Forest Soil Moisture Monitoring Using L-Band Passive Microwave and Machine Learning.
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| Title: | Forest Soil Moisture Monitoring Using L-Band Passive Microwave and Machine Learning. |
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| Authors: | Esmaeilisarteshnizi, Rouhollah1 (AUTHOR) rouhollah.esmaeilisarteshnizi@usherbrooke.ca, Magagi, Ramata1,2 (AUTHOR), Foucher, Samuel1,3 (AUTHOR), Berg, Aaron1,2 (AUTHOR), Colliander, Andreas2,3 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1970. 29p. |
| Subjects: | Machine learning, Brightness temperature, Moisture content of plants, Forest soils, Microwave remote sensing, Microwave radiometers |
| Abstract: | Highlights: What are the main findings? Current SMAP and SMOS soil moisture products show substantial uncertainty over boreal and temperate forests. Machine learning models significantly improved forest soil moisture estimation, with CatBoost achieving the best performance for both AM and PM overpasses. Brightness temperature was the most influential predictor, followed by vegetation water content, air and soil temperatures, and MPDI. What are the implications of the main findings? Combining L-band passive microwave observations with machine learning enhances soil moisture estimation in dense forest ecosystems. Accounting for vegetation, soil, and air temperature effects, particularly the differences between AM and PM overpasses, can improve future soil moisture retrieval algorithms. This study evaluates the potential of L-band passive microwave data for monitoring soil moisture (SM) in boreal and temperate forests using SMAP and SMOS AM and PM overpasses. SMAP and SMOS Level 3 SM products were first assessed for spring and summer seasons. SMOS showed lower accuracy (r2 = 0.04–0.24, ubRMSE = 0.09–0.13 m3/m3), while SMAP performed better (r2 = 0.18–0.62, ubRMSE = 0.05–0.07 m3/m3) across sites and overpasses. Given the larger number of SMAP TB observations at a fixed incidence angle and greater temporal coverage over the study area, SMAP was selected for SM estimation using ML models. Feature importance analysis identified brightness temperature (TB) as the most influential variable, followed by vegetation water content (VWC), air and soil temperatures, and the microwave polarization difference index (MPDI). Soil and air temperatures were interchangeable during AM overpasses, whereas PM overpasses showed distinct differences, likely due to thermal absorption by dense vegetation. Using optimal features, SM was estimated with CatBoost, Gradient Boosting (GB), Random Forest (RF), and Principal Component Regression (PCR), using stratified shuffle split (SSS) and leave-one-year-out cross-validation (LOYOCV). In SSS, CatBoost achieved slightly higher accuracy than the other ensemble models (AM: r2 = 0.73; PM: R2 = 0.74), while PCR yielded substantially lower accuracy across both overpasses. LOYOCV showed closer rankings among models, with CatBoost ranking highest overall (r2 = 0.58 for AM and 0.54 for PM). Results highlight the feasibility of improved SM estimation in forests using L-band TB, VWC, temperature variables, and MPDI. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Current SMAP and SMOS soil moisture products show substantial uncertainty over boreal and temperate forests. Machine learning models significantly improved forest soil moisture estimation, with CatBoost achieving the best performance for both AM and PM overpasses. Brightness temperature was the most influential predictor, followed by vegetation water content, air and soil temperatures, and MPDI. What are the implications of the main findings? Combining L-band passive microwave observations with machine learning enhances soil moisture estimation in dense forest ecosystems. Accounting for vegetation, soil, and air temperature effects, particularly the differences between AM and PM overpasses, can improve future soil moisture retrieval algorithms. This study evaluates the potential of L-band passive microwave data for monitoring soil moisture (SM) in boreal and temperate forests using SMAP and SMOS AM and PM overpasses. SMAP and SMOS Level 3 SM products were first assessed for spring and summer seasons. SMOS showed lower accuracy (r2 = 0.04–0.24, ubRMSE = 0.09–0.13 m3/m3), while SMAP performed better (r2 = 0.18–0.62, ubRMSE = 0.05–0.07 m3/m3) across sites and overpasses. Given the larger number of SMAP TB observations at a fixed incidence angle and greater temporal coverage over the study area, SMAP was selected for SM estimation using ML models. Feature importance analysis identified brightness temperature (TB) as the most influential variable, followed by vegetation water content (VWC), air and soil temperatures, and the microwave polarization difference index (MPDI). Soil and air temperatures were interchangeable during AM overpasses, whereas PM overpasses showed distinct differences, likely due to thermal absorption by dense vegetation. Using optimal features, SM was estimated with CatBoost, Gradient Boosting (GB), Random Forest (RF), and Principal Component Regression (PCR), using stratified shuffle split (SSS) and leave-one-year-out cross-validation (LOYOCV). In SSS, CatBoost achieved slightly higher accuracy than the other ensemble models (AM: r2 = 0.73; PM: R2 = 0.74), while PCR yielded substantially lower accuracy across both overpasses. LOYOCV showed closer rankings among models, with CatBoost ranking highest overall (r2 = 0.58 for AM and 0.54 for PM). Results highlight the feasibility of improved SM estimation in forests using L-band TB, VWC, temperature variables, and MPDI. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121970 |
