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Remote Sensing and Artificial Intelligence in Inland Waters Monitoring

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

Deadline for manuscript submissions: closed (1 February 2024) | Viewed by 20981

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Special Issue Editors

Prof. Dr. Miro Govedarica

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Guest Editor

Department of Computing and Control Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: geoinformatics; spatial databases; GeoAI; remote sensing; data analytics; big data; water resources monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Flor Álvarez-Taboada

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Guest Editor

Department of Cartographic Engineering, Faculty of Agriculture anf Forest Engineering, Universidad de Leon, 24401 Ponferrada, Spain
Interests: forest monitoring; NRT monitoring; LiDAR; 3D data; machine learning; optical satellite imagery
Special Issues, Collections and Topics in MDPI journals

Dr. Gordana Jakovljević

E-Mail Website
Guest Editor

Department of Geodesy, Faculty of Architecture, Civil Engineering and Geodesy, University of Banja Luka, 78000 Banja Luka, Bosnia and Herzegovina
Interests: remote sensing; artificial intelligence; plastic litter; water body monitoring

Special Issue Information

Dear Colleagues,

Water is the main fundamental element for life. Aquatic ecosystems are under great pressure due to different natural and anthropogenic factors increasing water crises, including water shortage, water pollution, and other water-related issues. Because of this, the comprehensive monitoring of water status on a local, regional, and global scale is needed to provide efficient and sustainable management of water resources, which is critical to the targets of the 2030 Agenda for Sustainable Development.

Remote sensing technologies and conjunction with in situ data can be used to reflect the spatial distribution and dynamic changes in water quality and quantity. Owing to the high frequency of data acquisition, large-scale coverage and different types of sensors combined with artificial intelligence and cloud computing can be used to understand complex and interconnected changes in aquatic environments.

This Special Issue focuses on papers describing how to improve inland water monitoring in terms of accuracy, and frequency, and add user value to derived data from remote sensing. In particular, this issue was designed to highlight currently applied research using optical, thermal and radar satellite images, LiDAR and UAV data, in situ instrumentation, GeoAI, deep and machine-learning algorithms, cloud computing, and big data processing application to better understand the current status and prevent feature degradation of water resources. Therefore, potential topics include, but are not limited to, the following:

Water flow dynamic monitoring;
Remote sensed monitoring of water quality parameters;
Water surface level monitoring;
GeoAI;
Plastic pollution;
Time-series analysis.

Prof. Dr. Miro Govedarica
Prof. Dr. Flor Álvarez-Taboada
Dr. Gordana Jakovljević
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
GeoAI
artificial intelligence
inland water bodies
water dynamic
water quality
time-series analysis
plastic pollution

Published Papers (12 papers)

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Research

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28 pages, 4379 KiB

Open AccessArticle

Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications

by Kaire Toming, Hui Liu, Tuuli Soomets, Evelyn Uuemaa, Tiina Nõges and Tiit Kutser

Remote Sens. 2024, 16(3), 464; https://doi.org/10.3390/rs16030464 - 25 Jan 2024

Viewed by 1030

Abstract

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO₄), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O₂), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle. Full article

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

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19 pages, 5916 KiB

Open AccessArticle

Long-Term Monitoring of Inland Water Quality Parameters Using Landsat Time-Series and Back-Propagated ANN: Assessment and Usability in a Real-Case Scenario

