ICESat-2 carries NASA’s next-generation laser altimeter, ATLAS, (Advanced Topographic Laser Altimeter System), designed to measure changes in ice sheet height, sea ice freeboard, and vegetation canopy height. ATLAS contains a photon-counting lidar which transmits green (532-nm) pulses at 10kHz. Each pulse is split into 3 pairs of beams (one strong and one weak). Approximately 1014photons per pulse travel from ATLAS through the atmosphere to reflect off the Earth’s surface. Some return back into the ATLAS telescope where they are recorded. Photons from sunlight and instrument noise at the same wavelength are also recorded. The flight software time tags all photons within a 500m to 6 km range window and generates histograms. Using the histograms, it selects a telemetry window which varies from 20m over flat surfaces to hundreds of meters over rougher terrain. ATL03 contains the time, height (relative to the WGS-84 ellipsoid), latitude and longitude of every photon within the telemetry window. The basic challenge is to determine which of these photons were reflected off the surface. We have developed an algorithm that identifies these signal photons and assigns a confidence level (low, medium, or high) to each signal photon based on the signal to noise ratio. We present an overview of the signal identification algorithm and show the results on actual ICESat-2 data over ice sheet, sea ice, vegetated, and water surfaces. Higher level ATLAS products work with aggregations of the photons in order to determine the ellipsoidal height of the Earth, canopy height and structure, and other quantities of geophysical interest.

The Advanced Topographic Laser Altimeter System (ATLAS) is the sole science instrument on NASA’s ICESat-2 mission. It was designed to measure elevation simultaneously along six tracks on the earth’s surface with centimeter-level vertical precision, demanding a picosecond-level precision in photon time of flight. To ensure this precision requirement was met, we developed for the Level-1B ATLAS data product a careful verification procedure. To quantify the amount of acceptable error we needed to understand the effects of the various floating-point precisions at all steps of the calculations and the limitations of the programming languages used for the production software (Fortran) and for the verification software (Python). For example, we found in the 64-bit photon time-of-flight calculations that differences even at 13 or 14 decimal places often revealed that an incorrect calibration value had been selected. Without first characterizing the acceptable error, such small errors could be overlooked and could propagate into critical inaccuracies in the science products. We describe our methods and lessons learned in order to inform future remote sensing verification efforts.