Oceans cover more than 70% of the Earth’s surface and provide over 50% of the world’s oxygen and store carbon dioxide. In addition, oceans transport heat from the equator for the poles and regulate climate patterns. Therefore, investigating ocean activities and comprehending the role of the oceans in global climate change is essential. However, existing ocean-observation data have deficiencies, such as inconsistent spatial distribution and restricted observation depth layers. Combining multi-source observation data (in situ observation data, sea surface data, numerical model data, and reanalysis data) can effectively predict and forecast the spatial-temporal evolution of ocean meteorology and marine environments.

This special section will focus on fusing multi-source ocean remote sensing data to address the challenges posed by the cross-modal features correlation and remote sensing big data. The aim is to solve the critical problems of artificial intelligence in oceanography. The Journal of Applied Remote Sensing invites researchers to contribute to this section, showcasing the latest advances and the applications of ocean remote sensing data.

Topics of interest include but are not limited to the following:

• Fusion techniques for multi-source ocean observation and remote sensing data.
• Physics-guided ocean data prediction and forecasting.
• Predicting and forecasting typical ocean disasters based on multi-source observation and remote sensing data.
• Cognition of ocean processes based on multi-source observation and remote sensing data.
• Ocean color observation and applications.
• Ocean internal wave observation and applications.
• Spectrum recovery from multispectral ocean color data.
• Coastal wetland mapping and monitoring.
• Deep learning techniques for ship performance monitoring.
• Modeling and change analysis of the Arctic.
• Oil spill detection on the ocean via multi-source data.

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system ( https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal’s established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks of manuscript submission. The special section is opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Water is a critical natural resource for humankind and the future of our planet. However, the development of society and economy has resulted in great pressure on water resources and the environment. In particular, climate change is irreversibly affecting water accessibility as extreme weather events increase, leading to more droughts and floods. Today, approximately 80% of the world’s population is exposed to high levels of threat to water scarcity, and 2.2 billion people are living without access to clean water. Fortunately, remote sensing, a technique of obtaining information from a distance, has emerged as a powerful tool to collect data on water resources. With the advent of earth observation satellites, better understanding of the complex water resource systems has become possible not only by monitoring and assessing the state of water resources but also through the environment at various spatial scales.

In recent years, remote sensing techniques have been widely used to delineate surface water bodies, estimate meteorological variables like temperature and precipitation, estimate hydrological state variables like soil moisture and land surface characteristics, and estimate fluxes such as evapotranspiration. Today, near-real-time monitoring of flood, drought events, and irrigation management are possible with the help of high resolution (i.e., high spatial, spectral, and radiometric resolutions) satellite data.

This special section calls for papers that focus on innovative algorithms and the diverse application of remote sensing to water resources, such as monitoring, mapping, and managing water-related issues. Additionally, the section will explore innovative techniques and emerging technologies that enhance the utilization of remote sensing data, such as machine-learning algorithms, data fusion approaches, and large-scale models. And state-of-the-art review papers about intelligent remote sensing for water resources are especially welcomed.

The Journal of Applied Remote Sensing invites researchers to contribute their expertise and insights to the section, showcasing the breadth and depth of the impact of satellite imagery in various water resources applications and environmental research including but not limited to the following:

• Water resource mapping
• Surface water monitoring
• Soil moisture estimation
• Water vapor retrieval
• Snow cover and water equivalent monitoring
• Snow and glacier parameter estimation
• Evapotranspiration estimation
• Irrigation monitoring
• Watershed planning and management
• Water availability assessing
• Water quality monitoring
• Flood monitoring and forecasting
• Drought assessment and prediction
• Wetland mapping and monitoring
• Coastal zone management
• Watershed land cover classification
• Remote sensing data fusion for water resources
• Advanced machine learning and artificial intelligence techniques for water resources

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal’s established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks of manuscript submission. The special section is opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Spectral analysis is a fundamental tool for extracting valuable information from spectral detection and imaging data. It can be utilized for material identification, image classification, and composition inversion, making it extremely crucial in various application fields, such as environment, geography, military, geology, and aerospace. With the development of optical sensors, traditional spectral analysis methods may face challenges that arise due to the higher spectral resolution of multispectral to hyperspectral data, wider spectral range including ultraviolet, visible, and infrared, and also due to new model spectral detection such as laser spectroscopy. Therefore, more sophisticated spectral analysis techniques are urgently required. In recent decades, machine learning, especially deep learning techniques, have brought spectral analysis into the era of artificial intelligence, enabling both qualitative and quantitative analysis in a more precise and robust way. The Journal of Applied Remote Sensing invites researchers to contribute to this section, showcasing the latest advances and the applications of spectral analysis. Topics of interest include but are not limited to the following:

• Multispectral/hyperspectral spectral-based detection
• Near-infrared/infrared spectral analysis
• Spectral sensing and imaging
• Raman spectral detection
• Laser spectroscopic detection
• Spectral feature extraction and transformation
• Spectral denoising and super-resolution
• Spectral unmixing
• Multitemporal spectral change detection
• Machine learning/deep learning in spectral analysis
• Spectral-analysis-based remote sensing applications

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal’s established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks of manuscript submission. The special section is opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.