|HUANG, E - University Of New South Wales|
|SCUDIERO, E - University Of California|
|CHOO, H - University Of New South Wales|
|TRIANTAFILIS, A - University Of New South Wales|
Submitted to: Soil Use and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/1/2015
Publication Date: 7/20/2016
Citation: Huang, E., Scudiero, E., Choo, H., Corwin, D.L., Triantafilis, A. 2016. Time-lapse monitoring of soil water content using electromagnetic conductivity imaging. Soil Use and Management. doi: 10.1111/sum.12261.
Interpretive Summary: The knowledge on spatiotemporal variations of water content in the root-zone is crucial for precision irrigation practices. Mobile electromagnetic induction (EMI) sensors measurements of soil apparent conductivity (ECa) can help mapping the changes of water content in time across a field. Indeed, ECa is a function of water content, soil type (e.g., texture), and salinity. The EMI sensors measure integral values of ECa for the entire soil profile (e.g., 0-0.75, 0-1.5 m). Recently developed inversion techniques allow estimating ECa values of soil properties at discrete points through the soil profile. This can potentially bring forward the quality of irrigation practices, leading to much more sustainable and profitable agricultural water use. In this study we monitor, in a 12 –days time-lapse, a 350m saline soil transect from the day following irrigations (soil close to field capacity) to 12 days later (soil close to wilting point), using EMI surveys and soil sampling for calibration and validation of the water content estimates. The proposed technique is very novel, especially for saline soils, in which EMI signal is often attenuated and derived water content estimations are generally biased.The results allowed us to monitor the spatio-temporal variations of soil volumetric water content across the surveyed area, over the 12-day period with prediction root mean square errors = 0.041 cm3/cm3, using a physical model that reduced bias due to soil salinity and texture on water content estimations. Farmers, agriculture consultants, extension specialists could benefit greatly from the application of this methodology.
Technical Abstract: The volumetric soil water content (VWC) is fundamental to agriculture. Unfortunately, the universally accepted thermogravimetric method is labour intensive and time-consuming to use for field-scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatio-temporal variation of VWC. However, depth-specific variation, which is important for irrigation management has not been explored. The objective of this study is to develop a relationship between VWC and estimates of true electrical conductivity (sigma) and to use this relationship to develop time-lapse images of soil VWC along a center-pivot irrigated alfalfa (Medicago sativa L.) field in San Jacinto, California, USA. We measured the bulk apparent electrical conductivity (ECa – mS/m) using a DUALEM-421 over a period of 12 days after an irrigation event (i.e., days 1, 2, 3, 4, 6, 8 and 12). We used EM4Soil to generate electromagnetic conductivity images (EMCI). We used a physical model to estimate ' from sigma, accounting for soil tortuosity and pore-water salinity, with a cross-validation RMSE of 0.04 cm3/cm3. Testing the scenario where no soil information is available, we used a 3-parameter exponential model to relate VWC to sigma and then to map VWC along the transect on different days. The results allowed us to monitor the spatio-temporal variations of soil VWC across the surveyed area, over the 12-day period. In this regard we were able to map the soil close to field capacity (0.27 cm3/cm3) and approaching permanent wilting point (0.03 cm3/cm3). The time-lapse soil VWC monitoring approach, developed using EMCI, has implications for soil and water use and management and will allow farmers and consultants to map and monitor soil moisture status and identify inefficiencies in water application rates and use. It can also be used as a research tool to potentially assist precision irrigation practices and to test the efficacy of different methods of irrigation in terms of water delivery and efficiency in water use in near real-time.