TY - JOUR
T1 - Integrating machine learning with spatial analysis for enhanced soil interpolation
T2 - Balancing accuracy and visualization
AU - Wang, Yuefan
AU - Yuan, Fei
AU - Cammarano, Davide
AU - Liu, Xiaojun
AU - Tian, Yongchao
AU - Zhu, Yan
AU - Cao, Weixing
AU - Cao, Qiang
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2025/8
Y1 - 2025/8
N2 - Soil interpolation at the village scale plays a critical role in precision agriculture, enabling accurate predictions of soil properties across unsampled locations. Traditional interpolation methods, such as Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Ordinary Kriging (Krig), and Empirical Bayesian Kriging (EBK), often face challenges in addressing the spatial heterogeneity and variability of soil data due to their linearity and stationarity assumptions and difficulties in handling nonlinear relationships. These limitations highlight the need to optimize soil interpolation approaches for improved accuracy, particularly at smaller scales. This study investigated the potential of integrating machine learning techniques with traditional interpolation methods to enhance prediction performance. The research collected data from two village scale study areas, comprising 208 and 309 sampling points, respectively. For each soil variable, traditional interpolation methods were compared to machine learning-enhanced approaches, including Multifactor Kriging (MFK), Random Forest Ordinary Kriging (RFOK), and Random Forest Inverse Distance Weighting (RFIDW). The performance of these methods was evaluated based on prediction efficiency and consistency analysis. Results demonstrated that the accuracy of interpolation methods was significantly influenced by the spatial correlation and variability of soil data. Overall, machine learning-integrated methods improved prediction efficiency by over 60 %. However, traditional Kriging methods produced smoother visualizations on interpolated maps compared to RFOK, which may be advantageous for specific applications, especially for large-scale applications. Consistency analysis revealed moderate similarity between traditional Kriging and IDW (Pearson correlation coefficients of 0.83 to 0.93), whereas RFOK and RFIDW exhibited stronger similarity (Pearson correlation coefficients of 0.96 to 0.98). This study underscores the innovative potential of integrating machine learning into soil interpolation, offering a significant contribution to advancing exploration of soil spatial heterogeneity. Through balancing the data accuracy and visualization effects, the findings provide actionable insights for optimizing soil management strategies and enhancing spatial prediction capabilities in agricultural research.
AB - Soil interpolation at the village scale plays a critical role in precision agriculture, enabling accurate predictions of soil properties across unsampled locations. Traditional interpolation methods, such as Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Ordinary Kriging (Krig), and Empirical Bayesian Kriging (EBK), often face challenges in addressing the spatial heterogeneity and variability of soil data due to their linearity and stationarity assumptions and difficulties in handling nonlinear relationships. These limitations highlight the need to optimize soil interpolation approaches for improved accuracy, particularly at smaller scales. This study investigated the potential of integrating machine learning techniques with traditional interpolation methods to enhance prediction performance. The research collected data from two village scale study areas, comprising 208 and 309 sampling points, respectively. For each soil variable, traditional interpolation methods were compared to machine learning-enhanced approaches, including Multifactor Kriging (MFK), Random Forest Ordinary Kriging (RFOK), and Random Forest Inverse Distance Weighting (RFIDW). The performance of these methods was evaluated based on prediction efficiency and consistency analysis. Results demonstrated that the accuracy of interpolation methods was significantly influenced by the spatial correlation and variability of soil data. Overall, machine learning-integrated methods improved prediction efficiency by over 60 %. However, traditional Kriging methods produced smoother visualizations on interpolated maps compared to RFOK, which may be advantageous for specific applications, especially for large-scale applications. Consistency analysis revealed moderate similarity between traditional Kriging and IDW (Pearson correlation coefficients of 0.83 to 0.93), whereas RFOK and RFIDW exhibited stronger similarity (Pearson correlation coefficients of 0.96 to 0.98). This study underscores the innovative potential of integrating machine learning into soil interpolation, offering a significant contribution to advancing exploration of soil spatial heterogeneity. Through balancing the data accuracy and visualization effects, the findings provide actionable insights for optimizing soil management strategies and enhancing spatial prediction capabilities in agricultural research.
KW - Multifactor kriging
KW - Random forest ordinary kriging
KW - Small scale
KW - Soil variables
UR - http://www.scopus.com/inward/record.url?scp=105006609357&partnerID=8YFLogxK
U2 - 10.1016/j.atech.2025.101032
DO - 10.1016/j.atech.2025.101032
M3 - Journal article
AN - SCOPUS:105006609357
SN - 2772-3755
VL - 11
JO - Smart Agricultural Technology
JF - Smart Agricultural Technology
M1 - 101032
ER -