The role of a novel normalized dynamically adjusted vegetation index (NDAVI) based on remotely sensed absorption coefficient for estimating crop fAPAR: a case study of rice ( Oryza sativa L.).

Abstract

The fraction of Absorbed Photosynthetically Active Radiation (fAPAR) is a crucial indicator of photosynthetic characteristics in crop growth monitoring and yield estimation. Traditional vegetation indices (VIs) struggle to achieve accurate estimations throughout the entire crop growth period due to canopy structural changes, particularly during the senescence phase. This study explores a novel VI, the Normalized Dynamically Adjusted Vegetation Index (NDAVI), for estimating green fAPAR (fAPAR green ) throughout the rice growth cycle, including periods of structural changes when senescent leaves are present in the canopy. A two-year replicated field experiment was designed, incorporating different nitrogen treatments and rice cultivars with varying plant architectures. Unmanned Aerial Vehicle (UAV) remote sensing technology was employed to investigate the effectiveness of VIs in estimating rice fAPAR green throughout the entire growth period. Results indicate that while traditional VIs show some correlation with fAPAR green across the whole growth period, the overall data distribution is relatively dispersed. The proposed NDAVI indirectly considered the relative changes in canopy chlorophyll concentration, effectively mitigating the impact of canopy senescence on fAPAR green estimation. In the two-year rice experiment, NDAVI demonstrated a significant linear relationship and goodness of fit with fAPAR green (R 2 ​> ​0.88). Furthermore, the NDAVI-based prediction model exhibited robust performance in inter-annual validation (R 2 ​> ​0.85, RMSE <0.06). This study integrates remote sensing absorption coefficients with vegetation indices to establish a novel index that accounts for crop absorption characteristics. The proposed method enables accurate estimation of crop fAPARgreen even in canopies with prominent senescent leaves.

Plant phenotyping relevance

UAVリモートセンシングと新規NDAVIにより、イネのfAPARを推定する方法を開発し、交差年検証まで実施しており、植物形質取得が研究の中心です。

Code and data availability

The paper describes UAV multispectral imagery, fAPAR/LAI/SPAD field measurements, and NDAVI modeling, but no public dataset, code, or model deposit is provided. Data are only available from the corresponding author upon reasonable request; the supplementary data link is just the article DOI, not a paper-specific asset.

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