Real-Time Agricultural Monitoring from Remotely Sensed Data (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 30 May 2025 | Viewed by 761
Special Issue Editors
Interests: time-series remote sensing; agricultural remote sensing; environmental remote sensing; vegetation/crop phenology; crop growth modelling
Special Issues, Collections and Topics in MDPI journals
Interests: reflectance spectroscopy; quantitative remote sensing of vegetation; crop growth monitoring; crop mapping; smart farming
Special Issues, Collections and Topics in MDPI journals
2. National Key Laboratory of Smart Farming Technologies and Systems, Harbin 150008, China
Interests: multi-source remote sensing; vegetation dynamics; plant stress
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing data have been successfully used to investigate various agricultural activities, such as crop type mapping, crop phenology detection, soil moisture assessment, and crop growth monitoring. From a practical point of view, agricultural management requires timely and accurate crop and soil information provided by remote sensing data in real-time. Real-time agricultural monitoring is still impeded by limitations in remote sensing data quality, monitoring algorithms, and computing platforms.
In this context, a Special Issue entitled “Real-Time Agricultural Monitoring from Remotely Sensed Data” is being planned in the journal Remote Sensing. We welcome all research or review articles on agricultural monitoring as long as they focus on work carried out during the crop-growing season. In addition, methodology papers on processing within-season remote sensing data (e.g., time-series data) are also welcome. This Special Issue has a broad range of topics, including crop monitoring (e.g., crop type classification, crop phenology detection, crop phenotyping, crop yield prediction) and agricultural condition investigations (e.g., agricultural drought, biotic/abiotic stresses). It should be noted that remotely sensed data from satellites, drones, or field instruments should be among the main data sources.
We look forward to receiving your contributions.
Prof. Dr. Ruyin Cao
Prof. Dr. Tao Cheng
Prof. Dr. Ran Meng
Guest Editors
Manuscript Submission Information
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Keywords
- agricultural remote sensing
- crop types
- crop phenotype
- crop yield
- in-season
- phenotyping
- plant stress
- precision agriculture
- time-series data
- within-season
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