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

2021 ◽  
Author(s):  
Stefan Schlaffer ◽  
Marco Chini ◽  
Wouter Dorigo ◽  
Simon Plank
Keyword(s):  
Surface Water ◽  
High Resolution ◽  
Prairie Pothole Region ◽  
Open Water ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Prairie Pothole ◽  
Small Water ◽  
Small Water Bodies

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.

Remote Sensing of Surface Water Dynamics Between 2015 and 2020 in the Prairie Pothole Region 

2021 ◽  
Author(s):  
Stefan Schlaffer ◽  
Marco Chini ◽  
Wouter Dorigo
Keyword(s):  
Surface Water ◽  
Prairie Pothole Region ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Drought Indices ◽  
Prairie Pothole ◽  
Small Water ◽  
Wide Range ◽  
Small Water Bodies

&lt;p&gt;The North American Prairie Pothole Region (PPR) consists of millions of wetlands and holds great importance for biodiversity, water storage and flood management. The wetlands cover a wide range of sizes from a few square metres to several square kilometres. Prairie hydrology is greatly influenced by the threshold behaviour of potholes leading to spilling as well as merging of adjacent wetlands. The knowledge of seasonal and inter-annual surface water dynamics in the PPR is critical for understanding this behaviour of connected and isolated wetlands. Synthetic aperture radar (SAR) sensors, e.g. used by the Copernicus Sentinel-1 mission, have great potential to provide high-accuracy wetland extent maps even when cloud cover is present. We derived water extent during the ice-free months May to October from 2015 to 2020 by fusing dual-polarised Sentinel-1 backscatter data with topographical information. The approach was applied to a prairie catchment in North Dakota. Total water area, number of water bodies and median area per water body were computed from the time series of water extent maps. Surface water dynamics showed strong seasonal dynamics especially in the case of small water bodies (&lt;&amp;#160;1&amp;#160;ha) with a decrease in water area and number of small water bodies from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. Inter-annual dynamics were strongly related to drought indices based on climate data, such as the Palmer Drought Severity Index. During the extremely wet period of late 2019 to 2020, 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. The results demonstrate the potential of Sentinel-1 data for long-term monitoring of prairie wetlands while limitations exist due to the rather low temporal resolution of 12 days over the PPR.&lt;/p&gt;

Constructing Mediterranean wetland open water dynamics using a new 18-year MODIS-derived surface water fraction dataset

2020 ◽  
Author(s):  
Linlin Li ◽  
Anton Vrieling ◽  
Andrew Skidmore ◽  
Tiejun Wang
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Temporal Dynamics ◽  
Open Water ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Water Fraction ◽  
Wide Range

&lt;p&gt;Wetlands are among the most biodiverse ecosystems in the world, due largely to their dynamic hydrology. Frequent observations by satellite sensors such as the Moderate Resolution Imaging Spectrometer (MODIS) allow for monitoring the seasonal, inter-annual and long-term dynamics of surface water extent. However, existing MODIS-based studies have only demonstrated this for large water bodies despite the ecological importance of smaller-sized wetland systems. In this paper, we constructed the temporal dynamics of surface water extent for 340 individual water bodies in the Mediterranean region between 2000 and 2017, using a previously developed 8-day 500 m MODIS surface water fraction (SWF) dataset. These water bodies has a wide range of size, specifically 0.01 km&lt;sup&gt;2&lt;/sup&gt; and larger. We then compared the water extent time series derived from MODIS SWF with those derived from a Landsat-based dataset. Results showed that MODIS- and Landsat-derived water extent time series showed a high correlation (r = 0.81) for more dynamic water bodies. Our MODIS SWF dataset can also effectively monitor the variability of very small water bodies (&lt;1 km&lt;sup&gt;2&lt;/sup&gt;) when comparing with Landsat data as long as the temporal variability in their surface water area was high. We conclude that MODIS SWF is a useful product to help understand hydrological dynamics for both small and larger-sized water bodies, and to monitor their seasonal, intermittent, inter-annual and long-term changes.&lt;/p&gt;

scholarly journals Spatial and Temporal Changes in Surface Water Area of Sri Lanka over a 30-Year Period

2020 ◽  
Vol 12 (22) ◽  
pp. 3701
Author(s):  
Deepakrishna Somasundaram ◽  
Fangfang Zhang ◽  
Sisira Ediriweera ◽  
Shenglei Wang ◽  
Junsheng Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Sri Lanka ◽  
Anthropogenic Activities ◽  
Water Area ◽  
Temporal Changes ◽  
Water Bodies ◽  
Water Dynamics ◽  
Annual Growth Rate ◽  
Spatial And Temporal Changes

Sri Lanka contains a large number of natural and man-made water bodies, which play an essential role in irrigation and domestic use. The island has recently been identified as a global hotspot of climate change extremes. However, the extent, spatial distribution, and the impact of climate and anthropogenic activities on these water bodies have remained unknown. We investigated the distribution, spatial and temporal changes, and the impacts of climatic and anthropogenic drivers on water dynamics in Dry, Intermediate, and Wet zones of the island. We used Landsat 5 and Landsat 8 images to generate per-pixel seasonal and annual water occurrence frequency maps for the period of 1988–2019. The results of the study demonstrated high inter- and intra-annual variations in water with a rapid increase. Further, results showed strong zonal differences in water dynamics, with most dramatic variations in the Dry zone. Our results revealed that 1607.73 km2 of the land area of the island is covered by water bodies, among this 882.01 km2 (54.86%) is permanent and 725.72 km2 (45.14%) is seasonal water area. Total inland seasonal water increased with a dramatic annual growth rate of 7.06 ± 1.97 km2 compared to that of permanent water (4.47 ± 2.08 km2/year). Sri Lanka has the highest permanent water area during December–February (1045.97 km2), and drops to the lowest in May–September (761.92 km2) when the seasonal water (846.46 km2) is higher than permanent water. The surface water area was positively related to both precipitation and Gross Domestic Product, while negatively related to the temperature. Findings of our study provide important insights into possible spatiotemporal changes in surface water availability in Sri Lanka under certain climate change and anthropogenic activities.

