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Remote Sensing in Monitoring Coastal and Inland Waters

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4845

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Special Issue Editors

Prof. Dr. Xiaoyun Wan

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Guest Editor

School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Interests: satellite altimetry; hydrology; bathymetry; satellite gravity

Dr. Qun Liu

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Guest Editor

State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Ningbo Research Institute, Zhejiang University, Hangzhou 310027, China
Interests: spaceborne oceanic lidar; remote sensing; hydrology; radiative transfer

Dr. Salem Morsy

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Guest Editor

School of Ocean Technology, Fisheries and Marine Institute, Memorial University of Newfoundland, St. John's, NL, Canada
Interests: LiDAR remote sensing; 3D point cloud processing; land cover classification; road features extraction; seafloor classification

Dr. Yongjun Jia

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Guest Editor

National Satellite Ocean Application Service, Beijing 100081, China
Interests: sea level change; satellite altimetry; marine gravity; sea ice
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Special Issue Information

Dear Colleagues,

Water resources are crucial for human use, especially in coastal and inland water bodies. These also provide important and diverse habitats and ecosystem services, support high levels of biodiversity, and are critical components of global carbon and nutrient cycles. However, like many other ecosystems, coastal and inland waters are threatened by the synergistic effects of multiple, co-occurring environmental pressures, including nutrient enrichment and other forms of organic and inorganic pollution, climate change, acidification, the establishment and spread of invasive species, and the diversion or extraction of upstream source waters. Their importance, as well as their sensitivity to and capacity to reflect climate and environmental changes, has garnered increasing attention over recent years. Assessing and monitoring coastal and inland waters is crucial to understanding and disentangling the effects of environmental change on freshwater ecosystems and estimating ocean carbon cycles.

Whereas conventional monitoring approaches tend to be limited in terms of spatial coverage and temporal frequency, remote sensing has the potential to provide an invaluable complementary source of data at the local to global scales. In particular, satellite remote sensing, which is often used in open ocean observation, has played an increasingly significant role in coastal and inland water monitoring.

The objective of this Special Issue is to publish novel methods and views using satellite remote sensing techniques, including optical remote sensing, satellite gravity, and satellite altimetry, in the field of coastal and inland waters. We seek to discuss the issues related to water—such as storage distribution and temporal variation, water quality, and water depth—from different perspectives using various remote sensing data, especially new satellite data, such as the Surface Water and Ocean Topography (SWOT) mission. Topics including new algorithms, findings, explanations, and performance of the new data to monitor coastal and inland waters are all within the scope of this section. We also invite papers on new technology or missions for monitoring coastal and inland waters.

This special issue seeks relevant methods of coastal and inland water environmental monitoring, including but not limited to simulations, algorithms, instruments, and applications.

Monitoring coastal levels, quality,and variation;
Monitoring inland water levels, quality, and discharge;
Explanation and prediction of coastal and inland water variation;
Inversion or mapping of coastal and inland water bathymetry;
New technology for monitoring coastal and inland water.

Prof. Dr. Xiaoyun Wan
Dr. Qun Liu
Dr. Salem Morsy
Dr. Yongjun Jia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

coastal and inland waters
satellite remote sensing
airborne/terrestrial/mobile LiDAR
water level/quality/depth
phytoplankton
carbon fluxes and stocks
Unmanned Aerial Vehicles (UAVs)

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Published Papers (5 papers)

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Research

18 pages, 4919 KB

Open AccessArticle

Satellite-Observed Acceleration in the Occurrence of Compound Marine Heatwave and Phytoplankton Bloom Events in the Global Coastal Ocean

