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

4 Discussion 
They were prepared for flooding due to their irrigation regime, as mentioned earlier by 
other researchers. As can be seen from Figure 9, there was a lack of water in May, since the 
growing season of wheat falls on this month. Since, in the second decade of June, the crop 
was harvested, the water demand dropped sharply, as can be seen in Figure 11a. 
The irrigation regime of dense grassy lands differs from arable lands and demonstrates 
an increase in vegetation and a decrease in the humidity of open surfaces from April to 
July. The acreage was also heavily overgrown with vegetation, and due to seasonal 
changes, they increased in the period from April to July. The restoration of agricultural 
areas is mentioned as important for improving the quality of life of the population and their 
socio-economic status [13]. Moreover, agricultural areas could be used as a source of 
ecosystem services for the population such as provision, the type of soil cover mainly 
occupied open or less vegetated land, and most pixels had values below zero, with some 
areas with high vegetation. 
The irrigated lands have not undergone any special changes, since they were sown with 
vegetation on the dates under consideration. Rare herbaceous types of soil cover were 
mostly without vegetation and had low values. The lands corresponded to vegetation. 
Rivers and lakes were well reflected with the maximum number of pixels in April. 
Thus, it has been observed with the help of Sentinel-2 that it is likely to be monitored 
remotely for the need for water and monitor the condition of vegetation. In particular, 
determining the type of land use can be an easy task to conduct. Remotely monitoring 
allows the collection of data about the area of observation into a single database, which 
facilitates the work of all stakeholders [15]. 
Remote sensing data can be generalized into an agricultural development model. Based 
on such models, it is possible to obtain accurate information about the terrain and the most 
promising types for cultivation in this area.