Giuseppe Satalino

On the characterization of agricultural soil roughness for radar remote sensing studies

The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation length l. However, soil moisture retrieval studies based on these models have yielded inconsistent results, not so much because of the failure of the models themselves, but because of the complexity of natural surfaces and the difficulty in estimating appropriate input roughness parameters. In this paper, the authors address the issue of soil roughness characterization in the case of agricultural fields having different tillage (roughness) states by making use of an extensive multisite database of surface profiles collected using a novel laser profiler capable of recording profiles up to 25 m long. Using this dataset, the range of RMS height and correlation values associated with each agricultural roughness state is estimated, and the dependence of these estimates on profile length is investigated. The results show that at spatial scales equivalent to those of the SAR resolution cell, agricultural surface roughness characteristics are well described by the superposition of a single scale process related to the tillage state with a multiscale random fractal process related to field topography.

Multitemporal C-band radar measurements on wheat fields

This paper investigates the relationship between C-band backscatter measurements and wheat biomass and the underlying soil moisture content. It aims to define strategies for retrieval algorithms with a view to using satellite C-band synthetic aperture radar (SAR) data to monitor wheat growth. The study is based on a ground-based scatterometer experiment conducted on a wheat field at the Matera site in Italy during the 2001 growing season. From March to June 2001, eight C-band scatterometer acquisitions at horizontal-horizontal and vertical-vertical polarization, with incidence angles ranging from 23/spl deg/ to 60/spl deg/, were taken. At the same time, soil moisture, wheat biomass, and canopy structure were collected. The paper describes the experiment and investigates the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages. Based on the experimental results, the retrieval of wheat biomass and soil moisture content using Advanced Synthetic Aperture Radar data is discussed.

Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops

This paper investigates the potential of multi-temporal C- and L-band SAR data, acquired within a short revisiting time (1-2 weeks), to map temporal changes of surface soil moisture content (mv) underneath agricultural crops. The analysed data consist of a new ground and SAR data set acquired on a weekly basis from late April to early August 2006 over the DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) agricultural site (Northern Germany) during the European Space Agency 2006 AgriSAR campaign. The paper firstly investigates the main scattering mechanisms characterizing the interaction between the SAR signal and crops, such as winter wheat and rape. Then, the relationship between backscatter and soil moisture content temporal changes as a function of different SAR bands and polarizations is studied. Observations indicate that rationing of the multi-temporal radar backscatter can be a simple and effective way to decouple the effect of vegetation and surface roughness from the effect of soil moisture changes, when volume scattering is not dominant. The study also assesses to which extent changes in the incidence angle between subsequent radar acquisitions may affect the radar sensitivity to soil moisture content. Finally, an algorithm based on the change detection technique retrieving superficial soil moisture content is proposed and assessed both on simulated and experimental data. Results indicate that for crops relatively insensitive to volume scattering in the vegetation canopy (as for instance winter wheat at C-band or winter rape and winter wheat at L-band), mv can be retrieved during the whole growing season, with accuracies ranging between 5% and 6% [m3/m3]. We also show that low incidence angles (e.g., 20-35 ) and HH polarization are generally better suited to mv retrieval than VV polarization and higher incidence angles.

On current limits of soil moisture retrieval from ERS-SAR data

Assesses the feasibility of retrieving soil moisture content over smooth bare-soil fields using European Remote Sensing synthetic aperture radar (ERS-SAR) data. The roughness conditions considered in this study correspond to those observed in agricultural fields at the time of sowing. Within this context, the retrieval possibilities of a single-parameter ERS-SAR configuration is assessed using appropriately trained neural networks. Three sources of error affecting soil moisture retrieval (inversion, measurement, and model errors) are identified, and their relative influence on retrieval performance is assessed using synthetic datasets as well as a large pan-European database of ground and ERS-1 and ERS-2 measurements. The results from this study indicate that no more than two soil moisture classes can reliably be distinguished using the ERS configuration, even for the restricted roughness range considered.

Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions

This paper presents a retrieval algorithm that estimates spatial and temporal distribution of volumetric soil moisture content, at an approximate depth of 5 cm, using multitemporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization images, acquired at low incidence angles (i.e., from 15/spl deg/ to 31/spl deg/). The algorithm appropriately assimilates a priori information on soil moisture content and surface roughness in order to constrain the inversion of theoretical direct models, such as the integral equation method model and the geometric optics model. The a priori information on soil moisture content is obtained through simple lumped water balance models, whereas that on soil roughness is derived by means of an empirical approach. To update prior estimates of surface parameters, when no reliable a priori information is available, a technique based solely on the use of multitemporal SAR information is proposed. The developed retrieval algorithm is assessed on the Matera site (Italy) where multitemporal ground and ASAR data were simultaneously acquired in 2003. Simulated and experimental results indicate the possibility of attaining an accuracy of approximately 5% in the retrieved volumetric soil moisture content, provided that sufficiently accurate a priori information on surface parameters (i.e., within 20% of their whole variability range) is available. As an example, multitemporal soil moisture maps at watershed scale, characterized by a spatial resolution of approximately 150 m, are derived and illustrated in the paper.

