Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

Ying-Qiang Song,Xue-Sen Cui,Wu Zhou,Lian-An Yang,Yi-Lun Liu,An-Le Wang,Bo Li,Yue-Ming Hu

doi:10.3390/su9050754

Abstract

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study was conducted in a regional scale study area in Shaanxi Province, China. A total of 472 topsoil (0–20 cm) samples were collected. A total of 14 auxiliary variables (predictors) were obtained from remote sensing data and environmental factors. The proposed method was compared with the ability of traditional geostatistical methods such as simple kriging (SK) and ordinary kriging (OK), in addition to hybrid geostatistical methods such as regression-ordinary kriging (ROK) and artificial neural network-ordinary kriging (ANNOK). The results showed that the extreme learning machines (ELM) model used principal components (PCs) as input variables, and performed better than both multiple linear regression (MLR) and artificial neural network (ANN) models. Compared with geostatistical and hybrid geostatistical prediction methods of SOM spatial distribution, the ELMOK model had the highest coefficient of determination (R2 = 0.671) and ratio of performance to deviation (RPD = 2.05), as well as the lowest root mean square error (RMSE = 1.402 g kg−1). In conclusion, the application of remote sensing imagery and environmental factors has a deeper driven significance of a non-linear and multi-dimensional hierarchy relationship for explaining the spatial variability of SOM, tracing local carbon sink and high quality SOM maps. More importantly, it is possibly concluded that the sustainable monitoring of SOM is a significant process through the pixel-based revisit sampling, an analysis of the mapping results of SOM, and methodological integration, which is the primary step in spatial variations and time series. The proposed ELMOK methodology is a promising and effective approach which can play a vital role in predicting the spatial variability of SOM at a regional scale.

Highlights

With rising concern regarding soil organic matter (SOM), it has generally been considered as a critical indicator of agricultural sustainability [1]
An extreme learning machine approach was used to predict the spatial distribution of SOM content with multi-source auxiliary variables of remote sensing, topographic, climate, and soil properties at a regional scale
A two-stage integrated process was proposed that incorporated an extreme learning machine (ELM) model with residual estimation by ordinary kriging, which was compared to the interpolation methods of artificial neural network-ordinary kriging (ANNOK) and regression-ordinary kriging (ROK) for assessing its feasibility, efficiency, and prediction accuracy

Summary

Introduction

With rising concern regarding soil organic matter (SOM), it has generally been considered as a critical indicator of agricultural sustainability [1]. SOM directly affects the sustainability of soil quality such as soil nutrients and soil texture. It indirectly impacts carbon emissions and, in turn, climate change, which limits the productive forces of food and the carrying capacity of the ecological environment [2,3,4]. The soil mapping of SOM can improve the response time of disturbances and the efficiency of the analysis of sustainable monitoring. Pixel-based mapping of SOM can be more displayed at higher resolutions, and reduces the cost and time required for field sampling by reducing the amount of sampling

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Methods

Results

Conclusion