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Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Attributes in Varying Forest Conditions
Highlighting the Use of UAV to Increase the Resilience of Native Hawaiian Coastal Cultural Heritage
LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert
Four Years of Atmospheric Boundary Layer Height Retrievals Using COSMIC-2 Satellite Data
Remote Sensing of Forests in Bavaria: A Review

Journal Description

Remote Sensing

Remote Sensing is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 24.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journal: Geomatics

Impact Factor: 4.2 (2023); 5-Year Impact Factor: 4.9 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2072-4292

Latest Articles

23 pages, 1814 KiB

Open AccessArticle

Doppler-Spread Space Target Detection Based on Overlapping Group Shrinkage and Order Statistics

by Linsheng Bu, Tuo Fu, Defeng Chen, Huawei Cao, Shuo Zhang and Jialiang Han

Remote Sens. 2024, 16(18), 3413; https://doi.org/10.3390/rs16183413 - 13 Sep 2024

The Doppler-spread problem is commonly encountered in space target observation scenarios using ground-based radar when prolonged coherent integration techniques are utilized. Even when the translational motion is accurately compensated, the phase resulting from changes in the target observation attitude (TOA) still leads to [...] Read more.

The Doppler-spread problem is commonly encountered in space target observation scenarios using ground-based radar when prolonged coherent integration techniques are utilized. Even when the translational motion is accurately compensated, the phase resulting from changes in the target observation attitude (TOA) still leads to extension of the target’s echo energy across multiple Doppler cells. In particular, as the TOA change undergoes multiple cycles within a coherent processing interval (CPI), the Doppler spectrum spreads into equidistant sparse line spectra, posing a substantial challenge for target detection. Aiming to address such problems, we propose a generalized likelihood ratio test based on overlapping group shrinkage denoising and order statistics (OGSos-GLRT) in this study. First, the Doppler domain signal is denoised according to its equidistant sparse characteristics, allowing for the recovery of Doppler cells where line spectra may be situated. Then, several of the largest Doppler cells are integrated into the GLRT for detection. An analytical expression for the false alarm probability of the proposed detector is also derived. Additionally, a modified OGSos-GLRT method is proposed to make decisions based on an increasing estimated number of line spectra (ENLS), thus increasing the robustness of OGSos-GLRT when the ENLS mismatches the actual value. Finally, Monte Carlo simulations confirm the effectiveness of the proposed detector, even at low signal-to-noise ratios (SNRs). Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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21 pages, 2709 KiB

Open AccessArticle

AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification

by Xiaofei Yang, Yuxiong Luo, Zhen Zhang, Dong Tang, Zheng Zhou and Haojin Tang

Remote Sens. 2024, 16(18), 3412; https://doi.org/10.3390/rs16183412 - 13 Sep 2024

Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively. However, CNNs struggle in modeling long-term dependencies, and transformers may miss [...] Read more.

Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively. However, CNNs struggle in modeling long-term dependencies, and transformers may miss subtle spatial-spectral features. To address these challenges, this paper proposes an innovative hybrid HSI classification method aggregating hierarchical spatial-spectral features from a CNN and long pixel dependencies from a transformer. The proposed aggregation multi-hierarchical feature network (AMHFN) is designed to capture various hierarchical features and long dependencies from HSI, improving classification accuracy and efficiency. The proposed AMHFN consists of three key modules: (a) a Local-Pixel Embedding module (LPEM) for capturing prominent spatial-spectral features; (b) a Multi-Scale Convolutional Extraction (MSCE) module to capture multi-scale local spatial-spectral features and aggregate hierarchical local features; (c) a Multi-Scale Global Extraction (MSGE) module to explore multi-scale global dependencies and integrate multi-scale hierarchical global dependencies. Rigorous experiments on three public hyperspectral image (HSI) datasets demonstrated the superior performance of the proposed AMHFN method. Full article

(This article belongs to the Section AI Remote Sensing)

28 pages, 20281 KiB

Open AccessArticle

Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery

by Seth Goodman, Ariel BenYishay and Daniel Runfola

Remote Sens. 2024, 16(18), 3411; https://doi.org/10.3390/rs16183411 - 13 Sep 2024

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development [...] Read more.

