An observing system simulation experiment for hydros radiometer-only soil moisture products

Abstract

Based on 1-km land surface model geophysical predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and parameter uncertainty on soil moisture products derived from the National Aeronautics and Space Administration Hydrosphere State (Hydros) mission. Simulated retrieved soil moisture products are created using three distinct retrieval algorithms based on the characteristics of passive microwave measurements expected from Hydros. The accuracy of retrieval products is evaluated through comparisons with benchmark soil moisture fields obtained from direct aggregation of the original simulated soil moisture fields. The analysis provides a quantitative description of how land surface heterogeneity, instrument error, and inversion parameter uncertainty impacts propagate through the measurement and retrieval process to degrade the accuracy of Hydros soil moisture products. Results demonstrate that the discrete set of error sources captured by the OSSE induce root mean squared errors of between 2.0% and 4.5% volumetric in soil moisture retrievals within the basin. Algorithm robustness is also evaluated for the case of artificially enhanced vegetation water content () values within the basin. For large (3 kg m 2), a distinct positive bias, attributable to the impact of sub-footprint-scale landcover heterogeneity, is identified in soil moisture retrievals. Prospects for the removal of this bias via a correction strategy for inland water and/or the implementation of an alternative aggregation strategy for surface vegetation and roughness parameters are discussed.

Ground Truth of Passive Microwave Radiative Transfer on Vegetated Land Surfaces

“…The 0.01 change in emissivity corresponds to the brightness temperature's change of <3 (K). Although this change can be detected in the synthetic analysis (e.g., [22,25]), it is not practically easy to detect this change in the real world applications. Since the signal from soil surface is strongly disturbed by vegetation, we cannot accurately observe SSM by 6.925 GHz microwave emissivity if VWC is large.…”

“…This assumption brings significant uncertainties of their retrievals [23,24]. In addition, since penetration depths of C-band and L-band microwaves are limited, the observed brightness temperatures lose their sensitivity to SSM and VWC as vegetation density increases, which also brings large uncertainty of the retrievals on vegetated surfaces (e.g., [25][26][27]). …”

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

“…Therefore, we discuss footprint-scale retrieval errors in this section, which are not dealt with upscaling in Section 1.1.2 and relative climatology errors in Section 2.1. At a footprint scale, a partial-derivative (or tangent space or Jacobian matrix) method may be employed for deterministically quantifying retrieval errors [59]. However, in practice, there are several difficulties in operationally implementing it.…”

“…The error sources to be considered for generating ensembles are largely classified into three main categories, as shown in Table 1. Instrument Measurements Measurement errors arise from calibration errors, vegetation attenuation, the water film formed by rain events, RFI, radiometric noise, instrument errors, bandwidth, sample integration time, structural uncertainty in surface backscatter or soil emission, and incidence angle interpolation errors and others [59]. It is a very important error source to consider, since several retrieval algorithms employ an inversion to minimize a mismatch with measurements.…”

Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

“…A number of studies on soil moisture estimations introduced the error sources that have degraded the accuracy of satellite remotely sensed soil moisture content such that it is critical to calibrate soil moisture estimation algorithms and to validate derived products using ground-truth data. The error sources comprise radio-frequency interference (RFI) [16], vegetation water content [13,17], surface roughness [16], and land surface heterogeneity [18]. It has been stated in the literature that a space-borne sensor designed to interpret SMC on the basis of soil microwave emission, and therefore the relationship between soil dielectric constant and water content, will show considerable systematic uncertainty of around 4% with maximum figures at relatively low water content in SMC retrieval [19].…”