Numerous technologies have contributed to the recent development of agriculture, especially the advancement in hyperspectral remote sensing (HRS) constituted a revolution in crop monitoring. The widespread use of HRS to obtain crop parameters suggests the need for a review of research advances in this area. HRS offers new theories and methods for studying crop parameters, but much work needs to be done both experimentally and theoretically before we can truly understand the physical and chemical processes that predict these crop parameters. The study focuses on the following elements: 1) The article provides a relatively comprehensive introduction to HRS and how it can be applied to crop monitoring; 2) Current state-of-the-art techniques are summarized and analyzed to inform further advances in crop monitoring; 3) Opportunities and challenges for crop monitoring applications using HRS are discussed, and future research is summarized. Finally, through a comprehensive discussion and analysis, the article proposes new directions for using HRS to study crop characteristics, such as new data mining techniques including deep learning provide opportunities for efficient processing of large amounts of HRS data; combining the temporal and dynamic characteristics of crop parameters and vegetation growth processes will greatly improve the accuracy of crop parameter detection and monitoring; multidata fusion and multiscale data assimilation will become HRS monitoring. Multidata fusion and multiscale data assimilation will become another research hotspot for HRS monitoring of crop parameters.

Simple active radiometer sensors, such as RapidScan, enable agronomic decision-making and phenotyping within commercial wheat fields and experiments. The objectives of this study were: 1 - to evaluate the accuracy of quantitative biomass and nitrogen uptake estimation by the RapidScan, and 2 - to evaluate yield loss estimation based on NDVI. The RapidScan sensor was used as an assessment tool in the following studies: (i) over 3 years, 518 wheat samples were monitored during the vegetative growth period for biomass and aboveground nitrogen uptake and (ii) wheat cultivars were tested in an additional 4 field experiments, which were scanned weekly and correlated with grain yield. Results showed that accurate biomass estimation is limited up to about 100 g DM m−2. Grain yield, actual and potential, estimation is highly affected by the emergence date. The results showed that the use of a proximal-sensing technique allows for rapid and accurate crop monitoring and yield estimation, but emphasizing limitations in future use as well.