Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis
Seasonal snowpack is a crucial water resource, making accurate Snow Water Equivalent (SWE) estimation essential for water management and environmental assessment. This study introduces a novel approach to Passive Microwave (PMW) SWE estimation, leveraging the strong, unexpected correlation between S...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2025.1554084/full |
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| author | Mahboubeh Boueshagh Joan M. Ramage Mary J. Brodzik David G. Long Molly Hardman Hans-Peter Marshall |
| author_facet | Mahboubeh Boueshagh Joan M. Ramage Mary J. Brodzik David G. Long Molly Hardman Hans-Peter Marshall |
| author_sort | Mahboubeh Boueshagh |
| collection | DOAJ |
| description | Seasonal snowpack is a crucial water resource, making accurate Snow Water Equivalent (SWE) estimation essential for water management and environmental assessment. This study introduces a novel approach to Passive Microwave (PMW) SWE estimation, leveraging the strong, unexpected correlation between SWE and the Spatial Standard Deviation (SSD) of PMW Calibrated Enhanced-Resolution Brightness Temperatures (CETB). By integrating spatial statistics, linear correlation, machine learning (Linear Regression, Random Forest, GBoost, and XGBoost), and SHapley Additive exPlanations (SHAP) analysis, this research evaluates CETB SSD as a key feature to improve SWE estimations or other environmental retrievals by investigating environmental drivers of CETB SSD. Analysis at three sites—Monument Creek, AK; Mud Flat, ID; and Jones Pass, CO—reveals site-specific SSD variability, showing correlations of 0.64, 0.82, and 0.72 with SNOTEL SWE, and 0.67, 0.89, and 0.67 with PMW-derived SWE, respectively. Among the sites, Monument Creek exhibits the highest ML model accuracy, with Random Forest and XGBoost achieving test R2 values of 0.89 and RMSEs ranging from 0.37 to 0.39 [K] when predicting CETB SSD. SHAP analysis highlights SWE as the driver of CETB SSD at Monument Creek and Mud Flat, while soil moisture plays a larger role at Jones Pass. In snow-dominated regions with less surface heterogeneity, such as Monument Creek, SSDs can improve SWE estimation by capturing snow spatial variability. In complex environments like Jones Pass, SSDs aid SWE retrievals by accounting for factors such as soil moisture that impact snowpack dynamics. PMW SSDs can enhance remote sensing capabilities for snow and environmental research across diverse environments, benefiting hydrological modeling and water resource management. |
| format | Article |
| id | doaj-art-e930cf1077e042a1b0789694dcc5a598 |
| institution | DOAJ |
| issn | 2673-6187 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Remote Sensing |
| spelling | doaj-art-e930cf1077e042a1b0789694dcc5a5982025-08-20T03:02:29ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872025-03-01610.3389/frsen.2025.15540841554084Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysisMahboubeh Boueshagh0Joan M. Ramage1Mary J. Brodzik2David G. Long3Molly Hardman4Hans-Peter Marshall5Department of Earth and Environmental Sciences, Lehigh University, Bethlehem, PA, United StatesDepartment of Earth and Environmental Sciences, Lehigh University, Bethlehem, PA, United StatesNational Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, United StatesDepartment of Electrical and Computer Engineering, Brigham Young University, Provo, UT, United StatesNational Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, United StatesDepartment of Geosciences, Boise State University, Boise, ID, United StatesSeasonal snowpack is a crucial water resource, making accurate Snow Water Equivalent (SWE) estimation essential for water management and environmental assessment. This study introduces a novel approach to Passive Microwave (PMW) SWE estimation, leveraging the strong, unexpected correlation between SWE and the Spatial Standard Deviation (SSD) of PMW Calibrated Enhanced-Resolution Brightness Temperatures (CETB). By integrating spatial statistics, linear correlation, machine learning (Linear Regression, Random Forest, GBoost, and XGBoost), and SHapley Additive exPlanations (SHAP) analysis, this research evaluates CETB SSD as a key feature to improve SWE estimations or other environmental retrievals by investigating environmental drivers of CETB SSD. Analysis at three sites—Monument Creek, AK; Mud Flat, ID; and Jones Pass, CO—reveals site-specific SSD variability, showing correlations of 0.64, 0.82, and 0.72 with SNOTEL SWE, and 0.67, 0.89, and 0.67 with PMW-derived SWE, respectively. Among the sites, Monument Creek exhibits the highest ML model accuracy, with Random Forest and XGBoost achieving test R2 values of 0.89 and RMSEs ranging from 0.37 to 0.39 [K] when predicting CETB SSD. SHAP analysis highlights SWE as the driver of CETB SSD at Monument Creek and Mud Flat, while soil moisture plays a larger role at Jones Pass. In snow-dominated regions with less surface heterogeneity, such as Monument Creek, SSDs can improve SWE estimation by capturing snow spatial variability. In complex environments like Jones Pass, SSDs aid SWE retrievals by accounting for factors such as soil moisture that impact snowpack dynamics. PMW SSDs can enhance remote sensing capabilities for snow and environmental research across diverse environments, benefiting hydrological modeling and water resource management.https://www.frontiersin.org/articles/10.3389/frsen.2025.1554084/fullsnow water equivalent (SWE)passive microwave remote sensingmachine learningenhanced-resolution dataspatial standard deviationSHapley additive exPlanation (SHAP) |
| spellingShingle | Mahboubeh Boueshagh Joan M. Ramage Mary J. Brodzik David G. Long Molly Hardman Hans-Peter Marshall Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis Frontiers in Remote Sensing snow water equivalent (SWE) passive microwave remote sensing machine learning enhanced-resolution data spatial standard deviation SHapley additive exPlanation (SHAP) |
| title | Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis |
| title_full | Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis |
| title_fullStr | Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis |
| title_full_unstemmed | Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis |
| title_short | Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis |
| title_sort | revealing causes of a surprising correlation snow water equivalent and spatial statistics from calibrated enhanced resolution brightness temperatures cetb using interpretable machine learning and shap analysis |
| topic | snow water equivalent (SWE) passive microwave remote sensing machine learning enhanced-resolution data spatial standard deviation SHapley additive exPlanation (SHAP) |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2025.1554084/full |
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