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J. Imaging 2018, 4(4), 52; https://doi.org/10.3390/jimaging4040052

Contribution of Remote Sensing on Crop Models: A Review

1
Department of Hydraulics, Soil Science and Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Institute for Climate and Atmospheric Science, School of Earth and Environment, The University of Leeds, Leeds LS2 9JT, UK
*
Author to whom correspondence should be addressed.
Received: 31 December 2017 / Revised: 16 March 2018 / Accepted: 20 March 2018 / Published: 23 March 2018
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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Abstract

Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products. View Full-Text
Keywords: crop models; earth observation; fusion; yield prediction; crop yield; vegetation indices; spatio-temporal scale crop models; earth observation; fusion; yield prediction; crop yield; vegetation indices; spatio-temporal scale
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kasampalis, D.A.; Alexandridis, T.K.; Deva, C.; Challinor, A.; Moshou, D.; Zalidis, G. Contribution of Remote Sensing on Crop Models: A Review. J. Imaging 2018, 4, 52.

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