This research presents a novel approach for using VIS-NIR spectroscopy for soil organic matter (SOM) estimation. Soil spectrum data is collected from 89 samples retrieved from the Aibi Lake wetland. The samples are measured using a first-order differential transformation achieved through a continuous projection algorithm, a principal component analysis, and a sparse auto-encoder (SAE). The extracted data is then combined with a partial least squares regression (PLSR) and backpropagation (BP) neural network for the purpose of building a SOM estimation model. Experimental results show that the SAE method is able to effectively compress the spectrum. The BP model is shown to handle the complex and nonlinear information of the spectrum better than the PLSR model. Meanwhile, the SAE-BP method has the highest accuracy for estimating SOM. The network model is shown to significantly improve the stability and accuracy of the vis-NIR spectrum inversion of the SOM model. This model shows a robust and strong analytical power when faced with complex nonlinear problems in the spectrum.