| Abstrakt: |
Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. Here we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross‐validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root‐mean‐square error of 0.034 m3/m3. It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets. Plain Language Summary: High‐resolution soil moisture data can be of great value for many practical applications, for example, flood forecasting, drought monitoring, crop management, weather forecasting, and better understanding the world's ecosystems. Recently, deep learning (DL) models directly learning patterns from observations have shown strong performance in modeling soil moisture and other environmental factors. Used directly, however, they inherit certain limitations of their training data such as low resolution and low accuracy; in other words, these models are students that cannot exceed their teacher (the data source). For soil moisture, just as for many other environmental variables of interest, observations are available on multiple scales, for example, probes in the ground and satellite‐based data. Here we show that learning from multiple data sources on different scales at the same time can allow DL models to overcome limitations of every single source: the student model can then outperform each teacher. Our multiscale model reported the best metrics for daily soil moisture prediction compared to alternatives and is no longer limited by poor satellite sensing capability in certain regions. This multiscale scheme is broadly applicable to many areas of environmental study. Key Points: We propose a novel multiscale soil moisture model (5 cm depth) learning from both satellite and in situ data, resulting in high accuracyIn situ data provides more information than satellite data, but if used together, they can overcome the limitations of each data sourceThe impacts of input resolution waned below the 9 km grid, and the results established the upper error bound (RMSE ∼0.034) due to resolution [ABSTRACT FROM AUTHOR] |
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