Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery

Abstract

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.