**6. Conclusions**

Remote sensing technology is developing at a rapid pace and non-destructive monitoring and assessment of crop N status is gaining importance. This paper analyses the physiological mechanisms and spectral response characteristics of remote sensing monitoring for canopy N. Taking the remote sensing monitoring platform, the correlation between remotely sensed data and N status, and the remote sensing retrieval methods as the entry point, this paper provides an in-depth summary of the research techniques in the field of remote sensing monitoring for canopy N. The factors affecting the accuracy of remote sensing monitoring are also discussed. To date, the research at field scale has been well validated. The development of sensors and spectral carrying platforms facilitates high-precision remote sensing monitoring of crop N at farm scale. Due to the amount of information that can be extracted from remote sensing data, the efficiency of model use has become a key research concern. The efficiency and flexibility of machine learning models and the explanatory nature of physical models have their own advantages. The hybrid of the two models is beginning to show results in improving model stability. In addition, the effective use of multi-source data, and the removal of confounding factors in crop N monitoring need to be further explored. In-depth understanding of the limitations of current technology will be necessary to enhance the understanding of the link between canopy optical properties and crop N status, and to identify more appropriate N retrieval methods. In the context of the current rapid development of smart agriculture, the combination of sensors, remote sensing platforms and the Internet of Things results in the initial formation of a crop growth monitoring IoT platform. It provides the development direction for real-time monitoring and early forecasting of crop N, making it more widely application in the fields of growth monitoring, yield prediction and precision fertilization. **Author Contributions:** Conceptualization, J.Z. and X.Y.; writing—original draft preparation, J.Z. and X.Y.; writing—review and editing, X.S., G.Y., X.D. and X.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Science and Technology Department of Guangdong Province (2019B020216001) and National Key Research and Development of China (2019YFE0125300).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
