Autor: |
Becker SM; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68503, USA., Franz TE; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68503, USA., Morris TC; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68503, USA., Mullins B; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68503, USA. |
Jazyk: |
angličtina |
Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 30; Vol. 24 (7). Date of Electronic Publication: 2024 Mar 30. |
DOI: |
10.3390/s24072223 |
Abstrakt: |
Gamma-ray spectroscopy (GRS) enables continuous estimation of soil water content (SWC) at the subfield scale with a noninvasive sensor. Hydrological applications, including hyper-resolution land surface models and precision agricultural decision making, could benefit greatly from such SWC information, but a gap exists between established theory and accurate estimation of SWC from GRS in the field. In response, we conducted a robust three-year field validation study at a well-instrumented agricultural site in Nebraska, United States. The study involved 27 gravimetric water content sampling campaigns in maize and soybean and 40 K specific activity (Bq kg -1 ) measurements from a stationary GRS sensor. Our analysis showed that the current method for biomass water content correction is appropriate for our maize and soybean field but that the ratio of soil mass attenuation to water mass attenuation used in the theoretical equation must be adjusted to satisfactorily describe the field data. We propose a calibration equation with two free parameters: the theoretical 40 K intensity in dry soil and a , which creates an "effective" mass attenuation ratio. Based on statistical analyses of our data set, we recommend calibrating the GRS sensor for SWC estimation using 10 profiles within the footprint and 5 calibration sampling campaigns to achieve a cross-validation root mean square error below 0.035 g g -1 . |
Databáze: |
MEDLINE |
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