Special Issue "Modelling Impacts of Climate Variability on Agricultural Crop Yields Using Remote Sensing Derived Information"

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: 15 May 2022.

Special Issue Editors

Dr. Louis Kouadio
E-Mail Website
Guest Editor
Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Interests: agrometeorology; crop growth modelling; crop yield forecasting; climate variability and change; remote sensing applications in agriculture; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Nathaniel K. Newlands
E-Mail Website
Guest Editor
Summerland Research and Development Centre, Agriculture and Agri-Food Canada, Summerland, BC V0H 1Z0, Canada
Interests: remote sensing applications in agriculture; ecosystem modeling; forecasting in agriculture; predictive analytics; artificial intelligence; machine learning; deep learning; integrated sensing and validation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Feeding an increasing human population (expected to reach about 9 billion in 2050) and combating hunger and poverty worldwide remains a tremendous challenge under a more variable and changing climate. A more comprehensive and reliable assessment of environmental-related vulnerabilities and risks affecting global crop production is therefore of paramount importance.

Remote sensing can provide spatially explicit and unbiased information across different spatial and temporal scales. When integrated with process-based and statistical models, such remote sensing data can help to explore how managed agroecosystems respond to a changing climate and can greatly improve the agricultural industry’s preparedness and productivity. Indeed, utilising such improved modelling systems can substantially facilitate longer-term climate change adaptation through incrementally shifting farm and agribusiness management practices according to the seasonal and longer-term crop yield forecasts.

This Special Issue invites high-quality and innovative scientific papers describing cutting-edge research on the application of remote sensing derived information from any platform (satellite, aircraft, UAVs/drones) to the study of agricultural climate risk-related issues. Potential topics include but are not limited to the following:

  • Innovative crop yield forecasting systems using RS-derived information;
  • Deep learning methodologies using Earth Observational data for crop yield forecasting;
  • Use of Earth Observational data to understand the impact of climate variability and change on crop growth and yield;
  • Innovative methodologies of Earth Observational data integration for tackling agricultural climate risks.

Dr. Louis Kouadio
Dr. Nathaniel K. Newlands
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.

Keywords

  • Climate variability and change
  • Crop model
  • Crop yield forecasting system
  • Climate risk management
  • Machine learning techniques
  • Deep learning
  • Food security

Published Papers (1 paper)

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Research

Article
UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions
Remote Sens. 2020, 12(15), 2445; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152445 - 30 Jul 2020
Cited by 8 | Viewed by 1558
Abstract
Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and [...] Read more.
Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and cost-effective data-driven variety selection and release in plant breeding. Traditional phenotyping methods that rely on trained experts are slow, costly, labor-intensive, subjective, and often require destructive sampling. We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle (UAV)-based multispectral remotely sensed data in maize (Zea mays L.) varietal response to maize streak virus (MSV) disease. Twenty-five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation. Ground scoring for MSV infection was carried out at mid-vegetative, flowering, and mid-grain filling on a scale of 1 (resistant) to 9 (susceptible). UAV-derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green (0.53–0.57 μm), Red (0.64–0.68 μm), Rededge (0.73–0.74 μm), and Near-Infrared (0.77–0.81 μm). The imagery captured was stitched together in Pix4Dmapper, which generates two types of multispectral orthomosaics: the NoAlpha and the transparent mosaics for each band. The NoAlpha imagery was used as input into QGIS to extract reflectance data. Six vegetation indices were derived for each variety: normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), Rededge NDVI (NDVIrededge), Simple Ratio (SR), green Chlorophyll Index (CIgreen), and Rededge Chlorophyll Index (CIrededge). The Random Forest (RF) classifier was used to evaluate UAV-derived spectral and VIs with and without variable optimization. Correlations between the UAV-derived data and manual MSV scores were significant (R = 0.74–0.84). Varieties were classified into resistant, moderately resistant, and susceptible with overall classification accuracies of 77.3% (Kappa = 0.64) with optimized and 68.2% (Kappa = 0.51) without optimized variables, representing an improvement of ~13.3% due to variable optimization. The RF model selected GNDVI, CIgreen, CIrededge, and the Red band as the most important variables for classification. Mid-vegetative was the most ideal phenological stage for accurate varietal phenotyping and discrimination using UAV-derived multispectral data with RF under artificial MSV inoculation. The results provide a rapid UAV-based remote sensing solution that offers a step-change towards data availability at high spatial (submeter) and temporal (daily/weekly) resolution in varietal analysis for quick and robust high-throughput plant phenotyping, important for timely and unbiased data-driven variety selection and release in plant breeding programs, especially as climate change accelerates. Full article
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