Document Type : Original Research Paper
Authors
Department of Surveying Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
Abstract
Background and Objectives: This research aims to present a novel approach for retrieving soil parameters from the combination of Sentinel-1 satellite data and the OH model. This information can aid in improving land management and increasing agricultural productivity. Accurate determination of soil parameters such as roughness and moisture is crucial for efficient land agriculture management and decision-making. Conventional ground-based methods for obtaining these parameters are often limited in spatial coverage and are frequently found to be time-consuming and costly. On the other hand, remote sensing techniques, especially those utilizing SAR satellite data, offer the potential for a more effective and comprehensive solution for monitoring soil conditions in vast areas. This study focuses on three main questions related to soil roughness and moisture parameters, emphasizing their significance for agriculture and their impact on soil science and agricultural processes. It also underscores the potential of remote sensing techniques, particularly the acquisition of satellite data, in providing effective and comprehensive solutions for monitoring soil conditions in extensive areas. Soil roughness and moisture are highly important for agriculture and can have significant impacts on crop growth.
Methods: This research is focused on investigating and analyzing the soil moisture and roughness parameters of an agricultural land in Nazarabad County. The process includes data collection, preprocessing, radar data calibration, and validation. Radar data for this study is obtained from the Sentinel-1 satellite. The use of radar data from this satellite for monitoring agricultural lands day and night and conducting comprehensive research on the subject is highly valuable. The input data underwent preprocessing in the SNAP software, involving the use of filters to remove noise spots and geometric corrections. The necessary inputs for solving the OH model equations from polarized images, especially HH and VV, were obtained after the aforementioned settings using SNAP software. Statistical analysis involves extracting vital information such as Sigma Naught (σ) and incidence angle (θ) for each pixel, which are crucial for the OH model. Polarized images, after adjustments, were further used for analysis. Next, the equations written for each pixel were individually solved in MATLAB programming software, and the values of the root mean square height (s) for obtaining roughness and the dielectric constant (ε) - a key parameter for estimating soil moisture content, i.e., soil moisture (mv), for all pixels were obtained. Finally, matrices related to these values were transformed into the output image, generating a map displaying information on soil moisture and roughness.
Findings: Based on the results obtained, it has been demonstrated that the values of dielectric constant, roughness, and humidity are very sensitive to the initial solver parameters. In particular, the dielectric constant exhibits significant sensitivity, which may be reduced by improving the solution method. Roughness profile analysis shows that the rms height varies in different regions and increases the scattering with the increase of roughness. Additionally, moisture content analysis indicates that the humidity is relatively uniform throughout the area.
Conclusion: This study demonstrates that the use of Sentinel-1 satellite data in conjunction with the OH model leads to a significant improvement in access to reliable information for enhancing agricultural management. This approach has the capability to analyze spatial and temporal variations in soil roughness and moisture, providing vital information for optimizing agricultural practices. Substantial soil condition improvements lead to more precise monitoring and better productivity in agriculture, offering the potential for more accurate monitoring of soil conditions and enhanced productivity in the agricultural domain. These assessments can provide valuable insights for agricultural land management and decision-making processes, contributing to increased efficiency and environmental conservation.
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COPYRIGHTS
© 2024 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)
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