Comparison of polarimetric SAR observables in terms of classification performance

The advent of fully polarimetric systems has led to an increased amount of information acquired by synthetic aperture radar (SAR) sensors but also to an increased complexity of the data to be analysed and interpreted. In particular, the choice of several representations of the data, in terms of different parameters with peculiar characteristics and physical meaning, has been offered. With this work, we intend to address their systematic investigation with a twofold goal: (1) to provide a brief review of the polarimetric representations under consideration; and (2) to characterize and compare them with respect to their usefulness for classification purposes. The analysis procedure consists of the accuracy estimation of classification tests performed on different parameters derived from L‐band polarimetric SAR data. In order to ensure a common basis for their comparison, a neural network classifier, the Multi‐Layer Perceptron trained by the Back‐Propagation learning rule, was used which permits us to operate on the data without making any a priori assumption on their statistics. In this way, the considered polarimetric parameters, in general characterized by different statistical distributions, may undergo the same classification process and the results compared. Our results indicate that the overall classification performance varies depending on the polarimetric parameters used. However, these variations are relatively limited and do not permit us, at this stage, to define an ‘absolute’ best representation to identify the classes under investigation in an optimal way.

Crop Classification Using Short-Revisit Multitemporal SAR Data

Classification of crops and other land cover types is an important application of both optical/infrared and SAR satellite data. It is already an import application of present satellite systems, as it will be for planned missions, such as the Sentinels. An airborne SAR data set with a short revisit time acquired by the German ESAR system during the ESA-campaign, AgriSAR 2006, has been used to assess the performance of different polarization modes for crop classification. Both C-and L-band SAR data were acquired over the Demmin agricultural test site in North Eastern Germany on a weekly basis during the growing season. Single-and dual-polarization, and fully polarimetric data have been used in the analysis (fully polarimetric data were only available at L-band). The main results of the analysis are, that multitemporal acquisitions are very important for single-and dual-polarization modes, and that cross-polarized backscatter produces the best results, with errors down to 3%-6% at the two frequencies. There is a trade-off between the polarimetric information and the multitemporal information, where the best overall results are obtained using the multitemporal information. If only a few acquisitions are available, the polarimetric mode may perform better than the single-and dual polarization modes.

Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

It is widely recognized that synthetic aperture radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such as, for example, hydraulic conductivity values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing and land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure based on the extended Kalman filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.

Contextual clustering for image segmentation

The unsupervised Pappas adaptive clustering (PAC) algo- rithm is a well-known Bayesian and contextual procedure for pixel label- ing. It applies only to piecewise constant or slowly varying intensity im- ages that may be corrupted by an additive white Gaussian noise field independent of the scene. Interesting features of PAC include multireso- lution implementation and adaptive estimation of spectral parameters in an iterative framework. Unfortunately, PAC removes from the scene any genuine but small region whatever the user-defined smoothing param- eter may be. As a consequence, PACs application domain is limited to providing sketches or caricatures of the original image. We present a modified PAC (MPAC) scheme centered on a novel class-conditional model, which employs local and global spectral estimates simulta- neously. Results show that MPAC is superior to contextual PAC and stochastic expectation-maximization as well as to noncontextual (pixel- wise) clustering algorithms in detecting image details.

Feature extraction and pattern classification of remote sensing data by a modular neural system

A modular neural network architecture has been used for the classification of remote sensed data in two experiments carried out to study two different but rather usual situations in real remote sensing applications. Such situations concern the availability of high-dimensional data in the first setting and an imperfect data set with a limited number of features in the second. The learning task of the supervised multilayer perceptron classifier has been made more efficient by preprocessing the input with unsupervised neural modules for feature discovery. The linear propagation network is introduced in the first experiment to evaluate the effectiveness of the neural data compression stage before classification, whereas in the second experiment data clustering before labeling is evaluated by the Kohonen self-organizing feature map network. The results of the two experiments confirm that modular learning performs better than nonmodular learning with respect to both learning quality and speed.