Multi-temporal analysis of inland water level change using ICESat-2 ATL-13 data in lakes and dams

Narin, Ömer Gökberk; Abdikan, Saygın

doi:10.1007/s11356-022-23172-9

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…We choose the ATL13 product to derive the water levels of the pan-Arctic lakes because the water mask for the ATL13 product adopts the HydroLAKES database. Of course, other data products (e.g., ATL03, ATL06, and ATL08) have also been reported in previous works to extract the lake or reservoir levels combined with the utilization of customized waterbody masks [52][53][54][55][56][57][58]. Moreover, we note that there is little difference in the water level extraction of the same lake with the different ICESat-2 products derived from ATL03 (Global Geolocated Photon Data) according to these studies.…”

Section: Icesat-2 Atl13 Product-derived Water Levelssupporting

confidence: 61%

How Many Pan-Arctic Lakes Are Observed by ICESat-2 in Space and Time?

et al. 2022

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High-latitude lakes are sensitive indicators of climate change. Monitoring lake dynamics in high-latitude regions (e.g., pan-Arctic regions) is essential to improving our understanding of the impacts of climate change; however, the lack of in situ water level measurements limits comprehensive quantification of the lake hydrologic dynamics in high-latitude regions. Fortunately, the newly launched ICESat-2 laser altimeter can provide finer footprint measurements and denser ground tracks, thus enabling us to measure the water level changes for more lakes than with conventional radar altimeters. This study aims to comprehensively assess the number and frequency of pan-Arctic lakes (>1 km2, north of 60°N) observable by the ICESat-2 in space and time over the past three years. Further, we analyze the spatial and temporal characteristics of the ICESat-2-based water level observations of these pan-Arctic lakes based on our customized classification of seasonal coverage patterns (wet/dry season, monthly, and ten-day). We find that the ICESat-2 observed 80,688 pan-Arctic lakes (97% of the total). Among the observed lakes, the ICESat-2 retrieved the seasonal coverage patterns for 40,192 lakes (~50% of observed lakes), accounting for nearly 84% of the area and 95% of the volumetric capacity. Most lakes (99%) have seasonal water-level fluctuation amplitudes within a range of 0–1 m. The latitudinal zonality analysis demonstrates that the seasonal change in pan-Arctic lake levels gently fluctuates around 0.5 m between 60°N and 74°N and becomes intense (range of level change from 1 m to 2 m) beyond 74°N. Our results are expected to offer an overall reference for the spatio-temporal coverage of the ICESat-2’s observations of pan-Arctic lakes, which is crucial for comprehending the hydrologic response of high-latitude lakes to ongoing climate change.

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Section: Icesat-2 Atl13 Product-derived Water Levelssupporting

confidence: 61%

How Many Pan-Arctic Lakes Are Observed by ICESat-2 in Space and Time?

et al. 2022

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“…In 2022, its data were used in seven studies for the extraction of inland water levels, a number substantially higher than any other mission's application. It is shown that laser altimeters have a unique role in monitoring water level changes in most rivers and lakes, especially for small ones, benefitting from their higher sampling densities and much smaller footprint than radar altimeters [93][94][95][96]. However, a significant limitation to their use is the temporal resolution, e.g., 91-day repeat cycle for ICESat-2 satellite, which is significantly lower than in radar altimeters.…”

Section: Discussionmentioning

confidence: 99%

“…As can be understood ICESat-2 has high spatial resolution and high density of observation. Thus, ICESat/ICESat-2 has a unique role in monitoring water level changes in most rivers and lakes, especially for small ones benefitting from their small footprints [93][94][95][96]. However, the water levels from ICESat-2 are much more accurate than those from ICESat [97].…”

Section: Laser Altimetry Satellite Missionsmentioning

confidence: 99%

Inland Water Level Monitoring from Satellite Observations: A Scoping Review of Current Advances and Future Opportunities

Kossieris,

Tsiakos,

Tsimiklis

et al. 2024

Remote Sensing

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Inland water level and its dynamics are key components in the global water cycle and land surface hydrology, significantly influencing climate variability and water resource management. Satellite observations, in particular altimetry missions, provide inland water level time series for nearly three decades. Space-based remote sensing is regarded as a cost-effective technique that provides measurements of global coverage and homogeneous accuracy in contrast to in-situ sensors. The advent of Open-Loop Tracking Command (OLTC), and Synthetic Aperture Radar (SAR) mode strengthened the use of altimetry missions for inland water level monitoring. However, it is still very challenging to obtain accurate measurements of water level over narrow rivers and small lakes. This scoping systematic literature review summarizes and disseminates the research findings, highlights major results, and presents the limitations regarding inland water level monitoring from satellite observations between 2018 and 2022. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and through a double screening process, 48 scientific publications were selected meeting the eligibility criteria. To summarize the achievements of the previous 5 years, we present fundamental statistical results of the publications, such as the annual number of publications, scientific journals, keywords, and study regions per continent and type of inland water body. Also, publications associated with specific satellite missions were analyzed. The findings show that Sentinel-3 is the dominant satellite mission, while the ICESat-2 laser altimetry mission has exhibited a high growth trend. Furthermore, publications including radar altimetry missions were charted based on the retracking algorithms, presenting the novel and improved methods of the last five years. Moreover, this review confirms that there is a lack of research on the collaboration of altimetry data with machine learning techniques.

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“…It provides regular measurements (footprints intersecting with inland water bodies, or for each transacted water body) of water height (same as WSE) at a river cross-section called a virtual station (VS). It has been successfully applied to many inland water studies [24][25][26]. Dandabathula and Rao (2020) [27] validated the ATL13 data product with 46 observations made with near real-time gauged data for 15 reservoirs/water bodies.…”

Section: Introductionmentioning

confidence: 99%

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

Chen,

Liu,

Ticehurst

et al. 2024

Remote Sensing

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The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect solely with water. It is acknowledged that the ATL13 product has noise elevations of the adjacent land, resulting in biased high mean WSEs at VSs. Earlier studies have relied on human intervention or water masks to resolve this. Both approaches are unsatisfactory solutions for large river basins where the issue becomes pronounced due to many tributaries and meanders. There is no automated procedure to partition the truly representative water height from the totality of the along-track ICESAT-2 photon segments (portions of photon points along a beam) for increasing precision of the mean WSE at VSs. We have developed an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparing the ATL13-derived WSEs with direct water level observations at 10 different gauging stations on 37 different dates along the Lower Murray River, Australia. The concordance between the two datasets is significantly high and without detectable bias. In addition, we evaluated the effects of four methods for calculating the mean WSEs at VSs after auto-segmentation processing. Our results reveal that all methods perform almost equally well, with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189 m) and MAE (0.130–0.142 m). We also found that the R2, RMSE and MAE are better under the high flow condition (0.999, 0.124 and 0.111 m) than those under the normal-low flow condition (0.997, 0.208 and 0.160 m). Overall, our auto-segmentation method is an effective and efficient approach for deriving accurate mean WSEs at river VSs. It will contribute to the improvement of ICESAT-2 ATL13 altimetry data utility on rivers.

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Multi-temporal analysis of inland water level change using ICESat-2 ATL-13 data in lakes and dams

Cited by 10 publications

References 39 publications

How Many Pan-Arctic Lakes Are Observed by ICESat-2 in Space and Time?

How Many Pan-Arctic Lakes Are Observed by ICESat-2 in Space and Time?

Inland Water Level Monitoring from Satellite Observations: A Scoping Review of Current Advances and Future Opportunities

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

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