Estimation of wheat kernel moisture content based on hyperspectral reflectance and satellite multispectral imagery

Abstract

The wheat kernel moisture content (KMC) plays a pivotal role in determining the quality, storage viability, and economic profitability of wheat. Monitoring wheat KMC in-field before harvest provides decision-making information to ensure harvested wheat meeting trade standards. The study aims to expand the feasibility of spectroscopy to estimate wheat KMC at multi-scales. In addition to using ground hyperspectral reflectance for single-point measurement of wheat KMC, satellite multispectral images were also utilized to monitor the field distribution of wheat KMC on large-scale. For each estimation task, we extracted sensitive spectral features and compared the accuracy of three machine learning methods including Ridge, Support Vector Regression (SVR) and Random Forest Regression (RFR). We also adopted Two-stage Tradaboost.R2 to address the issue of uneven distribution of samples corresponding to satellite imagery. Experimental results showed that, based on optimized spectral features, the R2 of RFR model outperformed other machine learning models (Ridge and SVR) for wheat KMC estimation (R2 > 0.85). Among all spectral features, the B5 band and PSRI vegetation index from PlanetScope satellite, as well as the B11, and B12 bands and VARI vegetation index from Sentinel-2, proved to be effective features for wheat KMC estimation. Moreover, the incorporation of hyperspectral reflectance and multispectral imagery can improve the estimation accuracy on large-scale through the transfer learning algorithm of Two-stage Tradaboost.R2. Leveraging its superior temporal and spatial resolution, PS imagery emerges as an ideal data source for monitoring rapid fluctuations in wheat KMC during the physiological maturity period, which enabled a multi-day wheat KMC simulation. Overall, This study contributes a reliable and convenient spectroscopy tool for wheat KMC estimation for multi-scenes, offering scientific insights to support agricultural water management and wheat quality.