Volume XLII-4/W14
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W14, 209–211, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W14-209-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W14, 209–211, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W14-209-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Aug 2019

23 Aug 2019

AUTOMATED GIS-BASED TECHNIQUE FOR EVALUATION OF INDIRECT GROWING SEASON ESTIMATIONS

I. Rykin1, E. Panidi1, and V. Tsepelev2 I. Rykin et al.
  • 1Department of Cartography and Geoinformatics, Saint Petersburg State University, St. Petersburg, Russia
  • 2Meteorological Department, Russian State Hydrometeorological University, St. Petersburg, Russia

Keywords: GIS, Remote Sensing, MODIS, NDWI, Climate Change, Growing Season, Vegetation

Abstract. This article is based on NDWI (Normalized Difference Water Index) which is automatically computed from the daily MODIS data. The main purpose of the article is to tell how the evaluation of NDWI-based growing season estimations can be automated. The NDWI is used as an indicator of liquid water quantity in vegetation, which is less sensitive to atmospheric scattering effect then the famous growing index (NDVI). The NDWI is computed using cloud-based platform (Google Earth Engine was applied) and compared with the daily meteorological data. The available meteorological data is collected for the past 130 years and NDWI data is collecting for the past 20 years. An automated technique has been probated on the example of republic of Komi, as it has a different climate forming factors. This approach can be used to evaluate growing season estimations for other territories that contain vegetation. Due to the accumulated amount of data, the study is relevant and has a special significance for areas with sparse hydrometeorological network.