Joint Laboratory for Environmental Remote Sensing and Data Assimilation

Shanghai, China

Joint Laboratory for Environmental Remote Sensing and Data Assimilation

Shanghai, China

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Wang M.,East China Normal University | Wang M.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Liu C.,East China Normal University | Liu C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

An online visual analytical system based on Java Web and WebGIS for air quality data for Shanghai Municipality was designed and implemented to quantitatively analyze and qualitatively visualize air quality data. By analyzing the architecture of WebGIS and Java Web, we firstly designed the overall scheme for system architecture, then put forward the software and hardware environment and also determined the main function modules for the system. The visual system was ultimately established with the DIV + CSS layout method combined with JSP, JavaScript, and some other computer programming languages based on the Java programming environment. Moreover, Struts, Spring, and Hibernate frameworks (SSH) were integrated in the system for the purpose of easy maintenance and expansion. To provide mapping service and spatial analysis functions, we selected ArcGIS for Server as the GIS server. We also used Oracle database and ESRI file geodatabase to store spatial data and non-spatial data in order to ensure the data security. In addition, the response data from the Web server are resampled to implement rapid visualization through the browser. The experimental successes indicate that this system can quickly respond to user's requests, and efficiently return the accurate processing results. © 2014 SPIE.


Du J.,East China Normal University | Du J.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Liu C.,East China Normal University | Liu C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

In this work, a soil moisture data assimilation scheme was developed based on the Community Land Model Version 3.0 (hereafter CLM) and Ensemble Kalman Filter. Soil moisture in the 1st soil layer was assimilated into CLM to evaluate the improvements of land surface process simulation. The results indicated that the assimilation system could improve the model accuracy effectively. It can transfer the variations of shallow soil layer's moisture to the deep soil and make great improvements to the soil water and heat status in an overall level. The system could improve the soil moisture accuracy from the 1st soil layer to the 6th soil layer by 50%. According to this experiment, the transfer depth of soil moisture was from 40 cm to 60 cm. After assimilation, the correlation coefficient of latent heat flux observation and simulation increased from 0.68 to 0.91 and the RMSE dropped from 86.7 W/m2 to 45.7 W/m2. For the sensible heat flux, the correlation coefficient increased from 0.69 to 0.80 and the RMSE reduced from 105.1 W/m2 to 71.3 W/m2. It was feasible and significant to assimilate soil moisture remote sensing products. © 2014 SPIE.


Zhou C.,East China Normal University | Zhou C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Shi R.,East China Normal University | Shi R.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 5 more authors.
International Journal of Remote Sensing | Year: 2013

As one of the major greenhouse gases, atmospheric carbon dioxide (CO2) concentrations have been monitored by both top-down satellite observations and air sampling systems on surface stations. The Atmospheric Infrared Sounder (AIRS) on board NASA's Aqua low Earth orbit (LEO) satellite is a high-resolution infrared sounder that has been in operation for more than 10 years. The World Data Centre for Greenhouse Gases (WDCGG) archives and provides data on CO2 and other greenhouse gases measured mainly from surface stations. In this article, we focus on the correlation between the two different sources of CO2 data and the influencing factors. In general, we find that a linear positive correlation occurs at most stations. However, the variation in the correlation coefficient is large, especially for stations in the Northern Hemisphere. The station's location, including its latitude, longitude, and altitude, is an important influencing factor because it determines how much its CO2 measurements are influenced by human activities. We also use root mean square difference (RMSD) and bias as evaluation indicators and find that they have similar trends like correlation coefficients. © 2013 © Taylor & Francis.


Wang C.,East China Normal University | Wang C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Shi R.,East China Normal University | Shi R.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 7 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011

Increased CO 2 (carbon dioxide) has been considered as one of key factors of global warming. Intending to describe the capability of CO 2 measurement by space-borne sensors quantitatively, this paper compares two data sets of CO 2 monthly products retrieved from AIRS and SCIAMACHY over China from 2003 to 2005. The increasing trend of CO 2 concentration can be detected consistently from both of the data sets. However, the seasonal variation of AIRS CO 2 is larger than SCIAMACHY CO 2 because the former represents CO 2 existing in the mid-troposphere while the latter represents in the lower-troposphere. CO 2 concentration reaches its yearly maximum in spring (April and May) and reaches its yearly minimum in late-autumn and winter (October to December and January) for both data sets. The coverage of AIRS monthly CO 2 is much better than that of SCIAMACHY over China and it shows that Xinjiang, Tibet, Inner Mongolia and northeast China have higher values than other regions in China especially in April and May due to local climate and vegetation growth process. © 2011 SPIE.


Chen Y.,East China Normal University | Chen Y.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Shi R.,East China Normal University | Shi R.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 5 more authors.
Atmospheric Environment | Year: 2013

An ensemble and enhanced PM10 (particulate matter with a diameter less than 10μm) concentration forecast model was established in eastern China based on data from 2005 to 2009. The enhanced model consists of a single stepwise regression forecast model and a combined forecast model based on wavelet decomposition and stepwise regression. Six individual forecast results were obtained with a combined model that can predict PM10 concentrations at multiple scales. By decomposing variables into detailed and approximated components in six scales and with the application of stepwise regression, the best-fitted forecast models were established in each component of the different scales. Then, the predicted results of the detail and approximation components were reconstructed in each scale as the enhanced prediction. A regional model was established for eastern China. The accuracy rate of each forecasted result by the regional model was calculated using testing data from 2010 based on the needs of operational forecasting. Precision evaluations were also performed. A comparatively higher accuracy was obtained by the combined model. The advantage of predicting the PM10 concentration with the combined model had wide spatial and temporal suitability. An enhanced forecast model was established for each city of eastern China with improvements, where all the predicted results in each city were evaluated by the accuracy rate and precision validation. In each city, the best-fitted model with the highest precision was selected and combined in an ensemble. The ensemble and enhanced forecast model had a significant improvement in accuracy rate and the highest precision of PM10 concentration forecasting in eastern China. © 2013 Elsevier Ltd.


