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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: 28 February 2025 | Viewed by 6855

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Special Issue Editors

Prof. Dr. John J. Qu

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Guest Editor

Professor and Director, GENRI & ESTC, Department of Geography and GeoInformation Science (GGS), Global Environment and Natural Resources Institute (GENRI), College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing; earth system and climate science; soil moisture and drought monitoring; water-energy-food nexus; environment and fire science
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xianjun Hao

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Guest Editor

GENRI & ESTC, Department of Geography and GeoInformation Science (GGS), Global Environment and Natural Resources Institute (GENRI), College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: satellite remote sensing applications; earth sciences and climate change; soil moisture and drought monitoring; data science and high performance computing
Special Issues, Collections and Topics in MDPI journals

Dr. Zhiliang Zhu

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Guest Editor

U.S. Geological Survey, Ecosystems Mission Area, Reston, VA 20192, USA
Interests: climate change

Special Issue Information

Dear Colleagues,

The changing climate threatens the very existence of the human community and the environment. It also functions to destabilize the resilience of global ecosystems as well as impact water, energy, food and public health. The Earth’s water–energy–food–health (WEFH) nexus is a complex system of interdependent relationships that requires using remote sensing technology for monitoring and research as well as valuable information to guarantee the well-being of society and the environment.

This Special Issue aims to showcase the latest advances in satellite-based applications in climate change and sustainability studies. The issue will focus on innovative approaches and techniques that utilize remote sensing measurements to address climate change challenges and promote sustainability as well as explore the vast potential of satellite-based applications in monitoring and understanding climate change phenomena.

Scope and Topics:

Applications of remote sensing observations in monitoring climate change and variations.
Satellite-based monitoring of greenhouse gas emissions and atmospheric composition.
Remote sensing of land use and land cover changes for climate change impact assessment.
Applications of remote sensing for water resource management and conservation.
Satellite-based monitoring of ecosystem functions, processes and biodiversity for sustainability assessments.
Applications of remote sensing technology in renewable energy planning and development.
Satellite-based approaches for disaster management and risk reduction in the context of climate change.
Remote sensing applications on the water–energy–food–health (WEFH) nexus.
Ecosystem vulnerability and resilience from remote sensing.

Submission Guidelines:

We invite researchers and practitioners from around the world to submit their original research articles, reviews and perspectives on satellite-based applications in climate change and sustainability. All submissions will be peer-reviewed by experts in the field, and accepted papers will be published online in Remote Sensing.

Prof. Dr. John J. Qu
Prof. Dr. Xianjun Hao
Dr. Zhiliang Zhu
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.

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Published Papers (7 papers)

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24 pages, 46868 KiB

Open AccessArticle

Thermal Profile Dynamics of a Central European River Based on Landsat Images: Natural and Anthropogenic Influencing Factors

by Ahmed Mohsen, Tímea Kiss, Sándor Baranya, Alexia Balla and Ferenc Kovács

Remote Sens. 2024, 16(17), 3196; https://doi.org/10.3390/rs16173196 - 29 Aug 2024

Viewed by 371

Abstract

River temperature is a critical parameter influencing aquatic ecosystems and water quality. However, it can be changed by natural (e.g., flow and depth conditions) and human factors (e.g., waste and industrial water drainage). Satellite-based monitoring offers a valuable tool for assessing river temperature on a large scale, elucidating the impacts of various factors. This study aims to analyze the spatiotemporal dynamics of surface water temperature (SWT) in the medium-sized Tisza River in response to natural and anthropogenic influences, employing Landsat satellites and in situ water temperature data. The validity of the Landsat-based SWT estimates was assessed across different channel sections with varying sizes. The longitudinal thermal profile of the Tisza was analyzed by mosaicking, monthly, four Landsat 9 images, covering the entire 962 km length of the Tisza in 2023. The impact of climate change was evaluated by analyzing SWT trends at a specific site from 1984 to 2024, utilizing 483 Landsat 4–9 images. The findings indicated elevated accuracy for Landsat-based SWT estimation (R² = 0.94; RMSE = 3.66 °C), particularly for channel sizes covering ≥ 3 pixels. Discharge, microclimatic conditions, and channel morphology significantly influence SWT, demonstrating a general increasing trend downstream with occasional decreases during the summer months. Dams were observed to lower the SWT downstream due to cooler bottom reservoir water discharge, with more pronounced differences during the summer months (1–3 °C). Tributaries predominantly (75%) elevated the SWT in the Tisza River, albeit with varying magnitudes across different months. Over the 40-year study period, an increasing trend in SWT was discerned, with an annual rise rate of 0.0684 °C. While the thermal band of Landsat satellites proved valuable for investigating the Tisza River’s thermal profile at a broad scale, finer spatial resolution bands are necessary for detecting small-scale phenomena such as thermal plumes and localized temperature variations in rivers. Full article

(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)

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21 pages, 15805 KiB

Open AccessArticle

Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data

by Ali Radman, Fariba Mohammadimanesh and Masoud Mahdianpari

Remote Sens. 2024, 16(14), 2673; https://doi.org/10.3390/rs16142673 - 22 Jul 2024

