Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.rse.2024.114197 |
| Title (Primary) | Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches |
| Author | Ma, H.; Zeng, J.; Zhang, X.; Peng, J.
; Li, X.; Fu, P.; Cosh, M.H.; Letu, H.; Wang, S.; Chen, N.; Wigneron, J.-P. |
| Source Titel | Remote Sensing of Environment |
| Year | 2024 |
| Department | RS |
| Volume | 308 |
| Page From | art. 114197 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Keywords | Surface soil moisture; SMAP; ASCAT; Active-passive microwave; Machine learning; Global dense network |
| Abstract | The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM mapping with improved accuracy. However, this integration remains largely underexplored. In this context, the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficients for global-scale SSM estimation was investigated, by fully considering the potential error sources in conventional radiative transfer models (RTMs) as well as other SSM linked factors. Based on ground measurements from globally distributed dense networks with mitigated mismatch issues and spatial/temporal independent evaluation strategies, this study: (i) comprehensively evaluated four classical machine learning approaches, including Random Forest (RF), Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Cascaded Neural Network (CNN), and chose the best performing RF method to implement the final integration of SSM; (ii) compared the integration retrievals to those made using data from a single sensor (SMAP or ASCAT) with the same machine learning framework, as well as to the SMAP passive, ASCAT active, and ESA CCI active-passive combined SSM products. The results show the integration retrievals achieve satisfactory performance by obtaining an averaged unbiased root mean squared error (ubRMSE) of 0.042 m3/m3 and a temporal correlation of 0.756, which are superior to machine learning based SSM estimated from a single active or passive sensor, and also outperform the SMAP, ASCAT, and ESA CCI products. Moreover, the temporal resolution is evidently improved compared to the SMAP and ASCAT SSM products, with a temporal ratio exceeding 60% for most areas across the globe. Therefore, blending active and passive measurements affords a more reliable SSM mapping with increased sampling at the global scale, and could contribute to improved hydro-ecological applications. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29234 |
| Ma, H., Zeng, J., Zhang, X., Peng, J., Li, X., Fu, P., Cosh, M.H., Letu, H., Wang, S., Chen, N., Wigneron, J.-P. (2024): Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches Remote Sens. Environ. 308 , art. 114197 10.1016/j.rse.2024.114197 |
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