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.

Since its launch in 1972, Landsat has revolutionized Earth observation and become an indispensable tool for scientists, policymakers, and enthusiasts. This special section will serve as a tribute to the groundbreaking achievements and enduring legacy of Landsat, showcasing its profound impact on our understanding of the Earth’s dynamic systems and its role in shaping the future of remote sensing.

Over the past five decades, Landsat has provided an unparalleled perspective of our planet, capturing vast amounts of high-quality, multispectral imagery that has transformed our knowledge of land cover, land use, and environmental change. The continuity, consistency, and quality of Landsat’s data have enabled researchers to monitor and analyze long-term trends, detect ecosystem shifts, assess the impacts of climate change, and make informed decisions for sustainable resource management.

In this special section, we would like to highlight the broad spectrum of applications and advancements made possible by Landsat over the 50 years from early first experiments with digital imaging to advanced integrated analytical systems with national and international information support roles. Additionally, the section will explore innovative techniques and emerging technologies that enhance the utilization of Landsat data, such as machine-learning algorithms, data fusion approaches, and cloud computing platforms.

As we reflect on the past 50 years of Landsat’s extraordinary journey, we also look ahead to the future. The section will discuss prospects for the next generation of Earth observation satellites and plans for the next 50 years of Earth observation system development and expansion. All papers accepted for this special section must include Landsat data.

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 Landsat satellite imagery in various domains of Earth observation and environmental research including but not limited to the following:

• Assessment of radiometric and geometric characteristics and performance of new remote sensing instruments
• Requirements, strategies, algorithms, and approaches for developing Landsat science products
• Advanced methods or strategies for multi-decadal and large land change assessments
• Defining new innovative Landsat standard and derived products for addressing societal issues
• Assessment of agricultural productivity and crop monitoring
• Mapping and monitoring water resources and water quality
• Assessment of natural hazards such as wildfires, floods, and landslides due to climate change
• Biodiversity conservation and habitat mapping
• Integration and data fusion with other remote sensing datasets for improved analysis
• Developing machine learning and artificial intelligence techniques for Landsat data analysis
• Assessing the accuracy and uncertainty of Landsat-derived products and classifications
• Evaluating the effectiveness of Landsat data in supporting land management and policy decisions
• Exploring the synergy between Landsat and other Earth observation missions for global monitoring
• Prospects for Landsat and the potential advancements in sensor technology and data processing

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.