Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network
- 3 Downloads
It is more difficult to retrieve land surface temperature (LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies (ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.
Keywordsradiometry Advanced Microwave Scanning Radiometer 2 (AMSR2) passive remote sensing inverse problem
Unable to display preview. Download preview PDF.
The author would like to thank for Ashcroft, P., and F. Wentz. 2003, updated daily. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) V001, September to October 2003. Boulder, CO, USA: National Snow and Ice Data Center. Digital media, NASA provides MODIS land surface temperature product.
- Aires F, Prigent C, Rossow W B et al., 2001. A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations. Journal of Geophysical Research, 106(D14): 14887–14907. doi: 10.1029/2001JD900085CrossRefGoogle Scholar
- Chen S S, Chen X Z, Chen W Q et al., 2011. A simple retrieval method of land surface temperature from AMSR-E passive microwave data-A case study over Southern China during the strong snow disaster of 2008. International Journal of Applied Earth Observation and Geoinformation, 13(1): 140–151. doi: 10.1016/j.jag.2010.09.007CrossRefGoogle Scholar
- Chen Z X, Davis D, Tsang L et al., 1992. Inversion of snow parameters by neural network with iterative inversion. In: 1992 International Geoscience and Remote Sensing Symposium. Houston, TX: IEEE, 1061–1063. doi: 10.1109/IGARSS.1992.578340Google Scholar
- Fily M, Royer A, Goïta K et al., 2003. A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas. Remote Sensing of Environment, 85(3): 328–338. doi: 10.1016/S0034-4257(03)00011-7CrossRefGoogle Scholar
- Mao K B, Shi J C, Tang H J et al., 2007c. A neural-network technique for retrieving land surface temperature from AMSR-E passive microwave data. In: 2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona: IEEE, 4422–4425. doi: 10.1109/IGARSS.2007.4423835Google Scholar
- Prata A J, 1994. Land surface temperatures derived from the advanced very high resolution radiometer and the along-track scanning radiometer: 2. Experimental results and validation of AVHRR algorithms. Journal of Geophysical Research, 99(D6): 13025–13058. doi: 10.1029/94JD00409CrossRefGoogle Scholar
- Prigent C, Aires F, Rossow W B, 2003. Land surface skin temperatures from a combined analysis of microwave and infrared satellite observations for an all-weather evaluation of the differences between air and skin temperatures. Journal of Geophysical Research, 108(D10): 4310, doi: 10.1029/2002JD002301CrossRefGoogle Scholar