Space-based Wetland Hydrology

Summary

Wetlands are transition zones, where water flow, nutrient cycling, and the Sun’s energy meet to produce unique and productive ecosystems. They provide critical habitats for a wide variety of plant and animal species, including the larval stages of many ocean fish. Wetlands also filter nutrients and pollutants from freshwater used by humans and provide aquatic habitats for outdoor recreation, tourism, and fishing. Globally, many such regions are under severe environmental stress, mainly from urban development, pollution, and rising sea level. However, there is increasing recognition of the importance of these habitats, and mitigation and restoration activities have begun in a few regions. A key element in wetlands conservation, management, and restoration involves monitoring its hydrologic system: the entire ecosystem depends on its water supply. In the past, hydrologic monitoring of wetlands was conducted almost exclusively by stage (water level) stations, which provide good temporal resolution, but suffer from low spatial resolution, as stage stations are typically distributed several, or even tens of kilometers, from each other.


Figure 1. Photographs of (a) freshwater herbaceous vegetation (saw grasses); (b) mixed vegetation—woody vegetation in the tree island and herbaceous around the islands—(c) freshwater woody vegetation (cypress); and (d) saltwater woody vegetation (mangroves) that grows along a tidal channel. [Source: Wdowinski and Hong, 2016]

Space-based wetland hydrology

Remote sensing observations, in particular satellite imagery, serve as very useful tools for characterizing spatial phenomena, such as land cover and its changes over time. Optical and radar imageries have been widely used to detect and monitor wetlands, mainly for classifying vegetation and estimating biological parameters, like aboveground biomass. Most of these remote sensing techniques cannot detect water level changes in wetlands, which occur beneath the vegetation cover. The one technique that is sensitive to water level (i.e., height) changes in vegetated aquatic environments is wetland InSAR (interferometric synthetic aperture radar). This technique provides detailed maps of water level changes between two acquisition times and can be used to detect water level changes in various wetland environments. The method has been successfully applied to study wetland hydrology in the Everglades, Louisiana, and other locations.


Figure 2. Figure 2. (a) Radarsat-1 interferogram showing phase changes in response to changes in water levels that occurred between the two acquisition dates. (b) InSAR–stage calibration plots for each water body. The water bodies and stage station locations are shown in Figure 7.5c. (c) InSAR-derived map of water level changes that occurred between March 23, 2005, and April 16, 2005. The black dots mark the location of the stage stations.

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Methods

Space-based Synthetic Aperture Radar (SAR) is a very reliable technique for monitoring changes of both the solid and aquatic surfaces of the Earth. SAR measures two independent observables, backscattered amplitude and phase, over a wide swath (10-400 km) with pixel resolution of 1-100 m depending on the satellite acquisition parameters. Backscattered amplitude, which is often presented as gray-scale images of the surface (Figure 1a), is very sensitive to the surface dielectric properties, surface inclination towards the satellite, and wave direction in oceans. Amplitude images are widely used for studying surface classification, soil moisture content, ocean waves, oil spill detection, and many other applications.

Figure 1. (a) Location map of our study area in South Florida. (b) RADARSAT-1 ScanSAR image of Florida showing location of study area (RADARSAT data © Canadian Space Agency / Agence spatiale canadienne 2002. Processed by CSTARS and distributed by RADARSAT International). (b) Cartoon illustrating the double-bounce radar signal return in vegetated aquatic environments. The red ray bounces twice and returns to the satellite, whereas the black ray bounces once and scattered away. [Source: Wdowinski and Hong, 2015]

The second observable, backscattered phase, measures the fraction of the radar wavelength that returns to the satellite’s antenna. It is mainly sensitive to the range between the surface and the satellite, but also to atmospheric conditions and changes in the surface dielectric properties. Phase data are mainly used in interferometric calculations (InSAR) for detecting cm-level displacements of the surface (Figure 2). The method compares pixel-by-pixel SAR phase observations of the same area acquired at different times from roughly the same location in space to produce high spatial-resolution displacement maps. Such maps, termed interferograms (Figure 2b-2d), are widely used in studies of earthquake induced crustal deformation, magmatic activity (volcanoes), land subsidence due to water extraction, glacier movements, and more.

