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Article: Assimilation of remote sensing into crop growth models: Current status and perspectives

TitleAssimilation of remote sensing into crop growth models: Current status and perspectives
Authors
KeywordsCrop growth models
Crop modelling
Crop monitoring
Crop yield prediction
Data assimilation
Remote sensing
Issue Date2019
Citation
Agricultural and Forest Meteorology, 2019, v. 276-277, article no. 107609 How to Cite?
AbstractTimely monitoring of crop lands is important in order to make agricultural activities more sustainable, as well as ensuring food security. The use of Earth Observation (EO) data allows crop monitoring at a range of spatial scales, but can be hampered by limitations in the data. Crop growth modelling, on the other hand, can be used to simulate the physiological processes that result in crop development. Data assimilation (DA) provides a way of blending the monitoring properties of EO data with the predictive and explanatory abilities of crop growth models. In this paper, we first provide a critique of both the advantages and disadvantages of both EO data and crop growth models. We use this to introduce a solid and robust framework for DA, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function (pdf) using Bayes’ rule. This treatment allows us to provide some recommendation on the choice of DA method for particular applications. We comment on current computational challenges in scaling DA applications to large spatial scales. Future areas of research are sketched, with an emphasis on DA as an enabler for blending different observations, as well as facilitating different approaches to crop growth models. We have illustrated this review with a large number of examples from the literature.
Persistent Identifierhttp://hdl.handle.net/10722/321848
ISSN
2023 Impact Factor: 5.6
2023 SCImago Journal Rankings: 1.677
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHuang, Jianxi-
dc.contributor.authorGómez-Dans, Jose L.-
dc.contributor.authorHuang, Hai-
dc.contributor.authorMa, Hongyuan-
dc.contributor.authorWu, Qingling-
dc.contributor.authorLewis, Philip E.-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorChen, Zhongxin-
dc.contributor.authorXue, Jing Hao-
dc.contributor.authorWu, Yantong-
dc.contributor.authorZhao, Feng-
dc.contributor.authorWang, Jing-
dc.contributor.authorXie, Xianhong-
dc.date.accessioned2022-11-03T02:21:51Z-
dc.date.available2022-11-03T02:21:51Z-
dc.date.issued2019-
dc.identifier.citationAgricultural and Forest Meteorology, 2019, v. 276-277, article no. 107609-
dc.identifier.issn0168-1923-
dc.identifier.urihttp://hdl.handle.net/10722/321848-
dc.description.abstractTimely monitoring of crop lands is important in order to make agricultural activities more sustainable, as well as ensuring food security. The use of Earth Observation (EO) data allows crop monitoring at a range of spatial scales, but can be hampered by limitations in the data. Crop growth modelling, on the other hand, can be used to simulate the physiological processes that result in crop development. Data assimilation (DA) provides a way of blending the monitoring properties of EO data with the predictive and explanatory abilities of crop growth models. In this paper, we first provide a critique of both the advantages and disadvantages of both EO data and crop growth models. We use this to introduce a solid and robust framework for DA, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function (pdf) using Bayes’ rule. This treatment allows us to provide some recommendation on the choice of DA method for particular applications. We comment on current computational challenges in scaling DA applications to large spatial scales. Future areas of research are sketched, with an emphasis on DA as an enabler for blending different observations, as well as facilitating different approaches to crop growth models. We have illustrated this review with a large number of examples from the literature.-
dc.languageeng-
dc.relation.ispartofAgricultural and Forest Meteorology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCrop growth models-
dc.subjectCrop modelling-
dc.subjectCrop monitoring-
dc.subjectCrop yield prediction-
dc.subjectData assimilation-
dc.subjectRemote sensing-
dc.titleAssimilation of remote sensing into crop growth models: Current status and perspectives-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.agrformet.2019.06.008-
dc.identifier.scopuseid_2-s2.0-85068037707-
dc.identifier.volume276-277-
dc.identifier.spagearticle no. 107609-
dc.identifier.epagearticle no. 107609-
dc.identifier.isiWOS:000500195900008-

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