by Gordana Jakovljevic, Flor Álvarez-Taboada and Miro Govedarica

Remote Sens. 2024, 16(1), 68; https://doi.org/10.3390/rs16010068 - 23 Dec 2023

Cited by 1 | Viewed by 1097

Abstract

Water scarcity and quality deterioration, driven by rapid population growth, urbanization, and intensive industrial and agricultural activities, emphasize the urgency for effective water management. This study aims to develop a model to comprehensively monitor various water quality parameters (WQP) and evaluate the feasibility of implementing this model in real-world scenarios, addressing the limitations of conventional in-situ sampling. Thus, a comprehensive model for monitoring WQP was developed using a 38-year dataset of Landsat imagery and in-situ data from the Water Information System of Europe (WISE), employing Back-Propagated Artificial Neural Networks (ANN). Correlation analyses revealed strong associations between remote sensing data and various WQPs, including Total Suspended Solids (TSS), chlorophyll-a (chl-a), Dissolved Oxygen (DO), Total Nitrogen (TN), and Total Phosphorus (TP). Optimal band combinations for each parameter were identified, enhancing the accuracy of the WQP estimation. The ANN-based model exhibited very high accuracy, particularly for chl-a and TSS (R² > 0.90, NRMSE < 0.79%), surpassing previous studies. The independent validation showcased accurate classification for TSS and TN, while DO estimation faced challenges during high variation periods, highlighting the complexity of DO dynamics. The usability of the developed model was successfully tested in a real-case scenario, proving to be an operational tool for water management. Future research avenues include exploring additional data sources for improved model accuracy, potentially enhancing predictions and expanding the model’s utility in diverse environmental contexts. Full article

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

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24 pages, 8423 KiB

Open AccessArticle

Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change

by Pingping Luo, Xiaohui Wang, Lei Zhang, Mohd Remy Rozainy Mohd Arif Zainol, Weili Duan, Maochuan Hu, Bin Guo, Yuzhu Zhang, Yihe Wang and Daniel Nover

Remote Sens. 2023, 15(24), 5778; https://doi.org/10.3390/rs15245778 - 18 Dec 2023

Cited by 3 | Viewed by 877

Abstract

Continuously global warming and landscape change have aggravated the damage of flood disasters to ecological safety and sustainable development. If the risk of flood disasters under climate and land-use changes can be predicted and evaluated, it will be conducive to flood control, disaster reduction, and global sustainable development. This study uses bias correction and spatial downscaling (BCSD), patch-generating land-use simulation (PLUS) coupled with multi-objective optimization (MOP), and entropy weighting to construct a 1 km resolution flood risk assessment framework for the Guanzhong Plain under multiple future scenarios. The results of this study show that BCSD can process the 6th Climate Model Intercomparison Project (CMIP6) data well, with a correlation coefficient of up to 0.98, and that the Kappa coefficient is 0.85. Under the SSP126 scenario, the change in land use from cultivated land to forest land, urban land, and water bodies remained unchanged. In 2030, the proportion of high-risk and medium-risk flood disasters in Guanzhong Plain will be 41.5% and 43.5% respectively. From 2030 to 2040, the largest changes in risk areas were in medium- and high-risk areas. The medium-risk area decreased by 1256.448 km² (6.4%), and the high-risk area increased by 1197.552 km² (6.1%). The increase mainly came from the transition from the medium-risk area to the high-risk area. The most significant change in the risk area from 2040 to 2050 is the higher-risk area, which increased by 337 km² (5.7%), while the medium- and high-risk areas decreased by 726.384 km² (3.7%) and 667.488 km² (3.4%), respectively. Under the SSP245 scenario, land use changes from other land use to urban land use; the spatial distribution of the overall flood risk and the overall flood risk of the SSP126 and SSP245 scenarios are similar. The central and western regions of the Guanzhong Plain are prone to future floods, and the high-wind areas are mainly distributed along the Weihe River. In general, the flood risk in the Guanzhong Plain increases, and the research results have guiding significance for flood control in Guanzhong and global plain areas. Full article

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

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24 pages, 34088 KiB

Open AccessArticle

Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico

by Freddy Hernán Villota-González, Belkis Sulbarán-Rangel, Florentina Zurita-Martínez, Kelly Joel Gurubel-Tun and Virgilio Zúñiga-Grajeda

Remote Sens. 2023, 15(23), 5505; https://doi.org/10.3390/rs15235505 - 26 Nov 2023

Viewed by 1512

Abstract

Remote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititlán and Zapotlán lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a significant decline in WQ, necessitating the adoption of advanced monitoring techniques. In this research, satellite-based remote sensing data were combined with ground-based measurements from the National Water Quality Monitoring Network of Mexico (RNMCA). These data sources were harnessed to train and evaluate the performance of six distinct categories of machine learning (ML) algorithms aimed at estimating WQ parameters with active spectral signals, including chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS). Various limitations were encountered during the study, primarily due to atmospheric conditions and cloud cover. These challenges affected both the quality and quantity of the data. However, these limitations were overcome through rigorous data preprocessing, the application of ML techniques designed for data-scarce scenarios, and extensive hyperparameter tuning. The superlearner algorithm (SLA), which leverages a combination of individual algorithms, and the multilayer perceptron (MLP), capable of handling complex and non-linear problems, outperformed others in terms of predictive accuracy. Notably, in Lake Cajititlán, these models provided the most accurate predictions for turbidity (r² = 0.82, RMSE = 9.93 NTU, MAE = 7.69 NTU), Chl-a (r² = 0.60, RMSE = 48.06 mg/m³, MAE = 37.98 mg/m³), and TSS (r² = 0.68, RMSE = 13.42 mg/L, MAE = 10.36 mg/L) when using radiometric data from Landsat-8. In Lake Zapotlán, better predictive performance was observed for turbidity (r² = 0.75, RMSE = 2.05 NTU, MAE = 1.10 NTU) and Chl-a (r² = 0.71, RMSE = 6.16 mg/m³, MAE = 4.97 mg/m³) with Landsat-8 radiometric data, while TSS (r² = 0.72, RMSE = 2.71 mg/L, MAE = 2.12 mg/L) improved when Sentinel-2 data were employed. While r² values indicate that the models do not exhibit a perfect fit, those approaching unity suggest that the predictor variables offer valuable insights into the corresponding responses. Moreover, the model’s robustness could be enhanced by increasing the quantity and quality of input variables. Consequently, remote sensing emerges as a valuable tool to support the objectives of WQ monitoring systems. Full article

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

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22 pages, 33889 KiB

Open AccessArticle

Machine and Deep Learning Regression of Chlorophyll-a Concentrations in Lakes Using PRISMA Satellite Hyperspectral Imagery

by Juan Francisco Amieva, Daniele Oxoli and Maria Antonia Brovelli

Remote Sens. 2023, 15(22), 5385; https://doi.org/10.3390/rs15225385 - 16 Nov 2023

Viewed by 1490

Abstract

The estimation of Chlorophyll-a concentration is crucial for monitoring freshwater ecosystem health, particularly in lakes, as it is closely linked to eutrophication processes. Satellite imagery enables synoptic and frequent evaluations of Chlorophyll-a in water bodies, providing essential insights into spatiotemporal eutrophication dynamics. Frontier applications in water remote sensing support the utilization of machine and deep learning models applied to hyperspectral satellite imagery. This paper presents a comparative analysis of conventional machine and deep learning models—namely, Random Forest Regressor, Support Vector Regressor, Long Short-Term Memory, and Gated Recurrent Unit networks—for estimating Chlorophyll-a concentrations. The analysis is based on data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral mission, complemented by low-resolution Chlorophyll-a concentration maps. The analysis focuses on three sub-alpine lakes, spanning Northern Italy and Switzerland as testing areas. Through a series of modelling experiments, best-performing model configurations are pinpointed for both Chlorophyll-a concentration estimations and the improvement of spatial resolution in predictions. Support Vector Regressor demonstrated a superior performance in Chlorophyll-a concentration estimations, while Random Forest Regressor emerged as the most effective solution for refining the spatial resolution of predictions. Full article

