Jun Fang Zhao

Validation of AMSR-E Soil Moisture Retrievals over Huaihe River Basin, in China

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.

Soil Moisture Inversion Using AMSR-E Remote Sensing Data: An Artificial Neural Network Approach

In this work artificial neural network with a back-propagation learning algorithm (BPNN) is employed to solve soil moisture retrieval for Sichuan Middle Hilly Area in China. Eighteen kinds of BPNN models have been developed using AMSR-E observations to retrieve soil moisture. The results show that the 18.7GHz band has some positive effect on improving soil moisture estimation accuracy while the 36.5GHz may interfere with deriving soil moisture, and vertical brightness temperature has a closer relationship with observed near-surface soil moisture than horizontal TB. The BPNN model driven by vertical and horizontal TB dataset at 6.9GHz and 10.7GHz frequency has the best performance of all the BPNN models withr value of 0.4968 and RMSE 10.2976%. Generally, the BPNN model is more suitable for soil moisture estimation than NASA product for the study area and can provide significant soil moisture information due to its ability of capturing non-linear and complex relationship.

Applicability of Modified TOPMODEL in the Arid Zone and the Humid Zone

This study aims at discussing the applicability of modified TOPMODEL in different areas with various climate conditions in China, choosing the Youshuijie catchment (the humid zone) and the Yingluoxia catchment (the arid zone) . Hydro-meteorological data and 90-m-resolution DEMs are used for driving the models. From the Nash-Sutcliffe efficiency coefficient (NE), we can see that TOPMODEL performed much better in the Youshujie catchment than in the Yingluoxia catchment, which suggests that modified TOPMODEL is much more suitable in the humid zone.

Soil Moisture Prediction for Huaihe River Basin Using Hydrological Model XXT and TOPMODEL

Soil moisture plays an important role in agricultural drought predicting. Hydrological models can be employed to forecast soil moisture. In order to get better predicted soil moisture information, we use two basin hydrological models, i.e. XXT and TOPMODEL, to forecast the soil moisture for Huaihe River watershed. The performance of both the two models was tested in the Linyi watershed with a drainage area of 10040 km2, a tributary of the Huaihe river, China. The results show that the soil moisture simulated by the XXT is more agree with the observed ones than that simulated by TOPMODE compared to the filed observed soil moisture at 10 cm or the mean ones of 10 cm, 20 com, and 40 cm from surface, and that the predicted soil moisture by both the models has the similar trend and temporal change pattern with the observed one. However, both the models need to be improved in soil moisture forecasting in the future work.

A Novel Integrated Rainfall-Runoff Model Based on TOPMODEL and Artificial Neural Network

Conventional process-based rainfall-runoff models are difficult to catch the non-linear factors and to take full advantages of previous information of rainfall and runoff. However, these factors are closely related to the initial watershed average saturation deficit at each time step. Therefore, in order to address the issue, this study selected the parameter about initial underground flow in TOPMODEL (TOPOgraphic driven Model) as the breakthrough point. Then we used the previous two-day observed runoff and rainfall data as the inputs of an artificial neural network (ANN) and initial subsurface flow of present day as an output, then integrated ANN into runoff generation module in TOPMODEL and finally applied the integrated model for daily runoff modeling in Yingluoxia watershed with 10009km2, China. In addition, this work also utilized particle swarm optimization technique (PSO) to avoid the local optimization, especially for the integration of black-box and physical models. The result shows that during the validation period the Nash-Sutcliffe efficiency coefficient (NE) and root mean square error (RMSE) of TOPMODEL are 0.45 and 3.88×10-4m respectively while the NE of 0.70 and RMSE of 2.85×10-4m for the integrated model. Significantly, the integrated model performs much better than the traditional model. Hence, this new method of integrating ANN with the runoff generation module of TOPMODEL is promising and easily extended to other process-based rainfall-runoff models as well.

Study of the Relationship between Climatic Variables and Potato in Northern Chinese Semi-Arid Potato Producing Areas

Based on the correlation analysis between climate and crop data in Gansu and Inner Mongolia, this study intends to calculate the climate impact factors, screen out the main climatic variable affecting potato and establish regional potato climate yield model to understand the level of influence about climate on potato yield. The result shows the main climatic variable affecting northern Chinese semi-arid potato include temperature, precipitation and sunhour; The model has an accurate simulation results, not only the fluctuation of climate yield is similar to that of climatic variables, but also it influenced by social and economic factors, regions with more developed economy and technology might mean a higher climate yield.

An Improved Mean Shift Segmentation Method of High-Resolution Remote Sensing Image Based on LBP and Canny Features

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.

A Novel Model Based on LBP and Meanshift for UAV Image Segmentation

This paper propose a hybrid model which combine LBP and Meanshift for unmanned aerial vehicle image segmentation. In order to take full advantage of UAV image,The segmentation start with the over-segmentation regions,where the image divided into many regions by Mean shift. Then the small regions are merge with their neighbors by the hybrid distance with spectral, spatial and LBP histogram.

Study on Insect Pests Detection Based on Digital Image

This paper mainly performs Cascade AdaBoost algorithm based on multi-feature to detect the images of Eurydema dominulus, which will cause harm to crucifer. Firstly, the mixing of HAAR features and LBP features is adopted instead of the single-feature of traditional model, which makes description of images more comprehensively from the angle of the gradient and texture. And then use the best features selected by Gentle AdaBoost algorithm to compose the weak classifier and the strong classifier. And the cascade detector is composed of the trained classifiers of each layer according to a certain screening rate. Experimental results show that the method of detection has the probability of dis-detecting and leak-detecting, but it still has a certain reference value in the field of agricultural plant diseases and insect pests detection for its good robustness.

Soil Moisture Inversion Based on AMSR-E and MODIS Data Fusion: A Case Study of Huaihe River Basin

In this paper, the correlation between fused data and original data, the measured soil and the precipitation data over Huaihe river basin by exploring the inversion of soil moisture from the time and space based on the method of multi-source remote sensing data fusion has been studied. In order to fuse the AMSR-E data which is all-day and all-weather and can penetrate the earth surface to some extent, with the MODIS data that can reflect the surface condition and temperature characteristics, the method of wavelet fusion was carried out in MATLAB. The conclusions of this study are listed as follows: (1) the inversion result of the fused data based on AMSE-E and MODIS is much better than a single remote sensing data inversion; (2) the fused data based on AMSE-E and MODIS is sensitive to soil moisture change trend when the seasons alternated every year, especially in the spring, summer and autumn.