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Remote Sens. 2018, 10(9), 1387; https://doi.org/10.3390/rs10091387

Determination of Soil Salt Content Using a Probability Neural Network Model Based on Particle Swarm Optimization in Areas Affected and Non-Affected by Human Activities

1
Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
2
College of Information Engineering, Qujing Normal University, 655011 Qujing, China
3
College of Applied Arts and Science, Beijing Union University, Beijing 100083, China
4
College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China
*
Author to whom correspondence should be addressed.
Received: 8 July 2018 / Revised: 19 August 2018 / Accepted: 29 August 2018 / Published: 31 August 2018
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Abstract

Traditional partial least squares regression (PLSR) and artificial neural networks (ANN) have been widely applied to estimate salt content from spectral reflectance in many different saline environments around the world. However, these methods entail a great amount of calculation, and their accuracy is low. To overcome these problems, a probability neural network (PNN) model based on particle swarm optimization was used in this study to build soil salt content models. Furthermore, there is a clear correlation between the level of human activities and the degree of salinization of an environment. This paper is the first to discuss this matter. Here, the performance of the PNN model to estimate soil salt content from reflectance data was investigated in areas non-affected (Area A) and affected (Area B) by human activities. The study area is located in Xingjinag, China. Different mathematical procedures, five wave band intervals, and two types of signal input sources were used for cross analysis. The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) index values were compared to verify the reliability of the model. Particle swarm optimization was used to adjust the optimal smoothing parameters of the PNN model and to avoid the long training processes required by the traditional ANN. The results show that the optimal wave band interval of the PNN is between 1000 nm and 1350 nm in Area A and between 400 nm and 700 nm in Area B. The reciprocal (1/R) transformation after Savitzky-Golay (SG) smoothing of the signal source is optimal for both areas. The RPD for both is greater than 30, which shows that the PNN model is applicable to areas with and without human activities and the prediction results are very good. The results indicated that the optimal wave band intervals for PNN modeling differed in areas affected and non-affected by human activities. The optimal interval of the artificial activities region falls in the visible light portion of the spectrum, and the optimized wave band region without human activities falls in the near-infrared short-wave portion of the spectrum. View Full-Text
Keywords: probability neural network; different degree of human activities; particle swarm optimization; soil salinity; spectrum mathematical transformation; hyperspectral remote sensing probability neural network; different degree of human activities; particle swarm optimization; soil salinity; spectrum mathematical transformation; hyperspectral remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Fu, C.; Gan, S.; Yuan, X.; Xiong, H.; Tian, A. Determination of Soil Salt Content Using a Probability Neural Network Model Based on Particle Swarm Optimization in Areas Affected and Non-Affected by Human Activities. Remote Sens. 2018, 10, 1387.

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