| Attention is paid to set up high-prediction method of soil nutrients, as well as improve prediction precision.With the development of information technology, especially the utilization of geo-statistics, remote sensing and expert knowledge in soil nutrients prediction, some new methods produced, which improve the efficiency and precision. However, the soil nutrients spatial variability characterized by scaling function, one method may not be suitable to various scales. Additionally, geographical condition is not always same in different areas, especially for south-western hilly and mountainous areas. Affected by new tectonic movement, topology there changed to many small tiny pieces, which makes dispersed farmland and complex environment. Doubtfully, the new prediction method can obtain a high prediction precision in small spatial scope with such dispersed pixels. As to choose a researching method, it is easier to find a better one after comparison. Generally, most reported comparisons are among different spatial interpolations, little attention was paid to new prediction method and technology such as comparison among spatial interpolation, remote sensing and expert knowledge.Taking Wangjiagou small watershed of Three Gorges Reservoir Area as researching zone with complex geophical environment,111samples were collected. Based on the soil physicochemical properties, reflection spectrum analysis and measurement, various methods of soil nutrients prediction were applied in such small spatial scope:spatial interpolation, remote sensing and expert knowledge, comparisons were done after that. Details are as follows:(1) Soil nutrients prediction using spatial interpolation methods. The accuracy of interpolation methods for soil nutrients prediction is influenced greatly by interpolation methods or interpolation parameters. Discussed impacts of inverse distance weighted (IDW), Kriging and interpolation parameters on prediction accuracy. Results show that kriging and IDW methods gave similar RMSE values for soil nutrients prediction. Kriging produced better results than IDW for interpolating soil TN, TP, AN, and AP. In all uses of IDW, the most accurate estimates were yielded by difficult powers and difficult neighbourhoods. We found no significant correlation between interpolation parameters used for IDW and variation coefficients, skewness. Fitted variograms for soil TN and TP are spherical models. AN and AP in the topsoil are best represented by the exponential model.Use best variogram models to do kriging interpolation, the highest precision has various neighborhoods, but small neighborhoods cannot get a high precision. There is no correlation between neighborhoods and variation coefficients, skewness.Therefore, before doing the interpolation, it is difficult to determine the interpolation parameters by statistical analysis. (2) Prediction based on high-spectum remote sensing. Soil reflection and absorption is not significant because of different physical influence. Firstly, removed continuum from the reflectance spectra of soil samples, made them into a same spectrum background, then chose the characteristic wave bands, and produced the prediction models of purple soil and paddy soil, with the method of partial least squares regressive analysis. Results show that except the TP, AP model of purple soil, the others'correlation coefficients between their predicted values and measured values are over0.5, but precision of TN in paddy soil is highest, with R reaches0.796. The above indicates, it is reasonable to use hyper-spectrum method.After cross-validation of soil samples from two soil-types, the prediction model shows some constrains in the two soil, which may because the different soil types. Since hyper-spectrum data is expensive, it is hard to use the method to do soil nutrients mapping. Using WorldView2and RapidEye satellite images and high-spectum based prediction models to map soil nutrients, after that we found absolute value of correlation coefficient between TN and red band of RapidEye simulated is0.53, that between TP and simulated all bands of RapidEye, WorldView2is lower than0.4. Absolute value correlation coefficient between real-time spectra and soil nutrients is lower0.3. So that, the method has some difficulty in real utilization.(3) Soil nutrients prediction with case-based reasoning. Extracted elevation, slope, plan curvature, profile curvature, topographic position index and topographic wetness index from DEM with1m grid size, soil type data measured land use types data as environment factors, using case-based reasoning to predict soil nutrients based on similarity between samples and cases. Results show that, the average relative errors of TN, TP, AN are lower than25%, whereas, AP is higher than80%. In method of case-based reasoning, prediction precision and coefficients of variation are correlated dramatically:a lower coefficient of variation brings about a higher prediction precision.(4) Comparisons among different prediction method. Since precision is influenced by sampling method, density and number. Methods of spatial interpolation, remote sensing and expert knowledge were evaluated. Results show that, the whole average relative errors of AP are more than80%, in most cases, case-based reasoning for AP prediction obtain the fine precision, but Kriging gets the lowest one. As to TN, TP, AN, hyper-spectrum prediction has a higher precision, which follows by case-based reasoning, Kriging has the lowest. Since hyper-spectrum images and multispectral images data in the same period was not collected, we no obtained soil nutrients maps by hyper-spectrum prediction models, however, the other two methods can easily to do the mapping. Case-based reasoning can get spatial distributionof soil nutrients with more details, spatially in the areas without soil samples, which can not be reached by spatial interpolation.Above all, we validated soil nutrients prediction methods of spatial interpolation, remote sensing and expert knowledgein small spatial scope. By comparing the different prediction methods, case-based reasoning method that with highest precision and best mapping potential and effects is most suitable in researching area, it offers reasonable basis and technology support for assessing soil nutrients distribution in small spatial scope with complex geophical environment.
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