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Article: Exploring the correlations between ten monthly climatic variables and the vegetation index of four different crop types at the global scale

TitleExploring the correlations between ten monthly climatic variables and the vegetation index of four different crop types at the global scale
Authors
Issue Date2017
Citation
Remote Sensing Letters, 2017, v. 8, n. 8, p. 752-760 How to Cite?
Abstract© 2017 Informa UK Limited. The relationship between vegetation index (VI) and climatic variables such as temperature (TEP) and precipitation (PRE) at local, regional and global scales are conventionally analysed to understand the responses of vegetation to climate change. Those unique responses also afford opportunities for using climate variables to discriminate vegetation types. This paper presents a data-driven analysis to explore correlations between ten monthly climatic variables (temperature, precipitation, potential evapotranspiration (PET), vapour pressure (VAP), wet days (WET), and others) and monthly VIs of four different crop types (maize, rice, soybeans, and wheat) at global scale. The purpose is to show the VI–climate correlations in a spatially explicit way, laying the foundation for better crop type mapping by integrating climatic variables and remote sensing. The results show large variations in VI–climate correlation for different crop types and regions. Most cropland areas in the world show strong correlations between VI and VAP, and other variables such as WET, PET, and monthly average daily minimum temperature (TMN). This result encourages future studies using additional climate variables (in addition to TMP and PRE) for detailed vegetation/crop-type mapping.
Persistent Identifierhttp://hdl.handle.net/10722/296840
ISSN
2023 Impact Factor: 1.4
2023 SCImago Journal Rankings: 0.458
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Xiaoxuan-
dc.contributor.authorYu, Le-
dc.contributor.authorWang, Hongshuo-
dc.contributor.authorZhong, Liheng-
dc.contributor.authorLu, Hui-
dc.contributor.authorYu, Chaoqing-
dc.contributor.authorGong, Peng-
dc.date.accessioned2021-02-25T15:16:47Z-
dc.date.available2021-02-25T15:16:47Z-
dc.date.issued2017-
dc.identifier.citationRemote Sensing Letters, 2017, v. 8, n. 8, p. 752-760-
dc.identifier.issn2150-704X-
dc.identifier.urihttp://hdl.handle.net/10722/296840-
dc.description.abstract© 2017 Informa UK Limited. The relationship between vegetation index (VI) and climatic variables such as temperature (TEP) and precipitation (PRE) at local, regional and global scales are conventionally analysed to understand the responses of vegetation to climate change. Those unique responses also afford opportunities for using climate variables to discriminate vegetation types. This paper presents a data-driven analysis to explore correlations between ten monthly climatic variables (temperature, precipitation, potential evapotranspiration (PET), vapour pressure (VAP), wet days (WET), and others) and monthly VIs of four different crop types (maize, rice, soybeans, and wheat) at global scale. The purpose is to show the VI–climate correlations in a spatially explicit way, laying the foundation for better crop type mapping by integrating climatic variables and remote sensing. The results show large variations in VI–climate correlation for different crop types and regions. Most cropland areas in the world show strong correlations between VI and VAP, and other variables such as WET, PET, and monthly average daily minimum temperature (TMN). This result encourages future studies using additional climate variables (in addition to TMP and PRE) for detailed vegetation/crop-type mapping.-
dc.languageeng-
dc.relation.ispartofRemote Sensing Letters-
dc.titleExploring the correlations between ten monthly climatic variables and the vegetation index of four different crop types at the global scale-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/2150704X.2017.1322732-
dc.identifier.scopuseid_2-s2.0-85034596991-
dc.identifier.volume8-
dc.identifier.issue8-
dc.identifier.spage752-
dc.identifier.epage760-
dc.identifier.eissn2150-7058-
dc.identifier.isiWOS:000401759600005-
dc.identifier.issnl2150-704X-

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