Abstract
We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.
Highlights
Wetland inundation is a key driver of ecosystem functions and associated services [1], and has important implications for water and wetland policies and management [2,3,4]
The errors in sub-pixel water fraction (SWF) estimates were largely due to a negative bias, where area estimates were usually lower than field measurements (Figure 4)
In the Everglades, the median nRMSE over 2815 reference SWF estimates (17 gage sites with an average of 166 sampled dates per sub-site) was found to be 0.19
Summary
Wetland inundation is a key driver of ecosystem functions and associated services [1], and has important implications for water and wetland policies and management [2,3,4]. As the importance of wetlands to regional hydrology [5,6], nutrient export to surface waters [7,8,9], greenhouse gas emissions [10,11,12], biodiversity [3,13] and other ecosystem services becomes increasingly apparent, there is a need for accurate and timely methods for monitoring wetland inundation dynamics. While large-area water datasets have proven useful in a range of application studies, their ability to accurately represent wetland inundation in space and time remains a challenge, due to the tendency of wetlands to be small in size and highly dynamic over time. There is a trade-off between spatial and temporal resolution among currently available global surface water data products, where medium spatial resolution data products (minimum mapping unit ~30-m) are limited to “snapshot” depictions of water cover and high temporal resolution data products are derived from coarse spatial resolution data [18,19,20,21,22,27]
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