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

作者：Li Wang, Shuisen Chen, Dan Li, Chongyang Wang, Hao Jiang, Qiong Zheng, Zhiping Peng · 发表于：Remote Sensing · 年份：2021 · DOI：10.3390/rs13152956 · 被引用次数：86 · 研究领域：Remote Sensing in Agriculture、Land Use and Ecosystem Services、Leaf Properties and Growth Measurement

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-b...