Classification of seasonal dynamics of soil moisture according to satellite data Sentinel-2, Jizzakh region, Uzbekistan

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Introduction
 
Currently, for agriculture, capturing satellite images of individual fields, regions, and 
districts with a certain cycling period is one of the most applicable approaches. 
Considering the possibility of obtaining information about the state of the soil, 
including recognition of crops, determination of the acreage of agricultural land, and the 
condition of crops, is an innovative approach in this area. Satellite data are used to manage 
and monitor agricultural performance at different levels [13]. This data is used to optimize 
farming and space-based management of technical operations. Satellite images significantly 
help to determine the location of crops and land depletion, and can then be used to develop 
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Corresponding author: 
[e-mail]
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons 
Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
E3S Web of Conferences 
386, 01005 (2023)
GISCA 2022 and GI 2022
https://doi.org/10.1051/e3sconf/202338601005

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and implement a land reclamation plan to improve the use of agricultural chemicals [2]. 
The statistics are known as NDWI, or Normalized Water Difference Index, which is 
usually calculated using data from optical remote sensing of the Earth. Normalized values 
of the near-infrared and vortex ranges were used to determine it. It is based on the high 
reflectivity of vegetation in certain bands, and changes in leaf moisture affect this indicator. 
In the same year, another MNDWI approach to land assessment was developed [13]. The 
processed Sentinel-2 data can be used as input parameters for the next Agro modelling 
chain, an open-access program such as DSSAT promoted in Central Asia to improve 
sustainable farming, increase profitability, reduce losses, and predict climate change 
adaptation [11].