Remote sensing retrieval of saline and alkaline land based on reflectance spectroscopy and RV-MELM in Zhenlai County

Abstract

The management of saline and alkaline land requires accurate distribution data, so it is necessary to construct a high-performance inversion model to get distribution data. The paper gets data of reflectance spectroscopy and salt content based on 62 samples in Zhenlai County. Then the paper builds the inversion model and calibration model, and draws a distribution map of salt content to make governance recommendations. The paper proposes the two-hidden-layer extreme learning machine with random values (RV-TELM) and the multiple hidden layers extreme learning machine with random values (RV-MELM) by using random value. Based on whether to specify the root mean square error (RMSE) and coefficient of determination (R2) of the test set that you want to obtain in the end, RV-MELM can be divided into two categories: RV-MELM with specified root mean square error and coefficient of determination (SRV-MELM) and RV-MELM with the largest number of searches (LRV-MELM). The paper selects LRV-MELM as the inversion model, and SRV-MELM as the calibration model. The experimental results show that the overall accuracy of the final map is 1.5286.