Papers by Marcela Piscitelli
Such is the importance to estimate and monitor the spatial and time variability from the superfic... more Such is the importance to estimate and monitor the spatial and time variability from the superficial features of agricultural soil that it contributes to carry out research on essential processes of the agro-ecosystems. Although spectral methods based on satellite data have been of great help, still they are not as accurate as they should at field level. Field radiometry helps to increase the accuracy of spectral response on agricultural grounds under different management systems. The aim of this work is to make a qualitative analysis on spectral signatures regarding different features, and considering current interactions on agricultural grounds under field conditions as well. Field-measured Spectroradiometric reflectances have been obtained (“ASDFieldSpec Pro FR”) over 8 transects in agricultural plots with no crops, under different soybean residue covers, and under different soil management systems. These acquisitions took place on soils with similar characteristics as regards gr...
Remote Sensing, 2016
The spatial sampling interval, as related to the ability to digitize a soil profile with a certai... more 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.
Revista de la Facultad de Agronomia
A Soil erodibility factor K values (for nomograph) has been obtained for 13 soils that represent ... more A Soil erodibility factor K values (for nomograph) has been obtained for 13 soils that represent the Azul stream upper watershed, which is the main creek of the District. Values obtained have low variation (cv:0,20). In reference to the correlations with the properties valuated, clay (R2 =0,71) is the most adjusted to them, followed by the sand with R2=0,68. Actual data also shows that silt (R2=0,06) as well as residue cover (R2 =0,01), have low correlation with K. Also, very fine sand (R2=0,20 ) and soil organic matter (R2=0,10) are low coefficients. Trends show that soil texture relates with K: they show good response to clay, total sands and organic matter theoretical principles, but they show no response to silt and very fine sand. Bulk density values of first ten centimetres show a positive correlation. Every property valuated has low variation coefficient (between 0,10 and 0,34). Erodibility quantification and its relationship with some soil properties may contribute to unders...
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
Soil moisture retrieval from SAR data presents two main sources of uncertainty: terrain heterogen... more Soil moisture retrieval from SAR data presents two main sources of uncertainty: terrain heterogeneity and speckle noise. In this paper, these issues will be addressed by using a Bayesian approach. Such a Bayesian approach (1) needs only a forward model (no retrieval model required), (2) gives the optimal unbiased estimator for the soil moisture and its error and (3) can include as many error sources as required. Through numerical simulations, a standard Oh retrieval procedure and the Bayesian approach were tested for different number of looks (n = 3 and n = 64). The results indicate that for a large number of looks the region of validity of both approaches are similar. Furthermore, contrary to the Oh model retrieval procedure which is only valid in a bounded region of the (hh, vv, hv)-space, the Bayesian approach gives an estimation of soil moisture and its error for any combination of hh, vv and hv, so enlarging the region where the retrieval is possible.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012
Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties ass... more 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 Oh's 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 minimizationbased 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. Index Terms-Bayesian methods, inverse problems, radar applications, soil moisture, synthetic aperture radar. I. INTRODUCTION S URFACE soil moisture content plays a key role in the interaction between the land surface and the atmosphere, and accurate knowledge about this variable is of interest for a variety of reasons. First, it is strongly related to vegetation development. Second, it determines the partitioning between rainfall into infiltration and runoff, which is strongly related to erosion of top soil through leaching. Third, when soil moisture is high,
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Papers by Marcela Piscitelli