Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 12
Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7287-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-7287-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 12
Model description paper
 | 
30 Nov 2021
Model description paper |  | 30 Nov 2021

Cosmic-Ray neutron Sensor PYthon tool (crspy 1.2.1): an open-source tool for the processing of cosmic-ray neutron and soil moisture data

Daniel Power, Miguel Angel Rico-Ramirez, Sharon Desilets, Darin Desilets, and Rafael Rosolem

Supplement

https://doi.org/10.5194/gmd-14-7287-2021-supplement

Data sets

COSMOS: the COsmic-ray Soil Moisture Observing System (http://cosmos.hwr.arizona.edu/) M. Zreda, W. J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem https://doi.org/10.5194/hess-16-4079-2012

CosmOz – The Australian Cosmic-ray Soil Moisture Sensor Network D. McJannet, M. Stenson, A. Sommer, and A. Hawdon https://doi.org/10.25901/5e7ab81af0394

Daily and sub-daily hydrometeorological and soil data (2013–2019) [COSMOS-UK] S. Stanley, V. Antoniou, A. Askquith-Ellis, L. A. Ball, E. S. Bennett, J. R. Blake, D. B. Boorman, M. Brooks, M. Clarke, H. M. Cooper, N. Cowan, A. Cumming, J. G. Evans, P. Farrand, M. Fry, O. E. Hitt, W. D. Lord, R. Morrison, G. V. Nash, D. Rylett, P. M. Scarlett, O. D. Swain, M. Szczykulska, J. L. Thornton, E. J. Trill, A. C. Warwick, and B. Winterbourn https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185

ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest aboveground biomass for the years 2010, 2017 and 2018 (http://cci.esa.int/data) M. Santoro and O. Cartus https://doi.org/10.5285/84403d09cef3485883158f4df2989b0c

ESA Land Cover Climate Change Initiative (Land_Cover_cci) (http://cci.esa.int/data) ESA Land Cover CCI project team and P. Defourny https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c

SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty (https://soilgrids.org/) L. Poggio, L. M. Sousa, N. H. de Batjes, G. B. M. Heuvelink, B. Kempen, E. Ribeiro, and D. Rossiter https://doi.org/10.5194/soil-7-217-2021

ERA5-Land hourly data from 1950 to 1980 J. Muñoz Sabater https://doi.org/10.24381/cds.e2161bac

AmeriFlux BASE US-ARM ARM Southern Great Plains site – Lamont S. Biraud, M. Fischer, S. Chan, and M. Torn https://doi.org/10.17190/AMF/1246027

Model code and software

crspy_v1.2.1 Daniel Power, Miguel Angel Rico-Ramirez, Sharon Desilets, Darin Desilets, and Rafael Rosolem https://doi.org/10.5281/zenodo.5543669

crspy_example: v1.2.2 Daniel Power, Miguel Angel Rico-Ramirez, Sharon Desilets, Darin Desilets, and Rafael Rosolem https://doi.org/10.5281/zenodo.5719063

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Short summary
Cosmic-ray neutron sensors estimate root-zone soil moisture at sub-kilometre scales. There are national-scale networks of these sensors across the globe; however, methods for converting neutron signals to soil moisture values are inconsistent. This paper describes our open-source Python tool that processes raw sensor data into soil moisture estimates. The aim is to allow a user to ensure they have a harmonized data set, along with informative metadata, to facilitate both research and teaching.
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