The inversion model of soil organic matter of cultivated land based on hyperspectral technology

Xiaohe Gu; Yancang Wang; Xiaoyu Song; Xingang Xu

doi:10.1117/12.2194287

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14 October 2015 The inversion model of soil organic matter of cultivated land based on hyperspectral technology

Xiaohe Gu; Yancang Wang; Xiaoyu Song; Xingang Xu

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Proceedings Volume 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII; 96370D (2015) https://doi.org/10.1117/12.2194287
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

Monitoring soil organic matter (SOM) in the cultivated land quantitively and mastering its spatial change are helpful for the adjustment of fertility and sustainable development of agriculture. The hyperspectral technology could be used to detect the targets quickly and nondestructively. The study aimed to develop a universal method to monitor SOM by hyperspectral data. The main idea of the study could be described as follows. Several mathematical transformations were used to improve the expression ability of hyperspectral data. The correlations between SOM and the hyperspectral reflectivity and its mathematical transformations were analyzed. Then the feature bands and its transformations were screened to develop the optimizing model of monitoring SOM based on the method of multiple linear regressions. The in-situ sample was used to evaluate the accuracy of the model. Results showed that the inversion model with the one differentiation of logarithmic reciprocal transformation ( ^{(1 lg P)'}) of reflectivity could reach highest correlation coefficient (0.643) with lowest RMSE (2.622 g/kg), which was considered as the optimizing inversion model of SOM. It indicated that the one differentiation of logarithmic reciprocal transformation of hyperspectral had good response with SOM of cultivated land. Based on this transformation, the optimizing inversion model of SOM could reach good accuracy with high stability.

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Xiaohe Gu, Yancang Wang, Xiaoyu Song, and Xingang Xu "The inversion model of soil organic matter of cultivated land based on hyperspectral technology", Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 96370D (14 October 2015); https://doi.org/10.1117/12.2194287

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