Cintia Bruscantini

L-Band Radar Soil Moisture Retrieval Without Ancillary Information

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 radiometer-based 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).

Speckle Noise and Soil Heterogeneities as Error Sources in a Bayesian Soil Moisture Retrieval Scheme for SAR Data

Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Ohs model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimization-based procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition.

Rationale Behind an Optimal Field Experiment to Assess the Suitability of Soil Moisture Retrieval Algorithms for SAR Data

Validation of soil moisture products derived from synthetic aperture radar (SAR) remotely sensed observations involves a comparison against ground-truth data. This validation step helps one to state the performance of competing retrieval algorithms. Nevertheless, the design of a field experiment in the context of SAR retrieval is not straightforward. Ground-based measurements are affected by instrument errors due to both the physical limitations of the measurement technique and the uncertainties related to the spatial variability of the soil moisture. To properly assess the performance of the retrieved estimates, both of the mentioned sources of uncertainties should be considered in the ground-based sampling and in the subsequent error assessment analysis. This letter addresses the rationale behind an optimal field experiment designed to assess the suitability of soil moisture retrieval algorithms.

Bayesian combined active/passive (B-CAP) soil moisture retrieval algorithm: A rigorous retrieval scheme for SMAP mission

This paper focused on exploiting remotely sensed active and passive observations over agricultural fields for soil moisture retrieval purposes. Co-polarized backscattering coefficients HH and VV and V-polarized brightness temperature TbV measurements were merged onto a Bayesian algorithm to enhance field-based retrieval estimates. The Bayesian algorithm relies on the use of active SAR to constrain passive information. It is assumed that observations are representative of an extent involving field sizes of about 800 m by 800 m, disregarding the scaling issues between the high resolution SAR pixel and the coarse resolution passive pixel. The integral equation model with multiple scattering at second order (IEM2M) and the ω - τ model were used as forward models for the backscattering coefficients and for the V-polarized brightness temperature, respectively. The Bayesian algorithm was assessed using datasets from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEx12). Such datasets are representative of contrasting soil conditions since soil moisture spanned almost its whole feasible range from 0.10 to 0.40 cm3 /cm3, at different observation geometries with incidence angles ranging from 35° to 55°. Also, the fairly large amount of measurements (97) made the dataset complete for assessment purposes. Soil moisture variability at field scale and dielectric probe error were accounted for in the comparison between retrieved estimates and in situ measurements. Performance metrics were used to quantify the agreement of the retrieval methodology to in situ information, and to assess the improvement in the combined methodology with respect to the single ones (active or passive). Overall, the root mean squared error (RMSE) showed an improvement from 0.08 to 0.11 cm3/cm3 (only active) or 0.03-0.12 cm3/cm3 (only passive, after bias correction) to 0.06-0.10 cm3/cm3 (combined), thus, demonstrating the potential of such combined soil moisture estimates. When analyzed each field separately, RMSE is less than 0.07 cm 3/cm3 and correlation coefficient r is greater than 0.6 for most of the fields.

An Observing System Simulation Experiment for the Aquarius/SAC-D Soil Moisture Product

An Observing System Simulation Experiment (OSSE) 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 the following: 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 performed by direct inversion of the forward model. The Aquarius OSSE attempts to capture the influence of various error sources, such as land surface heterogeneity, instrument noise, and retrieval ancillary parameter uncertainty, all 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 by 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 input to the forward model. Results show that, in heavily vegetated areas, soil moisture retrievals have 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 to 1-km VWC (which are intended to account for errors in VWC derived from remote sensing data). For soil moisture retrieval root-mean-square error on the order of 0.05 m3/m3, the error in VWC should be less than 12%.

Effect of Forward/Inverse Model Asymmetries Over Retrieved Soil Moisture Assessed With an OSSE for the Aquarius/SAC-D Mission

An Observing System Simulation Experiment (OSSE) for the Aquarius/SAC-D mission that includes different models for forward and retrieval processes is presented. This OSSE is implemented to study the errors related to the use of simple retrieval models in passive microwave applications. To this end, a theoretical forward model was introduced, which is suitable to reproduce some of the complexities related to canopy vegetation scattering. So far, this OSSE has been successfully exploited to study the artifacts in the retrieved soil moisture associated to: 1) uncertainties and aggregation of the ancillary parameters needed for the retrieval, and 2) instrumental noise effects. In this paper, we attempt to model the influence of this “model asymmetry” (different forward and inverse model) in the estimated soil moisture. These asymmetries are related to the fact that the emissivity of real surfaces is complex and strongly dependent on land cover type and condition. In particular, surface covered by average to dense vegetation presents complex scattering properties, related to canopy structure. Using this theoretical model, the difficulties related to retrieving soil moisture from passive data with a simple model are studied. The accuracy of the soil moisture estimation is analyzed in order to illustrate the impact of discrepancies between both models. In general, retrieved soil moisture performs worse over dense vegetated areas and under wet conditions. Furthermore, accuracy is highly dependent on land cover.

An Observing System Simulation Experiment (OSSE) for the Aquarius/SAC-D soil moisture product: An investigation of forward/retrieval model asymmetries

An Observing System Simulation Experiment (OSSE) for the Aquarius/SAC-D mission has been developed for assessing the accuracy of soil moisture retrieval from passive and active L band. So far, this OSSE has been successfully exploited to study the artifacts in the retrieved soil moisture associated to: (1) uncertainties and aggregation of the ancillary parameters needed for the retrieval and (2) instrumental noise effects. However, effects due to forward and retrieval model incompatibilities have not yet been studied. In this paper, OSSE attempts to capture the influence of this effect over estimated soil moisture. The emissivity of real surfaces is very complex and is strongly dependent on land cover type and condition. In particular, surface covered by average to dense vegetation presents complex scattering properties, heavily related to canopy structure. The OSSE implements a forward model using a theoretical approach based on the electromagnetic modeling of vegetation elements and high order radiative transfer theory. In this way, the difficulties related to retrieving soil moisture from passive data with a simple model are studied. The accuracy of the soil moisture estimation is analyzed on a set of selected footprints in order to illustrate the impact of discrepancies between both models. In general, retrieved soil moisture performs worse over dense vegetated areas and under wet conditions. Furthermore, accuracy is highly dependent on land cover.