Special Issue "Advances of Remote Sensing in Agriculture for Climate Change Adaptation"

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: 30 June 2021.

Special Issue Editors

Prof. Dr. Mario Cunha
Website
Guest Editor
1. Sciences Faculty, Porto University (FCUP) Rua do Campo Alegre, s.n. 4169-007 Porto, Portugal
2. Researcher at Institute for Systems and Computer Engineering, Technology (INESC TEC) Portugal, R. Dr. Roberto Frias, Porto, Portugal
Interests: remote sensing; crop modelling; climate change; precision agriculture; Orchards/Vineyards monitoring
Prof. Dr. Isabel Trigo
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Co-Guest Editor
(IPMA) EUMETSAT Land Surface Analysis - Satellite Application, Facility Project Manager Rua C ao Aeroporto, 1749-077 Lisboa, Portugal
Interests: Remote Sensing; Land Surface Temperature; Land Surface modelling
Special Issues and Collections in MDPI journals
Dr. Isabel Pôças
Website
Co-Guest Editor
Post-Doc Researcher at Center of Research for Geo-Space Sciences (CICGE), Sciences Faculty, Porto University (FCUP). Rua do Campo Alegre, s.n. 4169-007 Porto, Portugal
Interests: Remote sensing applications in agriculture; agricultural water management; crop monitoring; satellite-based surface energy balance models

Special Issue Information

Dear Colleagues,

Projections by FAO show that feeding a world population of 9.1 billion people in 2050 and meeting the United Nations’ Sustainable Development Goal of eradicating hunger (SDG2) would require raising overall food production by some 70% between 2005/07 and 2050. However, aggregate crop production at the world level is projected to grow over the period to 2030 at 1.4% p.a., down from the annual growth of 2.1% of the past 30 years. Increased production can be achieved through additional agricultural land (extensification) and/or improved productivity (intensification). Both strategies face the challenge of producing more with less in line with sustainably using natural resources and adapting agriculture to climate change.

The potential of remote sensing data for agriculture has long been recognized. In the 1960s, well before the advent of the digital era, innovative aerial photograph surveys were carried out using near infrared film. In the 1970s, with the start of the Landsat satellite missions, new sources of high spatial resolution data became available. The global coverage of different types of satellite data (currently including more than 300 Earth observation satellites) with repositories extending for more than 40 years, provides a unique historical data base, which allows us to develop, test and implement innovative measures to adapt agriculture to the foreseen climate scenarios. However, there is still a considerable gap between data and information. For the case of agriculture adaptation to changing climate, the great potential offered by remote sensed data is still yet to be fully explored.

This special issue comes at a time when climate change puts immense pressure on the sustainability of agricultural systems and food security in an increasingly over-populated world, themes that are on the top priority of the intricate political, economic, environmental, humanitarian and scientific agendas. Remote sensing can play an important role to support integrated climate change mitigation measures in agriculture emanated from such multi-agendas.

This special issue is calling for original and innovative manuscripts related to recent research and activities that demonstrate the proficient use of remote sensing techniques to advance applications for agriculture adaptation in the foreseen climate scenarios. The manuscripts must employ remote sensing data, such as multispectral and hyperspectral data, resulting from optical, thermal, or active and passive microwave sensors on-board of airborne or space-borne remote sensing platforms (proximity sensors may also be considered) to cope with issues of agriculture adaptation in a changing climate. Manuscripts related with modelling tools based on Earth observations applied in a wide spectrum of geographic (i.e., from local to regional to global levels) and temporal (i.e., daily to monthly to annual to decadal levels) scales aimed at mitigating climate change in agriculture are also welcome. Therefore, the focus of this special issue is broadly to explore the usefulness of remote sensing to improve agriculture performance in a changing climate and the potential topics include, but are not limited to:

