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Remote Sensing and Geophysical Tools for Land and Water System...
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Remote Sensing and Geophysical Tools for Land and Water System Analysis

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: 31 January 2026 | Viewed by 3633

Special Issue Editors

Dr. Vivek Agarwal

E-Mail Website
Guest Editor
Civil Engineering Department, Northumbria University, Newcastle, UK
Interests: environment engineering; earth sciences; InSAR; GIS; groundwater; land subsidence
Prof. Dr. Stuart Marsh

E-Mail Website
Guest Editor
Nottingham Geospatial Institute, University of Nottingham, Nottingham, UK
Interests: geoscience applications for earth observation (EO); remote sensing and GIS applications
Special Issues, Collections and Topics in MDPI journals
Dr. Anuj Tiwari

E-Mail Website
Guest Editor
Discovery Partners Institute (DPI), University of Illinois System, Chicago, IL, USA
Interests: satellite hydrology; UAV remote sensing; predictive modeling; feature engineering; public health; water monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing and geophysical tools have revolutionized the way land and water systems are studied, offering unprecedented insights into the Earth’s surface and subsurface. These technologies provide critical data for monitoring natural resources, assessing environmental changes, and managing ecosystems. With growing global challenges such as climate change, water scarcity, and land degradation, the integration of remote sensing with geophysical methods is vital for sustainable resource management. The ability to observe, model, and predict interactions between land and water systems is essential for addressing pressing ecological, agricultural, and urban challenges.

This Special Issue seeks to highlight cutting-edge research in remote sensing and geophysical tools for analyzing land and water systems. The aim is to explore innovative approaches, methodologies, and applications that advance understanding of these systems and their interactions. The topic aligns closely with the journal's scope, focusing on interdisciplinary studies that bridge geosciences, environmental sciences, and engineering. By emphasizing advancements in this field, the issue aims to foster new collaborations and provide a platform for the dissemination of transformative research.

Themes for submissions include the following:

  • Advances in remote sensing technologies for land and water system analysis;
  • Integration of remote sensing and geophysical methods for subsurface mapping;
  • Applications in hydrology, soil science, and land-use planning;
  • Data fusion and modeling approaches in resource management;
  • Case studies showcasing practical applications in diverse ecosystems.

Article types may include original research, review articles, technical notes, and case studies. Contributions that emphasize innovation, interdisciplinary approaches, or address real-world challenges are particularly encouraged.

Dr. Vivek Agarwal
Prof. Dr. Stuart Marsh
Dr. Anuj Tiwari
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

  • remote sensing
  • geophysical tools
  • land system analysis
  • water resource management
  • subsurface mapping
  • environmental monitoring
  • data integration and modeling

