E3S Web Conf.
Volume 227, 2021Annual International Scientific Conference on Geoinformatics – GI 2021: “Supporting sustainable development by GIST”
|Number of page(s)||10|
|Section||GIS in Agriculture|
|Published online||06 January 2021|
Application of GIS and RS in real time crop monitoring and yield forecasting: a case study of cotton fields in low and high productive farmlands
Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME), Koriy Niyaziy str., 39, 100000, Tashkent, Uzbekistan
2 National University of Uzbekistan named after Mirzo Ulugbek (NUUz), University str., 4, 100174, Tashkent, Uzbekistan
* Corresponding author: email@example.com
Badland reclamation and low productive farmlands always have been one of the most detrimental effects on the national economy, typically in agricultural sector of Uzbekistan. Nonetheless, such kind of lands has been used extensively for major crops like cotton and winter wheat. However, it is difficult to assessing real productivity of them. Advanced technologies as GIS and RS are vital tool for geospatially analysing and making decisions on this type of fields. This research was carried out for real-time crop monitoring and yield forecasting in case of low productive (3.5 ha) and high productive (8.3 ha) cotton areas of Jarkurgan district (Surkhandarya region, Uzbekistan) based on geospatial analyses of multi-temporal satellite images, condition of groundwater, soil salinity, and ground truth data. For monitoring vegetation phenology of cotton and forecasting its harvest, False Colour, NDVI (Normalized Difference Vegetation Index) and SI (Salinity Index) analyses of areas were carried out by using 6 temporal windows of multi-temporal Sentinel 2 from April through August 2019. Besides, groundwater condition data which was obtained from observation wells these located in massives consists of both cotton fields was analysed by IDW (Inverse Distance Weighting) interpolation algorithm method to determine groundwater’s effect to vegetation development and yield.
Key words: GIS / remote sensing / crop monitoring / yield forecasting / NDVI / soil index / Sentinel 2
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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