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods. Full article

(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)

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24 pages, 11964 KiB

Open AccessArticle

Projecting Response of Ecological Vulnerability to Future Climate Change and Human Policies in the Yellow River Basin, China

by Xiaoyuan Zhang, Shudong Wang, Kai Liu, Xiankai Huang, Jinlian Shi and Xueke Li

Remote Sens. 2024, 16(18), 3410; https://doi.org/10.3390/rs16183410 - 13 Sep 2024

Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV [...] Read more.

Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV in the Yellow River Basin (YRB), a climate-sensitive and ecologically fragile area, by integrating climate change, land management, and ecological protection policies under various scenarios. To achieve this, we developed an EV assessment framework combining a scenario weight matrix, Markov chain, Patch-generating Land Use Simulation model, and exposure–sensitivity–adaptation. We further explored the spatiotemporal variations of EV and their potential socioeconomic impacts at the watershed scale. Our results show significant geospatial variations in future EV under the three scenarios, with the northern region of the upstream area being the most severely affected. Under the ecological conservation management scenario and historical trend scenario, the ecological environment of the basin improves, with a decrease in very high vulnerability areas by 4.45% and 3.08%, respectively, due to the protection and restoration of ecological land. Conversely, under the urban development and construction scenario, intensified climate change and increased land use artificialization exacerbate EV, with medium and high vulnerability areas increasing by 1.86% and 7.78%, respectively. The population in high and very high vulnerability areas is projected to constitute 32.75–33.68% and 34.59–39.21% of the YRB’s total population in 2040 and 2060, respectively, and may continue to grow. Overall, our scenario analysis effectively demonstrates the positive impact of ecological protection on reducing EV and the negative impact of urban expansion and economic development on increasing EV. Our work offers new insights into land resource allocation and the development of ecological restoration policies. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring and Assessment of Forest, Grassland, Wetland and Urban Ecosystem)

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20 pages, 4165 KiB

Open AccessArticle

Identifying Conservation Priority Areas of Hydrological Ecosystem Service Using Hot and Cold Spot Analysis at Watershed Scale

by Srishti Gwal, Dipaka Ranjan Sena, Prashant K. Srivastava and Sanjeev K. Srivastava

Remote Sens. 2024, 16(18), 3409; https://doi.org/10.3390/rs16183409 - 13 Sep 2024

Hydrological Ecosystem Services (HES) are crucial components of environmental sustainability and provide indispensable benefits. The present study identifies critical hot and cold spots areas of HES in the Aglar watershed of the Indian Himalayan Region using six HES descriptors, namely water yield (WYLD), [...] Read more.

Hydrological Ecosystem Services (HES) are crucial components of environmental sustainability and provide indispensable benefits. The present study identifies critical hot and cold spots areas of HES in the Aglar watershed of the Indian Himalayan Region using six HES descriptors, namely water yield (WYLD), crop yield factor (CYF), sediment yield (SYLD), base flow (LATQ), surface runoff (SURFQ), and total water retention (TWR). The analysis was conducted using weightage-based approaches under two methods: (1) evaluating six HES descriptors individually and (2) grouping them into broad ecosystem service categories. Furthermore, the study assessed pixel-level uncertainties that arose because of the distinctive methods used in the identification of hot and cold spots. The associated synergies and trade-offs among HES descriptors were examined too. From method 1, 0.26% area of the watershed was classified as cold spots and 3.18% as hot spots, whereas method 2 classified 2.42% area as cold spots and 2.36% as hot spots. Pixel-level uncertainties showed that 0.57 km² and 6.86 km² of the watershed were consistently under cold and hot spots, respectively, using method 1, whereas method 2 identified 2.30 km² and 6.97 km² as cold spots and hot spots, respectively. The spatial analysis of hot spots showed consistent patterns in certain parts of the watershed, primarily in the south to southwest region, while cold spots were mainly found on the eastern side. Upon analyzing HES descriptors within broad ecosystem service categories, hot spots were mainly in the southern part, and cold spots were scattered throughout the watershed, especially in agricultural and scrubland areas. The significant synergistic relation between LATQ and WYLD, and sediment retention and WYLD and trade-offs between SURFQ and HES descriptors like WYLD, LATQ, sediment retention, and TWR was attributed to varying factors such as land use and topography impacting the water balance components in the watershed. The findings underscore the critical need for targeted conservation efforts to maintain the ecologically sensitive regions at watershed scale. Full article

