Abstract: 【Objective】The objective of this study is to explore the hyperspectral features and response regularity of the soil organic matter, and to select the sensitive wavelengths of soil organic matter, so as to reduce complexity of hyperspectral estimation model of soil organic matter and improve robustness of the model, which is to provide theoretical support to quantitatively monitor the soil fertility of farmland by using the hyperspectral technology. 【Method】 A total of 130 fluvo-aquic soil samples were collected from Jianghan plain, of which 40 were the training set samples. The soil organic matter content (SOMC_raw) and spectral reflectance (SR_raw) were measured from total samples, and an experiment of removal of organic matter was performed using the training set samples, and then we measured the soil organic matter content (SOMC_rem) and spectral reflectance (SR_rem) from samples of removal of organic matter. By calculating the difference and rate of change between SR_raw and SR_rem from training set samples, we could analyze how the content changes of soil organic matter itself influence the spectral features. The soil organic matter sensitive wavelengths were determined by the methods of uninformative variables elimination (UVE) and competitive adaptive reweighted sampling (CARS). The calibration set with 45 samples was utilized to build the soil organic matter estimation models base on partial least squares regression (PLSR) and back propagation neural network (BPNN), and the validation set of 45 samples was utilized to test whether sensitive wavelengths were suitable for the same type soil. 【Result】 The experiment of removal of organic matter showed that the average spectral reflectance of test soil samples increased at full-spectrum with removing organic matter content, especially at the visible spectrum; after the comparison of UVE, CARS, UVE-CARS, and CARS-UVE, the optimal method of variables selection was UVE-CARS. The method of UVE-CARS provided 84 selected variables which were the soil organic matter sensitive wavelengths with coverage area of 561-721, 1 920-2 280 nm. Based on soil organic matter sensitive wavelengths, the PLSR and BPNN had better performance than full-spectrum model, and BPNN was better than PLSR in predictive ability with its value of R², RMSE, RPD, MAE, MRE were 0.74, 1.33 g·kg^-1, 2.02, 1.04 g·kg^-1, 6.2%, respectively, so it could effectively estimate soil organic matter. 【Conclusion】 The soil organic matter sensitive wavelengths from training set could effectively estimate soil organic matter content in this test area with the same type samples. In addition, modeling of sensitive wavelengths by obtaining from the experiment of removal of organic matter and variables selection method could not only compress input wavelengths down into 4.2% of full-spectrum, but also enhance the estimation accuracy and reduce the model complexity. In this study, it provided a new approach to quickly and accurately evaluate soil organic matter content in the farmland.

Key words: soil organic matter, hyperspectra, variables selection, partial least squares regression, back propagation neural network, fluvo-aquic