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DTSTAMP:20210916T132454Z
LOCATION:Louis Favre
DTSTART;TZID=Europe/Stockholm:20210708T180000
DTEND;TZID=Europe/Stockholm:20210708T183000
UID:submissions.pasc-conference.org_PASC21_sess197_msa283@linklings.com
SUMMARY:Cyberinfrastructure Tools for Soil Moisture: Challenges and Applic
 ations
DESCRIPTION:Minisymposium\n\nCyberinfrastructure Tools for Soil Moisture: 
 Challenges and Applications\n\nVargas\n\nSoil moisture is a critical varia
 ble that links climate dynamics with water and food security. It regulates
  land-atmosphere interactions, and it is directly linked with plant produc
 tivity and survival. The current availability in soil moisture data over l
 arge areas comes from remote sensing (i.e., satellites), which provides ne
 arly global coverage at spatial resolution of tens of kilometers. Satellit
 e soil moisture data has two main shortcomings. First, although satellites
  can provide daily global information, they are limited to coarse spatial 
 resolution. Second, satellites are unable to measure soil moisture in area
 s of dense vegetation, snow cover, or extremely dry surfaces; this results
  in gaps in the data. We will present how we address these two shortc
 omings with a modular SOil MOisture Spatial Inference Engine (SOMOSPIE). S
 OMOSPIE consists of modular components including input of available data a
 t its native spatial resolution, selection of a geographic region of inter
 est, prediction of missing values across the entire region of interest (i.
 e., gap-filling), analysis of generated predictions, and visualization of 
 both predictions and analyses. To predict soil moisture, our engine levera
 ges hydrologically meaningful terrain parameters (e.g., slope, aspect) cal
 culated using an open-source platform for standard terrain analysis and a 
 suite of machine learning methods. We will present empirical studies of th
 e engine's functionality including a global assessment and another of data
  processing and fine-grained predictions across the United States. Fu
 rthermore, we will show how data can be traced and results can b
 e explained in SOMOSPIE thanks to the composition of f
 ine-grained containerized workflows.\n\nDomain: CS and Math, Emerging
  Applications, Chemistry and Materials, Climate and Weather, Physics
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