Preprints
https://doi.org/10.5194/hess-2021-330
https://doi.org/10.5194/hess-2021-330

  08 Jul 2021

08 Jul 2021

Review status: this preprint is currently under review for the journal HESS.

Monitoring Surface Water Dynamics in the Prairie Pothole Region Using Dual-Polarised Sentinel-1 SAR Time Series

Stefan Schlaffer1,2, Marco Chini3, Wouter Dorigo1, and Simon Plank2 Stefan Schlaffer et al.
  • 1Technische Universität Wien, Department of Geodesy and Geoinformation, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria
  • 2German Aerospace Center, Earth Observation Center, Münchener Str. 20, 82234 Wessling, Germany
  • 3Luxembourg Institute of Science and Technology, 41 rue du Brill, 4422 Belvaux, Luxembourg

Abstract. The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors have been widely used to calibrate and validate hydrological models of wetland dynamics. Yet, they are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors, such as the ones on board the Copernicus Sentinel-1 mission, can potentially overcome such limitations. However, water extent retrieval from SAR data is often affected by environmental factors, such as wind on water surfaces. Hence, for reliably monitoring water extent over longer time periods robust retrieval methods are required.

The aim of this study was to develop a robust approach for classifying open water extent dynamics in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the light of ancillary data. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of water bodies and the median area per water body were computed. The validation of the retrieved water maps yielded producer’s accuracies between 84 % and 95 % for calm days and between 74 % and 88 % on windy days. User’s accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information.

Surface water dynamics showed strong intra-annual dynamics especially in the case of small water bodies (< 1 ha). Water area and number of small water bodies decreased from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. During the extremely wet period between the autumn of 2019 and mid-2020, however, the dynamics of both small and large water bodies changed markedly. While a larger number of small water bodies was encountered, which remained stable throughout the wet period, also the area of larger water bodies increased, partly due to merging of smaller adjacent water bodies. However, the area covered by small water bodies was more stable than the area covered by large water bodies. This suggests that large potholes released water faster via the drainage network, while small potholes released water mainly to the atmosphere via evaporation. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations exist related to wind inhibiting correct water extent retrieval and due to the rather low temporal resolution of 12 days over the PPR.

Stefan Schlaffer et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-330', Anonymous Referee #1, 17 Aug 2021
    • AC1: 'Reply on RC1', Stefan Schlaffer, 01 Oct 2021
  • RC2: 'Comment on hess-2021-330', Geoff Pegram, 18 Aug 2021
    • AC3: 'Reply on RC2', Stefan Schlaffer, 01 Oct 2021
  • RC3: 'Comment on hess-2021-330', Anonymous Referee #3, 30 Aug 2021
    • AC2: 'Reply on RC3', Stefan Schlaffer, 01 Oct 2021
  • RC4: 'Comment on hess-2021-330', Anonymous Referee #4, 31 Aug 2021
    • AC4: 'Reply on RC4', Stefan Schlaffer, 01 Oct 2021

Stefan Schlaffer et al.

Stefan Schlaffer et al.

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Short summary
Prairie wetlands are important for biodiversity and water availability. Knowledge about their variability and spatial distribution is of great use in conservation and water resources management. In this study we propose a novel approach for the classification of small water bodies from satellite radar images and apply it for our study area over six years. The retrieved dynamics show the different responses of small and large wetlands to dry and wet periods.