Water Level measurement using COSMO-SkyMed Synthetic Aperture Radar

This paper addresses the challenge of identifying the polarimetric covariance matrix (PCM) structures associated with a polarimetric SAR image. Interestingly, such information can be used, for instance, to improve the scene interpretation or to enhance the performance of (possibly PCM-based) segmentation algorithms as well as other kinds of methods. To this end, a general framework to solve a multiple hypothesis test is introduced with the aim to detect and classify contextual spatial variations in polarimetric SAR images. Specifically, under the null hypothesis, only one unknown structure is assumed for data belonging to a 2-dimensional spatial sliding window, whereas under each alternative hypothesis, data are partitioned into subsets sharing different PCM structures. The problem of partition estimation is solved by resorting to hidden random variables representative of covariance structure classes and the expectation maximization (EM) algorithm. The effectiveness of the proposed detection strategies is demonstrated on both simulated and real polarimetric SAR data also in comparison with existing classification algorithms.

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“…and 2 denote by θ 0 (A 0 ) and θ 1 (A m ) the unknown parameters under H 0 , given A 0 , and under H 1,m , given A m , respectively.…”

Section: Sensor Model and Problem Formulationmentioning

confidence: 99%

Innovative Solutions Based on the EM-Algorithm for Covariance Structure Detection and Classification in Polarimetric SAR Images

Han

Addabbo

Biondi

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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“…Spaceborne reservoir water storage monitoring has been demonstrated using altimeters such as Jason-3 [7] or IceSat-2 [8], [9] to directly measure water surface heights combined with optical and/or hyperspectral sensors [4]- [6] that map reservoir surface areas [10]- [12]. Surface areas alone in some cases have also been used to estimate storage volumes using relationships between area and water surface heights [13].…”

Section: Introductionmentioning

confidence: 99%

Reservoir Water Level Monitoring Using CYGNSS's Level-1 Observations

Brendle,

Chan,

Hajj

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The use of measurements from NASA's CYGNSS mission for the mapping of inland water bodies has been extensively demonstrated in past works. Despite this progress, the use of CYGNSS measurements for monitoring dynamic properties of water bodies such as reservoir storage levels remains challenging given the need to combine observations over extended time periods or areas to improve water detection performance. This work explores the feasibility of an alternative sensing approach based on CYGNSS Level-1 products that has the potential to estimate reservoir water storage levels on intervals significantly finer compared to existing techniques. The method is explored using three example reservoirs with CYGNSS observations extending from October 2018 to December 2022. Retrieved water levels are found to be associated with relative water level root-meansquare-errors varying from 8-20% depending on the observed scenes' level of complexity. Correlations of up to ≈86% relative to in situ data are observed. The initial results presented in this work highlight the potential of spaceborne GNSS-R systems for reservoir water level monitoring.

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