Contrasting Drydown Time Scales: SMAP L-Band vs. AMSR2 C-Band Brightness Temperatures Against Ground Observations and SMAP Products
Surface water loss, regulated by natural factors such as surface properties and atmospheric conditions, is a complex process across multiple spatiotemporal scales. This study compared the statistical characteristics of drydown time scale (τ) derived from multi-frequency microwave brightness temperatures (TB, including L-band and C-band), SMAP (Soil Moisture Active Passive) soil moisture (SM) products, and in situ observation data. It mainly conducted a sensitivity analysis of τ to depth, climate type, vegetation coverage, and soil texture, and compared the sensitivity differences between signals of different frequencies. The statistical results of τ showed a pattern varying with sensing depth: C-band TB (0~3 cm) < L-band TB (0~5 cm) < in situ observation (4~8 cm), i.e., the shallower the depth, the faster the drying. τ was sensitive to Normalized Difference Vegetation Index (NDVI) when NDVI < 0.7 and climate types, but relatively insensitive to soil texture. The global median τ retrieved from TB aligned with the spatial pattern of climate classifications; drier climates and sparser vegetation coverage led to faster drying, and L-band TB was more sensitive to these factors than C-band TB. The attenuation magnitude of L-band TB was smaller than that of C-band TB, but the degree of change in its attenuation effect was greater than that of C-band TB, particularly regarding variations in NDVI and climate types. Furthermore, given the similar sensing depths of SMAP SM and L-band TB, their τ statistical characteristics were compared and found to differ, indicating that depth is not the sole reason SMAP SM dries faster than in situ observations.
Authors
- Shaoning Lv (ORCID: https://orcid.org/0000-0001-8957-6636)
- Hongxun Jiang (ORCID: https://orcid.org/0000-0003-0866-2546)
- Jun Wen (ORCID: https://orcid.org/0000-0003-1146-3628)
- Yin Hu (ORCID: https://orcid.org/0009-0005-3777-8590)
Institutions
- Chengdu University of Information Technology (CN)
- Fudan University (CN)
- Zhuhai Fudan Innovation Research Institute (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-09-26
- DOI
- https://doi.org/10.3390/rs17193307
- Citations
- 2
- Primary Topic
- Calibration and Measurement Techniques
- Type
- article
- Field-Weighted Citation Impact
- 9.99
Funders
- National Natural Science Foundation of China