No abstract
An Observing System Simulation Experiment for the Aquarius/SAC-D mission has been developed for assessing the accuracy of soil moisture retrievals from passive L-band remote sensing. The implementation of the OSSE is based on: a 1-km land surface model over the Red-Arkansas River Basin, a forward microwave emission model to simulate the radiometer observations, a realistic orbital and sensor model to resample the measurements mimicking Aquarius operation, and an inverse soil moisture retrieval model. The simulation implements a zero-order radiative transfer model. Retrieval is done by direct inversion of the forward model. The Aquarius OSSE attempts to capture the influence of different error sources: land surface heterogeneity, instrument noise and retrieval ancillary parameter uncertainty on the accuracy of Aquarius surface soil moisture retrievals. In order to assess the impact of these error sources on the estimated volumetric soil moisture, a quantitative error analysis is performed via the comparison of footprint-scale synthetic soil moisture with 'true' soil moisture fields obtained from the direct aggregation of the original 1-km soil moisture field fed into the forward model. Results show that, in heavily vegetated areas, soil moisture retrievals present a positive bias that can be suppressed with an alternative aggregation strategy for ancillary parameter vegetation water content (VWC). Retrieval accuracy was also evaluated when adding errors on 1-km VWC (which are intended to account for errors in VWC derived from remote sensing data). For soil moisture retrieval RMSE of the order of 0.05%vol/vol, relative error bias on VWC should be less than 12%.
Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainities associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a Bayesian model is proposed to address these issues. A soil moisture Bayesian estimator from polarimetric SAR images is presented. This estimator is based on a set of stochastic equations for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. Since it is a Bayesian estimator, it may extensively use a priori information about soil condition, enhancing the performance of the retrieval. The Oh model is used as scattering model, although it presents a limiting range of validity for retrieving. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval using Oh model is investigated. The Bayesian retrieval scheme is then compared with Oh's retrieval. The results indicate that Bayesian model enlarge the validity region of Oh's retrieval. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approachs to Oh's retrieval. On the other hand, an improvement in the accuracy of the retrieval is achieved by using a precise prior when speckle effects are large. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multi-loking process and prior information required to perform a precise retrieval in a given soil type/condition.
Abstract. This paper describes a procedure to estimate both the fraction of flooded area and the mean water level in vegetated river floodplains by using a synergy of active and passive microwave signatures. In particular, C band Envisat ASAR in Wide Swath mode and AMSR-E at X, Ku and Ka band, are used. The method, which is an extension of previously developed algorithms based on passive data, exploits also model simulations of vegetation emissivity. The procedure is applied to a long flood event which occurred in the Paraná River Delta from December 2009 to April 2010. Obtained results are consistent with in situ measurements of river water level.
A radar-only retrieval algorithm for soil moisture mapping is applied to L-band scatterometer measurements from the Aquarius. The algorithm is based on a nonlinear relation between L-band backscatter and volumetric soil moisture and does not require ancillary information on the surface, e.g., land classification, vegetation canopy, surface roughness, etc. It is based on the definition of three limiting cases or end-members: 1) smooth bare soil; 2) rough bare soil; and 3) maximum vegetation condition. In this study, an estimation method is proposed for the end-member parameters that is iterative and only uses space-borne measurements. The major advantages of the algorithm include an analytic formulation (direct inversion possible), and the fact that there is no requirement for ancillary information. Ancillary data often misclassify the surface and the parameterizations linking surface classification to model parameter values are often highly uncertain. The retrieval algorithm is tested using 3 years of space-borne scatterometer observations from the Aquarius/SAC-D. Retrieved soil moisture accuracy is assessed in several ways: comparison of global spatial patterns with other available soil moisture products (two reanalysis modeling products and retrievals based on the Aquarius radiometer), extended triple collocation (ETC) and time series analysis over selected target areas. In general, low bias and standard deviation are observed with levels comparable to independent radiometerbased retrievals. The errors, however, increase across areas with high vegetation density. The results are promising and applicable to forthcoming L-band radar missions such as SMAP-NASA (2015) and SAOCOM-CONAE (2016).
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