Li Z.,East China Normal University | Li Z.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Shi R.,East China Normal University | Shi R.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 2 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically. © 2015 SPIE.


Kan Y.,East China Normal University | Kan Y.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Liu C.,East China Normal University | Liu C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 4 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

The Weather Research and Forecast Model (WRF) version 3.5 has been used in this study to simulate a heavy rainfall event during the Meiyu season that occurred between 1 and 2 July 2014 over the Yangtze River valley (YRV) in China. The WRF model is driven by the National Centers for Environmental Predictions (NCEP) Final (FNL) global tropospheric analysis data, and eight WRF nested experiments using four different microphysics (MP) schemes and two cumulus parameterizations (CP) are conducted to evaluate the effects of these microphysics and cumulus schemes on heavy rainfall predictions over YRV region. The four MPs selected in this study are Lin et al., WRF Single-Moment 3-class scheme (WSM3), WRF Single-Moment 5-class scheme (WSM5) and WRF Single-Moment 6-class scheme (WSM6), and the two CPs are Kain-Fristch (KF) and Betts-Miller-Janjic (BMJ) schemes. Sensitivity studies showed that all MPs coupling with KF and BMJ CP schemes can well capture the major rain belt from the northeast to southwest with three rainfall centers, but largely overestimate the rainfall near the border between Anhui and Hubei provinces along with the Yellow Sea shore, which produce an opposite trend compared to the observations. Large discrepancies are also presented in WRF simulations of heavy rainfall centers regarding their locations and magnitudes. All MPs coupling with KF CP scheme produced the rainfall areas shifting towards east compared to the observations, while all MPs with BMJ CP scheme tend to better predict the rainfall patterns with slightly more fake precipitation centers. Among all the experiments, the BMJ cumulus scheme has superiority in simulating the Meiyu rainfall over the KF scheme, and the WSM5-BMJ combination shows the best predictive skills. © 2015 SPIE.


You C.,East China Normal University | You C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Gao Z.,CAS Yantai Institute of Coastal Zone Research | Ning J.,CAS Yantai Institute of Coastal Zone Research
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

This study examined the dynamic changes of the Bohai Sea coastline in recent 20 years through spatial-temporal analysis using combined data from remote sensing and GIS technology. Three standard false color remote sensing images derived from visual interpretation and the vectorization from artificial methods are adopted to complete the extraction of the Bohai Sea coastline information. The results show that the Bohai Sea coastline has an increasing trend from 1990 to 2010, especially with the fastest growth during 2000 to 2010.The coastlines along the Liaoning and Shandong Provinces generally had a growing trend, while the shoreline along the Beijing-Tianjin-Hebei region changes most rapidly. These analyses have suggested that human influence is the key factor in causing the significant changes of the Bohai Sea coastlines in recent years. © 2014 SPIE.


Wang H.,East China Normal University | Wang H.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Shi R.H.,East China Normal University | Shi R.H.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 2 more authors.
Applied Mechanics and Materials | Year: 2014

Remote sensing technology is one of the best methods for the large-scale monitoring of chlorophyll in crops. This study analyzes the feasibility of estimating the contents of chlorophyll by means of narrow band normalized difference vegetation indices (NDVInb). The reflectance of the two bands forming the NDVInb is from simulations run on the PROSPECT model. A traversal of possible combinations of NDVInb are examined from 400 nm to 800 nm. Our results indicate that, at the leaf level, estimation of chlorophyll content can be identified in NDVInb. Ranges for these bands include: 1) 720-735 nm combined with 400-428 nm; 2) 550-615 nm, 692-701 nm or 707-715 nm combined with 400-432 nm or 462-496 nm; 3) 562-589 nm, 616-662 nm or 729-737 nm combined with 434-454 nm; and 4) 664-687 nm combined with 550-615 nm or 692-701 nm. © (2014) Trans Tech Publications, Switzerland.


Zhang L.,East China Normal University | Zhang L.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Shi R.H.,East China Normal University | Shi R.H.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 2 more authors.
Advanced Materials Research | Year: 2014

"Kyoto Protocol" came into force on the February 16th, 2005. It introduced rules on the responsibilities of reducing greenhouse gas emission so as to alleviate and deal with problems caused by climate change. Among the three fulfillment mechanisms in "Kyoto Protocol", the Clean Development Mechanism (CDM) is the only one related to developing countries. As one of the most important developing countries in the world, it is urgent for China to make rational use of the CDM to support its high-speed economic development. At this point, nation-scale carbon related data are critical. This paper introduced the acquisition of soil, vegetation and land use/land cover data at a large scale using remotely sensed data and the simulation of carbon sink/source by means of ecosystem models. Remotely sensed data play an important role in the extraction of qualitative and quantitative information for CDM related researches and activities. © (2014) Trans Tech Publications, Switzerland.

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