During the Asian summer monsoon(ASM)season,the process of stratosphere-troposphere exchange significantly affects the concentration and spatial distribution of chemical constituents in the upper troposphere and lower ...During the Asian summer monsoon(ASM)season,the process of stratosphere-troposphere exchange significantly affects the concentration and spatial distribution of chemical constituents in the upper troposphere and lower stratosphere(UTLS).However,the effect of the intensity of the Asian summer monsoon anticyclone(ASMA)on the horizontal distribution of chemical species within and around the ASMA,especially on the daily time scale,remains unclear.Here,the authors use the MERRA-2 reanalysis dataset and Aura Microwave Limb Sounder observations to study the impact of ASMA intensity on chemical distributions at 100 hPa during the ASM season.The intraseasonal variation of ASMA is classified into a strong period(SP)and weak period(WP),which refer to the periods when the intensity of ASMA remains strong and weak,respectively.The relatively low ozone(O_(3))region is found to be larger at 100 hPa during SPs,while its mixing ratio is lower than during WPs in summer.In June,analysis shows that the O_(3) horizontal distribution is mainly related to the intensity of AMSA,especially during SPs in June,while deep convections also impact the O_(3) horizontal distribution in July and August.These results indicate that the intraseasonal variation of the ASMA intensity coupled to deep convection can significantly affect the chemical distribution in the UTLS region during the ASM season.展开更多
In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a ...In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.展开更多
Obtaining continuous and high-quality soil moisture(SM) data is important in scientific research and applications,especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing...Obtaining continuous and high-quality soil moisture(SM) data is important in scientific research and applications,especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing number of artificial satellites in China, the acquisition of SM data from remote sensing images has received increasing attention.In this study, we constructed an SM inversion model by using a deep belief network(DBN) to extract SM data from Fengyun-3 D(FY-3 D) Medium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) imagery;we named this model SM-DBN.The SM-DBN consists of two subnetworks: one for temperature and the other for SM. In the temperature subnetwork, bands 1, 2, 3, 4, 24, and 25 of the FY-3 D MERSI-Ⅱ imagery, which are relevant to temperature, were used as inputs while land surface temperatures(LST) obtained from ground stations were used as the expected output value when training the model. In the SM subnetwork, the input data included LSTs generated from the temperature subnetwork, normalized difference vegetation index(NDVI), and enhanced vegetation index(EVI);and the SM data obtained from ground stations were used as the expected outputs. We selected the Ningxia Hui Autonomous Region of China as the study area and used selected MERSI-Ⅱ images and in-situ observation station data from 2018 to 2019 to develop our dataset. The results of the SM-DBN were validated by using in-situ SM data as a reference, and its performance was also compared with those of the linear regression(LR) and back propagation(BP) neural network models. The overall accuracy of these models was measured by using the root mean square error(RMSE) of the differences between the model results and in-situ SM observation data. The RMSE of the LR, BP neural network, and SM-DBN models were 0.101, 0.083, and 0.032, respectively. These results suggest that the SM-DBN model significantly outperformed the other two models.展开更多
基金sponsored by Strategic Priority Research Program of the Chinese Academy of Science[grant No.XDA17010106]the National Key Research and Development Program of China[grant Nos.2018YFC1505703 and 2018YFC1506704].
文摘During the Asian summer monsoon(ASM)season,the process of stratosphere-troposphere exchange significantly affects the concentration and spatial distribution of chemical constituents in the upper troposphere and lower stratosphere(UTLS).However,the effect of the intensity of the Asian summer monsoon anticyclone(ASMA)on the horizontal distribution of chemical species within and around the ASMA,especially on the daily time scale,remains unclear.Here,the authors use the MERRA-2 reanalysis dataset and Aura Microwave Limb Sounder observations to study the impact of ASMA intensity on chemical distributions at 100 hPa during the ASM season.The intraseasonal variation of ASMA is classified into a strong period(SP)and weak period(WP),which refer to the periods when the intensity of ASMA remains strong and weak,respectively.The relatively low ozone(O_(3))region is found to be larger at 100 hPa during SPs,while its mixing ratio is lower than during WPs in summer.In June,analysis shows that the O_(3) horizontal distribution is mainly related to the intensity of AMSA,especially during SPs in June,while deep convections also impact the O_(3) horizontal distribution in July and August.These results indicate that the intraseasonal variation of the ASMA intensity coupled to deep convection can significantly affect the chemical distribution in the UTLS region during the ASM season.
基金Supported by National Key R&D Program of China(2017YFD0301004)Natural Science Foundation of Shandong Province,China(ZR2016DP04)Key Project of Shandong Provincial Meteorological Bureau(2017sdqxz03)
文摘In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.
基金Supported by the Science Foundation of Shandong(ZR2017MD018)Key Research and Development Program of Ningxia(2019BEH03008)+3 种基金Open Research Project of the Key Laboratory for Meteorological Disaster MonitoringEarly Warning and Risk Management of Characteristic Agriculture in Arid Regions(CAMF-201701 and CAMF-201803)Arid Meteorological Science Research Fund Project by the Key Open Laboratory of Arid Climate Change and Disaster Reduction of China Metrological Administration(IAM201801)Science Foundation of Ningxia(NZ12278)。
文摘Obtaining continuous and high-quality soil moisture(SM) data is important in scientific research and applications,especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing number of artificial satellites in China, the acquisition of SM data from remote sensing images has received increasing attention.In this study, we constructed an SM inversion model by using a deep belief network(DBN) to extract SM data from Fengyun-3 D(FY-3 D) Medium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) imagery;we named this model SM-DBN.The SM-DBN consists of two subnetworks: one for temperature and the other for SM. In the temperature subnetwork, bands 1, 2, 3, 4, 24, and 25 of the FY-3 D MERSI-Ⅱ imagery, which are relevant to temperature, were used as inputs while land surface temperatures(LST) obtained from ground stations were used as the expected output value when training the model. In the SM subnetwork, the input data included LSTs generated from the temperature subnetwork, normalized difference vegetation index(NDVI), and enhanced vegetation index(EVI);and the SM data obtained from ground stations were used as the expected outputs. We selected the Ningxia Hui Autonomous Region of China as the study area and used selected MERSI-Ⅱ images and in-situ observation station data from 2018 to 2019 to develop our dataset. The results of the SM-DBN were validated by using in-situ SM data as a reference, and its performance was also compared with those of the linear regression(LR) and back propagation(BP) neural network models. The overall accuracy of these models was measured by using the root mean square error(RMSE) of the differences between the model results and in-situ SM observation data. The RMSE of the LR, BP neural network, and SM-DBN models were 0.101, 0.083, and 0.032, respectively. These results suggest that the SM-DBN model significantly outperformed the other two models.
