Pablo Perna

Exploring the capacity of radar remote sensing to estimate wetland marshes water storage.

This paper focuses on the use of radar remote sensing for water storage estimation in wetland marshes of the Paraná River Delta in Argentina. The approach followed is based on the analysis of a temporal set of ENVISAT ASAR data which includes images acquired under different polarizations and incidence angles as well as different environmental conditions (water level, precipitation, and vegetation condition). Two marsh species, named junco and cortadera, were monitored. This overall data set gave us the possibility of studying and understanding the basic interactions between the radar, the soil under different flood conditions, and the vegetation structure. The comprehension of the observed features was addressed through electromagnetic models developed for these ecosystems. The procedure used in this work to estimate water level within marshes combines a direct electromagnetic model, field work data specifically obtained to feed the model, the actual ASAR measurements and a well known retrieval scheme based on a cost function. Results are validated with water level evaluations at specific points. A map showing an estimation of the water storage capacity and its error in junco and cortadera areas for the date where the investigation was done is also presented.

Model investigation about the potential of C band SAR in herbaceous wetlands flood monitoring

Wetlands are areas where the presence of water at or near the soil surface drives the natural system. Imaging radars (SARs) have distinct characteristics which make them of significant value for monitoring and mapping wetland inundation dynamics. The presence or absence of water (which has a much higher dielectric constant than dry or wet soil) in wetlands may significantly alter the signal detected from these areas depending on the dominant vegetation type, density, and height. The objective of this paper is to present our current research efforts to explain and correctly simulate the radar response of wetland vegetation/inundation mixtures, and use simulations as an aid for retrieval applications. The radar response of junco marshes under different flood conditions and vegetation stages is analysed using a set of 13 multipolarization ENVISAT ASAR scenes acquired over the Paraná River Delta marshes during the period 2003–2005. The main aspect of the approach followed is the simulation of SAR wave interactions with vegetation and water, using an adapted and improved version of the EM model developed at Tor Vergata University. The results obtained indicate that with the refined EM model, it is possible to represent with a good accuracy VV and HH SAR responses of junco marshes for a variety of environmental conditions. Further work and data are needed to explain measured HV backscattering. The general agreement obtained between simulations and observations permitted the development of a simple retrieval scheme, and estimates of water level below the canopy were obtained for different environmental conditions. RMS errors of forward simulations and retrievals are reported and discussed.

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Parana River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented.

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.

Effects of spatial sampling interval on roughness parameters and microwave backscatter over agricultural soil surfaces

The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm) to small ones (1 mm), with several intermediate values. Large- and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C- or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band.

Monitoring and modeling land surface dynamics in Bermejo River Basin, Argentina: Time series analysis of MODIS and AMSR-E data

In this paper, we show that MODIS NDVI and AMSR-E microwave vegetation indexes (MVI) data can be used to monitor land surface phenology in the Bermejo River Basin. For this purpose, the statistical nature of the study areas NDVI and MVI time series was analyzed. For NDVI, widely known time series models were tested and modified. NDVI temporal variation trends show functional forms that originate from the general annual performance of land surface phenology. Using these functional forms, a classification scheme is proposed. Furthermore, we also explored the possibility to use MVIs in order to improve the classification using assumptions about canopy structure that influence vegetation emissivity and opacity.

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.

SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina)

In this work, several retrieval algorithms were implemented to retrieve soil moisture (sm) and optical depth (τ) from Aquarius/SAC-D observations. Currently used sm retrieval algorithms (H- and V-pol Single Channel Algorithm, Microwave Polarization Difference Algorithm) were computed over Pampas Plains, Argentina. The methodology of a novel Bayesian algorithm developed was also presented, and its results were contrasted with the previous algorithms. Furthermore, an Artificial Neural Network (ANN) approach to retrieve sm from Aquarius brightness temperature was implemented and trained using SMOS Level-2 sm product. Finally, performance metrics for each algorithm were derived using SMOS L2 sm as benchmark product.

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.