by Jiajun Ma and Chunzai Wang

Remote Sens. 2026, 18(9), 1322; https://doi.org/10.3390/rs18091322 - 25 Apr 2026

Viewed by 243

Abstract

The occurrence of marine heatwaves (MHWs) and phytoplankton blooms is accelerating under climate change, yet the frequency and drivers of their compound co-occurrence remain poorly understood. Using coastal-optimized satellite observations from 2003–2020, we mapped global compound MHW–phytoplankton bloom (MHW-PB) events across coastal large marine ecosystems and quantified their spatiotemporal trends and environmental predictors. Compound events are increasing at 4.8% yr⁻¹, driven primarily by a 6.5% yr⁻¹ rise in MHW frequency; a temporal shuffle test confirms this trend falls below random co-occurrence expectation, indicating biological suppression actively constrains compound event growth. The compound independence factor (CIF) reveals latitudinal heterogeneity: low-latitude upwelling systems show MHW–PB mutual exclusivity, while high-latitude and eutrophic coastal regions show positive co-occurrence tendency. Interpretable machine learning further shows that nutrient availability dominates bloom responses at low latitudes whereas light dominates at high latitudes, with MHW intensity exhibiting nutrient-dependent non-linear associations with bloom probability. Paradoxically, compound frequency accelerates nearly twice as fast in low latitudes (6.1% yr⁻¹) as in high latitudes (3.5% yr⁻¹), driven by rapid tropical MHW acceleration. These diverging regimes signal dual ecological risks: trophic mismatches in upwelling systems and escalating hypoxia and harmful algal bloom hazards in eutrophic coastal waters. Full article

(This article belongs to the Special Issue Remote Sensing in Monitoring Coastal and Inland Waters)

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36 pages, 11911 KB

Open AccessArticle

Soil Moisture Retrieval Using Multi-Satellite Dual-Frequency GNSS-IR Considering Environmental Factors

by Shihai Nie, Yongjun Jia, Peng Li, Xing Wu and Yuchao Tang

Remote Sens. 2026, 18(6), 917; https://doi.org/10.3390/rs18060917 - 18 Mar 2026

Viewed by 472

Abstract

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. Full article

(This article belongs to the Special Issue Remote Sensing in Monitoring Coastal and Inland Waters)

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27 pages, 9820 KB

Open AccessArticle

Normalized Satellite-Derived Bathymetry Model from Landsat 8 Single-Band Image with Underwater Topography Trend for Nearshore Shallow Waters

by Jiasheng Xu, Jinfeng Ge, Guoqing Zhou, Ertao Gao, Xiang Zhou, Yuejun Huang, Juanfeng Li, Yang Yu, Zhenyin Yang, Yao Lei, Qiang Zhu, Yuhang Bai and Qinghu Teng

Remote Sens. 2026, 18(4), 660; https://doi.org/10.3390/rs18040660 - 21 Feb 2026

Viewed by 735

Abstract

Satellite-derived bathymetry holds significant value for acquiring nearshore bathymetric data. However, in coastal waters, bathymetry is affected by in-water particle scattering and seafloor substrate variability, leading to spatial inconsistency between the logarithmic green band profile derived from multispectral satellite imagery and the actual water depth profile. According to the position information of interpolated points and the inverse distance square relationship with the surrounding 16 points from low-reference bathymetric data (such as the bathymetric map from GEBCO, NOAA Electronic Navigational Charts), this model adopts a third-order inverse distance square bicubic convolution interpolation method to resample a high-resolution bathymetric map with the size of the satellite image. Normalized underwater topography trend data (derived from the low-resolution reference bathymetric map) were combined with normalized green band data to compute an averaged dataset. In this way, a linear bathymetric model was constructed. We invert this model’s parameters and calculate the water depth by using the average data and reference points from reference bathymetric data. Validation tests were conducted across three test areas using independent validation bathymetric data: Weizhou Island, China (Case II waters); Saipan, Northern Mariana Islands, USA (Case I waters); and Molokai Island, Hawaii, USA (Case I waters). Each test area was studied using five error analysis methods (i.e., scatterplot, error histogram, regional bathymetric error, three check lines, and seven check points). Compared to four classic bathymetric models (i.e., single-band model, log-ratio model, ratio-log model, and multi-band model), the proposed model achieved lower root mean square errors (RMSE) of 2.08 m, 1.40 m, and 2.01 m in the three test areas, representing reductions of 35%, 43%, 45%, and 20% and overall averages of 48%, 62%, 64%, and 43%, respectively. Its goodness of fit (R²) reached 0.87, 0.97, and 0.97, showing improvements of at least 5%, 5%, 9%, and 9% and overall averages of 17%, 77%, 84%, and 12%, respectively. The results demonstrate that the proposed model significantly improves bathymetry accuracy while maintaining algorithmic simplicity, providing a new model for acquiring nearshore foundational bathymetric maps. Full article

(This article belongs to the Special Issue Remote Sensing in Monitoring Coastal and Inland Waters)

