National Satellite Meteorological Center

Beijing, China

National Satellite Meteorological Center

Beijing, China
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Fang J.,Peking University | Fang J.,CAS Institute of Botany | Guo Z.,Peking University | Guo Z.,National Satellite Meteorological Center | And 4 more authors.
Global Change Biology | Year: 2014

Forests play an important role in regional and global carbon (C) cycles. With extensive afforestation and reforestation efforts over the last several decades, forests in East Asia have largely expanded, but the dynamics of their C stocks have not been fully assessed. We estimated biomass C stocks of the forests in all five East Asian countries (China, Japan, North Korea, South Korea, and Mongolia) between the 1970s and the 2000s, using the biomass expansion factor method and forest inventory data. Forest area and biomass C density in the whole region increased from 179.78 × 106 ha and 38.6 Mg C ha-1 in the 1970s to 196.65 × 106 ha and 45.5 Mg C ha-1 in the 2000s, respectively. The C stock increased from 6.9 Pg C to 8.9 Pg C, with an averaged sequestration rate of 66.9 Tg C yr-1. Among the five countries, China and Japan were two major contributors to the total region's forest C sink, with respective contributions of 71.1% and 32.9%. In China, the areal expansion of forest land was a larger contributor to C sinks than increased biomass density for all forests (60.0% vs. 40.0%) and for planted forests (58.1% vs. 41.9%), while the latter contributed more than the former for natural forests (87.0% vs. 13.0%). In Japan, increased biomass density dominated the C sink for all (101.5%), planted (91.1%), and natural (123.8%) forests. Forests in South Korea also acted as a C sink, contributing 9.4% of the total region's sink because of increased forest growth (98.6%). Compared to these countries, the reduction in forest land in both North Korea and Mongolia caused a C loss at an average rate of 9.0 Tg C yr-1, equal to 13.4% of the total region's C sink. Over the last four decades, the biomass C sequestration by East Asia's forests offset 5.8% of its contemporary fossil-fuel CO2 emissions. © 2014 John Wiley & Sons Ltd.


Grant
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: SPA.2013.3.2-01 | Award Amount: 2.63M | Year: 2013

PArtnership with chiNa on space DAta The objective of the PANDA Project is to establish a team of European and Chinese scientists who will jointly use space observations and in-situ data as well as advanced numerical models to monitor, analyse and forecast global and regional air quality. PANDA will provide to users and stakeholders knowledge, methodologies and toolboxes that will serve as a basis for global and regional air quality analysis and forecasts. It will provide science-based information that will improve air quality management by regional and local authorities. A strong dissemination and education activity will be established to train users to use the key products and data generated by the Project. Through the proposed cooperation between Europe and China, the following objectives will be reached before the completion of the Project: 1. Improvement of methods for monitoring air quality from combined space and in-situ observations 2. Elaboration of indicators for air quality, in support of European and Chinese policies 3. Development of toolboxes for air quality and emissions monitoring 4. Dissemination of information and educational activities The PANDA project is organized around 7 work-packages dealing with remote sensing data, in-situ observations, anthropogenic and natural emissions, integration of observations and models, toolbox development, cooperation, dissemination of knowledge and capacity building, and management and coordination. The project will support the European Space Policy and specifically the GMES Programme (Global Monitoring for Environment and Security). It will contribute to the development of the Global Earth Observation System of Systems (GEOSS).


Lu Q.,National Satellite Meteorological Center | Bell W.,ECMWF
Journal of Atmospheric and Oceanic Technology | Year: 2014

Passive microwave observations from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) have been exploited widely for numerical weather prediction (NWP), atmospheric reanalyses, and climate monitoring studies. The treatment of biases in these observations, with respect to models as well as between satellites, has been the focus of much effort in recent years. This study presents evidence that shifts, drifts, and uncertainties in pass band center frequencies are a significant contribution to these biases. Center frequencies for AMSU-A channels 6-14 and MSU channel 3 have been analyzed using NWP fields and radiative transfer models, for a series of operational satellites covering the period 1979-2012. AMSU-A channels 6 (54.40 GHz), 7 (54.94 GHz), and 8 (55.50 GHz) on several satellites exhibit significant shifts and drifts relative to nominal pass band center frequencies. No significant shifts were found for AMSU-A channels 9-14, most probably as a consequence of the active frequency locking of these channels. For MSU channel 3 (54.96 GHz) most satellites exhibit large shifts, the largest for the earliest satellites. For example, for the first MSU on the Television and Infrared Observation Satellite-N (TIROS-N), the analyzed shift is 68 MHz over the lifetime of the satellite. Taking these shifts into account in the radiative transfer modeling significantly improves the fit between model and observations, eliminates the strong seasonal cycle in the model-observation misfit, and significantly improves the bias between NWP models and observations. The study suggests that, for several channels studied, the dominant component of the model-observation bias results from these spectral errors, rather than radiometric bias due to calibration errors. © 2014 American Meteorological Society.