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

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17 pages, 9083 KiB

Open AccessArticle

Flood Monitoring Using Sentinel-1 SAR for Agricultural Disaster Assessment in Poyang Lake Region

by Hengkai Li, Zikun Xu, Yanbing Zhou, Xiaoxing He and Minghua He

Remote Sens. 2023, 15(21), 5247; https://doi.org/10.3390/rs15215247 - 5 Nov 2023

Cited by 1 | Viewed by 1499

Abstract

An extensive number of farmlands in the Poyang Lake region of China have been submerged due to the impact of flood disasters, resulting in significant agricultural economic losses. Therefore, it is of great importance to conduct the long-term temporal monitoring of flood-induced water body changes using remote sensing technology. However, the scarcity of optical images and the complex, fragmented terrain are pressing issues in the current water body extraction efforts in southern hilly regions, particularly due to difficulties in distinguishing shadows from numerous mountain and water bodies. For this purpose, this study employs Sentinel-1 synthetic aperture radar (SAR) data, complemented by water indices and terrain features, to conduct research in the Poyang Lake area. The results indicate that the proposed multi-source data water extraction method based on microwave remote sensing data can quickly and accurately extract a large range of water bodies and realize long-time monitoring, thus proving a new technical means for the accurate extraction of floodwater bodies in the Poyang Lake region. Moreover, the comparison of several methods reveals that CAU-Net, which utilizes multi-band imagery as the input and incorporates a channel attention mechanism, demonstrated the best extraction performance, achieving an impressive overall accuracy of 98.71%. This represents a 0.12% improvement compared to the original U-Net model. Moreover, compared to the thresholding, decision tree, and random forest methods, CAU-Net exhibited a significant enhancement in extracting flood-induced water bodies, making it more suitable for floodwater extraction in the hilly Poyang Lake region. During this flood monitoring period, the water extent in the Poyang Lake area rapidly expanded and subsequently declined gradually. The peak water area reached 4080 km² at the height of the disaster. The severely affected areas were primarily concentrated in Yongxiu County, Poyang County, Xinjian District, and Yugan County. Full article

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

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26 pages, 19002 KiB

Open AccessArticle

Retrieval of Water Quality Parameters in Dianshan Lake Based on Sentinel-2 MSI Imagery and Machine Learning: Algorithm Evaluation and Spatiotemporal Change Research

by Lei Dong, Cailan Gong, Hongyan Huai, Enuo Wu, Zhihua Lu, Yong Hu, Lan Li and Zhe Yang

Remote Sens. 2023, 15(20), 5001; https://doi.org/10.3390/rs15205001 - 18 Oct 2023

Cited by 2 | Viewed by 1123

Abstract

According to current research, machine learning algorithms have been proven to be effective in detecting both optical and non-optical parameters of water quality. The use of satellite remote sensing is a valuable method for monitoring long-term changes in the quality of lake water. In this study, Sentinel-2 MSI images and in situ data from the Dianshan Lake area from 2017 to 2023 were used. Four machine learning methods were tested, and optimal detection models were determined for each water quality parameter. It was ultimately determined that these models could be applied to long-term images to analyze the spatiotemporal variations and distribution patterns of water quality in Dianshan Lake. Based on the research findings, integrated learning algorithms, especially CatBoost, have achieved good results in the retrieval of all water quality parameters. Spatiotemporal analysis reveals that the overall distribution of water quality parameters is uneven, with significant spatial variations. Permanganate index (COD_Mn), Total Nitrogen (TN), and Total Phosphorus (TP) show relatively small interannual differences, generally exhibiting a decreasing trend in concentrations. In contrast, chlorophyll-a (Chl-a), dissolved oxygen (DO), and Secchi Disk Depth (SDD) exhibit significant interannual and inter-year differences. Chl-a reached its peak in 2020, followed by a decrease, while DO and SDD showed the opposite trend. Further analysis indicated that the distribution of water quality parameters is significantly influenced by climatic factors and human activities such as agricultural expansion. Overall, there has been an improvement in the water quality of Dianshan Lake. The study demonstrates the feasibility of accurately monitoring water quality even without measured spectral data, using machine learning methods and satellite reflectance data. The research results presented in this paper can provide new insights into water quality monitoring and water resource management in Dianshan Lake. Full article

