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Crop Yield Prediction Using Remote Sensing Techniques

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: 26 February 2026 | Viewed by 44

Special Issue Editor


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Guest Editor
Department of Remote Sensing, University of Wuerzburg, 97074 Würzburg, Germany
Interests: agricultural remote sensing; monitoring the harvest yield; sustainable agriculture; precision agriculture

Special Issue Information

Dear Colleagues,

Global agricultural systems are increasingly being challenged by climate variability, land-use change, and the growing demand for food security. Accurate, timely, and scalable methods for crop yield prediction are essential to support precision agriculture, optimize resource use, and guide policy decisions. In this context, remote sensing has emerged as a powerful tool for monitoring crop dynamics and estimating yield across spatial and temporal scales. With advances in satellite, UAV, and sensor technologies—as well as machine learning and data fusion approaches—yield modeling has entered a new era of precision and operational feasibility.

This Special Issue aims to explore cutting-edge research and innovative applications of remote sensing techniques for crop yield prediction. It aligns with the journal’s scope by promoting interdisciplinary studies that integrate Earth observation, agronomy, environmental science, and computational methods to address agricultural challenges. Contributions are encouraged from both methodological and applied perspectives, covering a wide range of crops and agroecological regions.

Topics of interest include, but are not limited to:

  • Yield estimation using multispectral, hyperspectral, radar, or thermal imagery;
  • Machine learning and hybrid modeling approaches (e.g., LUE, RF, CNNs);
  • Data fusion (e.g., MODIS–Landsat, Sentinel–UAV);
  • Phenological monitoring and crop growth modeling;
  • In-season yield forecasting and stress detection;
  • Integration of environmental, soil, and climate data;
  • Operational systems and platforms for yield prediction;
  • Ground truthing, uncertainty, and model validation.

Original research articles, reviews, and case studies are welcome.

Dr. Maninder Singh Dhillon
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Keywords

  • crop yield prediction
  • remote sensing
  • NDVI and vegetation indices
  • machine learning
  • light use efficiency (LUE)
  • data fusion (e.g., MODIS–Landsat)
  • phenology and crop growth models
  • climate and environmental drivers
  • UAV and satellite imagery
  • precision agriculture

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Published Papers

This special issue is now open for submission.
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