Multisource Remote Sensing Data-Driven Estimation of Rice Grain Starch Accumulation: Leveraging Matter Accumulation and Translocation Characteristics

Abstract

The expanding utilization of unmanned aerial vehicle (UAV) remote sensing (RS) technology has significantly advanced crop monitoring and detection. Despite its widespread application, the use of UAVs for examining rice grain starch accumulation (GSA) remains in its infancy. The preflowering nutritional organs’ nonstructural carbohydrate transport and the postflowering plant’s photosynthesis products are the primary sources of GSA. This study constructs a dynamic change curve based on the spectral index (SI) red edge re-normalized different vegetation index (RERDVI) before rice flowering. It introduces a novel indicator, the preflowering biomass accumulation dynamics (PBAD), identified through the dynamic curve’s distinct shape characteristics. Results show that PBAD has a good correlation with the aboveground biomass (AGB) at different preflowering stages. After flowering, a nutrient distribution composite index (NDCI) is developed by combining SIs and color indices (CIs), providing a precise monitoring tool for the nitrogen harvest index (NHI), which is important in GSA. By comprehensively considering preflowering nonstructural carbohydrate accumulation (AGB), postflowering photosynthetic capacity (NHI), canopy temperature depression (CTD) sensitive to GSA, and meteorological factors (sunshine duration (SSD) and precipitation), a GSA estimation model based on multisource RS data fusion was constructed using a multiple linear regression (MLR), random forest regression (RFR), and extreme gradient boosting (XGBoost). This approach significantly improved the accuracy of GSA estimation, with the XGBoost model achieving a validation $R^{2}$ of 0.76 and a root mean square error (RMSE) of 0.11 kg/m2 on a multiecological dataset, notably reducing the underestimation observed in traditional linear models.