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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 22530

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Dr. Sergey Lebedev

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1. Geophysical Center of the Russian Academy of Sciences, Bld. 3, Molodezhnaya St., 119296 Moscow, Russia
2. Department of Higher Mathematics No. 1, National Research University of Electronic Technology (MIET), Bld. 1, Shokin Square, Zelenograd, 124498 Moscow, Russia
3. Department of Physical Geography and Ecology, Faculty of Geography and Geoecology, Tver State University, Bld. 33, Zhelyabova St., 170100 Tver, Russia
Interests: retracking algorithms; calculating correction algorithms; satellite altimetry data interpretation; regional and global climatic change; Caspian Sea level and dynamics; water level of rivers; lakes and reservoirs
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Dear Colleagues,

Today’s remote sensing methods are becoming an integral part of monitoring various water areas, including both oceans and groundwater bodies (rivers, lakes, and reservoirs). The study of the hydrological regime of these objects is especially important for hard-to-reach water bodies, where in situ measurements are rarely or never performed. Active (synthetic aperture radar (SAR) and altimetry) and passive (microwave and infrared radiometry, spectrometry in the visible range) remote sensing data with high and ultra-high spatial resolution contain new information on the hydrological, hydrobiological, and other features of rivers, lakes, and reservoirs; the state of a basin’s natural environment; and the degree of anthropogenic impact. These data enable the significant expansion of the range of tasks that can be solved and the rapid monitoring of the occurrence and development of hazardous situations either natural or human-made in nature. Satellite information can be effectively used to monitor coastal reshaping, the water level and volume of water bodies, and river runoff, and map the consequences of natural disasters (floods, landslides, etc.).

Dr. Sergey Lebedev
Guest Editor

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Keywords

remote sensing
lake
river
reservoir

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20 pages, 6507 KiB

Open AccessArticle

Combining Satellite Imagery and a Deep Learning Algorithm to Retrieve the Water Levels of Small Reservoirs

by Jiarui Wu, Xiao Huang, Nan Xu, Qishuai Zhu, Conrad Zorn, Wenzhou Guo, Jiangnan Wang, Beibei Wang, Shuaibo Shao and Chaoqing Yu

Remote Sens. 2023, 15(24), 5740; https://doi.org/10.3390/rs15245740 - 15 Dec 2023

Cited by 1 | Viewed by 1936

Abstract

There are an estimated 800,000 small reservoirs globally with a range of uses. Given the collective importance of these reservoirs to water resource management and wider society, it is essential that we can monitor and understand the hydrological dynamics of ungauged reservoirs, particularly in a changing climate. However, unlike large reservoirs, continuous and systematic hydrological observations of small reservoirs are often unavailable. In response, this study has developed a retrieval framework for water levels of small reservoirs using a deep learning algorithm and remotely sensed satellite data. Demonstrated at four reservoirs in California, satellite imagery from both Sentinel-1 and Sentinel-2 along with corresponding water level field measurements was collected. Post-processed images were fed into a water level inversion convolutional neural network model for water level inversion, while different combinations of these satellite images, sampling approaches for training/testing data, and attention modules were used to train the model and evaluated for accuracy. The results show that random sampling of training data coupled with Sentinel-2 satellite imagery was generally the most accurate initially. Performance is improved by incorporating a channel attention mechanism, with the average R² increasing by 8.6% and the average RMSE and MAE decreasing by 15.5% and 36.4%, respectively. The proposed framework was further validated on three additional reservoirs in different regions. In conclusion, the retrieval framework proposed in this study provides a stable and accurate methodology for water level estimation of small reservoirs and can be a powerful tool for small reservoir monitoring over large spatial scales. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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21 pages, 5437 KiB

Open AccessArticle

An Application of Machine-Learning Model for Analyzing the Impact of Land-Use Change on Surface Water Resources in Gauteng Province, South Africa

by Eskinder Gidey and Paidamwoyo Mhangara

Remote Sens. 2023, 15(16), 4092; https://doi.org/10.3390/rs15164092 - 20 Aug 2023