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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19 pages, 3715 KB  
Article
Understanding Hydrological Changes at Chiang Saen in the Lancang–Mekong River by Integrating Satellite-Based Meteorological Observations into a Deep Learning Model
by Muzi Zhang, Jinqiang Wang, Hongbin Gu, Jian Zhou, Weiwei Wang, Yicheng Wang, Juanjuan Chen, Xueqian Yang, Qiyue Wang, Zhiwen Yi, Yi Huo and Wenchao Sun
Remote Sens. 2025, 17(24), 4002; https://doi.org/10.3390/rs17244002 - 11 Dec 2025
Viewed by 415
Abstract
Understanding the temporal variation in streamflow in the Lancang–Mekong River and its driving mechanism is essential for water resource management of this important international river. In this study, streamflow at the Chiang Saen gauging station was simulated using a long short-term memory (LSTM) [...] Read more.
Understanding the temporal variation in streamflow in the Lancang–Mekong River and its driving mechanism is essential for water resource management of this important international river. In this study, streamflow at the Chiang Saen gauging station was simulated using a long short-term memory (LSTM) model driven by satellite-based Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Multi-Source Weather (MSWX) datasets, with the aim of quantifying the contributions of climate change and human activities to streamflow variations. A key contribution of this work lies in the use of LSTM to reproduce naturalized streamflow conditions—using only climate inputs—thereby providing a data-driven alternative to conventional process-based modeling approaches in this data-scarce basin. The monthly precipitation and temperature data of Chiang Saen station from 1979 to 1991 are used for model training and validation. The natural streamflow of Chiang Saen station from 1992 to 2021 is reconstructed based on the trained model. The results show that the annual average precipitation of the basin from 1979 to 2021 only exhibits a statistically insignificant decreasing trend, while the annual average temperature shows a statistically significant upward trend, and the inter-annual variation in the annual average streamflow shows a non-significant downward trend. Periodic analysis shows that the main periodicity of precipitation, temperature, and streamflow data is 12 months, following annual periodicity in climate. LSTM simulations demonstrate high accuracy in predicting the streamflow in T month based on the MSWEP precipitation and MSWX temperature data in T-2, T-1, and T months. On an annual scale, the streamflow in the changing period (1992–2021) decreases by only 4.6% compared with the reference period (1979–1991). In spring, the streamflow in the changing period is 30.6% higher than that of the reference period, and climate change and human activities contribute 40.8% and 59.2%, respectively. Increases in streamflow (3.4%) are also detected in the winter, with human activity as the dominant contributing factor. For the summer, the streamflow in the changing period is −8.2% lower than that in the reference period, with a greater contribution from human activities (68.7%) than climate change (31.3%). The streamflow in autumn of the changing period is −12.1% lower than that in the reference period, with a greater contribution from human activities (90.2%) than climate change (9.8%). In general, the findings of this study indicate that the driving mechanisms behind streamflow changes at Chiang Saen are complex at different temporal scales, and they provide valuable insights for improving our understanding of hydrological changes within the Lancang–Mekong River Basin. Full article
(This article belongs to the Special Issue Remote Sensing and Geophysical Tools for Land and Water System Analysis)
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20 pages, 11133 KB  
Article
Geospatial Analysis of the Roman Site of Munigua Based on RGB Airborne Imagery
by Emilio Ramírez-Juidias and Daniel Antón
Remote Sens. 2025, 17(18), 3224; https://doi.org/10.3390/rs17183224 - 18 Sep 2025
Cited by 1 | Viewed by 716
Abstract
This study investigates the use of high-resolution RGB aerial imagery from Spain’s National Aerial Orthophotography Plan (PNOA) for archeological feature detection through spectral index analysis and unsupervised clustering. Focusing on the Roman site of Munigua, eight orthophotographs acquired between 2014 and 2024 were [...] Read more.
This study investigates the use of high-resolution RGB aerial imagery from Spain’s National Aerial Orthophotography Plan (PNOA) for archeological feature detection through spectral index analysis and unsupervised clustering. Focusing on the Roman site of Munigua, eight orthophotographs acquired between 2014 and 2024 were analyzed to compute five RGB-based spectral indices: VARI, GLI, ExG, CSI, and BI. These indices were used to detect surface spectral anomalies potentially linked to buried archeological structures. A multi-temporal approach was employed, with Principal Component Analysis (PCA) and K-Means clustering applied independently to each image. This allowed for the identification of temporally persistent anomalies (areas that remained within the same spectral cluster across multiple years), suggesting the presence of underlying anthropogenic features. Despite the lack of near-infrared data, the combination of RGB-based indices and temporal clustering proved effective for non-invasive prospection. The methodology is scalable, repeatable, and relies entirely on open-access datasets, making it suitable for broader applications in heritage monitoring and landscape archeology. The results underscore the potential of RGB imagery and time-series clustering in detecting subtle archeological signals within complex vegetated environments. Full article
(This article belongs to the Special Issue Remote Sensing and Geophysical Tools for Land and Water System Analysis)
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20 pages, 3124 KB  
Article
A Convergent Approach to Investigate the Environmental Behavior and Importance of a Man-Made Saltwater Wetland
by Luigi Alessandrino, Nicolò Colombani, Alessio Usai and Micòl Mastrocicco
Remote Sens. 2025, 17(12), 2019; https://doi.org/10.3390/rs17122019 - 11 Jun 2025
Viewed by 1336
Abstract
Mediterranean saline wetlands are significant ecological habitats defined by seasonal water availability and various biological communities, forming a unique ecotone that combines traits of both freshwater and marine environments. Moreover, they are regarded as notable natural and economic resources. Since the sustainable management [...] Read more.
Mediterranean saline wetlands are significant ecological habitats defined by seasonal water availability and various biological communities, forming a unique ecotone that combines traits of both freshwater and marine environments. Moreover, they are regarded as notable natural and economic resources. Since the sustainable management of protected wetlands necessitates a multidisciplinary approach, the purpose of this study is to provide a comprehensive picture of the hydrological, hydrochemical, and ecological dynamics of a man-made groundwater dependent ecosystem (GDE) by combining remote sensing, hydrochemical data, geostatistical tools, and ecological indicators. The study area, called “Le Soglitelle”, is located in the Campania plain (Italy), which is close to the Domitian shoreline, covering a surface of 100 ha. The Normalized Difference Water Index (NDWI), a remote sensing-derived index sensitive to surface water presence, from Sentinel-2 was used to detect changes in the percentage of the wetland inundated area over time. Water samples were collected in four campaigns, and hydrochemical indexes were used to investigate the major hydrochemical seasonal processes occurring in the area. Geostatistical tools, such as principal component analysis (PCA) and independent component analysis (ICA), were used to identify the main hydrochemical processes. Moreover, faunal monitoring using waders was employed as an ecological indicator. Seasonal variation in the inundation area ranged from nearly 0% in summer to over 50% in winter, consistent with the severe climatic oscillations indicated by SPEI values. PCA and ICA explained over 78% of the total hydrochemical variability, confirming that the area’s geochemistry is mainly characterized by the saltwater sourced from the artesian wells that feed the wetland. The concentration of the major ions is regulated by two contrasting processes: evapoconcentration in summer and dilution and water mixing (between canals and ponds water) in winter. Cl−/Br− molar ratio results corroborated this double seasonal trend. The base exchange index highlighted a salinization pathway for the wetland. Bird monitoring exhibited consistency with hydrochemical monitoring, as the seasonal distribution clearly reflects the dual behaviour of this area, which in turn augmented the biodiversity in this GDE. The integration of remote sensing data, multivariate geostatistical analysis, geochemical tools, and faunal indicators represents a novel interdisciplinary framework for assessing GDE seasonal dynamics, offering practical insights for wetland monitoring and management. Full article
(This article belongs to the Special Issue Remote Sensing and Geophysical Tools for Land and Water System Analysis)
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Other