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ENV.2013.6.2-6 | Award Amount: 11.20M | Year: 2013

By 2050, global agricultural productivity will need to increase with at least 70%. In order to guarantee food production for future generations, agricultural production will need to be based on sustainable land management practises. At present, earth observation based (global) crop monitoring systems focus mostly on short-term agricultural forecasts, thereby neglecting longer term environmental effects. However, it is well known that unsustainable cultivation practises may lead to a degradation of the (broader) environment resulting in lower agricultural productivity. As such, agricultural monitoring systems need to be complemented with methods to also assess environmental impacts of change in crop land and shifting cultivation practises. It is thereby important that this is addressed at the global level. SIGMA presents a global partnership of expert institutes in agricultural monitoring, with a strong involvement in GEO and the Global Agricultural Geo-Monitoring (GEO-GLAM) initiative. SIGMA aims to develop innovative methods, based upon the integration of in-situ and earth observation data, to enable the prediction of the impact of crop production on ecosystems and natural resources. The proposed project will address methods to: i. enable sharing and integration of satellite and in situ observations according to GEOSS Data CORE principles; ii. assess the impact of cropland areas and crop land change on other ecosystems; iii. understand and assess shifts in cultivation practises and cropping systems to evaluate impacts on biodiversity and the environment. Furthermore, dedicated capacity building activities are planned to increase national and international capacity to enable sustainable management of agriculture. Lastly, a strong coordinating mechanism will be put in place, through the project partners, between SIGMA and the G20 Global Agricultural Geo-Monitoring Initiative (GEOGLAM), in order to assure transparency and alignment of the SIGMA activities.


Liu Y.,CAS Yunnan Astronomical Observatory | Zhao L.,National Satellite Meteorological Center
Monthly Notices of the Royal Astronomical Society | Year: 2013

Using meteorological data is fundamental in any site survey for astronomical instruments. As a first step, the analyses of sunshine duration for candidate sites are crucial for remote site survey for solar observing instruments. In western China, some ground-based meteorological stations have collected daily sunshine data from only partial-sky area since they had to be constructed in valley. One aim of this study is to demonstrate the geographical properties of those meteorological stations. Our goal is to investigate the true sunshine durations obtained if the stations are installed at the top of nearby mountain without screen effect. We make use of the three-dimensional geographical data from the Google Earth software and the statistical meteorological data from the National Meteorology Administration and the meteorological stations. All the 76 national basic meteorological observing stations with altitude over 3000 m located in western China are measured. Since astronomical instruments tend to be installed at the top of a mountain, we need to take into account the loss of the sunshine durations in making the remote site survey. Our results, after the compensation, show that the condition of sunshine duration in the Hengduan-Shan Mountains (HSM) area can meet the basic requirement of at least 2500 h yr-1 for the Chinese Giant Solar Telescope (CGST) site survey. ©2013 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.


Yang H.,National Satellite Meteorological Center | Weng F.,National Oceanic and Atmospheric Administration
IEEE Transactions on Geoscience and Remote Sensing | Year: 2011

The retrieval of land surface emissivity from satellite passive microwave measurements often requires the knowledge of various radiative components (e.g., atmospheric upwelling and downwelling radiation) contributed to the measurements. Under a cloud-free condition, atmospheric and surface radiative components can be derived from atmospheric temperature and water vapor, and surface temperature data. Thus, the quality of these auxiliary data sets directly affects the emissivity accuracy. From an emission-based radiative transfer equation, a set of relationships is derived to study the sensitivity of surface emissivity to the errors of brightness temperature, atmospheric transmittance, and surface temperature. As an example, the uncertainties in the Advanced Microwave Scanning Radiometer-Earth Observing System emissivity at 23 and 89 GHz may be much larger than the uncertainties of emissivity at lower frequencies due to the higher uncertainties in computing the water vapor absorption. The error in the land surface temperature is a main source of error in emissivity at the frequencies less than 19 GHz. © 2011 IEEE.


