Remote sensing approaches for crop nutrition diagnosis and recommendations for nitrogen fertilizers in rice at canopy level

ABSTRACT

Nitrogen (N) fertilizer management plays a crucial role in high-yield rice production. To choose a well-performing rice N nutrient diagnosis indicator for developing rice production management strategies, this research conducted five field experiments under various N treatments. The results showed that machine learning and stepwise multiple linear regression suggested a strong relationship between vegetation indexes and agronomic indicators (0.70 > R² > 0.51). A strong correlation was obtained between red-edge based vegetation indexes and agronomic indicators (R² > 0.40). Additionally, the all-subset regression method results demonstrated that the red-edge basis vegetation indexes were generally applied during different vegetation index combinations. The red-edge basis vegetation indexes reached an approximately 40% contribution in nitrogen nutrient index prediction and an approximately 48% contribution in leaf area index monitoring. Furthermore, this study combined the normalized difference red-edge (NDRE) basis dynamic model to calculate the N dose, which ranged from 106 to 134 kg per hectare in large-scale N management according to the NDRE from Sentinel-2B images, a decrease of approximately 46 kg N ha⁻¹ fertilizer compared with farmers’ practices. Nevertheless, more refinements are needed to ensure that this strategy can be applied to farmers’ yield- and income-enhancing production.

KEYWORDS:

• Nitrogen
• vegetation index
• red-edge
• agronomic indicator

Acknowledgements

We are grateful to Professor Pablo J. Zarco-Tejada, Professor Tao Cheng, Dr. Wenhui Wang, and Gaoxiang Yang, who helped with the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [31971784, 32071903], the Earmarked Fund for Jiangsu Agricultural Industry Technology System, China [No. JATS (2022)168 and JATS (2022)468], the Jiangsu Provincial Cooperative Promotion Plan of Major Agricultural Technologies [No. 2021-ZYXT-01-1], and the China Scholarship Council (CSC) Doctor Joint Scholarship Program [No. 201906850067].