Soil moisture estimation of bare and vegetation-covered areas using a P/L/C-band SAR

TL;DR

This paper presents a novel multiband SAR-based model for estimating soil moisture in vegetated areas, incorporating crop height estimation and neural networks, validated with drone-borne SAR data showing high accuracy.

Contribution

It introduces a new multiband SAR model that accounts for crop height variations and employs neural networks for improved soil moisture estimation in vegetated areas.

Findings

Accurate soil moisture estimation with 0.05 cm³/cm³ RMSE.

Effective use of multiband SAR data and neural networks.

Model validated on real agricultural sites.

Abstract

The paper introduces a novel approach for estimating soil moisture in vegetated surfaces, specifically focusing on sugarcane crops throughout various growth stages in agriculture applications. While existing models typically address bare soil scenarios, this model utilizes data from P-, L-, and C-band Synthetic Aperture Radar (SAR) to estimate soil moisture. The semi-empirical Dubois model forms the basis of the proposed model, which has been adapted to accommodate multiband operation and crop height variations. Synthetic datasets are generated using the adjusted model to train two neural networks incorporated into the overall model. Additionally, a linear expression for estimating crop height is integrated into the model. The model is validated in an Experimental Site at the School of Agricultural Engineering, UNICAMP, and an independent area at the Sugarcane Technology Center in Piracicaba, Brazil. The model utilizes a multiband drone-borne SAR system with a 3-meter image resolution and radiometric accuracy of 0.5 dB. The results indicate that the model can estimate soil moisture with root-mean-square errors of 0.05 cm3.cm-3 (5 vol. %) across crop heights ranging from zero to 2.5 meters.