Improving the assessment of agricultural land reclamation condition using GIS based on the interdependence of the factors impact

One of the primary concerns in agricultural regions is the presence of soil salinity, which is especially prominent in the Amudarya district of Uzbekistan. This investigation aims to evaluate the salinity of the local soil utilizing data acquired through remote sensing. By integrating remote sensing data with modeling methodologies, this study offers a comprehensive understanding of the patterns of soil salinity. The outcomes of the study demonstrate the effectiveness of this approach, with a 72% accuracy in interpolation and a Kappa value of 81%. The study underscores the critical importance of amalgamating modeling techniques and remote sensing data to monitor local changes in soil salinity and assess the potential impact of climate change on soil salinity levels. These findings are of utmost significance in facilitating the planning of water resources and agricultural management, ultimately supporting the sustainable utilization of farmland in the region.

Study of soil salinity in agricultural land using Remote Sensing data in Amudarya district, Uzbekistan

Qobulov,

Botirova,

Tursunov

et al. 2024

Quantifying Water Bodies with Sentinel-2 Imagery and NDWI: A Remote Sensing Approach

Avezov,

Yunusova,

Yusupjonov

et al. 2024

This paper presents a comparative analysis of surface water dynamics using Sentinel-2 satellite imagery and the Normalized Difference Water Index (NDWI) obtained through the Google Earth Engine (GEE) platform. The study focuses on assessing changes in surface water area and water ratio in the area between the winter and summer seasons of 2017 and 2023. Our results indicate a notable fluctuation in water area over the study period, with the reservoir exhibiting varying extents of surface water coverage across different seasons and years. Specifically, in the summer of 2023, the water area was measured at 14.35 km2, compared to 14.98 km2 in 2017. Conversely, during the winter months, the water area decreased to 12.54 km2 in 2023, while it was 14.68 km2 in 2017. The findings suggest a shift in surface water dynamics over time, potentially influenced by climatic and environmental factors. Furthermore, the study highlights the efficiency of utilizing GEE and remote sensing techniques for surface water mapping and monitoring. Remote sensing provides a cost-effective and reliable means of monitoring surface water resources, enabling timely assessments and informed decision-making for water resource management and conservation efforts. This research underscores the importance of leveraging remote sensing technologies for effective resource management and environmental stewardship in the face of changing climatic conditions.

Design of an optimal irrigation system taking into account the characteristics of the earth using simulation-optimization models

Khakimova,

Mamanazarova,

Urinova

et al. 2024

This paper presents a novel algorithm for the design of an optimal irrigation system, which utilizes land topographic data, specifically digital land surface models (DSMs). The proposed approach incorporates an inter-farm irrigation network map, canal profile, and spatial model of the region as part of the topographic data. By dividing the land into sections with similar topographical characteristics, this method facilitates the identification and optimization of key parameters related to the land and irrigation system, ultimately leading to an optimal design of the irrigation system.