Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data

TL;DR

This study develops a machine learning framework utilizing VNIR hyperspectral and LWIR data to accurately estimate soil moisture content in real-world grassland conditions, demonstrating promising results.

Contribution

Introduces a novel regression framework combining feature selection, preprocessing, and supervised machine learning models for soil moisture estimation using hyperspectral data.

Findings

Extreme randomized trees achieved the best estimation accuracy.

Preprocessing methods significantly impact regression performance.

The framework shows potential for real-world soil moisture monitoring.

Abstract

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, LWIR, and soil-moisture data conducted on grassland site. We introduce a regression framework with three steps consisting of feature selection, preprocessing, and well-chosen regression models. The latter are mainly supervised machine learning models. An exception are the self-organizing maps which combine unsupervised and supervised learning. We analyze the impact of the distinct preprocessing methods on the regression results. Of all regression models, the extremely randomized trees model without preprocessing provides the best estimation performance. Our results reveal the potential of the respective regression framework combined with the VNIR hyperspectral data to estimate soil moisture measured under real-world conditions. In conclusion, the results of this paper provide a basis for further improvements in different research directions.