; Sokolov, M.; Murray, B.; Cameron, J.; Tsenov, R-M
• Is Part Of: Remote sensing letters. Volume 12:Issue 7(2021)
• Page Start: 645
• Page End: 653
• Publication Date: 2021-07-03
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2021.1918788
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: Estimating surface water using satellite data can improve estimates and predictions for hydroelectric energy generation. The goal of this research is to identify the best threshold method and image band combination to detect seasonal surface water at the basin scale. This paper explores Otsu, Minimum Entropy, and average of Otsu-Minimum Entropy threshold algorithms using Sentinel-2 narrow near-infrared band 8A (N-NIR), shortwave infrared bands 11 and 12 (SWIR-11 or SWIR-12) images for the Assiniboine River Basin (ARB). The highest precision (0.87) of surface water detection was obtained in autumn using Minimum Entropy (N-NIR & SWIR-11) threshold values for the ARB, however, the Minimum Entropy also had poor detection of surface water (low recall). Use of the naïve Bayesian classifier did not improve detection of surface water compared to using threshold values alone, and the use of threshold values generated for one basin was not transferable to another basin. We concluded that threshold methods should be assessed seasonally, at a per-tile scale before combining surface water products at a watershed scale, and products should be evaluated using non-balanced statistical measures for watersheds with high land-to-water ratios. Accuracy assessment is hampered by validation data which represents static hydrologic conditions, not contemporary, seasonal conditions.