Mapping high‐resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother

摘要: This study demonstrated a new method for mapping high resolution (spatial: 1 m, and temporal: hour) soil moisture by assimilating distributed temperature sensing (DTS) observed temperatures at intermediate scales. In order to provide robust property estimates, we first proposed an adaptive particle batch smoother algorithm (APBS). the APBS, tuning factor, which can avoid severe weight degeneration, is automatically determined maximizing reliability of estimates each window. A multiple truth synthetic test was used demonstrate APBS robustly estimate properties using two shallow depths. The then applied DTS data along 71 m transect, yielding hourly map with meter resolution. Results show draw prior guessed hydraulic thermal significantly closer field measured reference values. improved in turn remove biases between moisture, particularly noticeable depth above 20 cm. demonstrates potential characterizing temporal spatial variability reflects patterns consistent previous studies conducted intensive point scale samples. information derived from may facilitate remote product calibration validation. addition, this would also be applicable general hydrological assimilation problems model state parameter estimation. article protected copyright. All rights reserved.