Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2019-02, Vol.11 (4), p.387
• Main Authors: Zhang, Ke, Ge, Xiaokang, Shen, Pengcheng, Li, Wanyu, Liu, Xiaojun, Cao, Qiang, Zhu, Yan, Cao, Weixing, Tian, Yongchao
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Algorithms; Cereal crops; Correlation coefficient; Correlation coefficients; Crop yield; dynamic change model; Efficiency; Elongation; Experiments; Fertilization; Fertilizers; grain yield prediction; Methods; NDRE; Nitrogen; Nutrition; Predictions; red-edge; Remote sensing; Rice; Rice fields; sigmoid curve; Soils; Vegetation; Wheat
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11040387

Predicting the grain yield during early to mid-growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing. In this study, we conducted four experiments using different nitrogen (N) application rates (0–400 kg N∙ha−1) in three Japonica rice cultivars (Wuyunjing24, Ningjing4, and Lianjing7) grown in Jiangsu province, Eastern China, from 2015–2016. Spectral reflectance data were collected multiple times during early to mid-growth stages using an active mounted sensor (RapidScan CS-45, Holland Scientific Inc., Lincoln, NE, USA). Data were then used to calculate optimal vegetation indexes (normalized difference red edge, NDRE; normalized difference vegetation index, NDVI; ratio vegetation index, RVI; red-edge ratio vegetation index, RERVI), which were used to develop a dynamic change model and in-season grain yield prediction model. The NDRE index was more stable than other indexes (NDVI, RVI, RERVI), showing less standard deviation at the same N fertilizer rate. The R2 of the relationships between leaf area index (LAI), plant nitrogen accumulation (PNA), and NDRE also increased compared to other indexes. These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition. According to real-time series changes in NDRE, the resulting dynamic model followed a sigmoid curve, with a coefficient of determination (R2) >0.9 and relative root-mean-square error 0.7. For every 0.1 increase in SV, grain yield increased by 3608.1 kg·ha−1. Overall, our new dynamic model effectively predicted grain yield at stem elongation and booting stages, providing real-time crop N nutrition data for management of N fertilizer topdressing in rice production.