SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring

Estimation of regional soil carbon flux is very important for the study of the global carbon cycle. The spatial heterogeneity of soil respiration prevents the actual status of regional soil carbon flux from being revealed by measurements of only one or a few spatial sampling positions, which are usually used by traditional studies for the limitation of measurement instruments, so measuring in many spatial positions is very necessary. However, the existing instruments are expensive and cannot communicate with each other, which prevents them from meeting the requirement of synchronous measurements in multiple positions. Therefore, we designed and implemented an instrument for soil carbon flux measuring based on dynamic chamber method, SCFSen, which can measure soil carbon flux and communicate with each other to construct a sensor network. In its working stage, a SCFSen node measures the concentration of carbon in the chamber with an infrared carbon dioxide sensor for certain times periodically, and then the changing rate of the measurements is calculated, which can be converted to the corresponding value of soil carbon flux in the position during the short period. A wireless sensor network system using SCFSens as soil carbon flux sensing nodes can carry out multi-position measurements synchronously, so as to obtain the spatial heterogeneity of soil respiration. Furthermore, the sustainability of such a wireless sensor network system makes the temporal variability of regional soil carbon flux can also be obtained. So SCFSen makes thorough monitoring and accurate estimation of regional soil carbon flux become more feasible.

[1]  Yiqi Luo,et al.  Soil carbon sensitivity to temperature and carbon use efficiency compared across microbial-ecosystem models of varying complexity , 2014, Biogeochemistry.

[2]  J. J. de Gruijter,et al.  Effects of spatial pattern persistence on the performance of sampling designs for regional trend monitoring analyzed by simulation of space-time fields , 2013, Comput. Geosci..

[3]  Jake F. Weltzin,et al.  Responses of soil respiration to elevated CO2, air warming, and changing soil water availability in a model old‐field grassland , 2007 .

[4]  Ming Xu,et al.  Contribution of soil respiration to the global carbon equation. , 2016, Journal of plant physiology.

[5]  Wendi B. Heinzelman,et al.  Energy-Harvesting Wireless Sensor Networks (EH-WSNs) , 2018, ACM Trans. Sens. Networks.

[6]  R. M. Lark,et al.  Spatially nested sampling schemes for spatial variance components: Scope for their optimization , 2011, Comput. Geosci..

[7]  Xu Liang,et al.  Analysis of Power Characteristics for Sap Flow, Soil Moisture, and Soil Water Potential Sensors in Wireless Sensor Networking Systems , 2012, IEEE Sensors Journal.

[8]  Zigomar Menezes de Souza,et al.  Spatial and Temporal Variability of Soil CO2 Flux in Sugarcane Green Harvest Systems , 2016 .

[9]  Shaojie Tang,et al.  Canopy closure estimates with GreenOrbs: sustainable sensing in the forest , 2009, SenSys '09.

[10]  Renjie Huang,et al.  Design and Deployment of Sensor Network for Real-Time High-Fidelity Volcano Monitoring , 2010, IEEE Transactions on Parallel and Distributed Systems.

[11]  Jae Ho Lee,et al.  Effect of rainfall events on soil carbon flux in mountain pastures , 2017 .

[12]  Sini Niinistö,et al.  Comparison of different chamber techniques for measuring soil CO2 efflux , 2004 .

[13]  Dong-Sheng Xu,et al.  Early-Warning System With Quasi-Distributed Fiber Optic Sensor Networks and Cloud Computing for Soil Slopes , 2017, IEEE Access.

[14]  François Ingelrest,et al.  SensorScope: Out-of-the-Box Environmental Monitoring , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[15]  Euloge Kossi Agbossou,et al.  Reducing soil CO2 emission and improving upland rice yield with no-tillage, straw mulch and nitrogen fertilization in northern Benin , 2015 .

[16]  J. Yavitt,et al.  Plot-scale spatial variability of methane, respiration, and net nitrogen mineralization in muck-soil wetlands across a land use gradient. , 2018 .

[17]  Lan Fang,et al.  Variação temporal e espacial da respiração do solo sob cobertura em cultivo de pepino em estufa , 2016 .

[18]  Michael Bahn,et al.  Comparing ecosystem and soil respiration: Review and key challenges of tower-based and soil measurements , 2018 .

[19]  Ben Bond-Lamberty,et al.  The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling , 2017, Plant and Soil.

[20]  Johan J. Estrada-López,et al.  Smart Soil Parameters Estimation System Using an Autonomous Wireless Sensor Network With Dynamic Power Management Strategy , 2018, IEEE Sensors Journal.

[21]  Zhen Li,et al.  Practical deployment of an in-field soil property wireless sensor network , 2014, Comput. Stand. Interfaces.

[22]  W. Schlesinger Carbon Balance in Terrestrial Detritus , 1977 .

[23]  Antoine Diet,et al.  Soil Effects on the Underground-to-Aboveground Communication Link in Ultrawideband Wireless Underground Sensor Networks , 2017, IEEE Antennas and Wireless Propagation Letters.

[24]  Knute J. Nadelhoffer,et al.  Belowground Carbon Allocation in Forest Ecosystems: Global Trends , 1989 .

[25]  Poul Erik Lærke,et al.  Effect of chamber enclosure time on soil respiration flux: A comparison of linear and non-linear flux calculation methods , 2016 .

[26]  Xin Li,et al.  Regression Kriging-Based Upscaling of Soil Moisture Measurements From a Wireless Sensor Network and Multiresource Remote Sensing Information Over Heterogeneous Cropland , 2015, IEEE Geoscience and Remote Sensing Letters.

[27]  Lan Mu,et al.  Temporal and spatial variation of soil respiration under mulching in a greenhouse cucumber cultivation , 2016 .

[28]  Rosdiadee Nordin,et al.  Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review , 2017, Sensors.

[29]  Fuad E. Alsaadi,et al.  Collaborative fusion estimation over wireless sensor networks for monitoring CO2 concentration in a greenhouse , 2018, Inf. Fusion.

[30]  Cecilia Mascolo,et al.  WILDSENSING , 2012, ACM Trans. Sens. Networks.

[31]  J. J. de Gruijter,et al.  A hybrid design-based and model-based sampling approach to estimate the temporal trend of spatial means , 2012 .

[32]  Zhongyi Zheng,et al.  Practical Deployments of SEMAT on Wireless Sensor Networks in the Marine Environment , 2013, 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks.

[33]  Andreas Heinemeyer,et al.  Comparing the closed static versus the closed dynamic chamber flux methodology: Implications for soil respiration studies , 2011, Plant and Soil.

[34]  Mo Li,et al.  From Rateless to Distanceless: Enabling Sparse Sensor Network Deployment in Large Areas , 2016, IEEE/ACM Transactions on Networking.

[35]  Mark Farrell,et al.  Greater soil carbon stocks and faster turnover rates with increasing agricultural productivity , 2013 .