Improving the Accuracy of Cosmic-Ray Neutron Probe Estimate of Soil Water Content Using Multiple Detectors and Remote Sensing Estimates of Vegetation

Authors

Xiaochen Dong, University of Nebraska - LincolnFollow

First Advisor

Trenton Franz

Date of this Version

Winter 12-6-2017

Document Type

Article

Citation

Dong, Xiaochen. 2017.Improving the Accuracy of Cosmic-Ray Neutron Probe Estimate of Soil Water Content Using Multiple Detectors and Remote Sensing Estimates of Vegetation. MS Thesis, University of Nebraska.

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Natural Resource Sciences, Under the Supervision of Professor Trenton E. Franz. Lincoln, Nebraska: December 2017

Copyright 2017 Xiaochen Dong

Abstract

The recently developed Cosmic-ray Neutron Probe (CRNP) for estimating soil water content (SWC) fills a critical measurement gap between point scale methods and large scale measurements collected from remote sensing. CRNP works by measuring the change in low-energy neutron intensity over time. However, the accuracy of CRNP to measure SWC is well known to be affected by other hydrogen sources (e.g. soil organic content, atmospheric water vapor, vegetation and surface water). This study focuses on the influence of rapidly growing vegetation in agricultural fields on the accuracy of the CRNP method. Here we use data from three long-term CRNP study sites in central Nebraska (Paulman Farms, est. 2015), eastern Nebraska (US-Ne3, est. 2011), and central Iowa (IVS, est. 2010) that span a natural precipitation gradient of increasing precipitation from west to east. All three fields grow maize and soybean depending on rotation. At each CRNP site both hourly moderated and bare neutron counts are recorded. Previous research has shown that the bare to moderated ratio may be a good indicator of changing vegetation conditions and useful as a correction to estimating SWC. In addition, I use the MODIS remote sensing dataset to calculate a widely used index to monitor vegetation, Green Wide Dynamic Range Vegetation Index (GrWDRVI or WDRVI). Finally, observed vegetation data from US-Ne3 was collected biweekly from 2003-2016 and used as a benchmark for the CRNP and remote sensing analyses. My results indicate that biomass data determined from remote sensing (GrWDRVI) closely follows in-situ sampling of biomass (R2=0.677 for Maize and R2=0.567 for Soybean). The driest site (Paulman Farms) showed the best relationship between bare to moderated (B/M) ratios and GrWDRVI with an R2 = 0.9188, while the wettest site (IVS) showed the worst relationship with R2 = 0.09. I found that local correction factors using B/M ratio and moderated counts removing the influence of vegetation changes can be derived, thus removing bias in the CRNP SWC observations. The improved algorithm for estimating SWC from CRNP will be beneficial for long term monitoring as well as validating remote sensing SWC products. More experiments with direct biomass observations and needed to fully understand the relationship between GrWDRVI, bare and moderated neutron counts, and in-situ biomass.

Advisor: Trenton E. Franz