Cited by 6 | Viewed by 2156

Abstract

The change in land-use diversity is attributed to the anthropogenic factors sustaining life. The surface water bodies and other crucial natural resources in the study area are being depleted at an alarming rate. This study explored the implications of the changing land-use diversity on surface water resources by using a random forest (RF) classifier machine-learning algorithm and remote-sensing models in Gauteng Province, South Africa. Landsat datasets from 1993 to 2022 were used and processed in the Google Earth Engine (GEE) platform, using the RF classifier. The results indicate nine land-use diversity classes having increased and decreased tendencies, with high F-score values ranging from 72.3% to 100%. In GP, the spatial coverage of BL has shrunk by 100.4 km² every year over the past three decades. Similarly, BuA exhibits an annual decreasing rate of 42.4 km² due to the effect of dense vegetation coverage within the same land use type. Meanwhile, water bodies, marine quarries, arable lands, grasslands, shrublands, dense forests, and wetlands were expanded annually by 1.3, 2.3, 2.9, 5.6, 11.2, 29.6, and 89.5 km², respectively. The surface water content level of the study area has been poor throughout the study years. The MNDWI and NDWI values have a stronger Pearson correlation at a radius of 5 km (r = 0.60, p = 0.000, n = 87,260) than at 10 and 15 km. This research is essential to improve current land-use planning and surface water management techniques to reduce the environmental impacts of land-use change. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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18 pages, 5493 KiB

Open AccessArticle

Detection and Attribution of Alpine Inland Lake Changes by Using Random Forest Algorithm

by Wei Guo, Xiangnan Ni, Yi Mu, Tong Liu and Junzhe Zhang

Remote Sens. 2023, 15(4), 1144; https://doi.org/10.3390/rs15041144 - 20 Feb 2023

Cited by 3 | Viewed by 2001

Abstract

The alpine inland lake dynamics have been good indicators of changes in terrestrial hydrological cycles under global climate change. However, the relationship between alpine inland lake and climatic factors remained largely uncertain. This study examines the spatial-temporal change of the fluctuation of the lake by using dense time series Landsat TM/ETM/OLI images to delineate water boundary information based on the Random Forest algorithm and using ICESat (Ice, Cloud and land Elevation Satellite) dataset to monitor changes in variations of water level. Variations of Qinghai Lake (QHL) were analyzed from 1987 to 2020 and the mechanism of these changes was discussed with meteorological data. The results indicated that the QHL fluctuated strongly showing a pattern of shrinkage–expansion over the last three decades. The lake storage significantly decreased by −2.58 × 10⁸ m³·yr⁻¹ (R² = 0.86, p < 0.01) from 1989 to 2004 and sharply increased (6.92 × 10⁸ m³·yr⁻¹, R² = 0.92, p < 0.01) after 2004. The relationship between the lake and climate over the last 30 years implies that the decreasing evaporation and increasing precipitation were the major factors affecting the fluctuation of lake storage. Meanwhile, the temporal heterogeneity of the driving mechanism of climate change led to the phased characteristics of lake storage change. In detail, obvious warming led to the shrinkage of the QHL before 2004 through increasing evaporation, while humidifying and accelerating wind stilling dominated the expansion of the QHL after 2004 by increasing precipitation and decreasing evaporation. This paper indicated that the frameworks of multi-source remote sensing and accurate detection of water bodies were required to protect the high-altitude lakes from further climate changes based on the findings of this paper of the QHL recently. The framework presented herein can provide accurate detection and monitoring of water bodies in different locations in the Qinghai-Tibet Plateau, and provide a necessary basis for future political activities and decisions in terms of sustainable water resource management. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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19 pages, 5031 KiB

