Dynamic monitoring of crop phenotypic traits (e.g., LAI, plant height, biomass, nitrogen, yield et al.) is essential for exploring crop growth patterns, breeding new varieties, and determining optimized strategies for crop management. Traditional methods for determining crop phenotypic traits are mainly based on field sampling, handheld instrument measurement, and mechanized high-throughput platforms, which are time-consuming, and have low efficiency and incomplete spatial coverage. The development of crop science requires more rapid and accurate access to field-based crop phenotypes.
Remote sensing provides a novel solution to quantify crop structural and functional traits in a timely, rapid, non-invasive and efficient manner. With the development of burgeoning remote sensing sensors and diversified algorithms, a range of crop phenotypic traits have been determined, including morphological parameters, spectral and textural characteristics, physiological traits, and responses to abiotic/biotic stresses in different environments. In addition, research advances in varying disciplines beyond agricultural sciences, such as engineering, computer science, molecular biology, and bioinformatics, have brought new opportunities for further development of remote sensing-based methods and technologies to gain more quantitative information on crop structure and function in complex environments.
This Research Topic aims to collect studies on field-based crop phenotyping through different remote sensing platforms and sensors coupled with diversified algorithms. We welcome the latest research achievements for determining multi-sensor integration methods, image processing ways, and retrieval modeling algorithms to improve the accuracy and robustness of crop phenotype assessment, which can be used for accelerating crop research, breeding efficiency, and precise agricultural management. We also encourage research outputs from beyond agricultural sciences.
We welcome submissions of original research articles and reviews in remote sensing for field-based crop phenotyping, particularly studies involving multisource data integration and multiscale approaches. The following subthemes are covered but not limited to:
• High-throughput crop phenotyping
• Crop growth dynamics monitoring
• Crop morphological and physiological traits
• Crop yield prediction
• Crop response under abiotic/biotic stresses
• Multi-source data integration
Dynamic monitoring of crop phenotypic traits (e.g., LAI, plant height, biomass, nitrogen, yield et al.) is essential for exploring crop growth patterns, breeding new varieties, and determining optimized strategies for crop management. Traditional methods for determining crop phenotypic traits are mainly based on field sampling, handheld instrument measurement, and mechanized high-throughput platforms, which are time-consuming, and have low efficiency and incomplete spatial coverage. The development of crop science requires more rapid and accurate access to field-based crop phenotypes.
Remote sensing provides a novel solution to quantify crop structural and functional traits in a timely, rapid, non-invasive and efficient manner. With the development of burgeoning remote sensing sensors and diversified algorithms, a range of crop phenotypic traits have been determined, including morphological parameters, spectral and textural characteristics, physiological traits, and responses to abiotic/biotic stresses in different environments. In addition, research advances in varying disciplines beyond agricultural sciences, such as engineering, computer science, molecular biology, and bioinformatics, have brought new opportunities for further development of remote sensing-based methods and technologies to gain more quantitative information on crop structure and function in complex environments.
This Research Topic aims to collect studies on field-based crop phenotyping through different remote sensing platforms and sensors coupled with diversified algorithms. We welcome the latest research achievements for determining multi-sensor integration methods, image processing ways, and retrieval modeling algorithms to improve the accuracy and robustness of crop phenotype assessment, which can be used for accelerating crop research, breeding efficiency, and precise agricultural management. We also encourage research outputs from beyond agricultural sciences.
We welcome submissions of original research articles and reviews in remote sensing for field-based crop phenotyping, particularly studies involving multisource data integration and multiscale approaches. The following subthemes are covered but not limited to:
• High-throughput crop phenotyping
• Crop growth dynamics monitoring
• Crop morphological and physiological traits
• Crop yield prediction
• Crop response under abiotic/biotic stresses
• Multi-source data integration