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\begin{document}

\title{SoilMAP: An Open Source Python Library for Developing Algorithms and
Specialized User Interfaces that Integrate Multiple Disparate Data
Sources Including Near-Real-Time Sensor Data for Streamlined Monitoring
of Experiments and Analysis.}



\author[1]{Jerry Bieszczad}%
\author[1]{Mattheus Ueckermann}%
\author[1]{Rachel Gilmore}%
\author[2]{Marek Zreda}%
\affil[1]{Creare LLC}%
\affil[2]{Univ Arizona}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
COSMOS soil moisture sensors provide meso-scale area-averaged soil
moisture estimates, presenting a unique opportunity for validating
remotely sensed soil moisture data from satellite sensing platforms such
as SMAP. New, roving COSMOS sensors can provide greater spatial coverage
than their stationary counterparts. However, COSMOS sensors require
careful site-specific calibrations, which are not available for roving
sensors. As such, it is critically important for researchers to monitor
roving COSMOS collection campaigns in near-real-time. However,
specialized user interfaces are needed for rapid analysis. Moreover,
harmonizing remotely sensed data (such as Landsat, SSURGO, MODIS, SMAP,
and SRTM) with a roving COSMOS sensor is non-trivial and requires great
care that cannot be accomplished on-the-fly in the field. To address
these problems, we are developing the open source SoilMAP (Soil Moisture
Analysis and Processing) software, which is a specialized analysis
application for COSMOS and SMAP soil moisture data. We are developing
this application using PODPAC (https://podpac.org/), a cloud-ready open
source Python library for large-scale analysis and on-demand processing
of raw earth science data. Our soil moisture analysis application aims
to provide (1) customizable, rapid, near-real-time visualization and
analysis of COSMOS and SMAP data; (2) unified data access and automated
data wrangling to harmonize roving COSMOS measurements and SMAP L3 data;
and (3) a streamlined workflow for developing roving COSMOS sensor
calibrations with uncertainty estimates. We will demonstrate on-demand
processing of raw soil moisture data retrieved from COSMOS sensors and
SMAP L3 data using our SoilMAP software framework. We will also show our
user workflows specialized for (1) staging data from various
remotely-sensed and in-situ sensors, (2) monitoring a COSMOS data
collection campaign in near-real-time, and (3) analyzing the resultant
data with comparison to SMAP soil moisture. We will outline the steps
required to build and customize this application. SoilMAP greatly
reduces the burden of analyzing, comparing, and validating soil moisture
data using measurements from roving COSMOS sensors.%
\end{abstract}%



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