Chen B.,National Satellite Meteorological Center
Chinese Journal of Sensors and Actuators | Year: 2016

Non-Uniformity is a serious problem for the infrared focal plane array, for reducing the non-uniformity of the infrared focal plane, a new Non-Uniformity correction algorithm is suggested, it's called non-uniformity correction algorithm based on complex neural network based on fixed scene. It's different with the Non-Uniformity correction algorithms based on traditional BP neural network, which has only one learning-layer, the new algorithm uses two learning-layers, that has many advantages. The traditional Non-Uniformity correction algorithm based on traditional BP neural network blurs the image more when the more better correction result is the goal, however, the Non-Uniformity correction algorithm based on complex neural network uses two learning-layers, in the first learning-layer, the medial filter with a large scale neighbor data is used to generate the theory output data, and in the second learning-layer, the average filter with a small scale neighbor data is used to generate the theory output data, by using two learning-layers, not only the non-uniformity reduces, but also the image edge is kept. In the experiment, the Non-Uniformity correction algorithm based on complex neural network has the best result, the UN=0.75% for corrected image, and the UN=0.77% for the second best image, however, the image with UN=0.75% has a bigger average grads value than the image with UN=0.77%, that means the Non-Uniformity correction algorithm based on complex neural network has the advantage both on reducing the Non-Uniformity and avoiding the image edge bluring. © 2016, The Editorial Office of Chinese Journal of Sensors and Actuators. All right reserved.


Zheng W.,National Satellite Meteorological Center
Proceedings 2012 International Conference on System Science and Engineering, ICSSE 2012 | Year: 2012

In this paper, the methodology of monitoring flood using multi-source satellite sensors was put forward in order to systematically utilize advantages of each data to utmostly acquire surface flood information. Huaihe River Basin flood in the summer 2007 in China was chosen as the study case. For large-scale flood monitoring and warning, the WSF(water surface fraction) method based on passive microwave remote sensing data ASMR-E was employed to reveal soil wetness and flood patterns of the whole Huaihe River Basin; For moderate-scale food monitoring of Huaihe River mainstream, the middle spatial resolution data MODIS was used to map maximum flood extent and flood duration; For detailed flood monitoring of hardest-hit area, the Radar data RADARSAT and optical data TM with higher spatial resolution were made use of fusion analysis to estimate the losses. The results showed that comprehensive and effective use of multi-source data could give reliable early flood warning, real-time monitoring of flood development, and rapid and accurate assessment of flood losses. The information system framework of monitoring flood using multi-source satellite sensors was feasible. © 2012 IEEE.


Sun L.,National Satellite Meteorological Center | Hu X.,National Satellite Meteorological Center | Guo M.,State Oceanic Administration | Xu N.,National Satellite Meteorological Center
IEEE Transactions on Geoscience and Remote Sensing | Year: 2012

The MEdium-Resolution Spectral Imager (MERSI), onboard the second-generation Chinese polar-orbit meteorological satellite FY-3A, is a MODIS-like sensor with 19 solar bands and one thermal infrared band. Although there is a visible onboard calibration device, it can only be used for tracking temporal instrument degradation. The vicarious calibration (VC) campaign at the Dunhuang site, conducted once a year, has been the main postlaunch absolute radiometric calibration method for MERSI in the solar bands. To increase the in-flight calibration frequency, a multisite radiometric calibration tracking method is presented. This method relies on simulated radiation over several stable sites, and a daily calibration updating model is built from long-term trending of calibration coefficient series. The MERSI calibration reference is evaluated against the observations of Aqua MODIS, showing mean relative biases within 5% from 0.4 to 2.1 \mu\hbox{m}. The short-wave channels of MERSI are found to experience large degradation, particularly the 412-nm band with an annual degradation rate of 9.7%, whereas the red and near-infrared bands are relatively stable with annual degradation rates within \pm1%. Several approaches have been used to analyze the reliability of MERSI calibration results. A comparison of the calibration slopes shows that the relative biases between the multisite method and the annual Dunhuang VC campaign are below 3.8%. Aqua MODIS is used as a reference to monitor the data quality of the recalibrated MERSI. A double-difference analysis shows that the mean relative biases are almost within 5% over stable deserts, and the synchronous nadir observation analysis also reveals good agreement. © 2012 IEEE.


Chen B.-Y.,National Satellite Meteorological Center
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | Year: 2016

For improving the spatial resolution of FY-2G middle infrared band, this paper proposes a method to extract the edge from a moon image and to calculate the Modulation Transfer Function(MTF). It implements image restoration based on the image quality evaluation parameters and improves the image space resolution. Firstly, a moon visual position algorithm was designed based on the image navigation of FY-2 to search the moon image in the FY-2G full disk image. By using one-order differential and extreme value matching, the MTF evaluation value of FY-2G middle infrared band was calculate out by Fourier transform. Using the calculated MTF, the full disk image was restored by a Wiener filter. By using the pixel average gradient, power spectral components and image information entropy as evaluation functions, the reasonable filter parameters were determined. Finally, the high resolution image after MTF restoration was obtained. The experiments indicate that the MTF calculation is correct, image restoration is effective, and the spatial resolution has been improved greatly. Moreover, it ensures the consistency of image energy before and after restorations. © 2016, Science Press. All right reserved.

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