Work

Machine Learning-Based Soil Moisture Inversion from Drone-Borne X-Band Microwave Radiometry

Year: 2025

Type: article

Abstract: Surface soil moisture (SSM) is a critical land surface parameter affecting a wide variety of economically and environmentally important processes. Spaceborne microwave remote sensing has been extensively employed for monitoring SSM. Active microwave sensors offering high spatial resolution are typically utilized to capture dynamic fluctuations in soil moisture, albeit with low temporal resolution, whereas passive sensors are typically used to monitor the absolute values of large-scale soil moisture, but offer coarser spatial resolutions (~10 km). In this study, a passive microwave observation system using an X-band microwave radiometer mounted on a drone was established to obtain high-resolution (~1 m) radiative brightness temperature within the observation region. The region was a control experimental field established to validate the proposed approach. Additionally, machine learning models were employed to invert the soil moisture. Based on the site-based validation the trained inversion model performed well, with estimation accuracies of 0.74 and 2.47% in terms of the coefficient of determination and the root mean square error, respectively. This study introduces a methodology for generating high-spatial resolution and high-accuracy soil moisture maps in the context of precision agriculture at the field scale.

Source: Remote Sensing

DOI: 10.3390/rs17162781

Authors: Xiangkun Wan, Xiaofeng Li, Tao Jiang, Xingming Zheng, Lei Li

Institutions: Chinese Academy of Sciences, Northeast Institute of Geography and Agroecology

Language: English

Topic: Soil Moisture and Remote Sensing

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger

Open Access status: gold

Estimated APC paid: $3,258