Remote sensing of rice phenology and physiology via absorption coefficient derived from unmanned aerial vehicle imaging

Abstract

Rice (Oryza sativa L.) is the most important staple crop feeding more than half of the world’s population. Extensive effort currently undertaken to develop new and improve existing rice cultivars calls for high-throughput, ideally non-invasive methods for monitoring the phenology and performance of rice plants in the field. We report on the results of systematic application of canopy-level reflectance-derived absorption coefficients to the monitoring of rice stands with unmanned aerial vehicle multispectral sensors. The proposed approach was tested in the field on 39 rice varieties. It was capable of assessing rice phenology and physiology traits such as canopy absorption in different spectral regions, biomass productivity, panicle weight and, eventually, crop yield. Importantly, the proposed approach reflected the pigment transformation patterns accompanying the progression of rice phenological phases. Based on this information, our results showed it was possible to resolve with confidence the three key phases of rice phenology regardless of cultivar-specific variation in stand optical properties. The absorption coefficient in photosynthetically active radiation spectral range was significantly related to rice final yield. To the best of our knowledge, for the first time the absorption coefficients at blue and red bands were used to indicate panicle ripening, thus estimating panicle biomass accurately in the tested 39 varieties with the determination coefficient above 0.8. We argue that the proposed approach gives valuable insights into the rice phenology and physiology in the field, so it will become a useful complement to the traditional plant monitoring techniques welcomed by plant physiologists and practitioners, especially those involving in accelerated rice breeding.

Plant phenotyping relevance

UAVマルチスペクトル画像から吸収係数を算出し、イネの生育期・生理形質・穂重・収量を推定する方法を体系的に検証しており、表現型取得手法が研究の中心です。

Code and data availability

The paper reports UAV multispectral phenotyping of 39 rice varieties (absorption coefficients, biomass, panicle weight, yield), but no public dataset, image collection, or code repository is provided. The only availability statement requires contacting the corresponding author; the supplementary information link is not

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