Spatial Estimation of Daily Growth Biomass in Paddy Rice Field Using Canopy Photosynthesis Model Based on Ground and UAV Observations

Abstract

Precision farming, a labor-saving and highly productive form of management, is gaining popularity as the number of farmers declines in comparison to the increasing global food demand. However, it requires more efficient crop phenology observation and growth monitoring. One measure is the leaf area index (LAI), which is essential for estimating biomass and yield, but its validation requires destructive field measurements. Thus, using ground and UAV observation data, this study developed a method for indirect LAI estimation based on relative light intensity under a rice canopy. Daily relative light intensity was observed under the canopy at several points in paddy fields, and a weekly plant survey was conducted to measure the plant length, above-ground biomass, and LAI. Furthermore, images from ground-based and UAV-based cameras were acquired to generate NDVI and the canopy height (CH), respectively. Using the canopy photosynthetic model derived from the Beer–Lambert law, the daily biomass was estimated by applying the weekly estimated LAI using CH and the observed light intensity data as input. The results demonstrate the possibility of quantitatively estimating the daily growth biomass of rice plants, including spatial variation. The near-real-time estimation method for rice biomass by integrating observation data at fields with numerical models can be applied to the management of major crops.