Spectral analysis-artificial intelligence model with spatial variables for some soil chemical properties estimation

Introduction
The lack of scale-dependent information related to soil properties has largely led to limitations in agriculture, hydrology, climate, ecology, and environmental studies. In this regard, the study aims to analyze the scale-dependent changes in estimating the spatial distribution of soil pH and salinity in Tabriz. The proposed method applies the decomposition approach to increase the accuracy of soil properties spatial distribution estimation which includes creating a relationship between soil salinity and pH with the sub-series of input variables from the decomposition method, construction of a relationship between soil salinity and pH sub-series with sub-series of input variables and finally, using a data reduction approach for the effective sub-series determination from decomposition.
 
Materials and Methods
The sampling points of the study are related to Tabriz City in East Azerbaijan province. 60 and 38 samples were collected from zero to 20 cm depth for soil pH and salinity monitoring, respectively. Changes in soil properties are evident from one region to another. The cyclical behavior of soil properties is called periodicity. Previous studies in the spatial changes description have mainly focused on the spatial similarities obtained between properties in a region, in which the change of spatial arrangement-spatial dependence or periodicity of soil properties has not been regarded. Spectral analysis can measure the periodicity in spatial changes by approximating a series of spatial data with the sum of sine and cosine functions. The purpose of discrete wavelet transformation is to decompose the signal into the sub-series to obtain a comprehensive input signal analysis. Discrete wavelet transform is used to calculate approximation coefficients in a signal. Maximum overlap discrete wavelet transform (MODWT) is similar to discrete wavelet transform in which low and high pass filters are applied to the input signal at each level. However, the elimination of coefficients is not done by MODWT. Principle component analysis (PCA) was used as the reduction method to find the effective sub-series of decomposition.
 
Results and Discussion
In this study, 48 and 12 points were used in soil pH modeling, and 30 and eight points were used in soil salinity modeling in the calibration and validation periods, respectively. Longitude, latitude, height above sea level, slope, and slope aspect were considered as the spatial variables, and the longitude, latitude, and height of sampling points were recorded by GPS, but the slope and slope aspect of sampling points were taken from the production maps of slope and direction. They were extracted from the digital elevation model (DEM) map. The changes in accuracy measurement indices show the variation in the estimated soil salinity and pH against different inputs of the support vector regression (SVR) model. In the case of salinity, analyzing only the aspect of slope has been able to increase the accuracy of the measurement indices, the reduction rate of RMSE, RRMSE, and Var from the previous optimal state without decomposition to decomposition state is three, four, and 20%, respectively, and the rate of the residual predictive deviation (RPD) increase is equal to four percent. Based on the values of the accuracy measurement indices, PCA can increase the accuracy of the estimated values. The box plot of data related to the use of principal component analysis has become closer to the box plot of the observation data compared to the case where only the decomposition method was used. This problem shows the increase in accuracy with a combination of the MODWT spectral approach and the PCA data reduction method.
 
Conclusion
In recent years, there has been an increasing demand for soil spatial distribution information in environmental decision-making and land use management. One of the issues that can affect the accuracy of spatial modeling of soil properties is the spatial information increase of input variables to the model. Modeling sub-series of input variables with sub-series of soil properties and finally the sum of estimated sub-series of soil properties could not increase the accuracy of estimated values. Series decomposition could increase the accuracy of estimates. The factors that can affect the accuracy of the proposed method for determining the spatial changes of soil properties include the type of input variables, the type of used model in the modeling process, the use of the appropriate method in spectral analysis, and determining the effective factors in the decomposition method. Therefore, the combination of spectral analysis and artificial intelligence as an effective option can increase the accuracy of the spatial distribution of soil properties.