(This article belongs to the Special Issue Application of Remote Sensing and Spatial Analysis for Environmental Management in the Global South)

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31 pages, 17406 KiB

Open AccessArticle

Identification of Internal Tides in ECCO Estimates of Sea Surface Salinity in the Andaman Sea

by Bulusu Subrahmanyam, V. S. N. Murty, Sarah B. Hall and Corinne B. Trott

Remote Sens. 2024, 16(18), 3408; https://doi.org/10.3390/rs16183408 - 13 Sep 2024

We used NASA’s high-resolution (1/48° or 2.3 km, hourly) Estimating the Circulation and Climate of the Ocean (ECCO) estimates of salinity at a 1 m depth from November 2011 to October 2012 to detect semi-diurnal and diurnal internal tides (ITs) in the Andaman [...] Read more.

We used NASA’s high-resolution (1/48° or 2.3 km, hourly) Estimating the Circulation and Climate of the Ocean (ECCO) estimates of salinity at a 1 m depth from November 2011 to October 2012 to detect semi-diurnal and diurnal internal tides (ITs) in the Andaman Sea and determine their characteristics in three 2° × 2° boxes off the Myanmar coast (box A), central Andaman Sea (box B), and off the Thailand coast (box C). We also used observed salinity and temperature data for the above period at the BD12-moored buoy in the central Andaman Sea. ECCO salinity data were bandpass-filtered with 11–14 h and 22–26 h periods. Large variations in filtered ECCO salinity (~0.1 psu) in the boxes corresponded with near-surface imprints of propagating ITs. Observed data from the box B domain reveals strong salinity stratification (halocline) in the upper 40 m. Our analyses reveal that the shallow halocline affects the signatures of propagating semi-diurnal ITs reaching the surface, but diurnal ITs propagating in the halocline reach up to the surface and bring variability in ECCO salinity. In box A, the semi-diurnal IT characteristics are higher speeds (0.96 m/s) with larger wavelengths (45 km), that are closer to theoretical mode 2 estimates, but the diurnal ITs propagating in the box A domain, with a possible source over the shelf of Gulf of Martaban, attain lower values (0.45 m/s, 38 km). In box B, the propagation speed is lower (higher) for semi-diurnal (diurnal) ITs. Estimates for box C are closer to those for box A. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Ocean Salinity)

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31 pages, 7177 KiB

Open AccessArticle

Estimation Model and Spatio-Temporal Analysis of Carbon Emissions from Energy Consumption with NPP-VIIRS-like Nighttime Light Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

by Mengru Song, Yanjun Wang, Yongshun Han and Yiye Ji

Remote Sens. 2024, 16(18), 3407; https://doi.org/10.3390/rs16183407 - 13 Sep 2024

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial [...] Read more.

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations. Full article

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14 pages, 374883 KiB

Open AccessArticle

Revisiting the 2017 Jiuzhaigou (Sichuan, China) Earthquake: Implications for Slip Inversions Based on InSAR Data

by Zhengwen Sun and Yingwen Zhao

Remote Sens. 2024, 16(18), 3406; https://doi.org/10.3390/rs16183406 - 13 Sep 2024

The 2017 Jiuzhaigou earthquake (Ms = 7.0) struck the eastern Tibetan Plateau and caused extensive concern. However, the reported slip models of this earthquake have distinct discrepancies and cannot provide a good fit for GPS data. The Jiuzhaigou earthquake also presents a good [...] Read more.

