ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLII-4-W14-209-2019 AUTOMATED GIS-BASED TECHNIQUE FOR EVALUATION OF INDIRECT GROWING SEASON ESTIMATIONS Rykin I. 1 Panidi E. https://orcid.org/0000-0002-1492-4218 1 Tsepelev V. 2 Department of Cartography and Geoinformatics, Saint Petersburg State University, St. Petersburg, Russia Meteorological Department, Russian State Hydrometeorological University, St. Petersburg, Russia 23 08 2019 XLII-4/W14 209 211 Copyright: © 2019 I. Rykin et al. 2019 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLII-4-W14/209/2019/isprs-archives-XLII-4-W14-209-2019.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLII-4-W14/209/2019/isprs-archives-XLII-4-W14-209-2019.pdf

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.