Research Article | Open Access

Volume 2026 |Article ID 100128 | https://doi.org/10.1016/j.plaphe.2025.100128

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.)

Shenghui Fang,¹ Yuanjin Li ,^1,4 Mengyu Ge,² Ningge Yuan,¹ Yi Peng,¹ Yan Gong,¹ Yadong Liu,¹ Renshan Zhu,³ and Xianting Wu³

¹School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
²Hubei Institute of Land Surveying and Mapping, Wuhan, 430019, China
³College of Life Sciences, Wuhan University, Wuhan, 430079, China
⁴South China Sea Sea Area and Island Center, Ministry Of Natural Resources (South China Sea Standard Measurement and Information Center, Ministry of Natural Resources), Guangzhou, 510300, China

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 (fAPARgreen) 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 fAPARgreen throughout the entire growth period. Results indicate that while traditional VIs show some correlation with fAPARgreen 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 fAPARgreen estimation. In the two-year rice experiment, NDAVI demonstrated a significant linear relationship and goodness of fit with fAPARgreen (R2 > 0.88). Furthermore, the NDAVI-based prediction model exhibited robust performance in inter-annual validation (R2 > 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.