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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 1, pp. 76-87

Statistical and machine learning methods for river water level time series construction using satellite altimetry

N.K. Semenova 1, 2 , E.A. Zakharova 1, 3 , I.N. Krylenko 1, 2 , A.A. Sazonov 1, 2 
1 Water Problems Institute RAS, Moscow, Russia
2 Lomonosov Moscow State University, Moscow, Russia
3 Earth Observation for Learning and Application, Toulouse, France
Accepted: 08.12.2023
DOI: 10.21046/2070-7401-2024-21-1-76-87
The use of satellite altimetry data for monitoring the level regime of rivers in Arctic regions is limited due to the negative effect of complex fluvial morphology and ice cover on altimetric radar measurements. The construction of time series of river water levels consists of two main stages: 1) accurate geographic sampling of satellite measurements over the river channel and 2) calculation of the average level for a given date after filtering outliers. This work is based on measurements from the European altimetry satellites Sentintel-3A and Sentinel-3B. The paper proposes a method for determining aberrant values of altimetric measurements (outliers) over the wide floodplain section of the Kolyma River. The method improved the accuracy of calculation of satellite time series of water level by 0.04–1.59 m (or 4–85 %) compared to the widely used standard statistical method of filtering altimetric measurements. The suggested method is based on the combination of three algorithms of different complexity: statistical (Mahalanobis distance), clustering (Density-Based Spatial Clustering of Applications with Noise (DBSCAN)) and machine learning (Isolating Forest) methods. In the combined approach, values classified as outliers by at least two algorithms were considered outliers. This approach allowed us to reduce the impact of potential individual shortcomings of each of the three methods.
Keywords: satellite altimetry, Arctic rivers, water level, detection of outliers, machine learning methods
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