Increased anthropogenic stressors (e.g., warming, acidification, wildfires and other extreme events) present complex observational challenges for Earth science, and no one sensor can ‘do it all.’ While many remote sensing technologies are available at present, scientific disciplines are often trained to use only a specific subset, greatly limiting scientific advancements. Here we present open-source software (‘icepyx’) that lowers the barrier for entry for two remote platforms offering vertically-resolved information about the ocean’s subsurface: ICESat-2 (Ice, Cloud, and land Elevation Satellite 2) and Argo floats. icepyx provides object-oriented code for querying and downloading ICESat-2 and Argo data within a single analysis workflow. icepyx natively handles ICESat-2 data access and read-in; here we introduce the Query, Unify, Explore SpatioTemporal (QUEST) module as a framework for adapting icepyx to easily access and ingest other datasets and present Argo data as the initial use case. Seamless retrieval of coincident data from ICESat-2 and Argo enables improved targeted and exploratory studies across the cryosphere and open ocean realms. We close with recommendations for future work, a discussion of the value of open science, relevance of our work to upcoming satellite missions, and an invitation to join our programming community.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has collected global surface elevation measurements for over three years. ICESat-2 carries the Advanced Topographic Laser Altimeter (ATLAS) instrument, which emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat-2 acquisitions during the Northern Hemisphere melting season. In this work, we performed two case studies over the Greenland Ice Sheet to quantify volumetric scattering in ICESat-2 signals over snow. Elevation data from ICESat-2 was compared to Airborne Topographic Mapper (ATM) data to quantify bias. We used snow optical grain sizes derived from ATM and the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to attribute altimetry bias to snowpack properties. For the first case study, the mean optical grain sizes were 340±65 µm (AVIRIS-NG) and 670±420 µm (ATM), which corresponded with a mean altimetry bias of 4.81±1.76 cm in ATM. We observed larger grain sizes for the second case study, with a mean grain size of 910±381 µm and biases of 6.42±1.77 cm (ICESat-2) and 9.82±0.97 cm (ATM). Although these altimetry biases are within the accuracy requirements of the ICESat-2 mission, we cannot rule out more significant errors over coarse-grained snow, particularly during the Northern Hemisphere melting season.