Open AccessArticle

Continuous Intra-Annual Changes of Lake Water Level and Water Storage from 2000 to 2018 on the Tibetan Plateau

by Hengliang Guo, Bingkang Nie, Yonghao Yuan, Hong Yang, Wenhao Dai, Xiaolei Wang and Baojin Qiao

Remote Sens. 2023, 15(4), 893; https://doi.org/10.3390/rs15040893 - 6 Feb 2023

Cited by 2 | Viewed by 1968

Abstract

There is a large amount of lakes on the Tibetan Plateau (TP), which are very sensitive to climate change. Understanding the characteristics and driving mechanisms of lake change are crucial for understanding climate change and the effective use of water resources. Previous studies have mainly focused on inter-annual lake variation, but the continuous and long-term intra-annual variation of lakes on the TP remains unclear. To address this gap, we used the global surface water (GSW) dataset and the Shuttle Radar Topography Mission (SRTM) DEM to estimate the water level and storage changes on the TP. The results indicated that the average annual minimum lake water level (LWLmin) and the average annual maximum lake water level (LWLmax) increased by 3.09 ± 0.18 m (0.16 ± 0.01 m/yr) and 3.69 ± 0.12 m (0.19 ± 0.01 m/yr) from 2000 to 2018, respectively, and the largest change of LWLmin and LWLmax occurred in 2002–2003 (0.45 m) and 2001–2002 (0.39 m), respectively. Meanwhile, the annual minimum lake water storage change (LWSCmin) and annual maximum lake water storage change (LWSCmax) were 125.34 ± 6.79 Gt (6.60 ± 0.36 Gt/yr) and 158.07 ± 4.52 Gt (8.32 ± 0.24 Gt/yr) from 2000 to 2018, and the largest changes of LWSCmin and LWSCmax occurred in the periods of 2002–2003 (17.67 Gt) and 2015–2016 (17.51 Gt), respectively. The average intra-year changes of lake water level (LWLCintra-year) and the average intra-year changes of lake water storage (LWSCintra-year) were 0.98 ± 0.23 m and 40.19 ± 10.67 Gt, respectively, and the largest change in both LWLCintra-year (1.44 m) and LWSCintra-year (62.46 Gt) occurred in 2018. The overall trend of lakes on the TP was that of expansion, where the LWLC and LWSC in the central and northern parts of the TP was much faster than that in other regions, while the lakes in the southern part of the TP were shrinking, with decreasing LWLC and LWSC. Increased precipitation was found to be the primary meteorological factor affecting lake expansion, and while increasing glacial meltwater also had an important influence on the LWSC, the variation of evaporation only had a little influence on lake change. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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26 pages, 7672 KiB

Open AccessArticle

Toward Atmospheric Correction Algorithms for Sentinel-3/OLCI Images of Productive Waters

by Aleksandr Molkov, Sergei Fedorov and Vadim Pelevin

Remote Sens. 2022, 14(15), 3663; https://doi.org/10.3390/rs14153663 - 30 Jul 2022

Cited by 7 | Viewed by 2032

Abstract

Atmospheric correction of remote sensing imagery over optically complex waters is still a challenging task. Even algorithms showing a good accuracy for moderate and extremely turbid waters need to be tested when being used for eutrophic inland basins. Such a test was carried out in this study on the example of a Sentinel-3/OLCI image of the productive waters of the Gorky Reservoir during the period of intense blue-green algal bloom using data on the concentration of chlorophyll a and remote sensing reflectance measured from the motorboat at many points of the reservoir. The accuracy of four common atmospheric correction (AC) algorithms was examined. All of them showed unsatisfactory accuracy due to incorrect determination of atmospheric aerosol parameters and aerosol radiance. The calculated aerosol optical depth (AOD) spectra varied widely (AOD(865) = 0.005 − 0.692) even over a small area (up to 10 × 10 km) and correlated with the measured chlorophyll a. As a result, a part of the high water-leaving signal caused by phytoplankton bloom was taken as an atmosphere signal. A significant overestimation of atmospheric aerosol parameters, as a consequence, led to a strong underestimation of the remote sensing reflectance and low accuracy of the considered AC algorithms. To solve this problem, an algorithm with a fixed AOD was proposed. The fixed AOD spectrum was determined in the area with relatively “clean” water as 5 percentiles of AOD in all water pixels. The proposed algorithm made it possible to obtain the remote sensing reflectance with high accuracy. The slopes of linear regression are close to 1 and the intercepts tend to zero in almost all spectral bands. The determination coefficients are more than 0.9; the bias, mean absolute percentage error, and root-mean-square error are notably lower than for other AC algorithms. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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25 pages, 6628 KiB

