D. Solimini

The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass

Polarimetric radar data collected by AIRSAR and SIR-C over agricultural fields, forests, and olive groves of the Italian Montespertoli site are analyzed. The objective is to investigate the radar capability in discriminating among various vegetation species and its sensitivity to agricultural and arboreous biomass. Results indicate that a combined use of P(0.45 GHz) and L- (1.2 GHz) bands allows one to discriminate between agricultural fields and other targets, while a combined use of L- and C- (5.3 GHz) bands allows the authors to discriminate within agricultural areas. To monitor biomass, P-band gives the best results for forests and olive groves, L-band appears to be good for crops with low plant density (m/sup -2/), while for crops with high plant density, both L- and C-bands are useful. The availability of crosspolarized data is important for both classification and biomass retrieval.

Sensitivity to microwave measurements to vegetation biomass and soil moisture content: a case study

A comparative evaluation of the potential of active and passive microwave sensors in estimating vegetation biomass and soil moisture content is carried out. For this purpose, experimental data collected on an agricultural area by airborne scatterometers and radiometers during the AGRISCATT and AGRIRAD 1988 campaigns have been used. The results show that both microwave backscattering and emission are sensitive to vegetation biomass over a wide frequency range. Multifrequency observations seem to offer good probabilities for separating wide leaf from small leaf herbaceous crops, and for detecting different growth stages. Low frequency data (L band) at a steep incidence angle (10 degrees ) confirm that both the backscattering coefficient and the normalized temperature are correlated and sensitive to soil moisture content. >

Crop classification using multiconfiguration C-band SAR data

This paper reports on an investigation aimed at evaluating the performance of a neural-network based crop classification technique, which makes use of backscattering coefficients measured in different C-band synthetic aperture radar (SAR) configurations (multipolarization/multitemporal). To this end, C-band AirSAR and European Remote Sensing Satellite (ERS) data collected on the Flevoland site, extracted from the European RAdar-Optical Research Assemblage (ERA-ORA) library, have been used. The results obtained in classifying seven types of crops are discussed on the basis of the computed confusion matrices. The effect of increasing the number of polarizations and/or measurements dates are discussed and a scheme of interyear dynamic classification of five crop types is considered.

The SIR-C/X-SAR experiment on Montespertoli: sensitivity to hydrological parameters

Multi-frequency and multi-temporal polarimetric SAR measurements, carried out during SIR-C/X-SAR missions over the Montespertoli area have been analysed and compared with data collected at the same frequency and polarization, but at different dates, with the NASA/JPL AIRSAR. This paper presents an analysis of the achieved results aiming at evaluating the contribution of SAR data for estimating some geophysical parameters which play a significant role in hydrological processes and in particular soil moisture and roughness. The study has pointed out that in the scale of surface roughness typical of agricultural areas, a co-polar L-bandsensor gives the highest information content for estimating soil moisture and surface roughness. The sensitivity to soil moisture and surface roughness for individual fields is rather low since both parameters affect the radar signal. However, considering data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to...

Monitoring Urban Land Cover in Rome, Italy, and Its Changes by Single-Polarization Multitemporal SAR Images

This study contributes an assessment of the potential of single-polarization decametric synthetic aperture radar (SAR) images in classifying land cover within and around large urban areas and in monitoring their changes. The decision task is performed on a pixel basis and is carried out by supervised neural network algorithms fed by radar image features including backscattering intensity, coherence and textural parameters. Two configurations are considered: a short-term classification and change detection scheme intended for providing information in near-real time and a long-term scheme aimed at observing the urban changes at year time scales. We use a pair of interferometric images for the short-term case, while the long-term exercise utilizes two interferometric pairs and a fifth single acquisition. The images are acquired by the ERS SAR in late winter, spring and early summer over 836 square kilometers including Rome, Italy, and its surroundings. The accuracy of the short-term algorithm in discriminating seven types of surface is higher than 86%, while the accuracy of the long-term algorithm is beyond 88%. The many changes undergone by Rome from 1994 to 1999 have been identified by the postclassification comparison change detection procedure. The pixel-by-pixel analysis of the results has been carried out for a 160 square kilometers test area, obtaining a correct detection above 82% (less than 18% missed alarms and 0.3% false alarms).

Modeling polarization properties of emission from soil covered with vegetation

Polarization characteristics of centimetric microwave emission from canopy-covered fields are investigated. Experimental data are compared against theoretical predictions obtained by two different models. In a simple and direct approach, vegetation is considered as a uniform absorbing and scattering slab with plane parallel boundaries, while in a more realistic description, plants are modeled as an ensemble of lossy dielectric disks and thin cylinders (needles). A parametric analysis, carried out to assess the sensitivity of the polarization index (PI) to the most significant parameters of vegetation shows that, although the PI is mainly influenced by global parameters such as leaf area index and plant water content, morphological parameters such as disk or needle dimensions and orientation distribution may play a relevant role. In particular, a model composed of a mixture of disks and needles is able to represent the negative value of PI sometimes measured over fully grown vegetation. >

Model Analysis of Backscatter and Emission from Vegetated Terrains

A model representing vegetation as an ensemble of randomly oriented dielectric disks overlaying an infinite half space with a rough boundary has been implemented to compute in a unified approach both the microwave backscattering coefficient and the emissivity. A parametric analysis has been carried out at X-band to assess the effects of the single soil and vegetation parameters on the responses of active and passive sensors. To validate the model, its numerical results have also been compared with experimental data collected on the field by radar and radiometric systems at L-, X- and Ku- bands.

Effect of scattering mechanisms on polarimetric features of crops and trees

Abstract This paper discusses selected features of backscattcrig data collected in summer 1989 at C, Land P band over the Dutch Flevoland test site by the NASA/JPL airborne polarimetric SAR within the MAESTRO 1 Campaign. The dependence of the response of microwave active sensors on the different types and conditions of soils, crops and trees is analysed on the basis of polarization responses “or signatures”. backscattered powers at relevant polarizations and correlation coefficients. The scattering mechanisms that appear to be effective in controlling the copolar and cross-polar radar responses of different vegetation types at the three radar frequencies are discussed too.

Comarison between the microwave emissivity and backscatter coefficient of crops

Theoretical results are obtained by modeling the vegetation as an ensemble of small disks overlaying the ground; the emissivity and backscatter coefficient for different stages of growth are computed and compared. Experimental data collected by ground-based and airborne active and passive systems are then compared

Toward Fully Automatic Detection of Changes in Suburban Areas From VHR SAR Images by Combining Multiple Neural-Network Models

Recent X-band SAR missions, such as COSMO-SkyMed (CSK), which is able to provide very high spatial resolution images of an area of interest with a short revisit time, are expected to be quite useful sources of information for monitoring the terrestrial environment and its changes. On the other hand, the huge amount of data involved, as well as the need to promptly act in case of emergency, requires the development of automatic change detection tools. This paper reports on a novel automatic change detection algorithm combining multilayer perceptron neural networks (NNs) and pulse coupled NNs, which has been implemented and tested on pairs of Stripmap and Spotlight CSK images acquired on the Tor Vergata University area in the southeast outskirts of Rome, Italy, where a significant and continuous urbanization process is occurring.