Abstract
The NASA Soil Moisture Active Passive (SMAP) satellite mission aims to produce enhanced resolution surface soil moisture products by combining coincident but multiresolution L-band active and passive microwave measurements. Since the SMAP radar ceased operations early in the mission, Copernicus Sentinel-1 C-band radar observations are used in the combined product. The synergy is built on two basic foundations: first, active and passive signals covary in a known and systematic fashion, and second, measurements are available at multiple resolutions. In this study, we perform numerical simulations and assess global satellite observations to test the first foundation (covariation). Specific focus lies on the role of the vegetation canopy in modulating the active-passive relationship. We use a discrete radiative transfer model to simulate the slope beta and coefficient of determination R-2 of the relationship between active and passive signals, considering three vegetation types for which the model has been extensively assessed in previous experimental studies. We find that a linear relationship between backscatter and emissivity can be established over a range of vegetation conditions. The coupling between active and passive signals decreases with increasing vegetation water content, such that moderate or higher correlations (nonzero slopes) are retained up to 4 kg/m(2) (6.3 kg/m(2)) for L-band/L-band and 1.5 kg/m(2) (2 kg/m(2)) for the C-band/L-band configuration. We decompose the effects of different soil-vegetation scattering mechanisms, such as double-bounce, and different measurement error levels on the active-passive relationship. Comparisons with satellite data confirm that our simulations capture magnitudes and major trends found across global vegetated land masses.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Geowissenschaften > Department für Geographie |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 550 Geowissenschaften, Geologie |
ISSN: | 0196-2892 |
Sprache: | Englisch |
Dokumenten ID: | 100151 |
Datum der Veröffentlichung auf Open Access LMU: | 05. Jun. 2023, 15:34 |
Letzte Änderungen: | 05. Jun. 2023, 15:34 |