ESTIMATION OF WHEAT KERNEL MOISTURE CONTENT IN-FIELD BASED ON PLANETSCOPE AND SENTINEL-2 SATELLITE IMAGES

Abstract

Strict limitation of wheat kernel moisture content (KMC) has been set during wheat trading, as it determines the quality, storage safety, and economic efficiency. The acquisition of timely and precise wheat KMC data in-field constitutes a vital component of harvest management, as it can enable farmers to gather grain that meets industry standards, optimize their financial returns, and safeguard food resources. However, so far efficient monitoring methods have remained elusive. To address this challenge, this study utilized remote sensing satellite imagery, specifically, PlanetScope (PS) and Sentinel-2 (S2), to bridge this crucial gap. By leveraging the sensitive bands and vegetation indices for wheat KMC that were extracted from S2 and PS, respectively, this study constructed wheat KMC estimation models utilizing Random Forest Regression (RFR) to achieve high accuracy (R2>0.85). Furthermore, this study evaluated different spectral feature combinations to optimize the mapping retrieval quality of wheat KMC monitoring. Notably, the results revealed that the B5 band on PS was the most effective original band for wheat KMC monitoring, while B11 and B12 on S2 performed well but were susceptible to soil background interference along field edges. In terms of vegetation indices, the Plant Senescence Reflectance Index (PSRI) was deemed a reliable monitoring indicator. The practical implications of this study provided a dependable and convenient tool for monitoring wheat KMC in-field and scientific methods to assist harvest decision-making.