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

Ke Zhang,Weixing Cao,Pengcheng Shen,Wanyu Li,Yan Zhu,Xiaokang Ge,Xiaojun Liu,Yongchao Tian,Qiang Cao

doi:10.3390/rs11040387

Abstract

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 <5%. Moreover, the feature platform value (saturation value, SV) of the NDRE-based model accurately predicted the differences between treatments and the final grain yield levels. R2 values of the relationship between SV and yield were >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.

Highlights

Rice (Oryza sativa L.) is a staple food for billions of people worldwide
plant dry matter (PDM) increased at an invariable rate from tillering to flowering (Figure 2E–H), while plant nitrogen accumulation (PNA) was consistent with leaf N accumulation (LNA), continually increasing until flowering (Figure 2M–T)
We used the active spectrometer RapidScan CS-45 to determine the dynamic changes in different spectral indexes and agronomic parameters in rice

Summary

Introduction

Rice (Oryza sativa L.) is a staple food for billions of people worldwide. China is the largest rice producer in the world, with approximately 31 million hectares of paddy rice cultivation [1]. To ensure sustainable food production, agricultural producers invest in a significant amount of nitrogen (N) fertilizer [1]. Zhang et al reported that the N use efficiency (NUE) of China’s major food crops was less than 30% [2], representing 33% of the global NUE, and 40% of that in developed countries in Europe and America [3]. Scientific management of N fertilizer use is, crucial in realizing the goals of high yield, quality, and efficiency in rice production in eastern China

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