Timely and accurate large-scale data on agricultural activity are important for tracking and identifying management practices, cropland distribution, and for supporting food security programs. Needs of agricultural monitoring community involve a combination of high (<4 m) to moderate (<30 m) spatial resolutions, frequent revisit time, and open access, operational coverage for large spatial extents (Fritz et al., 2019). Because a majority of agricultural fields are just over 2 hectares in size, spatial resolutions should ideally be on the order of 100 m or less to adequately capture agricultural conditions and change (Yan & Roy, 2016). Revisit times on the order of weeks or less are desirable, because agricultural fields may undergo substantial change on diurnal or greater timescale due to processes such as tilling or precipitation (McNairn & Brisco, 2004). Collectively, since agriculture is closely tied to global markets and food security, there is a strong need for accurately monitoring agricultural activity at global scale (Becker-Reshef et al., 2019). Since retrievals made by Synthetic Aperture Radar (SAR) systems now meet or exceed these needs, they are well-suited for large-scale agricultural monitoring. Spaceborne SAR, such as the European Space Agency's (ESA) Sentinel-1 can map the Earth once every 6 to 12-days at moderate spatial resolution (Torres et al., 2012). SAR data provide valuable information useful for cropland identification, crop type classification and estimating yield (Betbeder et al., 2016; Huang et al., 2019; Whelen & Siqueira, 2018). For example, these data can be used to estimate biomass using backscatter magnitude, crop heights using interferometry and crop structure using polarimetry (Erten et al., 2016; Ferrazzoli et al., 1997; Wiseman et al., 2014). Also, unlike optical sensors, SAR can collect high quality data day and night and is largely unimpacted by atmospheric conditions, therefore having excellent potential for collecting dense time series. For these reasons, plus an increasing adoption of open data access policies, cheaper cloud computing resources, and a steady pipeline of future platforms, there has been a rapid increase in the use of SAR data sets with regards to agricultural applications and decision support systems.

Female students are less likely to participate in class than male peers in life sciences courses of all levels, but they are more likely to participate in classrooms with more female peers. Female students earn lower final course grades than males in classes taught by a male instructor and/or when female students are in the minority.

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