Special Issue "Monitoring Climate Impacts on Agriculture 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: 31 March 2022.

Special Issue Editors

Dr. Laura Harrison
E-Mail Website
Guest Editor
Climate Hazards Center, UC Santa Barbara, Santa Barbara, CA 93106, USA
Interests: drought monitoring and forecasting; climate trends and impacts; earth observation data; agroclimatology; hydrology; remote sensing
Dr. Hannah Kerner
E-Mail Website
Guest Editor
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Interests: machine learning; deep learning; artificial intelligence; crop type mapping; cropland mapping; remote sensing; Earth science; food security
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Special Issue Information

Dear Colleagues,

Significant progress is needed in the detection of climate impacts on agriculture. Climate impacts on agriculture are local-to-global scale issues that influence food production, food security, economic stability, and health. Modern day issues include a globally connected agriculture market, the threat of increasing climate extremes with anthropogenic climate change, and the severe consequences that climate impacts have on the world’s poor populations. Thanks to decades of exploration into novel methods and technological advancements, remote sensing techniques continue to provide opportunities for progress. Today’s agriculture monitoring applications draw from numerous accessible and expanding remotely sensed data streams. Use of remote sensing for monitoring agriculture is at an all-time high. 

The aim of this Special Issue on “Monitoring Climate Impacts on Agriculture Using Remote Sensing Techniques” is to showcase successful recent endeavors in climate impact detection using remote sensing data and to communicate about promising new methods and datasets. 

We invite you to share your research to further our understanding as a community of observed climate impacts on agriculture, new or best practices for remote monitoring, and opportunities for early identification of seasonal crop performance. We encourage submissions that focus on remote sensing of climate impacts that can determine the success or failure of seasonal crop production. Drought, flood, temperature extremes, and climate-associated pests, e.g., locusts, are example topics of interest. We also welcome investigations to remote sensing techniques that address climate impacts on crop suitability and longer-term management decisions. In addition to the points above, topics may include but are not limited to:

  • Recent climate extremes and hazards to agriculture production;
  • Links to climate trends and regional and global climate drivers;
  • Methods of impact detection, including machine learning or data science techniques;
  • Efforts to improve field or local scale accuracy of remote monitoring;
  • Validation of remote sensing estimates with ground observations;
  • Applications of new and in practice monitoring systems;
  • New public data sets for shared benchmarks or catalyzing future method development.

Dr. Laura Harrison
Dr. Hannah Kerner
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 papers will be 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 2400 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.


  • Agriculture monitoring
  • Climate extremes and climate change
  • Crop production estimates
  • Early detection and prediction
  • Water and temperature stress
  • Socioeconomic impacts
  • Machine learning
  • Public data sets

Published Papers (1 paper)

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Mapping the Location and Extent of 2019 Prevent Planting Acres in South Dakota Using Remote Sensing Techniques
Remote Sens. 2021, 13(13), 2430; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132430 - 22 Jun 2021
Cited by 1 | Viewed by 594
The inability of a farmer to plant an insured crop by the policy’s final planting date can pose financial challenges for the grower and cause reduced production for a widely impacted region. Prevented planting is primarily caused by excess moisture or rainfall such [...] Read more.
The inability of a farmer to plant an insured crop by the policy’s final planting date can pose financial challenges for the grower and cause reduced production for a widely impacted region. Prevented planting is primarily caused by excess moisture or rainfall such as the catastrophic flooding and widespread conditions that prevented active field work in the midwestern region of United States in 2019. While the Farm Service Agency reports the number of such “prevent plant” acres each year at the county scale, field-scale maps of prevent plant fields—which would enable analyses related to assessing and mitigating the impact of climate on agriculture—are not currently available. The aim of this study is to demonstrate a method for mapping likely prevent plant fields based on flood mapping and historical cropland maps. We focused on a study region in eastern South Dakota and created flood maps using Landsat 8 and Sentinel 1 images from 2018 and 2019. We used automatic threshold-based change detection using NDVI and NDWI to accentuate changes likely caused by flooding. The NDVI change detection map showed vegetation loss in the eastern parts of the study area while NDWI values showed increased water content, both indicating possible flooding events. The VH polarization of Sentinel 1 was also particularly useful in identifying potential flooded areas as the VH values for 2019 were substantially lower than those of 2018, especially in the northern part of the study area, likely indicating standing water or reduced biomass. We combined the flood maps from Landsat 8 and Sentinel 1 to form a complete flood likelihood map over the entire study area. We intersected this flood map with a map of fallow pixels extracted from the Cropland Data Layer to produce a map of predicted prevent plant acres across several counties in South Dakota. The predicted figures were within 10% error of Farm Service Agency reports, with low errors in the most affected counties in the state such as Beadle, Hanson, and Hand. Full article
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