Field scale root zone soil moisture estimation by coupling cosmic-ray neutron sensor with soil moisture sensors
- 1Soil Water Management and Crop Nutrition Laboratory, Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
- 2Soil and Water Management & Crop Nutrition Section, Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
- 3School of Natural Resources, University of Nebraska-Lincoln, Nebraska USA
Since it has become clear that climate change is having a major impact on water availability for agriculture and crop productivity, an accurate estimation of field-scale root-zone soil moisture (RZSM) is essential for improved agricultural water management. The Cosmic Ray Neutron Sensor (CRNS) has recently been used for field-scale soil moisture (SM) monitoring in large areas and is a credible and robust technique. Like other remote or proximal sensing techniques, the CRNS provides only SM data in the near surface. One of the challenges and needs is to extend the vertical footprint of the CRNS to the root zone of major crops. This can be achieved by coupling the CRNS measurements with conventional methods for soil moisture measurements, which provide information on soil moisture for whole rooting depth.
The objective of this poster presentation is to estimate field-scale RZSM by correlating the CRNS information with that from soil moisture sensors that provide soil moisture data for the whole root depth. In this study, the Drill and Drop probes which provide continuous profile soil moisture were selected. The RZSM estimate was calculated using an exponential filter approach.
Winter Wheat cropped fields in Rutzendorf, Marchfeld region (Austria) were instrumented with a CRNS and Drill & Drop probes. An exponential filter approach was applied on the CRNS and Drill and drop sensor data to characterize the RZSM. The preliminary results indicate the ability of the merging framework procedure to improve field-scale RZSM in real-time. This study demonstrated how to combine the advantages of CRNS nuclear technique (especially the large footprint and good representativeness of obtained data) with the advantages of conventional methods (providing data for whole soil profile) and overcome the shortcoming of both methods (the lack of information in the deeper part of soil profile being the major disadvantage of CRNS and the spatial limitation and low representativeness of point data being the major disadvantage of conventional capacitance sensors). This approach can be very helpful for improving agricultural water management.
How to cite: Said, H., Weltin, G., Heng, L. K., Franz, T., Fulajtar, E., and Dercon, G.: Field scale root zone soil moisture estimation by coupling cosmic-ray neutron sensor with soil moisture sensors, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4618, https://doi.org/10.5194/egusphere-egu2020-4618, 2020
Comments on the display
AC: Author Comment | CC: Community Comment | Report abuse
Hi, in Figure 6 you present SWC derived using exponential filter fit for depths 0-30 and 0-60 cm. Did you derive these estimates using only CRNS, D&D or both? Sorry, if I overlooked it on the poster?
Additionally, you say that this complementary approach would be helpful for agricultural management applications. While I completely agree with you, wondered if you have thought of technical difficulties related to D&D access tubes being damaged during agricultural management (ploughing and harvesting)? At a mixed-agricultural research site in Scotland, we found this to be a considerable challenge.
Thank you!
Dear Katya,
Thanks for your comment.
For your first question, We derive these estimates SWC using only CRNS.
Of course, you are entirely right there is technical diffcuties related to D&D but we can try to plan with the farmer his calendar for managing his plot and thus remove the probes during the period of ploughing and harvesting.
Great, thank you!
So, you mainly use the D&D data to validate your exponential filter derived estimates?
Hi Katya,
Yes, i will use the D&D data to validate and also to calibrate the exponential model.
Thank Katya.
Hami
Nice, thank you. A paper on the subject (I am sure you are already aware of it), I found very helpful Peterson et al., 2016, HESS.