In tropical climatic conditions, floods occur during heavy rainfall. Floods during this thick cloud cover partially stops the optical imagery to pass through the atmosphere and record the surface reflectance. Another kind of satellite imagery that is available is microwave remote sensing data that can pass through the clouds. However, the exploration of this microwave remote sensing began recently for earth observation applications. So, the algorithms and methods available for exploiting advantages from microwave data is still under research. The current part of the work is to explore the methods available to differentiate between the microwave data (Sentinel-1) and Optical imagery (Sentinel-2) in flooded and built-up area estimation. The ultimate aim is to conclude with most suitable datasets and fast computing methods in estimating the built-up area and flooded area during the emergency disaster time. Two case studies taken up for the study are August 2019 East Godavari floods and October 2019 Titli cyclone. So, the adopted method to estimate the flooded areas and built-up areas from the Sentinel-1A and Sentinel-2B was RGB clustering (Red, Green and Blue clustering) using the derived RGB colour combinations in snap 7.0 software. The datasets were classified into built-up, flooded area and vegetation areas using Random Forest supervised classification, a machine learning technique Validation of estimated built-up and flooded areas estimated from Sentinel-1A and Sentinel-2B was done using the random pixel distribution technique. Since the de-centralisation of estimated flooded areas and built-up area helps in fast distribution of the response forces to the affected area, estimation of built-up and flooded area was also taken up for the sub-districts of East Godavari district, India. Finally, the study estimates the damaged built-up and vegetation due to August 2019 East Godavari floods from Sentinel-1A and Sentinel-2B. Flooded area due to ‘Titli’ cyclone 2018 was estimated in East Godavari, Visakhapatnam and Vijianagaram districts of Andhra Pradesh state.

The article describes in detail the ways in which agricultural enterprises operating in irrigated regions, including farms, create automated systems for the development and implementation of internal land management projects, the use of specialized expert systems based on artificial intelligence in assessing projects and their economic efficiency. Geographical information for the internal organization of farmland, in particular, the design of irrigation plots, crop rotations, forest plantations, field paths and irrigation canals, which are key elements in the territorial arrangement of the proposed sowing areas; ways to create such projects with wide application of GIS technologies in a short amount of time at low cost, as well as promptly eliminate deficiencies identified by expert systems. It is explained that the introduction of expert systems based on artificial intelligence into the practice of projecting of land management is more cost-effective than traditional estimation methods.

The features of the organization of production units agroclusters, which are one of the components of land management projects which aimed at organizing the rational and efficient use of available land resources are described in this article. The term “cluster” is a French word that means “bundle”, “collection” in Uzbek. It can be taken as “the geographical proximity of enterprises and institutions cooperating with each other in a particular field”. The development of value-added production in the agricultural sector and the development of this market requires ensuring product quality standards, full use of scientific and scientific achievements in the processing process, development of existing research institutes and using the potential of geographical location. In our view, the solution of this problem can be found in the agricultural production system through the organization of cluster production, which is used by developed countries in America, Europe and Asia.

Uzbekistan has a wide range of land use categories, because of its best geographical location. The distribution of the land fund according to its specific purpose is always one of the most important issues in the country. This article provides an analysis of the use of the Republican land fund for the last decade. Explanations of the concept of the land fund have been studied and detailed clarifications have been given for each of its categories. Surkhandarya region was selected as the main study area and the land fund of the last two years was analysed. During the work, statistical land-use data which provided from the state committee on land resources, geodesy, cartography and state cadastre of the Republic of Uzbekistan were used. Besides that, using the importance of geographic information system (GIS) and remote sensing techniques was reviewed for rational land use.