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ORIGINAL RESEARCH article

Front. Remote Sens.
Sec. Microwave Remote Sensing
Volume 5 - 2024 | doi: 10.3389/frsen.2024.1481848

Advancing Terrestrial Snow Depth Monitoring with Machine Learning and L-band InSAR Data: A Case Study Using SnowEx 2017 Data

Provisionally accepted
Ibrahim Olalekan Alabi Ibrahim Olalekan Alabi 1*Hans-Peter Marshall Hans-Peter Marshall 2Jodi Mead Jodi Mead 3Ernesto Trujillo Ernesto Trujillo 2
  • 1 Computing Ph.D., Boise State University, Boise, United States
  • 2 Department of Geoscience, Boise State University, Boise, Idaho, United States
  • 3 Department of Mathematics, Boise State University, Boise, United States

The final, formatted version of the article will be published soon.

    Current terrestrial snow depth mapping from space faces challenges in spatial coverage, revisit frequency, and cost. Airborne lidar, although precise, incurs high costs and has limited geographical coverage, thereby necessitating the exploration of alternative, cost-effective methodologies for snow depth estimation. The forthcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, with its 12-day global revisit cycle and 1.25 GHz L-band frequency, introduces a promising avenue for costeffective, large-scale snow depth and snow water equivalent (SWE) estimation using L-band Interferometric SAR (InSAR) capabilities. This study demonstrates InSAR's potential for snow depth estimation via machine learning. Using 3 m resolution L-band InSAR products over Grand Mesa, Colorado, we compared the performance of three machine learning approaches (XGBoost, ExtraTrees, and Neural Networks) across open, vegetated, and the combined (open + vegetated) datasets using Root Mean Square Error (RMSE), Mean Bias Error (MBE), and R² metrics. XGBoost emerged as the superior model, with RMSE values of 9.85 cm, 10.46 cm, and 9.88 cm for open, vegetated, and combined regions, respectively. Validation against in-situ snow depth measurements resulted in an RMSE of approximately 16 cm, similar to in-situ validation of the airborne lidar. Our findings indicate that L-band InSAR, with its ability to penetrate clouds and cover extensive areas, coupled with machine learning, holds promise for enhancing snow depth estimation. This approach, especially with the upcoming NISAR launch, may enable high-resolution (~10 m) snow depth mapping over extensive areas, provided suitable training data are available, offering a cost-effective approach for snow monitoring.

    Keywords: Snow depth, InSAR, machine learning, NISAR, remote sensing

    Received: 16 Aug 2024; Accepted: 24 Dec 2024.

    Copyright: © 2024 Alabi, Marshall, Mead and Trujillo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ibrahim Olalekan Alabi, Computing Ph.D., Boise State University, Boise, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.