The 2017 Jiuzhaigou earthquake (Ms = 7.0) struck the eastern Tibetan Plateau and caused extensive concern. However, the reported slip models of this earthquake have distinct discrepancies and cannot provide a good fit for GPS data. The Jiuzhaigou earthquake also presents a good opportunity to investigate the question of how to avoid overfitting of InSAR observations for co-seismic slip inversions. To comprehend this shock, we first used pre-seismic satellite optical images to extract a surface trace of the seismogenic fault, which constitutes the northern segment of the Huya Fault. Then, we collected GPS observations as well as to measure the co-seismic displacements. Lastly, joint inversions were carried out to obtain the slip distribution. Our results showed that the released moment was 5.3 × 10¹⁸ N m, equivalent to Mw 6.4 with a rigidity of 30 GPa. The maximum slip at a depth of ~6.8 km reached up to 1.12 m, dominated by left-lateral strike-slip. The largest potential surface rupture occurred in the center of the seismogenic fault with strike- and dip-slip components of 0.4 m and 0.2 m, respectively. Comparison with the focal mechanisms of the 1973 Ms 6.5 earthquake and the 1976 triplet of earthquakes (Mw > 6) on the middle and south segments of the Huya Fault indicated different regional motion and slip mechanisms on the three segments. The distribution of co-seismic landslides had a strong correlation with surface displacements rather than surface rupture. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Interferometry Symposium 2024)

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35 pages, 6364 KiB

Open AccessArticle

Mapping the Influence of Olympic Games’ Urban Planning on the Land Surface Temperatures: An Estimation Using Landsat Series and Google Earth Engine

by Joan-Cristian Padró, Valerio Della Sala, Marc Castelló-Bueno and Rafael Vicente-Salar

Remote Sens. 2024, 16(18), 3405; https://doi.org/10.3390/rs16183405 - 13 Sep 2024

The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using [...] Read more.

The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data and cloud computing provided by Google Earth Engine (GEE), this study examines the effects of various forms of Olympic Games facility urban planning in different historical moments and location typologies, as follows: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games that occurred from Paris 2024 to Montreal 1976. However, this paper focuses specifically on the representative cases of Paris 2024, Tokyo 2020, Rio 2016, Beijing 2008, Sydney 2000, Barcelona 1992, Seoul 1988, and Montreal 1976. The study is not only concerned with obtaining absolute land surface temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, the locally normalized land surface temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it has been determined that Olympic planning has resulted in the development of green spaces, creating “green spots” that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become “hotspots.” Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context. Full article

(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology II)

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15 pages, 4826 KiB

Open AccessArticle

Assessing Evapotranspiration Changes in Response to Cropland Expansion in Tropical Climates

by Leonardo Laipelt, Julia Brusso Rossi, Bruno Comini de Andrade, Morris Scherer-Warren and Anderson Ruhoff

Remote Sens. 2024, 16(18), 3404; https://doi.org/10.3390/rs16183404 - 13 Sep 2024

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an [...] Read more.

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an increased frequency of drought events, leading to a series of conflicts among water users. However, detailed studies on the impacts of changes in water use due to agriculture expansion, including irrigation, are still lacking. Furthermore, the higher presence of clouds in tropical environments poses challenges for the availability of high-resolution data for vegetation monitoring via satellite images. This study aims to analyze 37 years of agricultural expansion using the Landsat collection and a satellite-based model (geeSEBAL) to assess changes in evapotranspiration resulting from cropland expansion in tropical climates, focusing on the São Marcos River Basin in Brazil. It also used a methodology for estimating daily evapotranspiration on days without satellite images. The results showed a 34% increase in evapotranspiration from rainfed areas, mainly driven by soybean cultivation. In addition, irrigated areas increased their water use, despite not significantly changing water use at the basin scale. Conversely, natural vegetation areas decreased their evapotranspiration rates by 22%, suggesting possible further implications with advancing changes in land use and land cover. Thus, this study underscores the importance of using satellite-based evapotranspiration estimates to enhance our understanding of water use across different land use types and scales, thereby improving water management strategies on a large scale. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM) (Second Edition))

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26 pages, 6509 KiB

Open AccessArticle

The Operational and Climate Land Surface Temperature Products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B

by Darren Ghent, Jasdeep Singh Anand, Karen Veal and John Remedios

Remote Sens. 2024, 16(18), 3403; https://doi.org/10.3390/rs16183403 - 13 Sep 2024

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth’s surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an [...] Read more.

