Traditional vegetation indices (VIs), while widely used in remote sensing, have struggled to provide reliable fAPAR estimates, particularly during critical stages like canopy senescence when aging leaves alter the canopy structure. To address this challenge, the team introduced a novel approach, the Normalized Dynamically Adjusted Vegetation Index (NDAVI). This innovative index effectively accounts for the impact of senescent leaves, ensuring a more accurate estimation of fAPARgreen even during the later stages of rice growth.
Rice, a staple food for over half the world’s population, is critical for food security, especially in regions heavily dependent on rice cultivation. Timely and precise monitoring of rice growth can improve crop management and yield prediction. fAPAR, the fraction of photosynthetically active radiation absorbed by a crop, plays a crucial role in understanding plant productivity and health. However, traditional methods for measuring fAPAR—often requiring labor-intensive, handheld equipment—are impractical for large-scale monitoring. Remote sensing, particularly unmanned aerial vehicles (UAVs), has emerged as a more efficient alternative. Despite its potential, traditional VIs often fail to accurately estimate fAPAR during the entire crop growth cycle, especially when senescent leaves complicate canopy structure.
A study (DOI: 10.1016/j.plaphe.2025.100128) published in Plant Phenomics on 11 October 2025 by Yuanjin Li’s team, Wuhan University, promises to advance the precision of crop monitoring, helping improve yield predictions and field management strategies.
The research aimed to analyze the changes in the fraction of absorbed photosynthetically active radiation (fAPARgreen) in rice under different nitrogen gradients throughout its growth period, using traditional vegetation indices (VIs) and the newly proposed Normalized Dynamically Adjusted Vegetation Index (NDAVI). Data from two consecutive years (2022 and 2023) revealed that fAPARgreen increased with days after transplanting (DAT) until it reached saturation points at DAT 38 in 2022 and DAT 58 in 2023. After this, fAPARgreen declined, particularly in 2022 when the senescence phase was included in the study. Nitrogen treatments significantly impacted the fAPARgreen levels, with the 2N treatment consistently showing higher values across both years. A correlation analysis between fAPARgreen and traditional VIs such as NDVI, EVI2, and kNDVI indicated strong correlations (R ≥ 0.88), but the data distribution remained somewhat dispersed, especially after the rice reached maturity, limiting the accuracy of these models. In contrast, the linear regression models between fAPARgreen and NDAVI exhibited a much more consistent and stronger correlation (R² = 0.91 in 2022, R² = 0.86 in 2023). Notably, NDAVI maintained a robust relationship with fAPARgreen even during the senescence stage, where traditional VIs showed poor fitting performance. Model accuracy verification, using cross-validation across different replicates, revealed that NDAVI significantly outperformed other VIs in both years, achieving R² = 0.884, RMSE = 0.058, and MAE = 0.046. These results demonstrate that NDAVI provides a highly reliable and interpretable method for estimating fAPARgreen throughout the rice growth period, including senescence, offering substantial improvements over traditional VIs.
The NDAVI index provides a significant advancement in crop monitoring by overcoming the challenges posed by canopy senescence. By accurately estimating fAPARgreen throughout the entire growth period, the index offers farmers and researchers a reliable tool for assessing crop health, improving yield predictions, and enhancing field management practices. Moreover, NDAVI's ability to account for the complex interactions between absorption and reflectance throughout the growth cycle means it can be applied to a range of crops beyond rice, potentially transforming how we monitor crop productivity on a global scale.
###
References
DOI
10.1016/j.plaphe.2025.100128
Original Source URl
https://doi.org/10.1016/j.plaphe.2025.100128
Funding information
This research was supported by Advanced Research Project: Remote sensing inversion of key land surface elements and development of quantitative products and Research Project: Research on Precise Monitoring of Sorghum Remote Sensing Phenotypes Based on UAV Technology (grant ID: MTGF2023048).
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.