Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm
Volume 8, Issue 6, December 2019, Pages: 335-345
Received: Sep. 27, 2019;
Accepted: Nov. 6, 2019;
Published: Nov. 22, 2019
Views 500 Downloads 225
Rong Chen, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Shishi Liu, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Yufei Yang, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Wei Huang, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Zongwei Han, Department of Tourism and Geography, Tongren University, Tongren, China
Peihong Fu, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
In this study, the spatial distribution pattern of the roads, historical samples, digital elevation data, and other available resources were incorporated into the design of a soil-sampling scheme to predict the soil organic matter (SOM) of the northern region of Zhongxiang City, Hubei Province, and simulated annealing (SA) was applied to optimize the sampling design. The sampling points determined after optimization were used to establish a multivariate linear regression model to adequately reproduce the intrinsic link between topographic factors and the SOM at 13 different sampling scales in areas nearby the existing roadways in the study area. The topographic factors included slope, plane curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). A multilayer perceptron (MLP) model was also constructed. Comparison of the accuracy of the multivariate linear regression and MLP models demonstrated the feasibility of an optimized soil sampling design based on the road network. With the optimized sampling design, accurate soil-landscape information can be obtained, and its precision is greater than that of the original sampling scheme before optimization. The optimized sampling design obtained reduces sampling costs, increases sampling efficiency, and provides an effective method for obtaining the spatial distribution pattern of organic matter in soils.
Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm, Earth Sciences.
Vol. 8, No. 6,
2019, pp. 335-345.
Franzen, D. W. and Peck, T. R. (1995). Field Soil Sampling Density for Variable Rate Fertilization. Journal of Postdoctoral Affairs 8, 568-574.
Cambardella, C. A., Moorman, T. B., Parkin, T. B., Karlen, D. L., Novak, J. M., Turco, R. F., et al (1994). Field-Scale Variability of Soil Properties in Central Iowa Soils. Soil Science Society of America Journal 58, 1501-1511.
Shi, X., Zhu, A. X., Burt, J. E., Qi, F. and Simonson, D. (2004). A Case-based Reasoning Approach to Fuzzy Soil Mapping. Soil Science Society of America Journal 68, 885-894.
Sakata, S., Ashida, F. and Zako, M. (2004). An efficient algorithm for Kriging approximation and optimization with large-scale sampling data. Computer Methods in Applied Mechanics and Engineering 193, 385-404.
Thompson, A. N., Shaw, J. N., Mask, P. L., Touchton, J. T. and Rickman, D. (2004). Soil Sampling Techniques for Alabama, USA Grain Fields. Precision Agriculture 5, 345-358.
An, Y., Yang, L., Zhu, A. X., Qin, C., and Shi, J. J. (2017). Identification of representative samples from existing samples for digital soil mapping. Geoderma 311, 109-119.
Yang, L., Zhu, A. X., Qin C. Z., Li B. L., Pei T., Qiu W. L., et al (2010). A purposive sampling design method based on typical points and its application in soil mapping. Progress in Geography 29, 279-286. (in Chinese)
Bertacchini, L., Durante, C., Marchetti, A., Sighinolfi, S., Silvestri, M., and Cocchi, M. (2012). Use of X-ray diffraction technique and chemometrics to aid soil sampling strategies in traceability studies. Talanta 98, 178-184.
Huang, J., Lark, R. M., Robinson, D. A., Lebron, I., Keith, A. M., Rawlins, B., et al (2014). Scope to predict soil properties at within-field scale from small samples using proximally sensed γ-ray spectrometer and EM induction data. Geoderma 232–234, 69-80.
Yao, R. j., Yang, J. s., Zhao, X. f., Chen, X. b., Han, J. j., Li, X. m., et al (2012). A New Soil Sampling Design in Coastal Saline Region Using EM38 and VQT Method. CLEAN – Soil, Air, Water 40, 972-979.
Quan, Q. and Shen, B. (2012). A Soil Sampling Method Based on Field Measurements, Remote Sensing Images and Kriging Technique. Advanced Materials Research 383-390, 5350-5356.
Wang, H., Yang, Q., Liu, Z. and Yang, C. (2006). Determining optimal density of grid soil-sampling points using computer simulation. Transactions of the Chinese Society of Agricultural Engineering 22, 145-148.
Zhu, A. X., Hudson, B., Burt, J., Lubich, K. and Simonson, D. (2001). Soil Mapping Using GIS, Expert Knowledge, and Fuzzy Logic. Soil Science Society of America Journal 65, 1463-1473.
Chan, S. H. Y., Donner, R. V. and Lämmer, S. (2011). Urban road networks — spatial networks with universal geometric features? The European Physical Journal B 84, 563-577.
Yang, L., Zhu, A. X., Qin C. Z., Li B. L. and Pei T. (2011). A soil sampling method based on representativeness grade of sampling points. Acta Pedologica Sinica 48, 938-946. (in Chinese)
Agbu, P. A., Ojanuga, A. G., and Olson, K. R. (1989). Soil-landscape relationships in the sokoto-rima Basin, Nigeria. Soil Science 148, 132-139.
Gessler, P. E., Moore, I. D., McKenzie, N. J. and Ryan, P. J. (1995). Soil-landscape modelling and spatial prediction of soil attributes. International Journal of Geographical Information Systems 9, 421-432.
Wang, J., Fu, B. and Qiu, Y. (2001). Soil nutrients in relation to land use and landscape position in the semi-arid small catchment on the loess plateau in China. Journal of arid environments 48, 537-550.
Shary, P. A., Sharaya, L. S. and Mitusov, A. V. (2002). Fundamental quantitative methods of land surface analysis. Geoderma 107, 1-32.
Tehrany, M. S., Pradhan, B., Mansor, S. and Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125, 91-101.
Moore, I. D., Gessler, P. E., Nielsen, G. A. and Peterson, G. A. (1993). Soil Attribute Prediction Using Terrain Analysis. Soil Science Society of America Journal 57, 443-452.
Moore, I. D. and Wilson, J. P. (1992). Length-slope factors for the Revised Universal Soil Loss Equation: Simplified method of estimation. Journal of Soil and Water Conservation 47, 423-428.
HJ/T166-2004, The technical specification for soil environmental monitoring. (in Chinese)
Hamid, T. S., and Sahar, S. (2016). Statistical modeling approaches for pm10 prediction in urban areas; a review of 21st-century studies. Atmosphere 7, 15.
Taewon Moon, Seojung Hong, Ha Young Choi, Dae Ho Jung, Se Hong Chang and Jung Eek Son (2019). Interpolation of greenhouse environment data using multilayer perceptron. Computers and Electronics in Agriculture 166, 105023.
Kalivas, J. H. (1992). Optimization using variations of simulated annealing. Chemometrics and Intelligent Laboratory Systems 15, 1-12.
Van Groenigen, J. W. and Stein, A. (1998). Constrained Optimization of Spatial Sampling using Continuous Simulated Annealing. Journal of environmental quality 27, 1078-1086.
Zhang, S. J., Zhu A. X., Liu J. and Yang L. (2013). Soil sampling scheme based on simulated annealing method. Chinese Journal of Soil Science 44, 820-825. (in Chinese)
Vašát, R., Heuvelink, G. B. M. and Borůvka, L. (2010). Sampling design optimization for multivariate soil mapping. Geoderma 155, 147-153.