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Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 6833

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Special Issue Editors

Dr. Nan Xu

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Guest Editor

School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Interests: remote sensing for coastal and hydrology applications
Special Issues, Collections and Topics in MDPI journals

Dr. Xin Li

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Guest Editor

College of Computer and Information, Hohai University, Nanjing 210098, China
Interests: remote sensing image processing; pansharpening; multimodal data fusion
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Dr. Junfeng Xiong

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Guest Editor

Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: remote sensing of inland waters; applications of machine learning for satellite monitoring

Dr. Linyang Li

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Guest Editor

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: computer communications (networks); programming languages; databases; remote sensing

Dr. Armin Moghimi

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Guest Editor

Ludwig-Franzius-Institute for Hydraulic, Estuarine and Coastal Engineering, Leibniz University Hannover, D-30167 Hannover, Germany
Interests: remote sensing; photogrammetry; registeration; classification; radiometric; normalization; radiometric correction; color consistency; random forest; iran; tehran; Sentinel 1; Sentinel 2; Landsat 8; Landsat 9; Landsat; IRS; UAV; wetland; change detection
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Dr. Arfan Arshad

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Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74075, USA
Interests: Transboundary water governance; food security; remote sensing; climate change; earth system predictability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inland water bodies around the world, such as lakes, reservoirs, rivers, canals, and ponds, play a crucial role in sustaining life, providing human well-being, supporting ecosystems, and ensuring water security for millions of people worldwide. However, in recent years, these valuable resources are under increasing pressure under the background of climate change, population growth, urbanization, and industrial activities. The ability to comprehensively monitor and assess the status and dynamics of inland water bodies (lakes, rivers, and reservoirs) from a local to global scale using remote sensing has become a critical challenge for hydrological, ecological, and environmental researchers, managers, and policy makers.

Remote sensing and artificial intelligence (AI) have emerged as powerful tools in addressing these above mentioned challenges. Remote sensing technologies, encompassing optical, thermal, radar, and lidar sensors aboard satellites and other platforms, offer the capability to acquire frequent, synoptic, and multidimensional data across large geographic areas over a long period with a given revisit frequency. When coupled with widely used AI techniques, such as machine learning and deep learning, these remote sensing datasets can be efficiently processed and analyzed to extract and invert valuable information (such as water area, water level, water storage, water quality, and wetland area) from inland water bodies across the globe. In addition, the obtained water-related information can further support water resource monitoring, assessment, management, and policy making.

Global-scale inland water body mapping and monitoring by remote sensing;
Artificial intelligence and machine learning approaches for water body detection and classification;
Estimation of water quality parameters from remote sensing data;
Remote sensing and AI in monitoring wetland ecosystems;
Monitoring changes in lake and river hydrology;
Synergistic use of multisource remote sensing data for water resource assessment;
Fusion of multisource remote sensing data for inland water studies;
Integration of remote sensing and GIS in water resource management.

Dr. Nan Xu
Dr. Xin Li
Dr. Junfeng Xiong
Dr. Linyang Li
Dr. Armin Moghimi
Dr. Arfan Arshad
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

remote sensing
inland water
artificial intelligence
water resource
hydrology
machine learning
global change and regional response

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Published Papers (6 papers)

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Research

22 pages, 10030 KiB

Open AccessArticle

Assessment of Atmospheric Correction Algorithms for Correcting Sunglint Effects in Sentinel-2 MSI Imagery: A Case Study in Clean Lakes

by Qingyu Wang, Hao Liu, Dian Wang, Dexin Li, Weixin Liu, Yunrui Si, Yuan Liu, Junli Li, Hongtao Duan and Ming Shen

Remote Sens. 2024, 16(16), 3060; https://doi.org/10.3390/rs16163060 - 20 Aug 2024

Viewed by 621

Abstract

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr⁻¹, R² = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr⁻¹, R² = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr⁻¹, R² ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio R_rs(490)/R_rs(560) (R² = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang’s clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters. Full article

(This article belongs to the Special Issue Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence)

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22 pages, 12746 KiB

Open AccessArticle

Monitoring the Vertical Variations in Chlorophyll-a Concentration in Lake Chaohu Using the Geostationary Ocean Color Imager

by Hanhan Li, Xiaoqi Wei, Zehui Huang, Haoze Liu, Ronghua Ma, Menghua Wang, Minqi Hu, Lide Jiang and Kun Xue

Remote Sens. 2024, 16(14), 2611; https://doi.org/10.3390/rs16142611 - 17 Jul 2024

Viewed by 614

Abstract

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (R_rs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field R_rs (R_rs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI R_rs data (R_rs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R² = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAI_Rrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAI_Rrs (R² = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with R_rs_G and environmental parameters (R² = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management. Full article

(This article belongs to the Special Issue Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence)

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16 pages, 8516 KiB

Open AccessArticle

Monitoring Monthly Net-Pen Aquaculture Dynamics of Shallow Lakes Using Sentinel-1 Data: Case Study of Shallow Lakes in Jiangsu Province, China

by Han Ding, Kang Xu, Chongbin Liu and Juanjuan Yu

Remote Sens. 2024, 16(11), 1922; https://doi.org/10.3390/rs16111922 - 27 May 2024

