Impacts of Phenological Dynamics on the Estimation of Leaf-scale Chlorophyll Content in Rice Using Hyperspectral Spectroscopy

Leaf Chlorophyll Content (LCC) is an important proxy for photosynthetic capacity. Despite a large body of literature on the LCC estimation, how and why changes in foliar structure influence LCC estimation remain uncertain. This uncertainty is particularly relevant for crops, in which foliar structure undergoes distinct transitions across growth stages.  As a result, the dynamics of the vegetative stage and the stability of the reproductive stage might cause different performances of LCC estimation approaches. To address this issue, here we aim to understand the impact of the crop growth phase on the LCC estimation performance and underlying mechanisms. We measured hyperspectral reflectance data and collected chlorophyll content destructively from the Seoul National University’s experiment farm in Suwon, South Korea. Using the dataset, we tested empirical, statistical, physical, and machine learning-based approaches for estimating rice LCC throughout the season from emergence to maturity. Our preliminary findings revealed that the estimation in the post-heading (i.e., reproductive) phase outperforms the pre-heading (i.e., vegetative) phase in all approaches. We further examine the mechanisms responsible for this phase-dependent performance difference and discuss strategies to achieve more robust LCC estimation. This study highlights the importance of understanding crop phenological dynamics in LCC estimation and will contribute to the improved monitoring of crop productivity as well as crop growth modeling.

 

This work was supported by NRF, SNU Creative Pioneering Research, KEITI, LS Mtron.