Contribution of Remote Sensing on Crop Models: A Review

Kasampalis, Dimitrios A.; Alexandridis, Thomas; Deva, Chetan; Challinor, Andrew J.; Moshou, Dimitrios; Zalidis, George C.

doi:10.3390/jimaging4040052

Cited by 180 publications

(120 citation statements)

References 111 publications

(131 reference statements)

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“…In order to ensure today's food security and in the coming decades, efforts have been made on establishment of crop simulation models aimed at predicting growth, development and yield potential of a crop under certain environmental conditions (Basso et al, 2013;Wang et al, 2018;. Several dynamic crop simulations models (CSMs) have been developed and used widely to study physiological, physical and chemical processes of crop productivity under a changing climate (Kasampalis et al, 2018;Shi, Tao, & Zhang, 2013;White, Hoogenboom, Kimball, & Wall, 2011). The crop models and their outputs are then used to guide agronomic decision-making aimed at sustainable management and development of adaptive strategies for responding to impacts of climate change (Basso et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Modelling Impacts of Climate Change on Maize (<i>Zea mays</i> L.) Growth and Productivity: A Review of Models, Outputs and Limitations

Kogo¹,

Kumar²,

Koech³

et al. 2019

GEP

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The use of crop modelling in various cropping systems and environments to project and upscale agronomic decision-making under the facets of climate change has gained currency in recent years. This paper provides an evaluation of crop models that have been used by researchers to simulate maize growth and productivity. Through a systematic review approach, a comprehensive assessment of 186 published articles was carried out to establish the models and parameterization features, simulated impacts on maize yields and adaptation strategies in the last three decades. Of the 23 models identified, CERES-maize and APSIM models were the most dominant, representing 49.7% of the studies undertaken between 1990 and 2018. Current research shows projected decline in maize yields of between 8%-38% under RCP4.5 and RCP8.5 scenarios by the end of the 21 st century, and that adaptation is essential in alleviating the impacts of climate change. Major agro-adaptation options considered in most papers are changes in planting dates, cultivars and crop water management practices. The use of multiple crop models and multi-model ensembles from general circulation models (GCMs) is recommended. As interest in crop modelling grows, future work should focus more on suitability of agricultural lands for maize production under climate scenarios.

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Section: Introductionmentioning

confidence: 99%

Modelling Impacts of Climate Change on Maize (<i>Zea mays</i> L.) Growth and Productivity: A Review of Models, Outputs and Limitations

Kogo¹,

Kumar²,

Koech³

et al. 2019

GEP

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“…NB: A brief review of crop yield estimation techniques can be found in the introduction to Doraiswamy et al (2005Doraiswamy et al ( , 2004Doraiswamy et al ( , 2003 and Kasampalis et al (2018) provide an overview of crop growth models.…”

Section: Indicator Example Methods Crop Yieldmentioning

confidence: 99%

Monitoring the Sustainable Intensification of Arable Agriculture: the Potential Role of Earth Observation

Hunt

Blackburn

Rowland

2019

International Journal of Applied Earth Observation and Geoinfor

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Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increase in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose a development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data.

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“…The use of ad hoc methodological approaches according to different objectives, scales, sensors, temporal, and spatial resolutions represents an important achievement. The continuous exploitation of new technological resources is one of the main characteristics of rs+pheno science, which guarantees this discipline to be up-to-date and always evolving, according to the availability of innovative instruments, the development of novel methodologies, and the emergence of new questions [70].…”

Section: Major Research Topicsmentioning

confidence: 99%

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

et al. 2019

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As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly difficult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network.

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Contribution of Remote Sensing on Crop Models: A Review

Cited by 180 publications

References 111 publications

Modelling Impacts of Climate Change on Maize (<i>Zea mays</i> L.) Growth and Productivity: A Review of Models, Outputs and Limitations

Modelling Impacts of Climate Change on Maize (<i>Zea mays</i> L.) Growth and Productivity: A Review of Models, Outputs and Limitations

Monitoring the Sustainable Intensification of Arable Agriculture: the Potential Role of Earth Observation

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

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Contribution of Remote Sensing on Crop Models: A Review

Cited by 180 publications

References 111 publications

Modelling Impacts of Climate Change on Maize (&lt;i&gt;Zea mays&lt;/i&gt; L.) Growth and Productivity: A Review of Models, Outputs and Limitations

Modelling Impacts of Climate Change on Maize (&lt;i&gt;Zea mays&lt;/i&gt; L.) Growth and Productivity: A Review of Models, Outputs and Limitations

Monitoring the Sustainable Intensification of Arable Agriculture: the Potential Role of Earth Observation

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

Contact Info

Product

Resources

About

Modelling Impacts of Climate Change on Maize (<i>Zea mays</i> L.) Growth and Productivity: A Review of Models, Outputs and Limitations

Modelling Impacts of Climate Change on Maize (<i>Zea mays</i> L.) Growth and Productivity: A Review of Models, Outputs and Limitations