TY - JOUR

T1 - AUTOMATED GIS-BASED TECHNIQUE for EVALUATION of INDIRECT GROWING SEASON ESTIMATIONS

AU - Rykin, I.

AU - Panidi, E.

AU - Tsepelev, V.

PY - 2019/8/23

Y1 - 2019/8/23

N2 - 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.

AB - 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.

KW - climate change

KW - gis

KW - growing season

KW - MODIS

KW - NDWI

KW - remote sensing

KW - vegetation

UR - http://www.scopus.com/inward/record.url?scp=85074343267&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-XLII-4-W14-209-2019

DO - 10.5194/isprs-archives-XLII-4-W14-209-2019

M3 - Conference article

AN - SCOPUS:85074343267

VL - 42

SP - 209

EP - 211

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 1682-1750

IS - 4/W14

T2 - 2019 Free and Open Source Software for Geospatial, FOSS4G 2019

Y2 - 26 August 2019 through 30 August 2019

ER -