Development of a Soil Temperature Map: A Remote Sensing and GIS Approach

Soil temperature is a crucial parameter in various fields such as agriculture, hydrology and climatology. Its precise knowledge is essential for decision making in natural resource management and land use planning.

This study focuses on the development of a soil temperature map of a specific area using a combination of remote sensing techniques and Geographic Information Systems (GIS). ArcGIS and QGIS software were used for data processing and analysis.

Methodology

Data acquisition: Satellite images of the study area were obtained containing information on land surface temperature (LST). Thermal sensors such as Landsat or MODIS, which capture infrared radiation emitted by the earth's surface, were used.

Data preprocessing: Satellite images were radiometrically and geometrically corrected to eliminate distortions and errors. Atmospheric correction algorithms were applied to minimize the influence of the atmosphere on the LST estimation.

Soil temperature estimation: Planck equation inversion methods were used to convert thermal radiance to land surface temperature. Corrections were applied to account for soil emissivity and other factors that influence LST.

Spatial analysis: ArcGIS software was used to create a soil temperature map from the LST data. Interpolation techniques were applied to estimate temperature in areas with no data. Spatial analysis was performed to identify patterns and trends in soil temperature distribution.

Validation of results: Soil temperature data obtained from satellite images were compared with soil temperature data measured in the field. Statistical techniques were used to assess the accuracy and reliability of the soil temperature map.

Integration with QGIS: QGIS tools were used to integrate the soil temperature map with other geographic information layers such as elevation data, land use, and climate characteristics. This allowed the relationship between soil temperature and other environmental factors to be analyzed.

Results

The soil temperature map generated revealed spatial patterns in temperature distribution. Areas with higher temperatures and areas with lower temperatures were identified, which were related to factors such as topography, vegetation cover, and soil moisture.