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Among passive microwave frequencies, sensors operating at C- and X-band frequencies have been used with some success to estimate near-surface soil moisture from aircraft and satellite platforms. The objective of this paper is to quantify the sensitivities of soil moisture retrieved via a single-channel single-polarization algorithm to the observed brightness temperature and to retrieval algorithm parameters of surface roughness, vegetation B parameter, and single-scattering albedo. Examination of the regions within the parameter space that produce accurate soil moisture retrievals reveals that reasonably accurate retrievals can be made over a range of conditions using a fixed set of input parameters. Retrievals with horizontally polarized brightness temperature observations are more consistent than with vertically polarized observations. At horizontal polarization, sensitivity to the input parameters is much greater for wet soils than for dry soils, whereas for vertical polarization the moisture dependence is much weaker. At vertical polarization, sensitivities to variations in all parameters are much lower. To ensure that retrieval accuracy specifications are consistently met, high soil moisture conditions should be used in defining parameter accuracy requirements. Given the spatial and temporal variability of vegetation and soil conditions, it seems unlikely that, for regions with substantial rapidly growing vegetation, the accuracy requirements for model parameters in a single-frequency, single-polarization retrieval algorithm can be met with current satellite products. For such conditions, any soil moisture retrieval algorithm using parameterizations similar to those of this study may require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates. [ABSTRACT FROM AUTHOR]

Summary: The objective of this study is to quantitatively evaluate the successive Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products and further to explore the improvements and error propagation of the latest 3B42V7 algorithm relative to its predecessor 3B42V6 using the Coupled Routing and Excess Storage (CREST) hydrologic model in the mountainous Wangchu Basin of Bhutan. First, the comparison to a decade-long (2001–2010) daily rain gauge dataset reveals that: (1) 3B42V7 generally improves upon 3B42V6’s underestimation both for the whole basin (bias from −41.15% to −8.38%) and for a 0.25°×0.25° grid cell with high-density gauges (bias from −40.25% to 0.04%), though with modest enhancement of correlation coefficients (CC) (from 0.36 to 0.40 for basin-wide and from 0.37 to 0.41 for grid); and (2) 3B42V7 also improves its occurrence frequency across the rain intensity spectrum. Using the CREST model that has been calibrated with rain gauge inputs, the 3B42V6-based simulation shows limited hydrologic prediction NSCE skill (0.23 in daily scale and 0.25 in monthly scale) while 3B42V7 performs fairly well (0.66 in daily scale and 0.77 in monthly scale), a comparable skill score with the gauge rainfall simulations. After recalibrating the model with the respective TMPA data, significant improvements are observed for 3B42V6 across all categories, but not as much enhancement for the already-well-performing 3B42V7 except for a reduction in bias (from −26.98% to −4.81%). In summary, the latest 3B42V7 algorithm reveals a significant upgrade from 3B42V6 both in precipitation accuracy (i.e., correcting the underestimation) thus improving its potential hydrological utility. Forcing the model with 3B42V7 rainfall yields comparable skill scores with in situ gauges even without recalibration of the hydrological model by the satellite precipitation, a compensating approach often used but not favored by the hydrology community, particularly in ungauged basins. [ABSTRACT FROM AUTHOR]