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Crop Classification Using Synthetic Aperture Radar and Optical Imagery

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (26 May 2024) | Viewed by 255

Special Issue Editors


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Guest Editor
Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, P.O. Box 757320, Fairbanks, AK 99775, USA
Interests: remote sensing; radar; SAR; optical; machine learning; deep learning; artificial intelligence; InSAR; polarimetry; geospatial analysis; hazard monitoring

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Guest Editor
Research & Development Corteva Agriscience™, 7000 NW 62nd Avenue, Johnston, IA 50131, USA
Interests: multisensor fusion for ecosystem monitoring; sustainable agriculture; smallholder farming; public private partnerships

Special Issue Information

Dear Colleagues,

Accurate and timely data on crop type and crop area are essential for successful agricultural management and environmental sustainability. However, obtaining large-scale maps of crop type and area using satellite imagery can be challenging in certain regions due to factors such as limited ground truth data, inadequate satellite data availability, computational difficulties, and lack of cropland data. Recently, Synthetic Aperture Radar (SAR) data have garnered attention due to their ability to penetrate clouds and operate in all weather conditions. Combining SAR data with optical data can provide valuable insights into crop characteristics over large areas; however, there have been few systematic efforts to integrate both data types. SAR technology is increasingly being used in agricultural monitoring as an active remote sensing system that is not affected by weather conditions or smoke. SAR satellites offer high spatial resolution and frequency, making them a complementary tool in optical imagery for crop monitoring. With the arrival of dense SAR time series from satellites like Sentinel-1 and upcoming missions such as the NASA-ISRO Synthetic Aperture Radar (NISAR), there is growing interest in exploring the potential of SAR for agricultural monitoring. Utilizing SAR technology can provide agronomy managers with a more accurate and reliable understanding of crop types and areas, enabling them to balance agricultural expansion with forest conservation and promote sustainable land management practices.

This Special Issue aims to present the state-of-the-art research in optical, SAR, PolSAR, and PolInSAR imagery for predictive agricultural crop monitoring using publicly available and commercial datasets. We solicit contributions from public and private sectors showcasing the contribution of optical and SAR datasets in agriculture spanning a wide range of topics, including but not limited to the following areas:

  • Crop classification using densely sampled time series information of optical and SAR imagery;
  • Application of machine learning algorithms in crop classification using SAR and optical imagery;
  • Fusion of radar and optical imagery for crop classification;
  • Comparison of crop classification using SAR and optical imagery;
  • Crop yield prediction using SAR and optical imagery;
  • Forest land cover mapping and pattern analysis;
  • Biomass estimation from SAR and optical imagery;
  • Exploring deep learning's potential for crop classification with SAR and optical imagery.

Dr. Olaniyi Ajadi
Dr. Anu Swatantran
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 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 classification
  • time series analysis
  • feature extraction
  • machine learning
  • deep learning
  • image fusion
  • synthetic aperture radar (SAR)
  • crop health
  • land use change detection
  • multi-temporal analysis

Published Papers

There is no accepted submissions to this special issue at this moment.
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