Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Modelling Impacts of Climate Variability on Agricultural Crop Yields Using...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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: closed (15 May 2022) | Viewed by 13125

Special Issue Editors

Dr. Louis Kouadio

E-Mail Website
Guest Editor
Centre for Applied Climate Sciences, Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Interests: interdisciplinary sciences; biophysical modelling; food security; environmental sustainability; remote sensing applications in agriculture; data analytics
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 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

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 3049 KiB  
Article
Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors
by A. A. Masrur Ahmed, Ekta Sharma, S. Janifer Jabin Jui, Ravinesh C. Deo, Thong Nguyen-Huy and Mumtaz Ali
Remote Sens. 2022, 14(5), 1136; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051136 - 25 Feb 2022
Cited by 19 | Viewed by 2804
Abstract
Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This [...] Read more.
Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture. Full article
(This article belongs to the Special Issue Modelling Impacts of Climate Variability on Agricultural Crop Yields Using Remote Sensing Derived Information)
►▼ Show Figures

Graphical abstract

18 pages, 29114 KiB  
Article
Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables
by S Janifer Jabin Jui, A. A. Masrur Ahmed, Aditi Bose, Nawin Raj, Ekta Sharma, Jeffrey Soar and Md Wasique Islam Chowdhury
Remote Sens. 2022, 14(3), 805; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030805 - 08 Feb 2022
Cited by 22 | Viewed by 3493
Abstract
Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes [...] Read more.
Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS–RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS–RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries. Full article
(This article belongs to the Special Issue Modelling Impacts of Climate Variability on Agricultural Crop Yields Using Remote Sensing Derived Information)
►▼ Show Figures

Figure 1

27 pages, 3896 KiB  
Article
UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions
by Walter Chivasa, Onisimo Mutanga and Chandrashekhar Biradar
Remote Sens. 2020, 12(15), 2445; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152445 - 30 Jul 2020
Cited by 38 | Viewed by 5531
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
(This article belongs to the Special Issue Modelling Impacts of Climate Variability on Agricultural Crop Yields Using Remote Sensing Derived Information)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop