Papers by Author: Jing Wen Xu

Abstract: For the point of high-resolution remote sensing image segmentation, this paper compared the segmentation effect between eCognition and EDISON through adjusting appropriate parameters. The experiment show that eCognition plays better than that of EDISON in segmenting more complex ground objects, while EDISON plays better in segmentation more uniform ground objects.

Abstract: Mean shift algorithm is a robust approach toward feature space analysis, which has been wildly used for natural scene image and medical image segmentation. Due to fuzzy boundary and low accuracy of Mean shift segmentation method, this paper puts forward to an improved Mean shift segmentation method of high-resolution remote sensing image based on LBP and Canny features. The results show that this improved Mean shift segmentation access can enhance segmentation accuracy compared to the traditional Mean shift.

Abstract: The two soil moisture retrieval methods based on the Advanced Microwave Scanning Radiometer of the Earth Observing System (AMSR-E) data, the standard algorithm by NASA and Land Parameter Retrieval Model (LPRM) have been validated at Xuchang site in Huaihe River basin, in China. The NASA dataset fails to capture main fluctuations of soil moisture, while the LPRM exhibits stronger agreement with the temporal dynamics and precipitation events associated with in situ soil moisture. The LPRM X-band product over ascending pass performs best with correlation coefficient value of 0.42, root mean square error ranging from 0.18 and mean absolute error of 0.14. Generally, the useful soil moisture information can be extracted over HRB from AMSR-E passive microwave data.

Abstract: Sichuan Hilly Area is selected as the study area. This paper uses ten brightness temperature AMSR-E data during 2006-2010. It constructs 8 preferred drought index by using the polarization ratio method (Polarization Rations, PR). This paper makes Pearson correlation analysis by using the 8 preferred drought index and the soil moisture of study area. Meanwhile, Linear regression and correlation analysis with the Daily precipitation and the standardized precipitation index SPI are also made. The results show: for example, in December 2009, drought index DI74, DI92, DI96 were basically consistent with the spatial distribution. Drought degree has an increasing trend from southeast to northwest regional gradually. And with the drought conditions in hilly area of actual and daily precipitation, SPI correlation between interannual and Sichuan are proper. So the drought index is more suitable for drought study in hilly area of central Sichuan Basin.

Abstract: Soil moisture plays an important role in agricultural drought monitoring. However, the traditional observed soil moisture data from meteorological stations has not been able to meet the demands for large-scale drought monitoring temporally and spatially. The microwave remote sensing is a new effective way for obtaining near surface soil moisture. The temporal and spatial variation in soil moisture in arid regions in northern China is examined based on the soil moisture data retrieved from AMSR-E (Advanced Microwave Scanning Radiometer-EOS), with which the observed soil moisture data at 10 cm depth from meteorological stations are compared. The results show that there is a small change in soil moisture with seasons for the central and west areas, while a large change for the east areas of the study region and the soil moisture in summer and autumn is greater than that in spring and winter. And that it decreases from southeast to northwest spatially, as is agree with the spatial distribution of precipitation in the study area. Moreover, there is a great difference in spatial distribution between the soil moisture retrieved from AMSR-E and the observed soil moisture data from meteorological stations for the central and west areas, while a small difference for the east areas of the study region.

Abstract: Soil moisture plays an important role in agricultural drought predicting, therefore there is an increasing demand for detailed predictions of soil moisture, especially at basin scales. However, so far soil moisture predictions are usually obtained as a by-product of climate and weather prediction models coupled with a land surface parameterization scheme, and there has been little dedicated work to meet this urgent need at basin scales. In order to improve the basin hydrological models’ performance in the soil moisture forecasting, an integrated soil moisture predicting model based on Artificial Neural Network (ANN) and Xinanjiang model is proposed and presented in this paper. The performance of the new integrated soil moisture predicting model was tested in the Linyi watershed with a drainage area of 10040 km2, located in the semi-arid area of the eastern China. The results suggest that the soil moisture simulated by the integrated ANN-Xinanjiang model is more agree with the observed ones than that simulated by Xinanjiang, and that the simulated soil moisture by both the models has the similar trend and temporal change pattern with the observed one.