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In recent years the satellite monitoring capabilities in particular to derive maps of aerosol optical depth (AOD) have increased tremendously. There are many aerosol retrieval algorithms for different satellites and sensors. In 2005, a new algorithm for AOD retrieval by synergetic use of of Terra and Aqua MODIS data (SYNTAM) was proposed by Tang et al. With this algorithm, surface reflectance and...
By analyzing the problem of the current monitoring system, the design solutions of the integrated remote monitoring system based on Internet / GPRS is proposed. The system is integrated multiple monitoring ways and it can monitor multiple locations simultaneously. By phone software, short message and Web browser, users can view video, temperature and humidity, smoke, infrared intrusion and other information...
On Oct. 28th, 2009, a heavy fog hit East China. From the MODIS RGB composite image, it can be seen clearly that, much of land of the area (110°-123°E, 30°-42°N) was covered by the heavy fog or cloud. Using MODIS multi-satellite algorithm for aerosol optical depth (AOD) retrieval, we get the AOD Map at 470, 550 and 660 nm from MODIS data at 1km × 1km resolution. By Validating with the AERONET data,...
This paper describes a case study of satellite data integrated with HYSPLIT forward-trajectory model to forecast air quality in regional scale. During mid-October 2009, a severe haze event occurs in eastern China. With the help of HYSPLIT Trajectory Model, a 24-hr, 48-hr and 72-hr forward trajectory from three regions with high aerosol optical depth (AOD) values were created. Forward trajectories...
Drought in sprint is one of the natural disasters that threatens our agriculture development and causes series environment, social and economical consequences. Remote sensing technology becomes possible to rapidly monitor soil moisture in regional scale by combining remote sensing and Geophysical Information Systems technology. In this paper, a thermal inertia map was obtained from MODIS 1B data on...
The remote sensing information service grid node (RSIN) is a tool for dealing with climate change and quantitative environmental monitoring. Based on the high-throughput computing grid, RSIN enables a workflow management system for data placement. The accompanying unified data-and-computation-schedule algorithm helps load balancing between and within workflow steps.
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