By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution a...By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.展开更多
With the development of Laser Induced Breakdown Spectroscopy （LIBS）, increasing numbers of researchers have begun to focus on problems of the application. We are not just satisfied with analyzing what kinds of eleme...With the development of Laser Induced Breakdown Spectroscopy （LIBS）, increasing numbers of researchers have begun to focus on problems of the application. We are not just satisfied with analyzing what kinds of elements are in the samples but are also eager to accomplish quantitative detection with LIBS. There are several means to improve the limit of detection and stability, which are important to quantitative detection, especially of trace elements, increasing the laser energy and the resolution of spectrometer, using dual pulse setup, vacuuming the ablation environment etc. All of these methods are about to update the hardware system, which is effective but expensive. So we establish the following spectrum data processing methods to improve the trace elements analysis in this paper： spectrum sifting, noise filtering, and peak fitting. There are small algorithms in these three method groups, which we will introduce in detail. Finally, we discuss how these methods affect the results of trace elements detection in an experiment to analyze the lead content in Chinese cabbage.展开更多
基金supported by the open research fund of the Key Laboratory of Agri-informatics,Ministry of Agriculture and the fund of Outstanding Agricultural Researcher,Ministry of Agriculture,China
文摘By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.
基金supported by National High-Tech R&D Program(863 Program),China(No.2013AA102402)
文摘With the development of Laser Induced Breakdown Spectroscopy （LIBS）, increasing numbers of researchers have begun to focus on problems of the application. We are not just satisfied with analyzing what kinds of elements are in the samples but are also eager to accomplish quantitative detection with LIBS. There are several means to improve the limit of detection and stability, which are important to quantitative detection, especially of trace elements, increasing the laser energy and the resolution of spectrometer, using dual pulse setup, vacuuming the ablation environment etc. All of these methods are about to update the hardware system, which is effective but expensive. So we establish the following spectrum data processing methods to improve the trace elements analysis in this paper： spectrum sifting, noise filtering, and peak fitting. There are small algorithms in these three method groups, which we will introduce in detail. Finally, we discuss how these methods affect the results of trace elements detection in an experiment to analyze the lead content in Chinese cabbage.
