International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 357–361, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-357-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 357–361, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-357-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Aug 2019

22 Aug 2019

HIGHLY DISCRETE MAPPING OF THE GROWING SEASON TIME FRAMES AND TIME DYNAMICS

I. Rykin1, A. Shagnieva1, E. Panidi1, and V. Tsepelev2 I. Rykin et al.
  • 1Saint Petersburg State University, St. Petersburg, Russia
  • 2Russian State Hydrometeorological University, St. Petersburg, Russia

Keywords: Growing Seasons, GIS-based Mapping, Vegetation Indexes, NDWI, Google Earth Engine

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.