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Accurate quantification of net primary productivity (NPP) is key to estimating vegetation productivity and studying the global carbon cycle. However, C3 and C4 plants vary in their living environments and potential productivity due to their different photosynthetic pathways. This study thus explored the potential of the near-infrared radiance of vegetation (NIRv,Rad) to track hourly and daily changes in C3 and C4 plants and investigate whether their photosynthetic pathways affect the relationship between NPP and NIRv,Rad. Statistics including long-term spectral data, the growth environment, and physiological indicators were collected using an automatic spectral monitoring system. The vegetation index NIRv,Rad was extracted from the collected spectral data and NPP was calculated using the collected net photosynthetic rate and leaf area index. The results showed that NIRv,Rad can effectively respond to NPP changes in C3 and C4 plants on hourly and daily scales. The NPP–NIRv,Rad relationship on the hourly scale was superior, with R2 values for winter wheat and summer maize of 0.81 and 0.70, respectively. Furthermore, when the accumulation of NPP was equal, the NIRv,Rad of summer maize showed larger changes, indicating that it was more sensitive to NPP change for this species than for winter wheat. Overall, the study demonstrated that NIRv,Rad can serve as an effective proxy indicator of NPP. In addition, this study provides a theoretical basis and scientific guidance for the construction of new simple models and realizing efficient agriculture, as well as a new method for rapid and accurate quantification of the NPP of C3 and C4 plants at geospatial scales in future research.

Time-of-arrival estimation is a key issue in the field of intelligent transportation research. In general, corresponding techniques are designed based on Global Positioning System (GPS) data. Unfortunately, the low precision of bus-mounted GPS system and lack of real-time traffic information lead to low estimation accuracy - greatly influence bus service performance. In this paper, a GPS calibration method is put forward, while projection rules of specific road shapes are discussed. Moreover, a real-time traffic evaluation algorithm is given. Then a hybrid dynamic prediction model, which achieves accuracy enhancement by taking into account traffic flow evaluation results and GPS position calibration, is proposed to predict the bus arrival time. Bus stop positions are used as reference points to verify effectiveness of proposed method. Bus data set is used to test effectiveness of proposed method. Test results demonstrate that our technique can provide a higher level of accuracy compared to methods based on traditional time-of-arrival techniques.