Invasive Spartina alterniflora (S. alterniflora), a native riparian species in the U.S. Gulf of Mexico, has led to serious degradation to the ecosystem and biodiversity as well as economic losses since it was introduced to China in 1979. Although multi-temporal remote sensing offers unique capability to monitor S. alterniflora over large areas and long time periods, three major hurdle exist: (1) in the coastal zone where S. alterniflora occupies, frequent cloud coverage reduces the number of available images that can be used; (2) prominent spectral variations exist within the S. alterniflora due to phonological variations; (3) poor spectral separability between S. alterniflora and its co-dominant native species is often presented in the territories where S. alterniflora intruded in. To articulate these questions, we proposed a new pixel-based phenological feature composite method (Ppf-CM) based on Google Earth Engine. The Ppf-CM method was brainstormed to battle the aforementioned three hurdles as the basic unit for extracting phonological feature is individual pixel in lieu of an entire image scene. With the Ppf-CM-derived phenological feature as inputs, we took a step further to investigate the performance of the latest deep learning method as opposed to that of the conventional support vector machine (SVM); Lastly, we strive to understand how S. alterniflora has changed its spatial distribution in the Beibu Gulf of China from 1995 to 2017. As a result, we found (1) the developed Ppf-CM method can mitigate the phonological variation and augment the spectral separability between S. alterniflora and the background species regardless of the significant cloud coverage in the study area; (2) deep learning, compared to SVM, presented better potentials for incorporating the new phenological features generated from the Ppf-CM method; and (3) for the first time, we discovered a S. alterniflora invasion outbreak occurred during 1996–2001.

The β-sialon:Eu²⁺ phosphor has superior luminescent properties as an excellent green down-conversion material for white light-emitting diodes. To take advantage of its high chromaticity and apply it to field emission displays (FEDs), the drawback of its electron insulation that impedes the electron transmission when excited by e-beam needs to be solved. In this work, a facile method was adopted to coat conductive In₂O₃ on the surface of β-sialon:Eu²⁺ phosphor, and the influence of In₂O₃ coating on the cathodoluminescent properties was investigated. The results show that the In₂O₃ coating contributes to charge elimination and significantly improves the cathodoluminescence (CL) of β-sialon:Eu²⁺. Under 19 kV/1.0 nA e-beam excitation, the In₂O₃ coated sample shows CL intensity about four times higher than that of the uncoated β-sialon:Eu²⁺. Moreover, no luminescence saturation occurs in coated β-sialon:Eu²⁺, indicating that the In₂O₃-coated β-sialon:Eu²⁺ would be a promising green emissive material for full-color FEDs.