Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster.Although spectral remote sensing data have many advantages for ground information observation,such as real time and high spa...Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster.Although spectral remote sensing data have many advantages for ground information observation,such as real time and high spatial resolution,they are often interfered by clouds,haze and rain.As a result,it is very difficult to retrieve ground information from spectral remote sensing data under those conditions.Compared with spectral remote sensing tech-nique,passive microwave remote sensing technique has obvious superiority in most weather conditions.However,the main drawback of passive microwave remote sensing is the extreme low spatial resolution.Considering the wide ap-plication of the Advanced Microwave Scanning Radiometer-Earth Observing System(AMSR-E) data,an AMSR-E data unmixing method was proposed in this paper based on Bellerby's algorithm.By utilizing the surface type classifi-cation results with high spatial resolution,the proposed unmixing method can obtain the component brightness tem-perature and corresponding spatial position distribution,which effectively improve the spatial resolution of passive microwave remote sensing data.Through researching the AMSR-E unmixed data of Yongji County,Jilin Provinc,Northeast China after the worst flood and waterlogging disaster occurred on July 28,2010,the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.展开更多
Terrestrial vegetation is one of the most important components of the Earth's land surface. Variations in terrestrial vegetation directly impact the Earth system's balance of material and energy. This paper de...Terrestrial vegetation is one of the most important components of the Earth's land surface. Variations in terrestrial vegetation directly impact the Earth system's balance of material and energy. This paper describes detected variations in vegetation activity at a national scale for China based on nearly 30 years of remote sensing data derived from NOAA/AVHRR(1982–2006) and MODIS(2001–2009). Vegetation activity is analyzed for four regions covering agriculture, forests, grasslands, and China's Northwest region with sparse vegetation cover(including regions without vegetation). Relationships between variations in vegetation activity and climate change as well as agricultural production are also explored. The results show that vegetation activity has generally increased across large areas, especially during the most recent decade. The variations in vegetation activity have been driven primarily by human factors, especially in the southern forest region and the Northwest region with sparse vegetation cover. The results further show that the variations in vegetation activity have influenced agricultural production, but with a certain time lag.展开更多
基金Under the auspices of National Natural Science Foundation of China (No. 40971189)Knowledge Innovation Programs of Chinese Academy of Sciences (No. KZCX2-YW-340)China Postdoctoral Science Foundation (No. 20100471276)
文摘Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster.Although spectral remote sensing data have many advantages for ground information observation,such as real time and high spatial resolution,they are often interfered by clouds,haze and rain.As a result,it is very difficult to retrieve ground information from spectral remote sensing data under those conditions.Compared with spectral remote sensing tech-nique,passive microwave remote sensing technique has obvious superiority in most weather conditions.However,the main drawback of passive microwave remote sensing is the extreme low spatial resolution.Considering the wide ap-plication of the Advanced Microwave Scanning Radiometer-Earth Observing System(AMSR-E) data,an AMSR-E data unmixing method was proposed in this paper based on Bellerby's algorithm.By utilizing the surface type classifi-cation results with high spatial resolution,the proposed unmixing method can obtain the component brightness tem-perature and corresponding spatial position distribution,which effectively improve the spatial resolution of passive microwave remote sensing data.Through researching the AMSR-E unmixed data of Yongji County,Jilin Provinc,Northeast China after the worst flood and waterlogging disaster occurred on July 28,2010,the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences,Climate Change:Carbon Budget and Relevant Issues(Grant No.XDA05050100)
文摘Terrestrial vegetation is one of the most important components of the Earth's land surface. Variations in terrestrial vegetation directly impact the Earth system's balance of material and energy. This paper describes detected variations in vegetation activity at a national scale for China based on nearly 30 years of remote sensing data derived from NOAA/AVHRR(1982–2006) and MODIS(2001–2009). Vegetation activity is analyzed for four regions covering agriculture, forests, grasslands, and China's Northwest region with sparse vegetation cover(including regions without vegetation). Relationships between variations in vegetation activity and climate change as well as agricultural production are also explored. The results show that vegetation activity has generally increased across large areas, especially during the most recent decade. The variations in vegetation activity have been driven primarily by human factors, especially in the southern forest region and the Northwest region with sparse vegetation cover. The results further show that the variations in vegetation activity have influenced agricultural production, but with a certain time lag.