Viewed by 725

Abstract

Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constrained in practical issues regarding efficiency while gaining high accuracy with limited training ground truth samples. To address these limitations, in this study, a novel deep learning model, namely Wet-ConViT, is designed for the precise mapping of wetlands using multi-source satellite data, combining the strengths of multispectral Sentinel-2 and SAR Sentinel-1 datasets. Both capturing local information of convolution and the long-range feature extraction capabilities of transformers are considered within the proposed architecture. Specifically, the key to Wet-ConViT’s foundation is the multi-head convolutional attention (MHCA) module that integrates convolutional operations into a transformer attention mechanism. By leveraging convolutions, MHCA optimizes the efficiency of the original transformer self-attention mechanism. This resulted in high-precision land cover classification accuracy with a minimal computational complexity compared with other state-of-the-art models, including two convolutional neural networks (CNNs), two transformers, and two hybrid CNN–transformer models. In particular, Wet-ConViT demonstrated superior performance for classifying land cover with approximately 95% overall accuracy metrics, excelling the next best model, hybrid CoAtNet, by about 2%. The results highlighted the proposed architecture’s high precision and efficiency in terms of parameters, memory usage, and processing time. Wet-ConViT could be useful for practical wetland mapping tasks, where precision and computational efficiency are paramount. Full article

(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)

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21 pages, 8897 KiB

Open AccessArticle

Satellite-Based Reconstruction of Atmospheric CO₂ Concentration over China Using a Hybrid CNN and Spatiotemporal Kriging Model

by Yiying Hua, Xuesheng Zhao, Wenbin Sun and Qiwen Sun

Remote Sens. 2024, 16(13), 2433; https://doi.org/10.3390/rs16132433 - 2 Jul 2024

Viewed by 717

Abstract

Although atmospheric CO₂ concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25° grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: R² = 0.936, RMSE = 1.3 ppm, MAE = 0.946 ppm; compared with TCCON: R² = 0.954, RMSE = 0.898 ppm and MAE = 0.741 ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO₂ concentrations, thereby supporting policy formulation and actions to address climate change. Full article

(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)

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21 pages, 8775 KiB

Open AccessArticle

Analysis of Meteorological Drivers of Taihu Lake Algal Blooms over the Past Two Decades and Development of a VOCs Emission Inventory for Algal Bloom

by Zihang Liao, Shun Lv, Chenwu Zhang, Yong Zha, Suyang Wang and Min Shao

Remote Sens. 2024, 16(10), 1680; https://doi.org/10.3390/rs16101680 - 9 May 2024

Viewed by 955

Abstract

Cyanobacterial blooms represent a common environmental issue in aquatic systems, and these blooms bring forth numerous hazards, with the generation of volatile organic compounds (VOCs) being one of them. Global climate change has led to alterations in various climatic factors affecting algal growth, indirectly impacting the quantity of VOCs released by algae. With advancements in remote sensing technology, exploration of the spatiotemporal distributions of algae in large water bodies has become feasible. This study focuses on Taihu Lake, characterized by frequent occurrences of cyanobacterial blooms. Utilizing MODIS satellite imagery from 2001 to 2020, we analyzed the spatiotemporal characteristics of cyanobacterial blooms in Taihu Lake and its subregions. Employing the LightGBM machine learning model and the (SHapley Additive exPlanations) SHAP values, we quantitatively analyzed the major meteorological drivers influencing cyanobacterial blooms in each region. VOC-related source spectra and emission intensities from cyanobacteria in Taihu Lake are collected based on the literature review and are used to compile the first inventory of VOC emissions from blue-green algae blooms in Taihu Lake. The results indicate that since the 21st century, the situation of cyanobacterial blooms in Taihu Lake has continued to deteriorate with increasing variability. The relative impact of meteorological factors varies across different regions, but temperature consistently shows the highest sensitivity in all areas. The VOCs released from the algal blooms increase with the proliferation of the blooms, posing a continuous threat to the atmospheric environment of the surrounding cities. This study aims to provide a scientific basis for further improvement of air quality in urban areas adjacent to large lakes. Full article

(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)

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24 pages, 8058 KiB

Open AccessArticle

Spatiotemporal Analysis of Drought Characteristics and Their Impact on Vegetation and Crop Production in Rwanda

by Schadrack Niyonsenga, Anwar Eziz, Alishir Kurban, Xiuliang Yuan, Edovia Dufatanye Umwali, Hossein Azadi, Egide Hakorimana, Adeline Umugwaneza, Gift Donu Fidelis, Justin Nsanzabaganwa and Vincent Nzabarinda