  • Prominency of remote sensing information to improve agricultural practices.
  • Uncertainty and accuracy of remote sensing techniques for assessment of climate change impacts on agriculture.
  • Multi-source data assimilation (satellite imagery, aerial photography, climate databases, weather forecast, census data, surveys databases, and local knowledge) for agricultural applications.
  • Integration of different scales of remote sensing measurements (e.g. satellite, UAV, ground-based measurements) towards further understanding scaling dynamics related to plant–climate interactions in agricultural systems.
  • Time series of remote sensing data: data fusion of multiple sensors, trend and cyclicality analysis of vegetation dynamics.
  • Remote sensing data to derive land surface temperature, radiation fluxes, and respective applications on agriculture.
  • Remote sensing data and techniques for estimating crop evapotranspiration.
  • Comparison of long-term vegetation dynamics based on remote sensing data and field-based measurements (phenology, permanent plots, eddy covariance, tree-ring parameters) in liaison with climate change and climate variability.
  • Integration of remote sensing, crop process modelling, and machine learning to advance agricultural monitoring and mitigate climate change induced impacts on crop functioning and yield.
  • Remotely sensed- assisted agricultural practices and decisions in the context of climate change: improved water productivity in irrigated systems, crop systems adaptation, crop protection strategies, soil fertility and conservation (e.g. salinity, soil organic matter, predictive modelling of soil erosion), modelling/monitoring land use/land cover changes, biodiversity, and carbon sequestration.
  • Relevance of remote sensing information to improve agricultural practices in a changing climate, taking into account the UN goals 13, related with “Climate action”, and 15, related with “Life on land”, as well as the “4 per 1000 initiative” launched at the COP 21 that aims at implementing practical actions on increasing carbon storage in soils under agriculture.
  • Actual and emerging monitoring systems for agriculture and food security: Design and implementation of remote sensing based institutional agricultural services to deliver early warning of climate infrequent events (e.g. droughts, floods) and associated food risk assessment

This special issue welcomes diverse types of articles including original research, reviews and perspective papers (upon consultation with the Editors).

Prof. Dr. Mario Cunha
Prof. Dr. Isabel Trigo
Dr. Isabel Pôças
Guest Editor

Keywords

  • Remote sensing
  • Climate change
  • Climate change mitigation
  • Weather forecast
  • Sustainable agriculture
  • Agricultural practices
  • Agricultural adaptation strategies
  • Phenology
  • Water use efficiency
  • Agricultural droughts
  • Land use land cover change
  • Food security

Published Papers (1 paper)

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Research

Open AccessArticle
Determining the Genetic Control of Common Bean Early-Growth Rate Using Unmanned Aerial Vehicles
Remote Sens. 2020, 12(11), 1748; https://doi.org/10.3390/rs12111748 - 29 May 2020
Abstract
Vigorous early-season growth rate allows crops to compete more effectively against weeds and to conserve soil moisture in arid areas. These traits are of increasing economic importance due to changing consumer demand, reduced labor availability, and climate-change-related increasing global aridity. Many crop species, [...] Read more.
Vigorous early-season growth rate allows crops to compete more effectively against weeds and to conserve soil moisture in arid areas. These traits are of increasing economic importance due to changing consumer demand, reduced labor availability, and climate-change-related increasing global aridity. Many crop species, including common bean, show genetic variation in growth rate, between varieties. Despite this, the genetic basis of early-season growth has not been well-resolved in the species, in part due to historic phenotyping challenges. Using a range of UAV- and ground-based methods, we evaluated the early-season growth vigor of two populations. These growth data were used to find genetic regions associated with several growth parameters. Our results suggest that early-season growth rate is the result of complex interactions between several genetic and environmental factors. They also highlight the need for high-precision phenotyping provided by UAVs. The quantitative trait loci (QTLs) identified in this study are the first in common bean to be identified remotely using UAV technology. These will be useful for developing crop varieties that compete with weeds and use water more effectively. Ultimately, this will improve crop productivity in the face of changing climatic conditions and will mitigate the need for water and resource-intensive forms of weed control. Full article
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