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\begin{document}

\title{On the Statistical Significance of Local Land Cover Measurements:~A
Comparative Analysis of Citizen Science and Remote Sensing Programs}



\author[1]{Aidan Schneider}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
Accurate land cover data can provide powerful insight into
characterizing the effects of climate change. Remote sensing satellites
enable state-of-the-art land cover measurements, but data collected on
the Earth's surface offers a new perspective on land cover
characteristics through its more localized scope. Areas that may be
generalized to a single pixel in a remote sensing satellite's data
products can be observed at a more granular level through on-site data
collection strategies. Low-cost sensors, such as NASA's Science and
Technology Education for Land/Life Assessment (STELLA), make such
granular data collection more cost-effective. Citizen Science programs,
like the Global Learning and Observations to Benefit the Environment
(GLOBE) Program, provide a blueprint for reliably scaling this type of
on-site data collection. STELLA is an open-source platform that allows
volunteers, like Citizen Scientists, to measure electromagnetic waves to
calculate irradiance and temperature in a specific Area Of Interest
(AOI). STELLA is cost-effective, as its kit can be assembled by any user
with access to makerspace tools commonly found in educational
institutions, like 3D printers and soldering irons. This work presents a
comparative analysis of the land cover measurements recorded by the
STELLA sensor and remote sensing satellites, such as LANDSAT9. Surface
temperatures were recorded hourly using the STELLA sensor on four
different types of land cover within a 500-square-meter area in Reno,
Nevada. The results indicate a statistically significant discrepancy
between measurements recorded by the STELLA sensor and LANDSAT,
highlighting an untapped data trove in localized sensor measurements.
Additionally, we present a data collection control flow for Citizen
Science volunteers to record reliable STELLA sensor data. We demonstrate
how such Citizen Science data can provide a valuable alternative
perspective when compared to its state-of-the-art counterparts,
rendering it a valuable tool for future studies.~

\href{https://agu23.ipostersessions.com/Default.aspx?s=D8-7A-70-1E-CA-E8-1B-D0-36-C1-35-AB-3F-87-8F-50}{AGU23
Poster Link}

\href{https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1435603}{AGU23
Abstract Link}%
\end{abstract}%



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