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth’s surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking. Full article

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16 pages, 7033 KiB

Open AccessArticle

Establishment of Remote Sensing Inversion Model and Its Application in Pollution Source Identification: A Case Study of East Lake in Wuhan

by Shiyue He, Yanjun Zhang, Lan Luo and Yuanxin Song

Remote Sens. 2024, 16(18), 3402; https://doi.org/10.3390/rs16183402 - 13 Sep 2024

In remote watersheds or large water bodies, monitoring water quality parameters is often impractical due to high costs and time-consuming processes. To address this issue, a cost-effective methodology based on remote sensing was developed to predict water quality parameters over a large and [...] Read more.

In remote watersheds or large water bodies, monitoring water quality parameters is often impractical due to high costs and time-consuming processes. To address this issue, a cost-effective methodology based on remote sensing was developed to predict water quality parameters over a large and operationally challenging area, especially focusing on East Lake. Sentinel-2 satellite image data were used as a proxy, and a multiple linear regression model was developed to quantify water quality parameters, namely chlorophyll-a, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand. This model was then applied to East Lake to obtain the temporal and spatial distribution of these water quality parameters. By identifying the locations with the highest concentrations along the boundaries of East Lake, potential pollution sources could be inferred. The results demonstrate that the developed multiple linear regression model provided a satisfactory relationship between the measured and simulated water quality parameters. The coefficients of determination R² of the multiple linear regression models for chlorophyll-a, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand were 0.943, 0.781, 0.470, 0.624 and 0.777, respectively. The potential pollution source locations closely matched the officially published information on East Lake pollutant discharges. Therefore, using remote sensing imagery to establish a multiple linear regression model is a feasible approach for understanding the exceedance and distribution of various water quality parameters in East Lake. Full article

(This article belongs to the Special Issue Monitoring Ecohydrology with Remote Sensing)

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20 pages, 11776 KiB

Open AccessArticle

Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile

by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart and Roberto Urrutia

Remote Sens. 2024, 16(18), 3401; https://doi.org/10.3390/rs16183401 - 13 Sep 2024

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning [...] Read more.

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R² = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)², consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R² = 0.96; MSE = 0.33 (µg/L)²; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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19 pages, 8280 KiB

Open AccessArticle

Estimating Three-Dimensional Resistivity Distribution with Magnetotelluric Data and a Deep Learning Algorithm

by Xiaojun Liu, James A. Craven, Victoria Tschirhart and Stephen E. Grasby

Remote Sens. 2024, 16(18), 3400; https://doi.org/10.3390/rs16183400 - 13 Sep 2024

In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric/magnetic field response by using a fully connected neural network [...] Read more.

In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric/magnetic field response by using a fully connected neural network framework (U-Net). Limited by computer hardware functionality, the resistivity models were generated by using a random walk technique to enlarge the generalization coverage of the neural network model, and 15,000 paired datasets were utilized to train and validate it. Grid search was used to select the optimal configuration parameters. With the optimal model framework from the parameter tuning phase, the metrics showed stable convergence during model training/validation. In the test period, the trained model was applied to predict the resistivity distribution by using both the simulated synthetic and the real MT data from the Mount Meager area, British Columbia. The reliability of the model prediction was verified with noised input data from the synthetic model. The calculated results can be used to reconstruct the position and shape trends of bodies with anomalous resistivity, which verifies the stability and performance of the DL-based 3D inversion algorithm and showcases its potential practical applications. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0)

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17 pages, 5434 KiB

Open AccessArticle

HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis

by Daniel La’ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona and Oscar Mendez

Remote Sens. 2024, 16(18), 3399; https://doi.org/10.3390/rs16183399 - 12 Sep 2024

The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective. However, RGB-based networks do not take full advantage of the spectral information in hyperspectral [...] Read more.

