[Prediction models of soil organic matter based on spectral curve in the upstream of Heihe basin].


Benefiting from the high spectral resolution, ground hyperspectral remote sensing technology can express the ground surface feature in detail, meanwhile, multispectral remote sensing has more advantages in studying the features in a large space time region, because of its long time-series images and wide coverage. Investigating the prediction models between the soil organic matter (SOM) content and the hyperspectral data and the sensitive bands based on different indices mathematically obtained from reflectance could combine the advantages of both kinds of spectral data, and provide a new method to search the spatio-temporal characteristics of SOM. Two hundred twenty three soil samples were chosen from the upper reaches of Heihe Basin to measure the SOM content and hyperspectral curve. Taking 181 of them, the stepwise linear regression methods were used to establish models between the SOM and five indices, including reflectance (lambda), reciprocal (REC), logarithm of the reciprocal (LR), continuum-removal (CR) and the first derivative reflectance (FDR). After then, the left 42 samples were used for model validation: firstly, the best model of the same index was chosen by the values of Pearson correlation coefficient (r) and Root mean squared error (RMSE) between the measured value and predicted value; secondly, the best models of different indices were compared. As a result, the model built by reflectance has a better estimation of SOM with the r: 0.863 and RMSE: 4.79. And the sensitive bands of the reflectance model contain 474 nm during TM1, 636 nm during TM3 and 1 632 nm during TM5. This result could be a reference for the retrieval of SOM content of the upper reaches by using the TM remote sensing data.

Cite this paper

@article{Liu2013PredictionMO, title={[Prediction models of soil organic matter based on spectral curve in the upstream of Heihe basin].}, author={Jiao Liu and Yi Li and Shi-Bin Liu}, journal={Guang pu xue yu guang pu fen xi = Guang pu}, year={2013}, volume={33 12}, pages={3354-8} }