Remote Sens. 2024, 16(8), 1455; https://doi.org/10.3390/rs16081455 - 20 Apr 2024

Cited by 1 | Viewed by 1193

Abstract

In recent years, Rwanda, especially its Eastern Province, has been contending with water shortages, primarily due to prolonged dry spells and restricted water sources. This situation poses a substantial threat to the country’s agriculture-based economy and food security. The impact may escalate with climate change, exacerbating the frequency and severity of droughts. However, there is a lack of comprehensive spatiotemporal analysis of meteorological and agricultural droughts, which is an urgent need for a nationwide assessment of the drought’s impact on vegetation and agriculture. Therefore, the study aimed to identify meteorological and agricultural droughts by employing the Standardized Precipitation Evapotranspiration Index (SPEI) and the Vegetation Health Index (VHI). VHI comprises the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), both derived from the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). This study analyzed data from 31 meteorological stations spanning from 1983 to 2020, as well as remote sensing indices from 2001 to 2020, to assess the spatiotemporal patterns, characteristics, and adverse impact of droughts on vegetation and agriculture. The results showed that the years 2003, 2004, 2005, 2006, 2013, 2014, 2015, 2016, and 2017 were the most prolonged and severe for both meteorological and agricultural droughts, especially in the Southern Province and Eastern Province. These extremely dry conditions led to a decline in both vegetation and crop production in the country. It is recommended that policymakers engage in proactive drought mitigation activities, address climate change, and enforce water resource management policies in Rwanda. These actions are crucial to decreasing the risk of drought and its negative impact on both vegetation and crop production in Rwanda. Full article

(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)

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21 pages, 12055 KiB

Open AccessArticle

A New Framework for the Reconstruction of Daily 1 km Land Surface Temperatures from 2000 to 2022

by Yuanjun Xiao, Shengcheng Li, Jingfeng Huang, Ran Huang and Chang Zhou

Remote Sens. 2023, 15(20), 4982; https://doi.org/10.3390/rs15204982 - 16 Oct 2023

Cited by 1 | Viewed by 1287

Abstract

Accurate, seamless, and long-term land surface temperature (LST) data sets are crucial for investigating climate change and agriculture production. However, factors like cloud contamination have led to invalid values in the LST product, which has restricted the application of the LST dataset. Therefore, the reconstruction of LST products is challenging, and it is attracting widespread attention. This study compared the performance of different algorithms (XGBoost, GBDT, RF, POLY, MLR) and different training sets (using only good-quality pixels or using both good-quality and other-quality pixels) in the estimation of missing pixels in the LST data, obtaining a seamless daily 1 km LST dataset of MODIS Terra-day, Aqua-day, Terra-night, and Aqua-night data for Zhejiang Province and its surrounding areas from 2000 to 2022. The results demonstrated that the performance of machine-learning models is significantly better than that of linear models, and among the five models, XGBoost performed the best, with an RMSE of less than 1 °C. The Wilcoxon test between the reconstructed LST and the true LST values revealed that including both good-quality and other-quality pixels for reconstruction resulted in a 33% increase in the number of days with non-significant differences compared with using only good-quality pixels. Moreover, the reconstructed nighttime LST has a lower RMSE compared with the reconstructed daytime LST, and the RMSE of the reconstructed LST on the Terra satellite is lower than the RMSE of the reconstructed LST on the Aqua satellite. The RMSEs for the reconstructed LSTs are 0.50 °C, 0.61 °C, 0.36 °C, and 0.39 °C, corresponding to Terra-day, Aqua-day, Terra-night, and Aqua-night for images with coverage reaching 70%, 0.65 °C, 0.83 °C, 0.49 °C, respectively, and 0.52 °C for images with coverage less than 70%. The accuracy of the reconstructed LSTs using our proposed framework outperforms the existing reconstruction methods. The 1 km daily seamless LST products can be applied in various fields, such as air temperature estimation, climate change, urban heat island, and crop temperature stress monitoring. Full article

(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)

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15 pages, 4772 KiB

Open AccessTechnical Note

Eutrophication and HAB Occurrence Control in Lakes of Different Origins: A Multi-Source Remote Sensing Detection Strategy

by Giovanni Laneve, Alejandro Téllez, Ashish Kallikkattil Kuruvila, Milena Bruno and Valentina Messineo

Remote Sens. 2024, 16(10), 1792; https://doi.org/10.3390/rs16101792 - 18 May 2024

Cited by 1 | Viewed by 815

Abstract

Remote sensing techniques have become pivotal in monitoring algal blooms and population dynamics in freshwater bodies, particularly to assess the ecological risks associated with eutrophication. This study focuses on remote sensing methods for the analysis of 4 Italian lakes with diverse geological origins, leveraging water quality samples and data from the Sentinel-2 and Landsat 5.7–8 platforms. Chl-a, a well-correlated indicator of phytoplankton biomass abundance and eutrophication, was estimated using ordinary least squares linear regression to calibrate surface reflectance with chl-a concentrations. Temporal gaps between sample and image acquisition were considered, and atmospheric correction dedicated to water surfaces was implemented using ACOLITE and those specific to each satellite platform. The developed models achieved determination coefficients higher than 0.69 with mean square errors close to 3 mg/m³ for water bodies with low turbidity. Furthermore, the time series described by the models portray the seasonal variations in the lakes water bodies. Full article

(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)

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