Open AccessArticle

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

by Bin Deng, Kai Xiong, Zhiyong Huang, Changbo Jiang, Jiang Liu, Wei Luo and Yifei Xiang

Remote Sens. 2022, 14(13), 3107; https://doi.org/10.3390/rs14133107 - 28 Jun 2022

Cited by 5 | Viewed by 2118

Abstract

The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km², respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km² for the training session and 4.6 km² for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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18 pages, 4366 KiB

Open AccessArticle

The Dynamic Changes of Lake Issyk-Kul from 1958 to 2020 Based on Multi-Source Satellite Data

by Yujie Zhang, Ninglian Wang, Xuewen Yang and Zhonglei Mao

Remote Sens. 2022, 14(7), 1575; https://doi.org/10.3390/rs14071575 - 24 Mar 2022

Cited by 14 | Viewed by 3632

Abstract

Lake Issyk-Kul is the largest alpine lake in arid Central Asia. In recent years, the lake has become a subject of special concern due to the dramatic fluctuations in its water level. In this study, the long-term continuous changes in the water level of Lake Issyk-Kul were derived from hydro-meteorological stations, CryoSat-2, and ICESat-2 satellites. Changes in area were analyzed by the Joint Research Centre (JRC) Global Surface Water (GSW) dataset based on the Google Earth Engine and the variations in water volume were estimated by an empirical formula. The results indicate that the water level of Lake Issyk-Kul fluctuated between 1606.06 m and 1608.32 m during 1958–2020, showing a slight decrease of 0.02 m/year on average. The water level first experienced a significant decreasing trend of 0.05 m/year from 1958 to 1998, and then began to rise rapidly by 0.10 m/year during 1998–2006, followed by a fluctuating decline after 2006. The area of Lake Issyk-Kul exhibited a downward trend before 1998, then a rapid expansion during 1998–2006, and short-term fluctuations in decline thereafter. Meanwhile, changes in water volume of Lake Issyk-Kul followed a similar pattern of variations in water level and area. According to comprehensive analyses, the continuous downward trend of the water level before 1998 was primarily affected by substantial anthropogenic water consumption in the basin. However, since the 21st century, the increases in precipitation and glacier meltwater and the reduced water consumption have collectively facilitated the short-term recovery of Lake Issyk-Kul in water level, area, and water volume. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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21 pages, 59735 KiB

Open AccessArticle

Analysis of Factors Influencing the Lake Area on the Tibetan Plateau Using an Eigenvector Spatial Filtering Based Spatially Varying Coefficient Model

by Zhexin Xiong, Yumin Chen, Huangyuan Tan, Qishan Cheng and Annan Zhou

Remote Sens. 2021, 13(24), 5146; https://doi.org/10.3390/rs13245146 - 18 Dec 2021

