Matias Barber

The Effect of Rain and Flooding Events on AMSR-E Signatures of La Plata Basin, Argentina

The objective of this paper is to describe and explain the effects on selected AMSR-E channels of two strong events, i.e., a rainstorm and a flooding, occurred in the Argentine section of La Plata basin. More specifically, the rainstorm took place within the Chaco region, which is covered by a continuous, moderately dense forest. The flooding affected the terminal part of Parana¿ River. The study is based on monitoring the temporal trends of the polarization indexes at various AMSR-E bands. In the forest, the rainstorm produces an effect on C band channels which is moderate, but well evident. The presence of this effect agrees with model simulations presented in previous papers. In the Parana¿ River, measurements of water level are available. Variations of polarization index at various frequencies are observed in correspondence with variations of water level in four different stations. However, the amount of the effect and the correlation between variables are dependent on the properties of the areas surrounding the stations. The Delta of Parana¿ river, where a land cover map is available, was selected for estimation of fraction of flooded area by using an algorithm available in the literature.

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.

A Bayesian approach to retrieve soil parameters from SAR data: effect of prior information

Soil moisture retrieval from SAR images is always affected by speckle noise, model errors and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. A Bayesian approach has been proposed to deal with these issues. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included. Based on simulations, the effect of prior information has been analyzed. It follows from simulations using the Ohs model that the soil moisture estimator is very sensitivity to the roughness prior.

Crop scattering analysis of L-band PolSAR data for vegetation and soil monitoring

Next L-band fully polarimetric Synthetic Aperture Radar (SAR) missions will provide meaningful and timely data over large agricultural areas. The purpose of this work is to evaluate the potential of L-band PolSAR (SAR polarimetry) for crop monitoring using incoherent target decomposition theorems applied to PolSAR data from the NASA/JPL UAVSAR airborne system over Canada. Polarimetric parameters Entropy, Mean Alpha Angle and Anisotropy are related to soil and vegetation water content, plant structure parameters (height and diameter) and the water distribution on the different plant parts (leaves, trunks, etc). Results are expected to contribute to the quantitative retrieval of physical parameters over croplands.

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.

Monitoring flooded area fraction in floodplains of Paraná basin using passive and active microwave systems

Over the past two decades, orbital passive microwave systems have proven to be sensitive to flood condition in large floodplains. This sensitivity is rooted in the well differentiated emission properties of calm water with respect to non-flooded land of any kind. In this paper, AMSR-E observations of an herbaceous wetland area on the Paraná River sub-basin were analyzed during the 2009–10 timeframe when this region was affected by a strong and long lasting flooding. Evident effects on the difference between vertically and horizontally polarized brightness temperatures (ΔT) were observed at C-band. The fraction of vegetated flooded area was estimated by applying an improved algorithm which uses ENVISAT ASAR data at specific dates to calibrate AMSR-E temporal series. Also, using a theoretical emission model, the behavior of ΔT flooded is discussed.

A Novel Method for 2-D Agricultural Soil Roughness Characterization Based on a Laser Scanning Technique

In this paper we present a laser profiler, whose main aim is the determination of agricultural soil roughness. Its working principle is based on the acquisition of an image of an object illuminated by a laser beam and on the use of 3D computer vision techniques to obtain the reconstruction of the scanned object. One of the most important purposes of this device is the attainment of the soil RMS height (s) and the correlation length (l) related to the autocorrelation function. These are fundamental inputs to derive soil moisture maps from soil backscattering data.

Modeling Bare Soil L-Band Polarimetric

Polarimetric soil moisture retrieval is among the main objectives of leading synthetic-aperture-radar satellite missions since it allows to systematically analyze costly-to-obtain polarization information to increase retrieval accuracy. In this letter, we present the results of modeling the L-band entropy

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