The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective. However, RGB-based networks do not take full advantage of the spectral information in hyperspectral data. This inspired the creation of HyperKon, a dedicated hyperspectral Convolutional Neural Network backbone built with self-supervised contrastive representation learning. HyperKon uniquely leverages the high spectral continuity, range, and resolution of hyperspectral data through a spectral attention mechanism. We also perform a thorough ablation study on different kinds of layers, showing their performance in understanding hyperspectral layers. Notably, HyperKon achieves a remarkable 98% Top-1 retrieval accuracy and surpasses traditional RGB-trained backbones in both pansharpening and image classification tasks. These results highlight the potential of hyperspectral-native backbones and herald a paradigm shift in hyperspectral image analysis. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)

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29 pages, 13136 KiB

Open AccessArticle

Assessing the Impact of Agricultural Practices and Urban Expansion on Drought Dynamics Using a Multi-Drought Index Application Implemented in Google Earth Engine: A Case Study of the Oum Er-Rbia Watershed, Morocco

by Imane Serbouti, Jérôme Chenal, Biswajeet Pradhan, El Bachir Diop, Rida Azmi, Seyid Abdellahi Ebnou Abdem, Meriem Adraoui, Mohammed Hlal and Mariem Bounabi

Remote Sens. 2024, 16(18), 3398; https://doi.org/10.3390/rs16183398 - 12 Sep 2024

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the [...] Read more.

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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23 pages, 4848 KiB

Open AccessArticle

Summer Chukchi Sea Near-Surface Salinity Variability in Satellite Observations and Ocean Models

by Semyon A. Grodsky, Nicolas Reul and Douglas Vandemark

Remote Sens. 2024, 16(18), 3397; https://doi.org/10.3390/rs16183397 - 12 Sep 2024

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may [...] Read more.

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may suggest that interannual changes in the Bering Strait mass transport are the sole and dominant factor shaping the salinity distribution in the downstream Chukchi Sea. Using satellite sea surface salinity (SSS) retrievals and altimetry-based estimates of the Bering Strait transport, the relationship between the Strait transport and Chukchi Sea SSS distributions is analyzed from 2010 onward, focusing on the ice-free summer to fall period. A comparison of five different satellite SSS products shows that anomalous SSS spatially averaged over the Chukchi Sea during the ice-free period is consistent among them. Observed interannual temporal change in satellite SSS is confirmed by comparison with collocated ship-based thermosalinograph transect datasets. Bering Strait transport variability is known to be driven by the local meridional wind stress and by the Pacific-to-Arctic sea level gradient (pressure head). This pressure head, in turn, is related to an Arctic Oscillation-like atmospheric mean sea level pattern over the high-latitude Arctic, which governs anomalous zonal winds over the Chukchi Sea and affects its sea level through Ekman dynamics. Satellite SSS anomalies averaged over the Chukchi Sea show a positive correlation with preceding months’ Strait transport anomalies. This correlation is confirmed using two longer (>40-year), separate ocean data assimilation models, with either higher- (0.1°) or lower-resolution (0.25°) spatial resolution. The relationship between the Strait transport and Chukchi Sea SSS anomalies is generally stronger in the low-resolution model. The area of SSS response correlated with the Strait transport is located along the northern coast of the Chukotka Peninsula in the Siberian Coastal Current and adjacent zones. The correlation between wind patterns governing Bering Strait variability and Siberian Coastal Current variability is driven by coastal sea level adjustments to changing winds, in turn driving the Strait transport. Due to the Chukotka coastline configuration, both zonal and meridional wind components contribute. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Coastline Monitoring)

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29 pages, 14739 KiB

Open AccessArticle

Use of SLSTR Sea Surface Temperature Data in OSTIA as a Reference Sensor: Implementation and Validation

by Chongyuan Mao, Simon Good and Mark Worsfold

Remote Sens. 2024, 16(18), 3396; https://doi.org/10.3390/rs16183396 - 12 Sep 2024

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office’s Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and [...] Read more.

