Accuracy Assessment and Normalisation of Water Spread Area Estimate from Multi-sensor Satellite Data

Kompella, Sai Santosh; Kadapala, Bharath Kumar Reddy; Hakeem, K. Abdul; Issac, Annie Maria; Annamalai, Lesslie

doi:10.1007/s12524-020-01185-6

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…We empirically determined the 0.3 km 2 threshold by evaluating the accuracy of the satellite-derived water area measurements against in situ water-level data for a subset of MA lakes ranging in size from 0.1 km 2 to 15 km 2 . Other studies have also found that accuracy of satellite-based lake water area extraction decreases for smaller water bodies without specifying any size threshold [43][44][45]. While the 0.3 km 2 cutoff was suitable for reliably monitoring water level fluctuations in our MA lake dataset, further accuracy assessments would be required to determine an appropriate minimum lake size threshold if applying this method in other geographic regions.…”

Section: Statewide Classification Of Wd Lakesmentioning

confidence: 89%

A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

Kumar,

Roy,

Andreadis

et al. 2024

Remote Sensing

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Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.

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Section: Statewide Classification Of Wd Lakesmentioning

confidence: 89%

A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

Kumar,

Roy,

Andreadis

et al. 2024

Remote Sensing

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“…The impact of co-localization and spatial resampling errors was minimized and/or evaluated by 6% of the eligible papers: 20 eligible papers published in 2022, 2021, and 2020; 8 eligible papers published in 2011 and 2010; 1 eligible paper published in 1996. In order to minimized the errors, Arai et al [368], Cao et al [164], Li et al [107], Soenen et al [500], and Zurita-Milla et al [419] carefully chose the size of the reference maps; Bair et al [254], Cavalli [114,145], Ding et al [152], Fernandez-Garcia et al [256], Hamada et al [441], Hajnal et al [169], Lu et al [435], Ma & Chan [78], Rittger et al [262], Sun et al [263], Yang et al [488], and Yin et al [151] spatially resampled the reference fractional abundance maps; Estes et al [447] compared different windows of pixels (i.e., 3 × 3, 7 × 7, 11 × 11, 15 × 15, and 21 × 21); Pacheco & McNairn [480] selected the size and the spatial resolution of the reference maps; Ben-dor et al [507], Fernandez-Guisuraga et al [342], Kompella et al [328], Laamarani et al [343], and Plaza & Plaza [465] carefully co-localized the reference fractional abundance maps on the reference maps; Wang et al [366] expanded the windows of the field sample size; Zhu et al [64] resampled at "four kinds of grids" (i.e., 1100 × 1100 m, 2200 × 2200 m, 4400 × 4400 m, and 8800 × 8800 m) the reference fractional abundance map and compared the results. Bair et al [254], Binh et al [341], Cavalli [114,145], Cheng et al [543], and Ruescas et al [448] evaluated the errors in co-localization and spatial-resampling due to the comparison of different data at different s...…”

Section: Error In Co-localization and Spatial Resamplingmentioning

confidence: 99%

Spatial Validation of Spectral Unmixing Results: A Systematic Review

Cavalli

2023

Remote Sensing

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The pixels of remote images often contain more than one distinct material (mixed pixels), and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared effort has enabled the development of techniques for retrieving information from mixed pixels. The most analyzed, implemented, and employed procedure is spectral unmixing. Among the extensive literature on the spectral unmixing, nineteen reviews were identified, and each highlighted the many shortcomings of spatial validation. Although an overview of the approaches used to spatially validate could be very helpful in overcoming its shortcomings, a review of them was never provided. Therefore, this systematic review provides an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing the papers that were published not only in 2011and 2010, but also in 1996 and 1995. The key criterion is that the results of the spectral unmixing were spatially validated. The Web of Science and Scopus databases were searched, using all the names that were assigned to spectral unmixing as keywords. A total of 454 eligible papers were included in this systematic review. Their analysis revealed that six key issues in spatial validation were considered and differently addressed: the number of validated endmembers; sample sizes and sampling designs of the reference data; sources of the reference data; the creation of reference fractional abundance maps; the validation of the reference data with other reference data; the minimization and evaluation of the errors in co-localization and spatial resampling. Since addressing these key issues enabled the authors to overcome some of the shortcomings of spatial validation, it is recommended that all these key issues be addressed together. However, few authors addressed all the key issues together, and many authors did not specify the spatial validation approach used or did not adequately explain the methods employed.

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Accuracy Assessment and Normalisation of Water Spread Area Estimate from Multi-sensor Satellite Data

Cited by 2 publications

References 10 publications

A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

Spatial Validation of Spectral Unmixing Results: A Systematic Review

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