Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Remote Sensing |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2024.1481848/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591639547215872 |
|---|---|
| author | Ibrahim Olalekan Alabi Ibrahim Olalekan Alabi Hans-Peter Marshall Jodi Mead Ernesto Trujillo |
| author_facet | Ibrahim Olalekan Alabi Ibrahim Olalekan Alabi Hans-Peter Marshall Jodi Mead Ernesto Trujillo |
| author_sort | Ibrahim Olalekan Alabi |
| collection | DOAJ |
| description | 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 cost-effective, 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. The code and data used in this work are available at https://github.com/cryogars/uavsar-lidar-ml-project. |
| format | Article |
| id | doaj-art-084cb35c006d4f2fbee25df23978f46a |
| institution | Kabale University |
| issn | 2673-6187 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj-art-084cb35c006d4f2fbee25df23978f46a2025-01-22T07:13:20ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872025-01-01510.3389/frsen.2024.14818481481848Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 dataIbrahim Olalekan Alabi0Ibrahim Olalekan Alabi1Hans-Peter Marshall2Jodi Mead3Ernesto Trujillo4Computing PhD Program, Boise State University, Boise, ID, United StatesDepartment of Geoscience, Boise State University, Boise, ID, United StatesDepartment of Geoscience, Boise State University, Boise, ID, United StatesDepartment of Mathematics, Boise State University, Boise, ID, United StatesDepartment of Geoscience, Boise State University, Boise, ID, United StatesCurrent 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 cost-effective, 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. The code and data used in this work are available at https://github.com/cryogars/uavsar-lidar-ml-project.https://www.frontiersin.org/articles/10.3389/frsen.2024.1481848/fullsnow depthInSARmachine learningNISARremote sensing |
| spellingShingle | Ibrahim Olalekan Alabi Ibrahim Olalekan Alabi Hans-Peter Marshall Jodi Mead Ernesto Trujillo Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data Frontiers in Remote Sensing snow depth InSAR machine learning NISAR remote sensing |
| title | Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data |
| title_full | Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data |
| title_fullStr | Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data |
| title_full_unstemmed | Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data |
| title_short | Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data |
| title_sort | advancing terrestrial snow depth monitoring with machine learning and l band insar data a case study using nasa s snowex 2017 data |
| topic | snow depth InSAR machine learning NISAR remote sensing |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2024.1481848/full |
| work_keys_str_mv | AT ibrahimolalekanalabi advancingterrestrialsnowdepthmonitoringwithmachinelearningandlbandinsardataacasestudyusingnasassnowex2017data AT ibrahimolalekanalabi advancingterrestrialsnowdepthmonitoringwithmachinelearningandlbandinsardataacasestudyusingnasassnowex2017data AT hanspetermarshall advancingterrestrialsnowdepthmonitoringwithmachinelearningandlbandinsardataacasestudyusingnasassnowex2017data AT jodimead advancingterrestrialsnowdepthmonitoringwithmachinelearningandlbandinsardataacasestudyusingnasassnowex2017data AT ernestotrujillo advancingterrestrialsnowdepthmonitoringwithmachinelearningandlbandinsardataacasestudyusingnasassnowex2017data |