Viewed by 688

Abstract

Net-pen aquaculture, one of the primary forms of aquaculture in shallow lakes, has generated substantial economic benefits but has also triggered eutrophication and ecological degradation. Therefore, the accurate and scientific dynamic monitoring of aquaculture areas is of the utmost importance. However, existing studies have predominantly employed annual monitoring, failing to fully exploit the unique advantage of synthetic aperture radar (SAR) imagery for seasonal, monthly or more frequent time scale monitoring. This study aimed to develop an approach for monitoring the monthly dynamics of net-pen aquaculture using Sentinel-1 time series images. We integrated threshold segmentation and temporal consistency checking to extract spatial data on the net-pen aquaculture from eight typical shallow lakes in Jiangsu Province, one of the eastern coastal provinces of China, from 2016 to 2021. This study yielded the following results: (1) The VV polarization image offers a particularly noticeable contrast between net-pen aquaculture structures and their surroundings and checking for temporal consistency can help alleviate the problem of distinguishing between changes in error caused by misclassification and actual changes that occurred. The overall accuracy of the net-pen aquaculture extraction results was over 85%. (2) This approach not only enables the analysis of changes in the aquaculture area and fence length but can also identify key time points. For instance, the net-pen aquaculture area in Gehu Lake experienced a rapid decline from May to November 2018, the decrease in while Eastern Taihu Lake occurred from September 2018 to June 2019. (3) The removal of net-pen aquaculture in lakes within the Jiangsu Province is primarily attributed to policy responses. In response to the ecological river and lake action plan implemented in Jiangsu Province, a decrease was observed in both the aquaculture areas and fence lengths across most lakes from 2016 to 2021. These results can provide a reference for lake management and ensuring the effective implementation of related policies. Full article

(This article belongs to the Special Issue Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence)

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21 pages, 3207 KiB

Open AccessArticle

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

by Xiying Sun, Lizhi Miao, Xinkai Feng and Xixing Zhan

Remote Sens. 2024, 16(10), 1661; https://doi.org/10.3390/rs16101661 - 8 May 2024

Viewed by 982

Abstract

The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region. Full article

(This article belongs to the Special Issue Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence)

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23 pages, 17055 KiB

Open AccessArticle

Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

by Yue Song, Yue Ma, Zhibiao Zhou, Jian Yang and Song Li

Remote Sens. 2024, 16(7), 1127; https://doi.org/10.3390/rs16071127 - 22 Mar 2024

Cited by 2 | Viewed by 1100

Abstract

The highly accurate data of topography and bathymetry are fundamental to ecological studies and policy decisions for coastal zones. Currently, the automatic extraction and classification of signal photons in coastal zones is a challenging problem, especially the surface type classification without auxiliary data. The lack of classification information limits large-scale bathymetric applications of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). In this study, we propose a photon extraction–classification method to process geolocated photons in coastal areas from the ICESat-2 ATL03 product. The basic idea is to extract the signal photons using an adaptive photon clustering algorithm, and the extracted signal photons are classified based on the accumulated histogram and triangular grid. We also generate the bottom profile using the weighted interpolation. In four typical coastal areas (artificial coast, natural coast, island, and reefs), the extraction accuracy of a signal photons exceeds 0.90, and the Kappa coefficients of four surface types exceed 0.75. This method independently extracts and classifies signal photons without relying on auxiliary data, which can greatly improve the efficiency of obtaining bathymetric points in all kinds of coastal areas and provide technical support for other coastal studies using ICESat-2 data. Full article

(This article belongs to the Special Issue Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence)

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20 pages, 6576 KiB

Open AccessArticle

Spatio-Temporal Variation of Cyanobacteria Blooms in Taihu Lake Using Multiple Remote Sensing Indices and Machine Learning

by Xin Pan, Jie Yuan, Zi Yang, Kevin Tansey, Wenying Xie, Hao Song, Yuhang Wu and Yingbao Yang

Remote Sens. 2024, 16(5), 889; https://doi.org/10.3390/rs16050889 - 2 Mar 2024

Viewed by 1200

Abstract

In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects multiple remote sensing input indices that can represent the characteristics of the primary underlying type in Taihu Lake. The proposed method performs best, with an F1 score of 0.91–0.98. Based on this method, the spatio-temporal variation of cyanobacteria blooms in the Taihu Lake complex was analyzed. During 2010–2022, the average area of cyanobacteria blooms in Taihu Lake increased slightly. Severe-scale cyanobacteria blooms occurred in 2015–2019. Cyanobacteria blooms were normally concentrated from May to November. However, the most prolonged extended duration occurred in 2017, lasting for eight months. Spatially, cyanobacteria blooms were mainly identified in the northwestern part of Taihu Lake, with an average occurrence frequency of about 10.0%. The cyanobacteria blooms often began to grow in the northwestern part of the lake and then spread to the Center of the Lake, and also dissipated earliest in the northwestern part of the lake. Our study is also beneficial for monitoring the growth of cyanobacteria blooms in other similar large lakes in long time series. Full article

(This article belongs to the Special Issue Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence)

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