Deriving High-Resolution Reservoir Bathymetry From ICESat-2 Prototype Photon-Counting Lidar and Landsat Imagery

Li, Yao; Gao, Huilin; Jasinski, Michael F.; Zhang, Shuai; Stoll, J.

doi:10.1109/tgrs.2019.2917012

Cited by 88 publications

(65 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The underwater lake floor level can be extracted using the Multiple Altimeter Beam Experiment Lidar photon-counting lidar data (Y. Li et al, 2019;Y. Ma et al, 2019;McGill et al, 2013).…”

Section: Monitoring Lake Level/volume Changesmentioning

confidence: 99%

Tibetan Plateau's Lake Level and Volume Changes From NASA's ICESat/ICESat‐2 and Landsat Missions

Zhang

Chen

Xie

2019

Geophysical Research Letters

134

View full text Add to dashboard Cite

Lake level change is an effective indicator of climate change, because it reflects the dynamic balance of water input and loss. Here we provide a first look of Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) on the lakes of the Tibetan Plateau. ICESat‐2 doubles the number of lakes observed using predecessor ICESat data. The ICESat/ICESat‐2 altimetry data between 2003 and 2018 are in excellent agreement with in situ measurements from Qinghai Lake (R2 = 0.95, root‐mean‐square error = 0.10 m) and with gauge observations made on 3 December 2018 (< 2 cm). The 62 lakes examined show a mean rate of water level change of 0.28 ± 0.03 m/year in 2003–2018, with 58 lakes increasing (mean rate 0.30 ± 0.03 m/year), and 4 lakes decreasing (mean rate −0.12 ± 0.06 m/year). A total change of lake water storage of ~14 Gt/year is estimated. The results suggest that ICESat‐2 data will be an important tool for future lake level change studies.

show abstract

“…The underwater lake floor level can be extracted using the Multiple Altimeter Beam Experiment Lidar photon-counting lidar data (Y. Li et al, 2019;Y. Ma et al, 2019;McGill et al, 2013).…”

Section: Monitoring Lake Level/volume Changesmentioning

confidence: 99%

Tibetan Plateau's Lake Level and Volume Changes From NASA's ICESat/ICESat‐2 and Landsat Missions

Zhang

Chen

Xie

2019

Geophysical Research Letters

134

View full text Add to dashboard Cite

show abstract

“…Human-made reservoirs, which are managed by storing and releasing water under predetermined operation rules, play an important role in mitigating floods and improving the efficiency of the water supply for municipal, industrial, and agricultural demands [1][2][3][4]. Although most (if not all) human operated reservoirs are monitored in real-time, reservoir storage information is not commonly available to the public.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the limited availability of gauge observations-especially with regard to remote locations, restricted locations, and/or observations over large geographical areas-remote sensing technology provides a promising alternative by monitoring reservoirs from space [4,[9][10][11][12]. With remotely sensed water surface area and elevation data, reservoir storage information can be inferred.…”

Section: Introductionmentioning

confidence: 99%

“…Reservoir surface area is commonly estimated by classifying optical satellite imageries [13,14] and surface elevation values are typically obtained from satellite radar altimetry [15,16]. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat) and the Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 were used to measure the elevation values of relatively small lakes and reservoirs [4,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using the Digital Elevation Model (DEM) to Improve the Spatial Coverage of the MODIS Based Reservoir Monitoring Network in South Asia

Zhang

Gao

2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Satellite remote sensing of near real-time reservoir storage variations has important implications for flood monitoring and water resources management. However, satellite altimetry data, which are essential for estimating storage variations, are only available for a limited number of reservoirs. This lack of high-density spatial coverage directly hinders the potential use of remotely sensed reservoir information for improving the skills of hydrological modeling over highly regulated river basins. To solve this problem, a reservoir storage dataset with high-density spatial coverage was developed by combining the water surface area estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) imageries with the Digital Elevation Model (DEM) data collected by the Shuttle Radar Topography Mission (SRTM). By including more reservoirs, this reservoir dataset represents 46.6% of the overall storage capacity in South Asia. The results were validated over five reservoirs where gauge observations are accessible. The storage estimates agree well with observations, with coefficients of determination ranging from 0.47 to 0.91 and normalized root mean square errors (NRMSE) ranging from 15.46% to 37.69%. Given the general availability of MODIS and SRTM data, this algorithm can be potentially applied for monitoring global reservoirs at a high density.

show abstract

“…Furthermore, the direct retrieval of bathymetry from satellite imagery is limited for situations in clear and shallow water bodies where the remotely detected signal is dominated by bottom reflected radiation (Carbonneau et al, 2006;Dilbone et al, 2018;Gao, 2009;Legleiter et al, 2009;Legleiter & Overstreet, 2012;Sandidge & Holyer, 1998). Alternatively, the topography can be estimated between the smallest and largest water surface areas using water level and flood extent data, such as the approaches presented by Feng et al (2011), Arsen et al (2013), Getirana et al (2018), Tseng et al (2016), Li et al (2019Li et al ( , 2020, and Armon et al (2020). These methods, however, have limitations.…”

Section: Introductionmentioning

confidence: 99%

Lake Topography and Active Storage From Satellite Observations of Flood Frequency

Fassoni‐Andrade

Paiva

Fleischmann

2020

Water Resources Research

View full text Add to dashboard Cite

Topography is critical information for water resources management in lakes, and remote sensing provides a unique opportunity to estimate topography in ungauged regions. We introduce here a new method that estimates nearshore topography of water bodies based on a flood frequency map and time series of water levels by assuming the equivalence between flood frequency and water level exceedance probability at a given area. Test cases are performed for two lakes and 12 hydropower reservoirs using the proposed Flood2Topo app. This new application generates the bottom level pixel by pixel and level‐area‐active storage relationship directly from the topography map, without the need to fit functions. Flood extent estimates from the Landsat based Global Surface Water (GSW) data set, the current state‐of‐the‐art, were used to run Flood2Topo, together with water levels from satellite altimetry and in situ gauges. Results show bottom level root mean square deviation (RMSD) values of 18.5 and 146 cm for Lake Poopó (Bolivia) and Lake Curuai (Amazon basin), respectively. For reservoir active storage, RMSD normalized values ranged from 2% to 11% for 11 reservoirs (average NRMSD of 6.4%). The method can be applied to any seasonally flooded area using any data set. Considering the surface water occurrence map from the GSW data set, the method is applicable to 35.8% (86%) of the global seasonally flooded areas delimited by flood frequencies between 0 and 95% (99%) over 35 years. The flood frequency‐based method is a promising tool for obtaining data for hydrodynamic simulations and monitoring of ungauged water bodies.

show abstract

Deriving High-Resolution Reservoir Bathymetry From ICESat-2 Prototype Photon-Counting Lidar and Landsat Imagery

Cited by 88 publications

References 50 publications

Tibetan Plateau's Lake Level and Volume Changes From NASA's ICESat/ICESat‐2 and Landsat Missions

Tibetan Plateau's Lake Level and Volume Changes From NASA's ICESat/ICESat‐2 and Landsat Missions

Using the Digital Elevation Model (DEM) to Improve the Spatial Coverage of the MODIS Based Reservoir Monitoring Network in South Asia

Lake Topography and Active Storage From Satellite Observations of Flood Frequency

Contact Info

Product

Resources

About