Integrating Remote Sensing and Machine Learning for Intelligent Crop Monitoring and Yield Mapping
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: 28 February 2027 | Viewed by 372
Editors
Interests: crop mapping; growth monitoring; yield estimation; subtropical agricultural remote sensing; transfer learning; hyperspectral remote sensing; time-series analysis; graph-spectrum cognition
Interests: remote sensing big data; intelligent computing; geo-object-based image analysis (GEOBIA); high-resolution remote sensing; spatio-temporal collaborative computing; land cover information extraction; geospatial artificial intelligence (GeoAI)
Special Issue Information
Dear Colleagues,
With the compounding pressures of climate change and a growing global population, ensuring food security requires a transition toward intelligent, predictive digital farming. Remote sensing has become a cornerstone of precision agriculture, offering continuous multi-scale observations. Concurrently, the explosion of multi-source Earth observation data—from high-resolution optical and SAR satellites to UAV platforms—presents unprecedented opportunities. However, extracting actionable insights from these massive, complex, and non-linear agricultural datasets remains a major bottleneck.
The integration of advanced machine learning, particularly deep learning and domain adaptation algorithms, offers a powerful paradigm to bridge this gap. By fusing computational intelligence with remote sensing physics, researchers can model intricate crop growth dynamics and map yields with unprecedented precision, even across highly fragmented or complex agricultural landscapes.
This Special Issue aims to gather innovative research that exploits the synergy between multi-source remote sensing and advanced machine learning to address critical challenges in crop classification, phenology tracking, stress detection, and yield forecasting. We encourage submissions that propose innovative algorithms, multi-sensor data fusion frameworks, and transferable artificial intelligence (AI) models applicable from local fields to global scales. Original research articles, comprehensive reviews, and technical notes are all welcome.
Articles may address, but are not limited to, the following topics:
- Intelligent crop identification and classification mapping;
- Sub-pixel unmixing and mapping of fragmented agricultural landscapes;
- Domain adaptation and model transferability across agro-ecological zones;
- Multi-source data fusion (Optical, SAR, Hyperspectral, Thermal);
- Phenology tracking and biophysical/biochemical parameter inversion;
- Machine learning-driven yield prediction and forecasting;
- Early detection and monitoring of crop stresses.
Dr. Yingpin Yang
Prof. Dr. Jiancheng Luo
Prof. Dr. Tianjun Wu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
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-anonymized 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.
Keywords
- crop classification and mapping
- yield prediction
- phenology monitoring
- machine learning
- transfer learning
- domain adaptation
- multi-source data fusion
- time-series analysis
- AI agents
- explainable AI
- climate change
- sustainable agriculture
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