Q-DAI: Design and Implementation of a QGIS Plugin for Disaggregation of Soil Moisture Content at 30 m Spatial Resolution

Soil moisture content (SMC) plays a significant role in land surface water and energy cycle and is essential in performing various field-related studies. It is a crucial parameter provided by passive L-band sensors on soil moisture active passive/soil moisture ocean salinity satellite missions at a resolution of ~36–40 km. To obtain inference from the SMC data and apply it to different applications, its study and analysis are required that is achievable using any geographic information systems software. Quantum Geographic Information System (QGIS) is an open-source software with a user-friendly graphical user interface (GUI) and a repository of application-specific plugins. However, no plugin provides SMC or downscales the SMC product for a required location. Q-Daily Arial Image (Q-DAI), the QGIS plugin proposed here, implements a downscaling algorithm to obtain the low-resolution SMC product from SMAP/SMOS at fine resolution using inputs from high-resolution satellite imagery. The plugin is developed by designing a GUI using Qt Creator and defining its functionality using Python. Q-DAI is tested on QGIS 3.16.16 on Windows 10, 8 GB RAM PC and QGIS 3.22 on a macOS Ventura laptop. Q-DAI can be used to obtain high-resolution SMC for any location, and in this article, sample results of Q-DAI implemented for Delhi region data have been shown.