fix ref (#72)

Author: e-marshall

paper.md

@@ -42,7 +42,7 @@ bibliography: "paper.bib"
 Advances in cloud computing, remote sensing, and engineering are transforming earth system science into an increasingly data-intensive field, requiring students and scientists to learn a broad range of new skills related to scientific programming, data management, and cloud infrastructure [@abernathey_2021_cloud; @gentemann_2021_science; @guo_2017_big; @mathieu_2017_esas; @ramachandran_2021_open; @wagemann_2021_user]. This work contains educational modules designed to reduce barriers to interacting with large, complex, cloud-hosted remote sensing datasets using open-source computational tools and software. The goal of these materials is to demonstrate and promote the rigorous investigation of n-dimensional multi-sensor satellite imagery datasets through scientific programming. These tutorials feature publicly available satellite imagery with global coverage and commonly used sensors such as optical and synthetic aperture radar data with different levels of processing. We include thorough discussions of specific data formats and demonstrate access patterns for two popular cloud infrastructure platforms (Amazon Web Services and Microsoft Planetary Computer) as well as public cloud computational resources for remote sensing data processing at Alaska Satellite Facility (ASF).
 
 # Statement of Need
-Research on the transition to data-intensive, cloud-based science highlights the need for knowledge development to accompany technological advances in order to realize the benefit of these transformations [@abernathey_2021_cloud; @gentemann_2021_science; @guo_2017_big; @mathieu_2017_esas; @palumbo_2017_building; @radocaj_2020_global; @ramachandran_2021_open; @sudmannsBigEarthData2020; @wagemann_2021_user].
+Research on the transition to data-intensive, cloud-based science highlights the need for knowledge development to accompany technological advances in order to realize the benefit of these transformations [@abernathey_2021_cloud; @gentemann_2021_science; @guo_2017_big; @mathieu_2017_esas; @palumbo_2017_building; @radocaj_2020_global; @ramachandran_2021_open; @Sudmanns_2020_big; @wagemann_2021_user].
 
 These educational modules address this need and are guided by principles identified in Diataxis [@Procida_Diataxis_documentation_framework] in order to help analysts engage in data-driven scientific discovery using cloud-based data and open-source tools.