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.
Highlights
Soil is an important natural resource that provides food and fiber for the survival of earth organisms, as well as the sustainable development of ecosystems [1,2,3,4]
Several statistical investigations have shown that 9.55 × 108 hm2 of soil is under threat of salinization across the globe [10,11]
Particle swarm calculation was used to adjust the node parameters of the model, increase calculation speed of the probability neural network (PNN) model, and avoid the huge volume of calculations required by the traditional artificial neural networks (ANN) model
Summary
Soil is an important natural resource that provides food and fiber for the survival of earth organisms, as well as the sustainable development of ecosystems [1,2,3,4]. Liu et al [29] utilized a visible and near-infrared spectrum method in a feasibility study for the assessment of soil potassium content in Qian County yellow soil, Shaanxi They built a soil potassium content calculation model using laboratory spectral reflectance, total soil potassium, available potassium content, multiple linear regression (MLR), and PLSR. Liu et al [30] utilized visible/short wave near infrared spectroscopy (Vis/SW-NIRS) to determine the available nitrogen and potassium in soil In their study, they used the standard normal variation (SNV) method, multiple scattering correction (MSC) method, and Savitzky-Golay smoothing and combined them in a first-order differential algorithm for impellent spectrum pre-processing analysis. Data Colleecffteicotsnof human activities, the optimized wave band regions with and without human activities are discussed in this study
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