Figure 2. Interferograms showing phase changes, which were induced by water level changes in the Everglades wetlands, south Florida. (a) JERS-1 amplitude image of South Florida showing the location of the three interferograms. (b) L-band (24 cm wavelength) ALOS interferogram of 90 km wide ascending track. Each color cycle corresponds to 15 cm of water level change. (c) C-band (5.6 cm) Radarsat-1 interferogram of a 75 km wide descending swath. Each color cycle represents 4 cm of water level change. (d) X-band (3.1 cm) TSX interferogram of a 30 km wide descending swath. Some of the observed changes reflect changes in atmospheric moisture between the acquisitions. Each color cycle represents 2 cm of water level changes. L.O. – Lake Okeechobee; EAA – Everglades Agricultural Area; WCA – Water Conservation Area; ENP – Everglades National Park. [Source: Wdowinski and Hong, 2015]

 

Wetland InSAR

Wetland Interferometric Synthetic Aperture Radar (InSAR) is a unique application of the InSAR technique, which detects water level changes in aquatic environments with emergent vegetation. It provides high spatial resolution hydrological observations of wetland and floodplains that cannot be obtained by any terrestrial-based methods. Here we present wetland InSAR observations acquired over various wetland environments by various sensors (L-, C-, and X-bands) and polarizations (Figure 2). The quality of the wetland InSAR observations is evaluated by calculating interferometric coherence and the accuracy by comparing the space-based observations with ground-based stage (water level) measurements. Coherence analyses indicate that L-band data are most suitable for the wetland InSAR application, but also C- and X-band data with short time span between acquisition dates are very useful. The comparison between InSAR and stage observations indicates an accuracy level of 3-8 cm, depending on the data type. Our studies present the more advanced wetland InSAR time series techniques that provide multi-temporal high-resolution maps of absolute water levels. Useful applications of wetland InSAR observations include high spatial resolution water level monitoring, detection of flow patterns and flow discontinuities, and constraining high-resolution flow models.

Here we presents results of our recent study using Sentinel-1 observations for routine water level change measurements over the entire south Florida Everglades wetlands (Liao et al., 2020). The study utilizes 91 Sentinel-1 images acquired over a three-year period (Sep 2016 to Nov 2019) and generates routine 12-days Interferograms and correspondingly 30 m spatial resolution water level change maps over the entire Everglades. The high spatial resolution interferograms detect hydrological signals induced by both natural- and human-induced flow, including tides, gate operations, and canal overflow; all these cannot be detected by terrestrial measurements. The large number of both InSAR and ground-based gauge observations allow us to quantify the overall accuracy of the Sentinel-1 InSAR measurements, which is 3.9 cm for the entire wetland area, but better for smaller hydrological units within the Everglades. Our study reveals that the tropospheric delay for individual interferograms can be very large, as much as 30 cm (~10 fringes). When applying tropospheric corrections to all three years of Sentinel-1 InSAR observations, the overall accuracy level improved by 13% to 3.4 cm. Although our study is focused on the Everglades, its implications in term of the suitability of Sentinel-1 observations for space-based hydrological monitoring of wetlands and the derived accuracy level are applicable to other wetlands with similar vegetation types, located all over the world.


Figure 3. A representative Sentinel-1 Interferogram (20161009–20161021) showing phase change over south Florida. (a) Most phase changes reflect surface water level change, but also tropospheric phase delay. The white solid lines mark the boundaries of the hydrologic units (WCA1, WCA2A, WCA2B, WCA3A, WCA3B, BCNP, ENP). Interesting hydrological signal are marked by red dash boxes are enlarged in figures (a1), (a2), and (a3). Each fringe cycle (from red to yellow to green to blue and back to red) in the interferogram represents a 3.6 cm vertical elevation change. All interferograms presented in the following share the same colour scale as presented here. [Source: Liao et al., 2020]


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Additional


Publications

 

2020

Liao, H., S. Wdowinski, and S. Li, Regional-scale hydrological monitoring of wetlands with Sentinel-1 InSAR Observations: Case Study of the South Florida Everglades, (2020), Remote Sensing for Environment, 251, https://doi.org/10.1016/j.rse.2020.112051. [PDF,Electronic Supplement]

2018

Jaramillo, F., I. Brown, P. Castellazzi, L. Espinosa, A. Guittard, S-H Hong, V. Rivera-Monroy, S. Wdowinski, (2018), Assessment of hydrologic connectivity in an ungauged wetland with InSAR observations, Environmental Research Letters, vol. 13, no. 2, 024003. [PDF]

Jaramillo, F., Licero, L., Åhlen, I., Manzoni, S., Rodríguez-Rodríguez, J.A., Guittard, A., Hylin, A., Bolaños, J., Jawitz, J., Wdowinski, S. and Martínez, O., (2018), Effects of Hydroclimatic Change and Rehabilitation Activities on Salinity and Mangroves in the Ciénaga Grande de Santa Marta, ColombiaWetlands, pp.1-13. [PDF]

2017

Hong, S. H., & Wdowinski, S. (2017). A Review on Monitoring the Everglades Wetlands in the Southern Florida Using Space-based Synthetic Aperture Radar (SAR) Observations 33(4), 377-390.

