Highly discrete mapping of the growing season time frames and time dynamics

Research output

Abstract

Growing season time frames can be estimated and mapped using the vegetation indexes mapping and analysis. This approach brings significant benefit consisted in the ability of detailed (highly discrete in the meaning of spatial resolution) mapping of spatial differences in growing season stage and length. In comparison with interpolation of ground air temperature (applied when using temperature to detect growing seasons), real spatial resolution raises to kilometers per pixel and higher, while nodes of ground observation network can be spaced by thousands of kilometers in some regions. Our ongoing study is devoted to design a processing chain for mapping of growing season time frames basing on vegetation indexes data with close-to-one-day time resolution. We used MOD09GA dataset as an initial data. Data processing was implemented in Google Earth Engine big geospatial data platform.

Original languageEnglish
Pages (from-to)357-361
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume42
Issue number3/W8
DOIs
Publication statusPublished - 20 Aug 2019
Event2019 GeoInformation for Disaster Management, Gi4DM 2019 - Prague
Duration: 3 Sep 20196 Sep 2019

Fingerprint

growing season
vegetation index
spatial resolution
search engine
Interpolation
Pixels
Earth (planet)
air
Engines
interpolation
engine
Temperature
pixel
air temperature
ability
Processing
Air
time
temperature
Big data

Scopus subject areas

  • Information Systems
  • Geography, Planning and Development

Cite this

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abstract = "Growing season time frames can be estimated and mapped using the vegetation indexes mapping and analysis. This approach brings significant benefit consisted in the ability of detailed (highly discrete in the meaning of spatial resolution) mapping of spatial differences in growing season stage and length. In comparison with interpolation of ground air temperature (applied when using temperature to detect growing seasons), real spatial resolution raises to kilometers per pixel and higher, while nodes of ground observation network can be spaced by thousands of kilometers in some regions. Our ongoing study is devoted to design a processing chain for mapping of growing season time frames basing on vegetation indexes data with close-to-one-day time resolution. We used MOD09GA dataset as an initial data. Data processing was implemented in Google Earth Engine big geospatial data platform.",
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AU - Rykin, I.

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