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

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19 pages, 23302 KiB

Open AccessArticle

Mapping Underwater Aquatic Vegetation Using Foundation Models With Air- and Space-Borne Images: The Case of Polyphytos Lake

by Leonidas Alagialoglou, Ioannis Manakos, Sofia Papadopoulou, Rizos-Theodoros Chadoulis and Afroditi Kita

Remote Sens. 2023, 15(16), 4001; https://doi.org/10.3390/rs15164001 - 12 Aug 2023

Cited by 1 | Viewed by 1773

Abstract

Mapping underwater aquatic vegetation (UVeg) is crucial for understanding the dynamics of freshwater ecosystems. The advancement of artificial intelligence (AI) techniques has shown great potential in improving the accuracy and efficiency of UVeg mapping using remote sensing data. This paper presents a comparative study of the performance of classical and modern AI tools, including logistic regression, random forest, and a visual-prompt-tuned foundational model, the Segment Anything model (SAM), for mapping UVeg by analyzing air- and space-borne images in the few-shot learning regime, i.e., using limited annotations. The findings demonstrate the effectiveness of the SAM foundation model in air-borne imagery (

G S D

= 3–6 cm) with an

F 1

score of

86.5 % \pm 4.1 %

when trained with as few as 40 positive/negative pairs of pixels, compared to

54.0 % \pm 9.2 %

using the random forest model and

42.8 % \pm 6.2 %

using logistic regression models. However, adapting SAM to space-borne images (WorldView-2 and Sentinel-2) remains challenging, and could not outperform classical pixel-wise random forest and logistic regression methods in our task. The findings presented provide valuable insights into the strengths and limitations of AI models for UVeg mapping, aiding researchers and practitioners in selecting the most suitable tools for their specific applications. Full article

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

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27 pages, 3421 KiB

Open AccessArticle

Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes

by Christine L. Bunyon, Benjamin T. Fraser, Amanda McQuaid and Russell G. Congalton

Remote Sens. 2023, 15(11), 2839; https://doi.org/10.3390/rs15112839 - 30 May 2023

Cited by 4 | Viewed by 2296

Abstract

With the increasing occurrence of cyanobacteria blooms, it is crucial to improve our ability to monitor impacted lakes accurately, efficiently, and safely. Cyanobacteria are naturally occurring in many waters globally. Some species can release neurotoxins which cause skin irritations, gastrointestinal illness, pet/livestock fatalities, and possibly additional complications after long-term exposure. Using a DJI M300 RTK Unmanned Aerial Vehicle equipped with a MicaSense 10-band dual camera system, six New Hampshire lakes were monitored from May to September 2022. Using the image spectral data coupled with in situ water quality data, a random forest classification algorithm was used to predict water quality categories. The analysis yielded very high overall classification accuracies for cyanobacteria cell (93%), chlorophyll-a (87%), and phycocyanin concentrations (92%). The 475 nm wavelength, normalized green-blue difference index—version 4 (NGBDI_4), and normalized green-red difference index—version 4 (NGRDI_4) indices were the most important features for these classifications. Logarithmic regressions illuminated relationships between single bands/indices with water quality data but did not perform as well as the classification algorithm approach. Ultimately, the UAS multispectral data collected in this study successfully classified cyanobacteria cell, chlorophyll-a, and phycocyanin concentrations in the studied NH lakes. Full article

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

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31 pages, 11866 KiB

Open AccessArticle

LASSO (L1) Regularization for Development of Sparse Remote-Sensing Models with Applications in Optically Complex Waters Using GEE Tools

by Anna Catherine Cardall, Riley Chad Hales, Kaylee Brooke Tanner, Gustavious Paul Williams and Kel N. Markert

