利用微波遥感的发射率数据反演裸土壤湿度，不可避免需要结合地表面和土壤层的一些先验知识，而先验知识的准确度又将对反演结果的准确度产生一定的影响。文章讨论了地表的高度起伏相关函数形式、土壤温度和土壤质地等三类先验知识，定义了几种不同的土壤湿度反演误差，从而定量地给出三类先验知识的不确定性对土壤湿度反演的影响，指出：基于BSM散射模型和人工神经网络（ANN）的土壤湿度的反演方法是可行的，向ANN输入两种极化的裸土壤表面发射率数据便可反演出裸土壤的湿度，在上述三种先验知识具有一定的不确定性时仍可保证较好土壤湿度反演准确度。

It's pointed out by this paper that the priori knowledge of random rough soil surface is needed in soil moisture inversion by using passive remote sensing, so the precision of priori knowledge has effect on the inversion result. Several kinds of inversion error are defined in this paper to depict the influence of three kinds of priori knowledge on the inversion, these three kinds of priori knowledge are the type of soil surface height distribution, soil temperature, and soil texture. Simulation result shows that it's feasible to invert soil moisture by neural network (NN) based on bi-spectrum model (BSM) . Using two kinds of emissivity data of two polarizations as the input of NN, the inversion error of soil moisture is allowable even there is some uncertainty on these three kinds of priori knowledge.