Since ancient times, agriculture has played a vital role in a country’s growth and forms the lifeline of the global economy. Worldwide, agriculture has brought in progressive changes across the production and manufacturing sectors. The constant evolution of modern technologies has introduced various advancements in agriculture and has led to the establishment of scientific farming methodologies. Production in agriculture greatly influences the rise and fall of the country’s economy and determines the success of the country in the global market. This phenomenon is specifically veracious for developing countries with increasing population measures. This is because growing population results in increased food demands with a requirement for increased agriculture productivity measures. The rise of population in a global context with lesser agricultural productivity can often result in global food demands. Achieving the goal of higher productivity is difficult, as the current agricultural sector struggles with changing climatic conditions and natural disasters.

Remote sensing in agriculture has significant benefits that help with the acquisition of agricultural field data or other significant phenomena without any physical contact with the object. It provides a timely and accurate picture of the agricultural regions with high revisit frequency measures. The application of remote sensing in agriculture minimizes environmental impacts over crops and maximizes productivity measures with earlier detection and prevention of environmental hazards and deadly diseases. Some of the recent research on remote sensing for agriculture includes forecasting crop production rates, horticulture, crop identification, cropping system analysis, crop progress, and damage assessment, pest identification and disease prevention measures, soil mapping, drought monitoring, land cover and degradation mapping, crop nutrition deficiency monitoring, crop yield forecasting, etc. In an agro-based remote sensing environment, the sensors are far from the agricultural object being observed; the input data is captured from the agricultural fields and transmitted to the destination through physical carriers, such as electromagnetic radiation. The output is usually produced in the form of an image that represents the captured objects. Some of the considerable advancements in further analysis and interpretation of the sensed data with improved remote sensing techniques could significantly increase the growth of agricultural productivity. Further, the integration of remote sensing techniques with advanced computing technologies, such as the Internet of Things (IoT), artificial intelligence, deep learning, and federated learning, could enhance yield productivity and efficiently fulfill global food demands.   

The Special Section  on Improving Agricultural Productivity to Meet Increased Food Demands using Remote Sensing aims to increase global agricultural productivity with advanced remote sensing techniques. The topics of the special section include but are not limited to the following:

• Advancement in low spatial resolution sources for monitoring global environmental changes and agriculture
• Earlier pest detection and control using improved spectral properties
• Effective measures to enhance precision/information accuracy in remote-sensing techniques
• Frontiers in remote sensing for agricultural productivity
• Airborne and unmanned aerial vehicle remote sensing techniques for flexible monitoring of agricultural fields using different spatial scales
• Innovative remote sensing operational techniques for agricultural field study using on-site and space data
• Integrating new sensing techniques with advanced computational intelligence algorithms (deep learning, machine learning, federated learning, artificial intelligence) to improve crop yield across agricultural fields.
• Effective ways of representation learning and big data analytics for remote sensing in agriculture
• Remote sensing and IoT integrated approach to meet global food demands
• Recent advancement in remote sensing techniques to detect land use and land covers for improving agriculture
• Significance of hyperspectral remote sensing in agriculture and its future directions
• Combined image processing approach (spatial and spectral) for remote sensing
• Target and anomaly detection with image processing and remote sensing for precision agriculture

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 6 weeks of manuscript submission. Special sections are 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.

Hyperspectral imaging has emerged as a new generation of remote sensing technology since the late 1980s when the first airborne hyperspectral instrument, AVIRIS, became operational, followed by the launch of the first spaceborne hyperspectral sensor, Hyperion, onboard NASA’s EO-1 satellite in 2000. The detailed spectral information provided by hyperspectral imagery and the spatial relationships among the different spectra in a neighborhood often provide results not possible with multispectral or other types of imagery. Hyperspectral imaging technology goes well beyond the traditional remote sensing applications ranging from agriculture, forestry, environmental monitoring, geology, defense, intelligence, and law enforcement to food safety and inspection, and medical imaging. Many imaging spectrometers have been designed and built for spaceborne, airborne, UAV/UAS, and ground-based systems. The R&D on processing techniques of the hyperspectral imaging and hyperspectral applications is one of the most hot areas in remote sensing community. Many papers on this topic have been published. The Journal of Applied Remote Sensing will publish a special section on hyperspectral remote sensing and imaging spectrometer design. Potential topics include but are not limited to the following:

Hyperspectral Sensor Design and Implementation

• Design and development of imaging spectrometers for all spectral regions from the UV to the thermal IR, for spaceborne, airborne, UAV/UAS, and ground-based systems
• Novel architectures and spectrometer designs
• Verification and calibration methods and techniques
• Simulation techniques in sensor design and characterization
• Sensor artifact assessment and suppression
• Enabling technologies
  
  

Hyperspectral Data Applications and Processing

• Geology and mineralogy for earth and planetary applications
• Vegetation monitoring and health assessment
• Ocean, coastal ocean and inland waters
• Atmospheric sounding and imaging for greenhouse gas and air quality
• Defense and homeland security
• Emergency response, disaster recovery, and remediation
• Data processing and exploitation methods and algorithms
• Spectral signature libraries and databases
• Radiative transfer modeling
• Atmospheric correction techniques
• Detection, classification and characterization
• Sensor and data fusion,
• Hyperspectral image sharpening and super-resolution
• De-noising
• Physics-based spectral phenomenology understanding and modeling
• Deep learning or machine learning based approaches for spectral 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 6 weeks of manuscript submission. Special sections are 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.