Search

• A locally modelling approach was proposed to build spectral model for estimating leaf nutrients at regional scale. • This approach considered the varied relations between leaf nutrients and leaf spectra over space. • This approach was applied to a case study in Hainan Island to estimate leaf phosphorus concentration of rubber trees. • The proposed approach outperformed the commonly used globally modelling method. The globally modelling method was commonly used to build spectral models for estimating leaf nutrients of plants. However, predictive ability of such models was rather limited at regional scale because of the unreasonable assumption inherent in the globally modelling method which assumed that relationships between leaf nutrients and leaf spectra were stable over space. In fact, the leaf nutrients-spectra relationships generally varied across extensive area due to influences of various environmental factors. In order to accommodate the varied leaf nutrients-spectra relationships at regional scale, the locally modelling approach was proposed to develop spectral models for predicting leaf nutrients. The locally modelling approach assumed that the leaf nutrients-spectra relationships were stable at local spaces within which environmental conditions were relatively uniform. According to this basic idea, the proposed approach used environmental factor as grouping variable to classify leaf samples into several homogenous sub-datasets. Within these sub-datasets, the leaf nutrients-spectra relationships would be more stable. Then, linear or non-linear method were employed to fit model for each sub-dataset. In order to verify the effectiveness of this method, the locally modelling approach was applied to a case study to estimate leaf phosphorus concentration (LPC) of rubber trees (Hevea brasiliensis) in Hainan Island, China. In this case study, three scenarios were set up. In the first scenario, the local model was built using the partial least squares regression (PLSR) with full wavelengths as input variables. In the second scenario, the local model was also developed using the PLSR but with manually selected wavelengths as input variables. In the third scenario, the local model was constructed using the artificial neural networks with feature bands as input variables. Performances of these local models were compared to that of the corresponding global models. Results showed that for the three scenarios local models always yielded higher prediction accuracies than that of the global models in the test dataset. For the first scenario, values of coefficient of determination (r2) and root mean square error (RMSE) ranged from 0.652 to 0.735 and from 0.030% to 0.034%, respectively, for the local model, whereas those varied from 0.458 to 0.637 and from 0.037% to 0.041%, respectively, for the global model. For the second scenario, values of r2 and RMSE ranging from 0.565 to 0.734 and from 0.031% to 0.046%, respectively, for the local model, while those varied from 0.275 to 0.559 and from 0.038% to 0.053%, respectively, for the global model. For the third scenario, values of r2 and RMSE ranging from 0.678 to 0.863 and from 0.021% to 0.033%, respectively, for the local model, while those varied from 0.441 to 0.726 and from 0.030% to 0.043%, respectively, for the global model. This result demonstrates that the locally modelling approach was more effective in estimating LPC of rubber trees than the globally modelling method at the regional scale. In the future, more environmental factors should be considered and their combined effects on leaf sample classification need to be investigated. [ABSTRACT FROM AUTHOR]

Airglow observation is a very effective method to investigate plasma bubbles, and can obtain their horizontal structure. In this study, the image processing method was used to process airglow data, including image enhancement, azimuth correction, and image projection, and the clear image products of equatorial plasma bubbles (EPBs) were obtained. Based on the optical data of the airglow imager in Hainan, we investigated the main optical features of EPBs, and statistically analyzed the occurrence of EPBs from September 2014 to August 2015. The observation results show that EPB exhibits plume-shaped structures, usually tilting westward, and EPB extends to a long distance along the geomagnetic field lines. It is found that the west wall of EPB is relatively stable, while there are some bifurcations on the east wall of EPB, and the bifurcation of EPB becomes more pronounced with time. Moreover, the spatial scale of EPB gradually increases with time, which is about several hundred kilometers, and the drift velocity of EPB is in the range of 40–130 m/s (+/−20 m/s). The statistical results show that EPBs mainly occur in the months of September to November and February to April, with a higher occurrence rate. In terms of seasonal occurrence, EPBs tend to appear more frequently in spring and autumn, and the occurrence rate of EPBs is relatively low in winter and summer. [ABSTRACT FROM AUTHOR]