Spatio-temporal variation analysis of soil temperature based on wireless sensor network
Keywords:
precision agriculture, soil temperature dynamics, spatial-temporal variability, spatial variation, wireless sensor network (WSN)Abstract
Abstract: Soil temperature is a key factor for best planting dates decision-making in the large scale farming areas of northeast China because of high latitudes and frigid environment. Continuous data were collected from a wireless sensor network (WSN)-based monitoring system to exactly analyze and understand soil temperature of the whole farmland. Using the classical statistics and geo-statistics methods, real-time monitoring data were analyzed with three aspects, i.e. temporal variation, spatial variation and spatio-temporal variation. Temporal variation analysis of each sensor node showed a sinusoidal curve of daily soil temperature and gave the long-term trend of daily average soil temperature in a certain period. Spatial variation analysis provided the spatial distribution map of daily average soil temperature within a study region for a certain day. Spatio-temporal variation analysis quantified the variation process of the spatial distribution over time by the monitored classes distribution indicator (MCDI) proposed. Experimental results showed that the above variations analysis of the real-time data provide an effective approach to determine whole-farmland soil temperature. Keywords: precision agriculture, soil temperature dynamics, spatial-temporal variability, spatial variation, wireless sensor network (WSN) DOI: 10.3965/j.ijabe.20160906.1871 Citation: Liu H, Meng Z J, Wang H, Xu M. Spatio-temporal variation analysis of soil temperature based on wireless sensor network. Int J Agric & Biol Eng, 2016; 9(6): 131-138.References
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[19] Zhang R D. The theory and application of spatial variation. Beijing, China: Science Press, 2005.
[2] Zhang N, Wang M, Wang N. Precision agriculture-a worldwide overview. Computers and Electronics in Agriculture, 2002; 36: 113–132.
[3] Amirinejad A A, Kamble K, Aggarwal P, Chakraborty D, Pradhan S, Mittal R B. Assessment and mapping of spatial variation of soil physical health in a farm. Geoderma, 2011; 160(3-4): 292–303.
[4] Ojha T, Misra S, Raghuwanshi N S. Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers & Electronics in Agriculture, 2015; 118: 66–84. DOI: 10.1016/j.compag.2015.08.011
[5] Zhang M, Wang W, Liu C, Gao H, Li M. Development of a wireless sensor network for soil moisture monitoring in precision agriculture. American Society of Agricultural and Biological Engineers Annual International Meeting 2012, Dallas, Texas, July 29 - August 1, 2012.
[6] Dong X, Vuran M C, Irmak S. Autonomous precision agriculture through integration of wireless underground sensor networks with center pivot irrigation systems. Ad Hoc Networks, 2013; 11(7): 1975–1987.
[7] Li Z, Wang N, Franzen A, Taher P, Godsey C, Zhang H, et al. Practical deployment of an in-field soil property wireless sensor network. Computer Standards & Interfaces, 2014; 36(2): 278–287.
[8] Majone B, Viani F, Filippi E, Bellin A, Massa A, Toller G, et al. Wireless sensor network deployment for monitoring soil moisture dynamics at the field scale. Procedia Environmental Sciences, 2013; 19: 426–435
[9] Vuran M C, Akan O B, Akyildiz I F. Spatio-temporal correlation: theory and applications for wireless sensor networks. Computer Networks, 2004; 45: 245–259.
[10] Heathman G C, Cosh M H, Han E, Jackson T J, McKee L, McAfee S. Field scale spatiotemporal analysis of surface soil moisture for evaluating point scale in situ networks, Geoderma, 2012; 170: 195–205
[11] Zhang M, Li M, Wang W, Liu C, Gao H. Temporal and spatial variability of soil moisture based on WSN. Mathematical & Computer Modelling, 2013; 58(3-4): 826–833.
[12] Liu H, Meng Z J, Xu M, Shang Y Y. Sensor nodes deployment based on regular patterns in farmland environmental monitoring. Transactions of the CSAE, 2011; 27(8): 265–270. (in Chinese with English abstract)
[13] Rains G C, Thomas D L, Vellidis G. Soil-sampling issues for precision management of crop production. Applied Engineering in Agriculture, 2001; 17(6): 769–775.
[14] Webster R, Oliver M A. Chapter 4. Characterizing spatial processes: The Covariance and Variogram. Geostatistics for Environmental Scientists, Second Edition. John Wiley & Sons, Ltd, 2008; pp. 47–76.
[15] Fischer M M. Handbook of Applied Spatial Analysis Handbook of applied spatial analysis: Springer, 2010; pp. 27–41.
[16] Cui X W, Zhang L, Zhu L, Song G, Wu B. Changes of landscape pattern and its characteristics in Kaixian county before and after impoundment of Three Gorges Dam Project. Transactions of the CSAE, 2012; 28(4): 227–234. (in Chinese with English abstract)
[17] Zeng Y N, Jin W P, Wang H M, Zhang H. Simulation of land-use changes and landscape ecological assessment in eastern part of Qinghai Plateau. Transactions of the CSAE, 2014; 30(4): 185–194.
[18] Awe G O, Reichert J M, Wendroth O O. Temporal variability and covariance structures of soil temperature in a sugarcane field under different management practices in southern Brazil. Soil & Tillage Research, 2015; 150: 93–106.
[19] Zhang R D. The theory and application of spatial variation. Beijing, China: Science Press, 2005.
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Published
2016-12-01
How to Cite
Hui, L., Zhijun, M., Hua, W., & Min, X. (2016). Spatio-temporal variation analysis of soil temperature based on wireless sensor network. International Journal of Agricultural and Biological Engineering, 9(6), 131–138. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1871
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Information Technology, Sensors and Control Systems
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