| Cite this article: | 张晓光,孔繁昌.基于模拟蒸发数据的滨海盐渍土水分光谱模型[J].灌溉排水学报,0,():-. |
| ZHANG Xiao-Guang,KONG Fan-Chang.基于模拟蒸发数据的滨海盐渍土水分光谱模型[J].灌溉排水学报,0,():-. |
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| Prediction model of soil moisture based on spectrum and simulated evaporation data of coastal saline soil |
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ZHANG Xiao-Guang, KONG Fan-Chang
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Qingdao Agricultural University
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| Abstract: |
| 【Objective】Soil moisture is a key influence factor to the movement of soil water and salt. Area of saline soil in the coastal region is broad. In order to better grasping and using the potential land resources, soil moisture need to be monitoring by establishing efficient spectral models. 【Method】In this paper, coastal saline soil was selected in Yellow River delta area as the research object. Salt solutions with different salt concentration were added to the same type of soils separately, and then the process of soil water evaporation in natural condition was simulated. Spectral data with different water content and salt content were obtained by the surface hyperspectral remote sensing technology. 18 different spectral transformations were done separately to the processed spectral data. partial least squares regression(PLSR) models were established between the soil moisture content and soil spectral data base on the above processed spectral data. 【Result】Results showed that Among the all types of spectral transformation, models based on smoothing + normalization spectral transformation (contained normalization of variables, range normalization, maximum normalization, and area normalization) has achieved good results. Model based on smoothing + normalization of variables had the best accuracy(R2 = 0.7131,RMSE = 0.0950,ratio of standard deviation to root mean square error (RPD) = 1.8237). It indicated that this model could be directly applied to saline soil moisture inversion. Many combinations of spectral transformation were formed from the several spectral transformation methods. Found by combinations of spectral transformation, model established after the transformation of smoothing+ normalization of variables+ multiplicative scatter correction model got obvious improvement and achieved good prediction (R2 = 0.8661, RMSE = 0.8661, RPD = 2.7643). Accuracies of the models established after other combinations of spectral transformation presented significant decline. It indicated that suitable spectral transformation need to chose for modelling. 【Conclusion】The established model of this paper has the characters of strong stability and high prediction accuracy. The data values of soil moisture can be used to the model was distributed in a wide extent. So it could be applied to the monitoring of different soil moisture states by remote sensing in the coastal saline soil area. |
| Key words: Soil moisture content;Hyperspectral model;Spectral transformation;Coastal saline soil;Artificial simulation |
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