New Perspectives in Plant Phenotyping: Satellite-Based Multispectral Remote Sensing

Dear Colleagues,

Plant phenotyping is a rapidly evolving field, and due to technological advancements, researchers are now able to assess plant performance more accurately and efficiently; this allows crop yields to be increased, crop adaptability to be enhanced, and sustainable agriculture to be promoted. Traditional plant phenotyping often involves manual or low-throughput methods to measure plant attributes such as height, leaf area, and fruit size. Proximal sensing and near-ground remote sensing technologies can rapidly and non-destructively gather information about plant phenotypes at the field level by using advanced imaging methods, sensor technology, and automated tools. However, it is challenging to effectively obtain phenotypic data at the regional scale when employing these methods. Multispectral satellite remote sensing technology provides a macroscopic, dynamic, and comprehensive perspective for plant phenotyping research, which promotes the development of agricultural science, ecology, and environmental science.

Multispectral remote sensing has emerged a powerful tool for monitoring and managing agricultural systems. By capturing data across various wavelengths, multispectral sensors provide critical information about plant health, growth stages, and environmental interactions.

This Special Issue, entitled “New Perspectives in Plant Phenotyping: Satellite-based Multispectral Remote Sensing”, seeks to explore the ability of multispectral remote sensing to revolutionize agricultural monitoring and advance sustainable farming practices. We welcome submissions that delve into innovative techniques, practical applications, and comparative studies that enhance our understanding and utilization of multispectral data in agriculture. The scope of this Special Issue includes, but is not limited to, the following topics:

• The Development of Novel Approaches in Agricultural Applications
• Innovative techniques using machine learning and deep learning for crop identification and classification.
• High-Precision Crop Identification and Classification
• Development of high-precision products for single crop identification or multi-crop classification.
• Global Variations in Crop Phenology and Planting Patterns
• Exploration of global variations in crop phenology and planting patterns, including multi-cropping systems.
• Long-term Changes in Crop Phenology Due to Climate Change
• The analysis of changes in crop phenological characteristics over long time series, influenced by factors such as climate change.
• Performance Comparison of Multispectral Sensors
• The comparative performance analysis of different multispectral sensors in extracting the phenological characteristics of crops.
• The Fusion of Multispectral Images for Enhanced Accuracy
• Techniques for multisource multispectral remote sensing data to enhance accuracy in agricultural applications.
• Multi-source and Multi-Scale Spectral Data Fusion to Enhance Regional Phenotype Mapping
• Spectral data fusion from the ground, drones, and satellites to improve the performance of plant phenotyping.

Dr. Luwei Feng
Dr. Xiaoyan Kang
Dr. Yan Zhao
Guest Editors

Manuscript Submission Information

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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.

• multispectral remote sensing
• crop classification
• phenology
• deep learning
• radiative transfer model
• plant parameter retrieval
• vegetation productivity simulation
• crop yield prediction