Cited by 4 | Viewed by 2631

Abstract

Lakes on the Tibet Plateau (TP) have a significant impact on the water cycle and water balance, and it is important to monitor changes in lake area and identify the influencing factors. Existing research has failed to quantitatively identify the changes and influencing factors of lakes in different regions of the TP. Thus, an eigenvector spatial filtering based spatially varying coefficient (ESF-SVC) model was used to analyze the relationship between lake area and climatic and terrain factors in the inner watershed of the TP from 2000 to 2015. A comparison with ordinary regression and spatial models showed that the ESF-SVC model eliminates spatial autocorrelation and has the best model fit and complexity. The experiments demonstrated that precipitation, snow melt, and permafrost moisture release, as well as the area of vegetation and elevation difference in the watershed, can significantly promote the expansion of lakes, while evapotranspiration and days of mean daily temperature above zero have an inhibitory effect on lake area expansion. The degree of influence of each factor also differs significantly over time and across regions. Spatially quantitative modeling of lake area in the TP using the ESF-SVC method is a new attempt to provide novel ideas for lake research. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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14 pages, 3147 KiB

Open AccessTechnical Note

Preliminary Performance Assessment of the Wave Parameter Retrieval Algorithm from the Average Reflected Pulse

by Yuriy Titchenko, Guo Jie, Vladimir Karaev, Kirill Ponur, Maria Ryabkova, Vladimir Baranov, Vladimir Ocherednik and Yijun He

Remote Sens. 2024, 16(2), 418; https://doi.org/10.3390/rs16020418 - 21 Jan 2024

Cited by 2 | Viewed by 1193

Abstract

To obtain new information about surface waves, it is proposed to use an underwater acoustic wave gauge, and an assessment of its effectiveness can be performed using a numerical simulation and field experiment. A new device, an underwater acoustic wave gauge named “Kalmar”, was developed by the Institute of Applied Physics of the Russian Academy of Sciences for long-term, all-weather monitoring of wind waves. The instrument uses ultrasound to probe the water surface from underwater and can be used to verify remote sensing data. In this work, the capabilities of the device are tested and compared with ADCP data. Two independent methods for processing underwater acoustic wave gauge data are discussed and compared. One of them is completely new for acoustic measurements and is based on the analysis of the shape of the reflected acoustic pulse averaged over space and time. The other allows processing individual reflected pulses and calculating the time implementation of the distance to the water surface. It is shown that two independent methods of significant wave height retrieval from the acoustic wave gauge measurements are highly correlated. The “Kalmar” acoustic wave gauge and the RDI WH-600 acoustic Doppler current profiler operated simultaneously at the test site in Gelendzhik from 1 February to 10 February 2020. The significant wave heights measured by the two instruments are in good agreement. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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12 pages, 1649 KiB

Open AccessTechnical Note

The Study of the Bistatic Cross-Correlation Function of Two Signals Separated in Frequency Reflected by the Water Surface

by Yury Titchenko, Jie Guo, Vladimir Karaev, Dmitry Kovaldov and Yijun He

Remote Sens. 2023, 15(16), 4049; https://doi.org/10.3390/rs15164049 - 16 Aug 2023

Viewed by 1046

Abstract

The purpose of this study is to analyze the applicability conditions for the significant wave height (SWH) measurement approach based on measuring the cross-correlation function of two signals with similar frequencies reflected by the sea surface in the bistatic problem statement (the transmitting antenna and the receiving antenna are separated in space). When implementing this approach, the modulus of the normalized cross-correlation function for several pairs of signals with different frequency bases will be measured in the experiment. The advantage of this approach over the traditional method for radar altimetry, based on the analysis of the shape of the reflected pulse, is the high accuracy in measuring the SWH for weak waves. In the bistatic formulation of the problem, an important advantage of the approach under study is the possibility of obtaining analytical formulas for solving the direct problem. This paper presents the derivation of a formula for the modulus of the normalized cross-correlation function of reflected signals, which expresses an explicit relationship with the parameters of sea waves and the measurement geometry in the bistatic formulation of the problem. This paper considers the influence on the modulus of the normalized cross-correlation function of the antenna patterns of the transmitting and receiving antennas, the distances to the sea surface, the wave slope variances, the SWH and the frequency base of the transmitted signals. The optimal variants of the measurement scheme are discussed. The results and conclusions obtained can be easily expanded to underwater acoustic sounding. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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