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19 pages, 3145 KB

Open AccessArticle

Optical Water Type Guided Benchmarking of Machine Learning Generalization for Secchi Disk Depth Retrieval

by Bo Jiang, Hanfei Yang, Lin Deng and Jun Zhao

Remote Sens. 2026, 18(2), 287; https://doi.org/10.3390/rs18020287 - 15 Jan 2026

Viewed by 574

Abstract

Secchi disk depth (SDD) is a widely critical indicator of water transparency. However, existing retrieval models often suffer from limited transferability and biased predictions when applied to optically diverse waters. Here, we compiled a dataset of 6218 paired in situ SDD and remote sensing reflectance (R_rs) measurements to evaluate model generalization. We benchmarked nine machine learning (ML) models (RF, KNN, SVM, XGB, LGBM, CAT, RealMLP, BNN-MCD, and MDN) under three validation scenarios with progressively decreasing training-test overlap: Random, Waterbody, and Cross-Optical Water Type (Cross-OWT). Furthermore, SHAP analysis was employed to interpret feature contributions and relate model behaviors to optical properties. Results revealed a distinct scenario-dependent generalization gradient. Random splits yielded minimal bias. In contrast, Waterbody transfer consistently shifted predictions toward underestimation (SSPB: −16.9% to −3.8%). Notably, Cross-OWT extrapolation caused significant error inflation and a bias reversal toward overestimation (SSPB: 10.7% to 88.6%). Among all models, the Mixture Density Network (MDN) demonstrated superior robustness with the lowest overestimation (SSPB = 10.7%) under the Cross-OWT scenario. SHAP interpretation indicated that engineered indices, particularly NSMI, functioned as regime separators, with substantial shifts in feature attribution occurring at NSMI values between 0.4 and 0.6. Accordingly, feature sensitivity analysis showed that removing band ratios and indices improved Cross-OWT robustness for several classical ML models. For instance, KNN exhibited a significant reduction in Median Symmetric Accuracy (MdSA) from 96% to 40% after feature reduction. These findings highlight that model applicability must be evaluated under scenario-specific conditions, and feature engineering strategies require rigorous testing against optical regime shifts to ensure generalization. Full article

(This article belongs to the Special Issue Remote Sensing in Monitoring Coastal and Inland Waters)

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27 pages, 8424 KB

Open AccessArticle

Research on the Algorithm of Lake Surface Height Inversion in Qinghai Lake Based on Sentinel-3A Altimeter

by Chuntao Chen, Xiaoqing Li, Jianhua Zhu, Hailong Peng, Youhua Xue, Wanlin Zhai, Mingsen Lin, Yufei Zhang, Jiajia Liu and Yili Zhao

Remote Sens. 2025, 17(4), 647; https://doi.org/10.3390/rs17040647 - 14 Feb 2025

Cited by 4 | Viewed by 1595

Abstract

Lakes are a crucial component of inland water bodies, and changes in their water levels serve as key indicators of global climate change. Traditional methods of lake water level monitoring rely heavily on hydrological stations, but there are problems such as regional representativeness, data stability, and high maintenance costs. The satellite altimeter is an essential tool in lake research, with the Synthetic Aperture Radar (SAR) altimeter offering a high spatial resolution. This enables precise and quantitative observations of lake water levels on a large scale. In this study, we used Sentinel-3A SAR Radar Altimeter (SRAL) data to establish a more reasonable lake height inversion algorithm for satellite-derived lake heights. Subsequently, using this technology, a systematic analysis study was conducted with Qinghai Lake as the case study area. By employing regional filtering, threshold filtering, and altimeter range filtering techniques, we obtained effective satellite altimeter height measurements of the lake surface height. To enhance the accuracy of the data, we combined these measurements with GPS buoy-based geoid data from Qinghai Lake, normalizing lake surface height data from different periods and locations to a fixed reference point. A dataset based on SAR altimeter data was then constructed to track lake surface height changes in Qinghai Lake. Using data from the Sentinel-3A altimeter’s 067 pass over Qinghai Lake, which has spanned 96 cycles since its launch in 2016, we analyzed over seven years of lake surface height variations. The results show that the lake surface height exhibits distinct seasonal patterns, peaking in September and October and reaching its lowest levels in April and May. From 2016 to 2023, Qinghai Lake showed a general upward trend, with an increase of 2.41 m in lake surface height, corresponding to a rate of 30.0 cm per year. Specifically, from 2016 to 2020, the lake surface height rose at a rate of 47.2 cm per year, while from 2020 to 2022, the height remained relatively stable. Full article

(This article belongs to the Special Issue Remote Sensing in Monitoring Coastal and Inland Waters)

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