Paolo Ferrazzoli

Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans

Abstract Surface soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition on a daily basis and on a regional scale (∼10 km). However, the effects of vegetation cover, soil temperature, snow cover, topography, and soil surface roughness also play a significant role in the microwave emission from the surface. Different soil moisture retrieval approaches have been developed to account for the various parameters contributing to the surface microwave emission. Four main types of algorithms can be roughly distinguished depending on the way vegetation and temperature effects are accounted for. These algorithms are based on (i) land cover classification maps, (ii) ancillary remote sensing indexes, and (iii) two-parameter or (iv) three-parameter retrievals (in this case, soil moisture, vegetation optical depth, and effective surface temperature are retrieved simultaneously from the microwave observations). Methods (iii) and (iv) are based on multiconfiguration observations, in terms of frequency, polarization, or view angle. They appear to be very promising as very few ancillary information are required in the retrieval process. This paper reviews these various methods for retrieving surface soil moisture from microwave radiometric systems. The discussion highlights key issues that will have to be addressed in the near future to secure operational use of the proposed retrieval approaches.

Passive microwave remote sensing of forests: a model investigation

A model, based on the radiative transfer theory and the matrix doubling algorithm, is described and used to compute the emissivity e of forests. According to model simulations, the L-band emissivity trend versus forest biomass is more gradual than that of the backscatter coefficient. This gradual behavior is observed, in absence of leaves, also at C- and X-bands, while leaves anticipate saturation and make e higher in coniferous forests and lower in deciduous forests. Model results are successfully validated by some available experimental data. Operational aspects, concerning the potential of airborne and spaceborne radiometers in identifying forest type and estimating biomass, are discussed.

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.

Simulating L-band emission of forests in view of future satellite applications

The microwave model developed at the Tor Vergata University is used to simulate the emissivity of forests, in order to study the performance of an L-band spaceborne radiometer, similar to that carried by the Soil Moisture Ocean Salinity mission. The model is first validated, and the importance of a correct vegetation growth parametrization in the modeling procedure is pointed out. This model is also used to calibrate a simple zero-order radiative transfer model, since simple models have been recognized to be useful in retrieval applications at a global scale. We show that although a zero-order approximation cannot be directly used for forests, a simple formulation may be applied, provided the albedo and the optical depth are defined as equivalent parameters.

SAR polarimetric features of agricultural areas

Abstract The potential of synthetic aperture radar (SAR) in monitoring soil and vegetation parameters is being evaluated in extensive investigations, worldwide. A significant experiment on this subject, the Multi-sensor Airborne Campaign (MAC 91), was carried out in the summer of 1991 on several sites in Europe, based on the NASA/JPL polarimetric synthetic aperture radar (AIR-SAR). The site of Montespertoli (Italy) was imaged three times during this campaign at P-, L-, and C-band and at different incidence angles between 20° and 50°. Calibrated full polarimetric data collected over the agricultural area of this site have been analysed and a critical analysis of the information contained in linear and circular co-polar and cross-polar data has also been carried out. Here a guideline for the formulation of crop discrimination algorithms is suggested. It has been found that P-band data are rather effective only in discriminating broad classes of agricultural landscape, while finer detail can be obtained by i...

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. >

Two-year global simulation of L-band brightness temperatures over land

This letter presents a synthetic L-band (1.4 GHz) multiangular brightness temperature dataset over land surfaces that was simulated at a half-degree resolution and at the global scale. The microwave emission of various land-covers (herbaceous and woody vegetation, frozen and unfrozen bare soil, snow, etc.) was computed using a simple model [L-band Microwave Emission of the Biosphere (L-MEB)] based on radiative transfer equations. The soil and vegetation characteristics needed to initialize the L-MEB model were derived from existing land-cover maps. Continuous simulations from a land-surface scheme for 1987 and 1988 provided time series of the main variables driving the L-MEB model: soil temperature at the surface and at depth, surface soil moisture, proportion of frozen surface soil moisture, and snow cover characteristics. The obtained global maps constitute a useful dataset for a first evaluation of the sensitivity of future satellite-based L-band radiometry data to soil moisture.

Retrieving soil moisture and agricultural variables by microwave radiometry using neural networks

Two neural network algorithms trained by a physical vegetation model are used to retrieve soil moisture and vegetation variables of wheat canopies during the whole crop cycle. The first algorithm retrieves soil moisture using L band, two polarizations and multiangular radiometric data, for each single date of radiometric acquisition. The algorithm includes roughness and vegetation effects, but does not require a priori knowledge of roughness and vegetation parameters for the specific field. The second algorithm retrieves vegetation variables using dual band, V polarization and multiangular radiometric data. This algorithm operates over the whole multitemporal data set. Previously retrieved soil moisture values are also used as a priori information. The algorithms have been tested considering measurements carried out in 1993 and 1996 over wheat fields at the INRA Avignon test site.

Experimental and model investigation on radar classification capability

The capability of multifrequency polarimetric synthetic aperture radar (SAR) to discriminate among nine vegetation classes is shown using both experimental data and model simulations. The experimental data were collected by the multifrequency polarimetric AIRSAR at the Dutch Flevoland site and the Italian Montespertoli site. Simulations are carried out using an electromagnetic model, developed at Tor Vergata University, Rome, Italy, which computes microwave vegetation scattering. The classes have been defined on the basis of geometrical differences among vegetation species, leading to different polarimetric signatures. It is demonstrated that, for each class, there are some combinations of frequencies and polarizations producing a significant separability. On the basis of this background, a simple, hierarchical parallelepiped algorithm is proposed.

A simple approach to monitor crop biomass from C-band radar data

Abstract A simple two-term model of the radar backscattering coefficient of crops, designed for the retrieval of the amount of water in the canopy, is described and analyzed. The principle of the method is to calibrate the simple model from the simulations of a discrete first-order radiative transfer model during crop development. The canopy structure is taken into account in the discrete model to compute the relationships between a) the vegetation direct contribution to backscattering σ ° v and the optical depth τ and b) the optical depth τ and the amount of water in the canopy. The two-term model is tested against C-band radar data acquired over a soybean crop during the whole vegetation cycle. The simulations correlate well with the measurements and the retrieval of the amount of water in the canopy Wc (kg/m 2 ) can be carried out. Accurate temporal information on the crop growth could be derived from the radar data. Ancillary information about soil moisture are required, but it is found that rough estimates on a 4–5 day basis are sufficient.