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| 1 | Generating high spatiotemporal resolution LAI based on MODIS/GF-1 data and combined Kriging-Cressman interpolation显示文摘Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this study,a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer(MODIS)characterized by coarse spatial resolution and high temporal resolution and Gaofen-1(GF-1)with fine spatial resolution and coarse temporal resolution.This method was applied to the Huangpu district of Guangzhou,Guangdong,China.The results showed that compared to field observation,the predicted values of LAI had an acceptable accuracy of 73.12%.Using Moran’s I index and Kolmogorov-Smirnov tests,it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions.Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38%compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging.Moreover,the regression models that accounts for the relationship between NDVI(Normalized Difference Vegetation Index)and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870.Finally,the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps,which showed reasonably spatial and temporal variability.The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products. | Liu Zhenhua Huang Rugen Hu Yueming Fan Shudi Feng Peihua | 2016 | International Journal of Agricultural and Biological Engineering2016，9，5： | 3 |
| 2 | The 3rd-order nonlinearity of bacteriorhodopsin by four-wave mixing显示文摘The 3rd-order nonlinear optical susceptibility x(3) and the response time of the light-transducing biomolecule bacteriorhodopsin were measured with the four-wave mixing technique and a picosecond frequency-doubled Nd: YAG laser (532 nm). The x(3) and the response time measured are 10-9 esu and 20 ps , respectively. The possible mechanism for generating the 3rd-order nonlinear optical susceptibility x(3) and response time were discussed. | HUANG Yanping WANG Liying CHEN Shudi WANG Zugeng LI Qingguo | 2000 | Chinese Science Bulletin2000，45，19： | 1 |
| 3 | Halophyte Vegetation Influences Soil Microbial Community of Coastal Salt Marsh显示文摘Coastal wetlands are the most productive ecosystems worldwide and can provide important ecosystem services,yet the characteristics of microbial community within these systems remain poorly understood.Microbial community of salt marsh vegetation and the associated soil physio-chemical properties were investigated in this study.Three typical Suaeda australis,Phragmites australis,Spartina alterniflora wetlands,and non-vegetated bare mudflats in the Zhoushan Islands were studied to advance the understanding of the characteristics of soil bacterial communities in coastal wetlands.Results showed that the bare mudflats exhibited high pH value and soil moisture content compared with the vegetated samples.In different vegetation types,the organic matter content,total nitrogen,and total potassium content decreased in the order:S.alterniflora wetland>P.australis wetland>S.australis wetland,and there was no obvious difference in total phosphorous content.The halophytes could decrease soil salinity compared with bare mudflats.Proteobacteria,Nitrospinae,Bacteroidetes,Acidobacteria,and Nitrospirae were the predominant level across all samples.Functional prediction showed that SPA-covered soil might play vital roles in sulphur cycling,while SUA and PHR covered soils were involved in nitrogen cycling.This study could provide the first insight into the microbial community of this study area and contribute to a better understanding of vegetation microbiota and bioremediation in coastal wetland ecosystem. | GU Chen SHI Jiyan RUI Jianliang YU Yanming HUANG Weibin LU Zhinai CHEN Yao CHEN Xiaojun DONG Shudi HU Zhijun YE Chenghua | 2022 | Journal of Ocean University of China2022，21，6： | 1 |
| 4 | Transformer-based ensemble deep learning model for EEG-based emotion recognition显示文摘Emotion recognition is one of the most important research directions in the field of brain–computer interface(BCI).However,to conduct electroencephalogram(EEG)-based emotion recognition,there exist difficulties regarding EEG signal processing;moreover,the performance of classification models in this regard is restricted.To counter these issues,the 2022 World Robot Contest successfully held an affective BCI competition,thus promoting the innovation of EEG-based emotion recognition.In this paper,we propose the Transformer-based ensemble(TBEM)deep learning model.TBEM comprises two models:a pure convolutional neural network(CNN)model and a cascaded CNN-Transformer hybrid model.The proposed model won the abovementioned affective BCI competition’s final championship in the 2022 World Robot Contest,demonstrating the effectiveness of the proposed TBEM deep learning model for EEG-based emotion recognition. | Xiaopeng Si Dong Huang Yulin Sun Shudi Huang He Huang Dong Ming | 2023 | Brain Science Advances2023，9，3： | 0 |
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