Evaluation of the ZigBee based wireless soil moisture sensor network SoilNet

A remaining challenge in hydrology is to explain the observed patterns of hydrological behaviour over multiple spacetime scales as a result of interacting environmental factors. The large spatial and temporal variability of soil water content is determined by factors like atmospheric forcing, topography, soil properties and vegetation, which interact in a complex nonlinear way (e.g. Western et al., 2004). A promising new technology for environmental monitoring is the wireless sensor network (Cardell-Oliver et al., 2005). The wireless sensor network technology allows the real-time soil water content monitoring at high spatial and temporal resolution for observing hydrological processes in small water-sheds (0.1-80 sqkm). Although wireless sensor networks can still be considered as an emerging research field, the supporting communication technology for low cost, low power wireless networks has matured greatly in the past decade (Robinson et al., 2008). Wireless environmental sensor networks will play an important role in the emerging terrestrial environmental observatories (Bogena et al., 2006), since they are able to bridge the gap between local (e.g. lysimeter) and regional scale measurements (e.g. remote sensing). This paper presents a first application of the novel wireless soil water content network SoilNet, which was developed at the Forschungszentrum Jülich using the new low-cost ZigBee radio network

Sensor-to-sensor variability of ECH2O EC-5, TE and 5TE sensors used for wireless sensor networks

Towards an improvement of measurement accuracy for the low-budget soil water content sensors ECH2O EC-5, TE and 5TE used in the wireless sensor network SoilNet, the application of a sensor-specific calibration procedure based on dielectric standard liquids reduce the RMSE of approximately 0.010 to 0.015 cm^3 cm^-3 in high soil water content range

An empirical vegetation correction for soil water content quantification using cosmic ray probes

Cosmic ray probes are an emerging technology to continuously monitor soil water content at a scale significant to land surface processes. However, the application of this method is hampered by its susceptibility to the presence of aboveground biomass. Here we present a simple empirical framework to account for moderation of fast neutrons by aboveground biomass in the calibration. The method extends the N0-calibration function and was developed using an extensive data set from a network of 10 cosmic ray probes located in the Rur catchment, Germany. The results suggest a 0.9% reduction in fast neutron intensity per 1 kg of dry aboveground biomass per m2 or per 2 kg of biomass water equivalent per m2. We successfully tested the novel vegetation correction using temporary cosmic ray probe measurements along a strong gradient in biomass due to deforestation, and using the COSMIC, and the hmf method as independent soil water content retrieval algorithms. The extended N0-calibration function was able to explain 95% of the overall variability in fast neutron intensity

Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables

This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly observations of latent heat flux, sensible heat flux, groundwater recharge, soil moisture and soil temperature from an agricultural observatory in Denmark. The results show that multi-objective calibration in combination with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using lookup tables to define parameter values in land surface models. Using measurements of turbulent fluxes as the target variable, parameter optimization is capable of matching simulations and observations of latent heat, especially during the summer period, whereas simulated sensible heat is clearly biased. Of the 30 parameters considered, the soil texture, monthly leaf area index (LAI) in summer, stomatal conductance and root distribution have the highest influence on the local-scale simulation results. The results from this study contribute to improvements of the model characterization of water and energy fluxes. This work highlights the importance of performing parameter calibration using observations of hydrologic and energy fluxes and variables to obtain the optimal parameter values for a land surface model.</p

Comment on ‘Examining the variation of soil moisture from cosmic‑ray neutron probes footprint: experimental results from a COSMOS‑UK site’ by Howells, O.D., Petropoulos, G.P., et al., Environ Earth Sci 82, 41 (2023)

The published article by Howells et al. (2023) attempts to empirically derive the lateral footprint for a single cosmic-ray neutron sensor (CRNS), which is part of the COSMOS-UK network (Evans et al. 2016). The main result is the “true” footprint to be 50 m in radius, substantially smaller than previously published estimates. Their conclusion contradicts more than 15 peer-reviewed studies and more than a decade of research on the subject conducted by various international research groups, and thus, it would be considered as a ground-breaking finding if the methods were scientifically sound. However, the methods and arguments presented by the authors have major errors and the presented conclusions are consequently wrong

Underground Environment Aware MIMO Design Using Transmit and Receive Beamforming in Internet of Underground Things

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required derived the degrees of freedom of the UG MIMO interference channel. The underground receiver needs to perfectly cancel the interference from the three different components of the EM-waves propagating in the soil medium. This concept is based upon reducing the interference the undesired components to minimum at UG receiver using the receive beamforming. In this paper, underground environment aware MIMO using transmit and receive beamforming has been developed. The optimal transmit beamforming and receive combin- ing vectors under minimal inter-component interference constraint are derived. It is shown that UG MIMO performs best when all three component of the wireless UG channel are leveraged for beamforming. The environment aware UG MIMO technique leads to three-fold performance improvements and paves the wave for design and development of next generation sensor-guided irrigation systems in the field of digital agriculture

Reanalysis in Earth System Science: Towards Terrestrial Ecosystem Reanalysis

A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modelled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyses, and more in detail biogeochemical ocean and terrestrial reanalyses. In particular, we identify land surface, hydrologic and carbon cycle reanalyses which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics

Atmospheric deposition and precipitation are important predictors of inorganic nitrogen export to streams from forest and grassland watersheds: a large-scale data synthesis

Previous studies have evaluated how changes in atmospheric nitrogen (N) inputs and climate affect stream N concentrations and fluxes, but none have synthesized data from sites around the globe. We identified variables controlling stream inorganic N concentrations and fluxes, and how they have changed, by synthesizing 20 time series ranging from 5 to 51 years of data collected from forest and grassland dominated watersheds across Europe, North America, and East Asia and across four climate types (tropical, temperate, Mediterranean, and boreal) using the International Long-Term Ecological Research Network. We hypothesized that sites with greater atmospheric N deposition have greater stream N export rates, but that climate has taken a stronger role as atmospheric deposition declines in many regions of the globe. We found declining trends in bulk ammonium and nitrate deposition, especially in the longest time-series, with ammonium contributing relatively more to atmospheric N deposition over time. Among sites, there were statistically significant positive relationships between (1) annual rates of precipitation and stream ammonium and nitrate fluxes and (2) annual rates of atmospheric N inputs and stream nitrate concentrations and fluxes. There were no significant relationships between air temperature and stream N export. Our long-term data shows that although N deposition is declining over time, atmospheric N inputs and precipitation remain important predictors for inorganic N exported from forested and grassland watersheds. Overall, we also demonstrate that long-term monitoring provides understanding of ecosystems and biogeochemical cycling that would not be possible with short-term studies alone.publishedVersio

Wireless Underground Channel Modeling

A comprehensive treatment of wireless underground channel modeling is presented in this chapter. The impacts of the soil on bandwidth and path loss are analyzed. A mechanism for the UG channel sounding and multipath characteristics analysis is discussed. Moreover, novel time-domain impulse response model for WUC is reviewed with the explanation of model parameters and statistics. Furthermore, different types of the through-the-soil wireless communications are surveyed. Finally, the chapter concludes with discussion of the UG wireless statistical model and path loss model for through-the-soil wireless communications in decision agriculture. The model presented in this chapter is also validated with empirical data