Remote Sens. 2023, 15(6), 1670; https://doi.org/10.3390/rs15061670 - 20 Mar 2023

Cited by 2 | Viewed by 1832

Abstract

Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then creating empirical water quality models using approaches such as multi-linear regression or step-wise linear regression. These approaches, which require modelers to select model parameters, may not be well suited for optically complex waters, where interference from suspended solids, dissolved organic matter, or other constituents may act as “confusers”. For these waters, it may be useful to include non-standard terms, which might not be considered when using traditional methods. Recent machine-learning work has demonstrated an ability to explore large feature spaces and generate accurate empirical models that do not require parameter selection. However, these methods, because of the large number of included terms involved, result in models that are not explainable and cannot be analyzed. We explore the use of Least Absolute Shrinkage and Select Operator (LASSO), or L1, regularization to fit linear regression models and produce parsimonious models with limited terms to enable interpretation and explainability. We demonstrate this approach with a case study in which chl-a models are developed for Utah Lake, Utah, USA., an optically complex freshwater body, and compare the resulting model terms to model terms from the literature. We discuss trade-offs between interpretability and model performance while using L1 regularization as a tool. The resulting model terms are both similar to and distinct from those in the literature, thereby suggesting that this approach is useful for the development of models for optically complex water bodies where standard model terms may not be optimal. We investigate the effect of non-coincident data, that is, the length of time between satellite image collection and in situ sampling, on model performance. We find that, for Utah Lake (for which there are extensive data available), three days is the limit, but 12 h provides the best trade-off. This value is site-dependent, and researchers should use site-specific numbers. To document and explain our approach, we provide Colab notebooks for compiling near-coincident data pairs of remote-sensing and in situ data using Google Earth Engine (GEE) and a second notebook implementing L1 model creation using scikitlearn. The second notebook includes data-engineering routines with which to generate band ratios, logs, and other combinations. The notebooks can be easily modified to adapt them to other locations, sensors, or parameters. Full article

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

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27 pages, 5814 KiB

Open AccessArticle

Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning

by Leonardo F. Arias-Rodriguez, Ulaş Firat Tüzün, Zheng Duan, Jingshui Huang, Ye Tuo and Markus Disse

Remote Sens. 2023, 15(5), 1390; https://doi.org/10.3390/rs15051390 - 1 Mar 2023

Cited by 7 | Viewed by 4153

Abstract

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R² = 0.68) but lower performances for TSM and NO3-N (R² = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring. Full article

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

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Review

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19 pages, 7076 KiB

Open AccessReview

Monitoring Inland Water Quantity Variations: A Comprehensive Analysis of Multi-Source Satellite Observation Technology Applications

by Zhengkai Huang, Xin Wu, Haihong Wang, Cheinway Hwang and Xiaoxing He

Remote Sens. 2023, 15(16), 3945; https://doi.org/10.3390/rs15163945 - 9 Aug 2023

Cited by 1 | Viewed by 1246

Abstract

The advancement of multi-source Earth observation technology has led to a substantial body of literature on inland water monitoring. This has resulted in the emergence of a distinct interdisciplinary field encompassing the application of multi-source Earth observation techniques in inland water monitoring. Despite this growth, few systematic reviews of this field exist. Therefore, in this paper, we offer a comprehensive analysis based on 30,212 publications spanning the years 1990 to 2022, providing valuable insights. We collected and analyzed fundamental information such as publication year, country, affiliation, journal, and author details. Through co-occurrence analysis, we identified country and author partnerships, while co-citation analysis revealed the influence of journals, authors, and documents. We employed keywords to explore the evolution of hydrological phenomena and study areas, using burst analysis to predict trends and frontiers. We discovered exponential growth in this field with a closer integration of hydrological phenomena and Earth observation techniques. The research focus has shifted from large glaciers to encompass large river basins and the Tibetan Plateau. Long-term research attention has been dedicated to optical properties, sea level, and satellite gravity. The adoption of automatic image recognition and processing, enabled by deep learning and artificial intelligence, has opened new interdisciplinary avenues. The results of the study emphasize the significance of long-term, stable, and accurate global observation and monitoring of inland water, particularly in the context of cloud computing and big data. Full article

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

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