diff --git a/paper/paper.bib b/paper/paper.bib
index 3b1d292..3b30910 100644
--- a/paper/paper.bib
+++ b/paper/paper.bib
@@ -26,41 +26,6 @@ @ARTICLE{Sullivan2019
   doi       = "10.21105/joss.01450"
 }
 
-@ARTICLE{Liu2021,
-  title    = "{Biogeographical trends in phytoplankton community size structure
-              using adaptive sentinel 3-OLCI chlorophyll a and spectral
-              empirical orthogonal functions in the estuarine-shelf waters of
-              the northern Gulf of Mexico}",
-  author   = "Liu, B and D'Sa, Eurico J and Maiti, Kanchan and
-              Rivera-Monroy, Victor H and Xue, Zuo",
-  journal  = "Remote sensing of environment",
-  volume   =  252,
-  pages    = "112154",
-  year     =  2021,
-  url      = "https://www.sciencedirect.com/science/article/pii/S0034425720305277",
-  keywords = "Phytoplankton size fraction; Chlorophyll a; Adaptive; EOF;
-              Sentinel 3-OLCI; Northern Gulf of Mexico",
-  issn     = "0034-4257",
-  doi      = "10.1016/j.rse.2020.112154"
-}
-
-@ARTICLE{Liu2023,
-  title     = "{Quantifying the Potential Contribution of Submerged Aquatic
-               Vegetation to Coastal Carbon Capture in a Delta System from
-               Field and Landsat 8/9-Operational …}",
-  author    = "Liu, B and Sevick, T and Jung, H and Kiskaddon, E and
-               Carruthers, T",
-  abstract  = "Submerged aquatic vegetation (SAV) are highly efficient at
-               carbon sequestration and, despite their relatively small
-               distribution globally, are recognized as a potentially valuable
-               …",
-  journal   = "Remote Sensing",
-  publisher = "mdpi.com",
-  year      =  2023,
-  url       = "https://www.mdpi.com/2072-4292/15/15/3765",
-  doi       = "10.3390/rs15153765"
-}
-
 @MISC{De_La_Pena2017,
   title     = "{hyperspy/hyperspy: HyperSpy 1.3}",
   author    = "De La Pe{\~n}a, Francisco and Ostasevicius, Tomas and Tonaas
diff --git a/paper/paper.md b/paper/paper.md
index 0b1b3ba..88bcd44 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -26,7 +26,7 @@ bibliography: paper.bib
 
 # Summary
 
-HyperCoast is a Python package designed to provide an accessible and comprehensive set of tools for visualizing and analyzing hyperspectral data in coastal environments. Leveraging the capabilities of popular packages like Leafmap [@Wu2021] and PyVista [@Sullivan2019], HyperCoast streamlines the exploration and interpretation of complex hyperspectral remote sensing data from existing spaceborne and airborne missions. It is also poised to support future hyperspectral missions, such as NASA's SBG and GLIMR [@Dierssen2021]. It enables researchers and environmental managers to gain deeper insights into the dynamic processes occurring in aquatic environments [@Liu2021; @Liu2023].
+HyperCoast is a Python package designed to provide an accessible and comprehensive set of tools for visualizing and analyzing hyperspectral data in coastal environments. Leveraging the capabilities of popular packages like Leafmap [@Wu2021] and PyVista [@Sullivan2019], HyperCoast streamlines the exploration and interpretation of complex hyperspectral remote sensing data from existing spaceborne and airborne missions. It is also poised to support future hyperspectral missions, such as NASA's SBG and GLIMR [@Dierssen2021].
 
 HyperCoast supports the reading and visualization of hyperspectral data from various missions, including AVIRIS [@Green1998], NEON [@Kampe2010], PACE [@Gorman2019], EMIT [@Green2021], and DESIS [@Alonso2019], along with other datasets like ECOSTRESS [@Fisher2020]. Users can interactively explore hyperspectral data, extract spectral signatures, change band combinations and colormaps, visualize data in 3D, and perform interactive slicing and thresholding operations (see Figure 1). Additionally, by leveraging the earthaccess [@barrett2024] package, HyperCoast provides tools for interactively searching NASA's hyperspectral data. This makes HyperCoast a versatile and powerful tool for working with hyperspectral data globally, with a particular focus on coastal regions.