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40 pages, 8521 KB  
Systematic Review
Nutrient and Dissolved Oxygen (DO) Estimation Using Remote Sensing Techniques: A Literature Review
by Androniki Dimoudi, Christos Domenikiotis, Dimitris Vafidis, Giorgos Mallinis and Nikos Neofitou
Remote Sens. 2025, 17(24), 4044; https://doi.org/10.3390/rs17244044 - 16 Dec 2025
Viewed by 414
Abstract
Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl [...] Read more.
Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl a). Although chl a is a crucial indicator of phytoplankton biomass and nutrient overloading, it reflects the outcome of eutrophication rather than its cause. Nutrients, the primary “drivers” of eutrophication, are essential indicators for predicting the potential phytoplankton growth in water bodies, allowing adoption of effective preventive measures. Long-term monitoring of nutrients combined with multiple water quality indicators using remotely sensed data could lead to a more precise assessment of the trophic state. Retrieving non-optically active constituents, such as nutrients and DO, remains challenging due to their weak optical characteristics and low signal-to-noise ratios. This work is an attempt to review the current progress in the retrieval of un-ionized ammonia (NH3), ammonium (NH4+), ammoniacal nitrogen (AN), nitrite (NO2−), nitrate (NO3−), dissolved inorganic nitrogen (DIN), phosphate (PO43−), dissolved inorganic phosphorus (DIP), silicate (SiO2) and dissolved oxygen (DO) using remotely sensed data. Most studies refer to Case II highly nutrient-enriched water bodies. The commonly used spaceborne and airborne sensors, along with the selected spectral bands and band indices, per study area, are presented. There are two main model categories for predicting nutrient and DO concentration: empirical and artificial intelligence (AI). Comparative studies conducted in the same study area have shown that ML and NNs achieve higher prediction accuracy than empirical models under the same sample size. ML models often outperform NNs when training data are limited, as they are less prone to overfitting under small-sample conditions. The incorporation of a wider range of conditions (e.g., different trophic state, seasonality) into model training needs to be tested for model transferability. Full article
(This article belongs to the Special Issue Remote Sensing and Geophysical Tools for Land and Water System Analysis)
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