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office’s Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and Sentinel-3B data since December 2019). The impacts of using SLSTR SSTs and the SLSTR as the reference sensor for the bias correction of other satellite data have been assessed using independent Argo float data. Combining Sentinel-3A and -3B SLSTRs with two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (onboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership and National Oceanic and Atmospheric Administration-20 satellites) in the reference dataset has also been investigated. The results indicate that when using the SLSTR as the only reference satellite sensor, the OSTIA system becomes warmer overall, although there are mixed impacts in different parts of the global ocean. Using both the VIIRS and the SLSTR in the reference dataset leads to moderate but more consistent improvements globally. Numerical weather prediction (NWP) results also indicate a better performance when using both the VIIRS and the SLSTR in the reference dataset compared to only using the SLSTR at night. Combining the VIIRS and the SLSTR with latitudinal weighting shows the best validation results against Argo, but further investigation is required to refine this method. Full article

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17 pages, 6251 KiB

Open AccessArticle

Asymmetric Response of the Indonesian Throughflow to Co-Occurring El Niño–Southern Oscillation–Indian Ocean Dipole Events

by Aojie Li, Yongchui Zhang, Mei Hong, Tengfei Xu and Jing Wang

Remote Sens. 2024, 16(18), 3395; https://doi.org/10.3390/rs16183395 - 12 Sep 2024

The Indonesian Throughflow (ITF) is significantly modulated by Indo-Pacific climate forcing, especially the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). However, when ENSO and IOD occur concurrently, they tend to play different roles in the ITF volume transport. By employing [...] Read more.

The Indonesian Throughflow (ITF) is significantly modulated by Indo-Pacific climate forcing, especially the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). However, when ENSO and IOD occur concurrently, they tend to play different roles in the ITF volume transport. By employing an improved Constructed Circulation Analogue (CCA) method, the relative contributions of these climate events to the ITF inflow and outflow transport in the upper and lower layers were quantified. The results indicate that during co-occurring El Niño and positive IOD events, ENSO is the dominant influence, with ratio values of 5.5:1 (3.5:1) in the upper layer and 1.7:1 (1.6:1) in the lower layer of the inflow (outflow). Conversely, during co-occurring La Niña and negative IOD events, the IOD predominates, with ratio values of 1:6 (1:6.5) in the upper layer and 1:4 (1:3) in the lower layer of the inflow (outflow). The mechanisms underlying these variations in the upper and lower layers can be explained by the differences in sea level anomaly (SLA) and wave propagation, respectively. This study provides a new insight into distinct roles of climate forcing on the ITF volume transport during the simultaneous occurrence of multiple climate modes. Full article

(This article belongs to the Special Issue Air-Sea Interaction and Climate Variability in the Ocean: Observations and Modeling Based on Remote Sensing Techniques)

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53 pages, 32149 KiB

Open AccessReview

Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation

by Kai Hu, Xinyan Feng, Qi Zhang, Pengfei Shao, Ziran Liu, Yao Xu, Shiqian Wang, Yuanyuan Wang, Han Wang, Li Di and Min Xia

Remote Sens. 2024, 16(18), 3394; https://doi.org/10.3390/rs16183394 - 12 Sep 2024

With the rapid development of satellite remote sensing technology, carbon-cycle research, as a key focus of global climate change, has also been widely developed in terms of carbon source/sink-research methods. The internationally recognized “top-down” approach, which is based on satellite observations, is an [...] Read more.

With the rapid development of satellite remote sensing technology, carbon-cycle research, as a key focus of global climate change, has also been widely developed in terms of carbon source/sink-research methods. The internationally recognized “top-down” approach, which is based on satellite observations, is an important means to verify greenhouse gas-emission inventories. This article reviews the principles, categories, and development of satellite detection payloads for greenhouse gases and introduces inversion algorithms and datasets for satellite remote sensing of XCO₂. It emphasizes inversion methods based on machine learning and assimilation algorithms. Additionally, it presents the technology and achievements of carbon-assimilation systems used to estimate carbon fluxes. Finally, the article summarizes and prospects the future development of carbon-assimilation inversion to improve the accuracy of estimating and monitoring Earth’s carbon-cycle processes. Full article

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