Mahmoudi, M., Garcia, R., Cline, E., Price, R. M., Scinto, L. J., Wdowinski, S., & Miralles-Wilhelm, F. (2017). Fine spatial resolution simulation of two-dimensional modeling of flow pulses discharge into wetlands: Case study of Loxahatchee impoundment landscape assessment, the evergladesJournal of Hydrologic Engineering22(1), D5015001. [PDF]

2016

Oliver-Cabrera, T., and S. Wdowinski. InSAR-Based Mapping of Tidal Inundation Extent and Amplitude in Louisiana Coastal Wetlands. Remote Sensing, 8, 393. [PDF]

2015

Brisco, B., K. Murnaghan, S. Wdowinski & Sang-Hoon Hong (2015): Evaluation of RADARSAT-2 Acquisition Modes for Wetland Monitoring Applications, Canadian Journal of Remote Sensing, 41, 431-439, DOI: 10.1080/07038992.2015.1104636. [PDF]

Hong, S.-H., H.-O. Kim, S. Wdowinski, and E. Feliciano (2015), Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types. Remote Sens., 7, 8563-8585. [PDF]

Brisco, B., F. Ahern, S-H Hong, S. Wdowinski, K. Murnaghan, L. White, and D.K., Atwood (2015), Porlarimetric decomposition of temperate wetlands at C-band, IEEE J. Selected Topics in App. Earth Observations and Remote Sensing, 8, 3585-3594, DOI 10.1109/JSTARS.2015.2414714. [PDF]

Wdowinski, S. and S-H. Hong, Wetland InSAR: A review of the technique and applications, Edited by R.W. Tiner, M.W. Lang, and V.V. Klemas, Remote Sensing of Wetlands Applications and Advances, CRC Press, Pages 137-154, DOI: 10.1201/b18210-10, 2015. [PDF]

2014

Xiao, X., S. Wdowinski, and Y. Wu, Improved Water Classification Using an Application-oriented Processing of Landsat ETM+ and ALOS PALSAR, International Journal of Control & Automation, 7 (11), 355-370, 2014. [PDF]

Hong, S-H, and S. Wdowinski, Multi-temporal, multi-track monitoring of wetland water levels in the Florida Everglades using ALOS PALSAR data with interferometric processing, IEEE Geosciences and Remote Sensing Letters, DOI 10.1109/LGRS.2013.2293492, 2014. [PDF]

Hong, S-H, and S. Wdowinski, Double bounce component in cross-polarimetric SAR from a new scattering target decomposition, IEEE Geosciences and Remote Sensing, DOI 10.1109/TGRS.2013.2268853, 2014. [PDF]

2013

Wdowinski, S., S.-H. Hong, A. Mulcan, and B. Brisco. Remote-sensing monitoring of tide propagation through coastal wetlands. Oceanography 26(3):64–69, DOI 10.5670/ oceanog.2013.46, 2013. [PDF]

Kim, S-W, S. Wdowinski, F. Amelung, T. H. Dixon, and J-S Won, Interferometric coherence analysis of the Everglades wetlands, South Florida, in press, IEEE Geosciences and Remote Sensing, DOI 10.1109/TGRS.2012.2231418, 2013. [PDF]

2011

Hong, S-H, and S. Wdowinski, Evaluation of the quad-polarimetric RADARSAT-2 observations for the wetland InSAR application, Canadian Journal of Remote Sensing, Vol. 37, Issue 5, pp. 484-492, 2011.[PDF]

2010

Hong, S-H, S. Wdowinski, S-W Kim, Space-based multi-temporal monitoring of wetland water levels: Case study of WCA1 in the Everglade, Remote Sensing for Environment, 2010b. [PDF]

Gondwe, B.R.N., S.-H. Hong, S. Wdowinski, and P. Bauer-Gottwein, Hydrodynamics of the groundwater-dependent Sian-Kaan wetlands, Mexico, from InSAR and SAR data, Wetlands, 30, 1-13, 2010. [PDF]

Hong, S-H, S. Wdowinski, S-W Kim, Evaluation of TerraSAR-X observations for Wetland InSAR application, IEEE Geosciences and Remote Sensing, 48, 864-873, 2010a. [PDF]

2008

Wdowinski, S., S. Kim, F. Amelung, T. Dixon, F. Miralles-Wilhelm, and R. Sonenshein, Space-based detection of wetlands surface water level changes from L-band SAR interferometry, Remote Sensing for Environment, 112/3, 681-696, 2008. [PDF]

2004

Wdowinski, S., F. Amelung, F. Miralles-Wilhelm, T. Dixon, and R. Carande, Space-based measurements of sheet-flow characteristics in the Everglades wetland, Florida, Geophys. Res. Lett., 31, L15503, 10.1029/2004GL020383, 2004. [PDF]

Links:

 

Shimon Wdowinski's research pages on:

 

Everglades research and data

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