scholarly journals Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

2018 ◽  
Vol 22 (8) ◽  
pp. 4349-4380 ◽  
Author(s):  
Andrew Ogilvie ◽  
Gilles Belaud ◽  
Sylvain Massuel ◽  
Mark Mulligan ◽  
Patrick Le Goulven ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Landsat Imagery ◽  
Water Monitoring ◽  
Water Bodies ◽  
Small Lakes ◽  
Small Water ◽  
Water Detection ◽  
Automated Method ◽  
Small Water Bodies

Abstract. Hydrometric monitoring of small water bodies (1–10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies, but the influence of their limited surface areas, vegetation growth, and rapid flood dynamics on long-term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimize the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these small water bodies. Extensive hydrometric field data (1999–2014) for seven small reservoirs within the Merguellil catchment in central Tunisia and SPOT imagery are used to calibrate the method and explore its limits. The Modified Normalised Difference Water Index (MNDWI) is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15 %. Applied to 546 Landsat 5, 7, and 8 images over 1999–2014, the method reproduces surface water extent variations across small lakes with high skill (R2=0.9) and a mean root mean square error (RMSE) of 9300 m2. Comparison with published global water datasets reveals a mean RMSE of 21 800 m2 (+134 %) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3 ha with Landsat (Normalised RMSE = 27 %). Interferences from clouds and scan line corrector failure on ETM+ after 2003 also decrease the number of operational images by 51 %, reducing performance on lakes with rapid flood declines. Combining Landsat observations with 10 m pansharpened Sentinel-2 imagery further reduces RMSE to 5200 m2, displaying the increased opportunities for surface water monitoring in small water bodies after 2015.

scholarly journals A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

2019 ◽  
Vol 11 (18) ◽  
pp. 2163 ◽  
Author(s):  
Ethan D. Kyzivat ◽  
Laurence C. Smith ◽  
Lincoln H. Pitcher ◽  
Jessica V. Fayne ◽  
Sarah W. Cooley ◽  
...  
Keyword(s):  
Surface Water ◽  
High Resolution ◽  
Power Law ◽  
Environmental Gradients ◽  
Water Bodies ◽  
Detection Methods ◽  
Prairie Pothole ◽  
Small Surface ◽  
Water Classification ◽  
Surface Water Bodies

The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km2 of open surface water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km2 (40 m2) to 15 km2. Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km2 in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here.

scholarly journals Monitoring the Water Quality of Small Water Bodies Using High-Resolution Remote Sensing Data

2019 ◽  
Vol 8 (12) ◽  
pp. 553 ◽  
Author(s):  
Zehra Yigit Avdan ◽  
Gordana Kaplan ◽  
Serdar Goncu ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
High Resolution ◽  
Quality Parameters ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
Water Turbidity ◽  
Small Water ◽  
Red Edge ◽  
Small Water Bodies

Remotely sensed data can reinforce the abilities of water resources researchers and decision-makers to monitor water quality more effectively. In the past few decades, remote sensing techniques have been widely used to measure qualitative water quality parameters. However, the use of moderate resolution sensors may not meet the requirements for monitoring small water bodies. Water quality in a small dam was assessed using high-resolution satellite data from RapidEye and in situ measurements collected a few days apart. The satellite carries a five-band multispectral optical imager with a ground sampling distance of 5 m at its nadir and a swath width of 80 km. Several different algorithms were evaluated using Pearson correlation coefficients for electrical conductivity (EC), total dissolved soils (TDS), water transparency, water turbidity, depth, suspended particular matter (SPM), and chlorophyll-a. The results indicate strong correlation between the investigated parameters and RapidEye reflectance, especially in the red and red-edge portion with highest correlation between red-edge band and water turbidity (r2 = 0.92). Two of the investigated indices showed good correlation in almost all of the water quality parameters with correlation higher than 0.80. The findings of this study emphasize the use of both high-resolution remote sensing imagery and red-edge portion of the electromagnetic spectrum for monitoring several water quality parameters in small water areas.

scholarly journals Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

2018 ◽  
Author(s):  
Andrew Ogilvie ◽  
Gilles Belaud ◽  
Sylvain Massuel ◽  
Mark Mulligan ◽  
Patrick Le Goulven ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Landsat Imagery ◽  
Water Monitoring ◽  
Water Bodies ◽  
Small Lakes ◽  
Small Water ◽  
Water Detection ◽  
Automated Method ◽  
Small Water Bodies

Abstract. Hydrometric monitoring of small water bodies (1–10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies but the influence of their limited surface areas, vegetation growth and rapid flood dynamics on long term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimise the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these smallest water bodies. Extensive hydrometric field data (1999–2014) for 7 small reservoirs within the Merguellil catchment in Central Tunisia are used to calibrate the method and explore its limits. MNDWI is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15 %. Applied to 546 Landsat 5, 7 and 8 images over 1999–2014, the method reproduces surface water extent variations across small lakes with high skill (R2 = 0.9) and mean RMSE of 9 300 m2. Comparison with published global water data sets reveals a mean RMSE of 21 800 m2 (+134 %) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3 ha with Landsat (NRMSE = 27 %). Interferences from clouds &amp; scan line corrector failure on ETM+ after 2003 also decrease the number of operational images by 51 %, reducing performance on lakes with rapid flood declines.

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