Emanuele Santi

A multifrequency algorithm for the retrieval of soil moisture on a large scale using microwave data from SMMR and SSM/I satellites

The sensitivity of microwave emission at different frequencies to soil moisture in bare and vegetated soils has been investigated using experimental data. Since the best frequency for the measurement of soil moisture (L-band) is absent in current satellite sensors, it is necessary to seek alternative solutions. An algorithm is proposed for the retrieval of soil moisture based on the sensitivity to moisture of both the brightness temperature and the polarization index at C-band, one that is able to correct for the effect of vegetation by means of the polarization index at X-band. The algorithm has been tested by using experimental data collected with airborne microwave radiometers on agricultural areas and validated by using the data sets of special sensor microwave/imager (SMM/I) and scanning multichannel microwave radiometer (SMMR). These research activities are planned in view of coming new satellites: AQUA (NASA) and ADEOS-II (NASDA), which will be launched by the end of 2001. These will have new generation microwave radiometers (AMSR-E and AMSR) onboard, which show much better characteristics with respect to the previous sensors, in particular an enhanced spatial resolution.

Soil Moisture Estimates From AMSR-E Brightness Temperatures by Using a Dual-Frequency Algorithm

This paper investigates the possibility of estimating the soil moisture content (SMC) on a global scale from dual-frequency (C- and X-bands) microwave data of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Because some anomalous behavior was occasionally found in AMSR-E C- and X-band data, a calibration check compared the AMSR-E data with measurements from the SSM/I sensor over two reference targets, namely a Russian evergreen forest and the sea surface, both of which have already been studied in the past. The algorithm for retrieving soil moisture uses both the brightness temperature at C-band in horizontal polarization and the polarization index at X-band for correcting the effects of vegetation. This algorithm is based on a simplified radiative transfer (tau-omega) model, which has been inverted by using the Nelder-Mead iterative minimization method. The algorithm was validated with microwave data collected on two sites during the Microwave Alpine Soil Moisture Experiment 2002 (MASMEx02) and the Soil Moisture Experiment 2002 (SMEX02), respectively. The first site, in Italy, was characterized by natural vegetation covers, whereas the second site, in Iowa (U.S.), was covered primarily in agricultural crops. In general, the soil moisture estimated by the algorithm from AMSR-E data and the SMC measured on the ground were in good agreement with each other in both sites, and five classes of soil moisture were easily identified

Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation

Soil moisture content (SMC) and above-ground biomass (AGB) are key parameters for the understanding of both the hydrological and carbon cycles. From an economical perspective, both SMC and AGB play a significant role in the agricultural sector, one of the most relevant markets worldwide. This paper assesses the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to soil moisture and vegetation biomass from an experimental point of view. For that, three scientific flights were performed in order to acquire GNSS reflectometry (GNSS-R) polarimetric observations over a wide range of terrain conditions. The GNSS-R data were used to obtain the right-left and right-right reflectivity components, which were then georeferenced according to the transmitting GNSS satellite and receiver positions. It was determined that for low-altitude GNSS-R airborne platforms, the reflectivity polarization ratio provides a highly reliable observable for SMC due to its high stability with respect to surface roughness. A correlation coefficient of 0.93 and a sensitivity of 0.2 dB/SMC (%) were obtained for moderately vegetated fields with a surface roughness standard deviation below 3 cm. Similarly, the copolarized reflection coefficient shows a stable sensitivity to forest AGB with equal to 0.9 with a stable sensitivity of 1.5 dB/(100 t/ha) up to AGB values not detectable by other remote sensing systems.

Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture

The use of Global Navigation Satellite Systems (GNSS) signals for remote sensing applications, generally referred to as GNSS-Reflectometry (GNSS-R), is gaining increasing interest among the scientific community as a remote sensing tool for land applications. This paper describes a long term experimental campaign in which an extensive dataset of GNSS-R polarimetric measurements was acquired over a crop field from a ground-based stationary platform. Ground truth ancillary data were also continuously recorded during the whole experimental campaign. The duration of the campaign allowed to cover a full crop growing season, and as a consequence of seasonal rains on the experimental area, data could be recorded over a wide variety of soil conditions. This enabled a study on the effects of different land bio-geophysical parameters on GNSS scattered signals. It is shown that significant power variations in the measured GNSS reflected signals can be detected for different soil moisture and vegetation development conditions. In this work we also propose a technique based on the combination of the reflected signal’s polarizations in order to improve the integrity of the observables with respect to nuisance parameters such as soil roughness.

Ground-Based Microwave Investigations of Forest Plots in Italy

In this paper, the result obtained on two forest stands of poplar (Populus alba) and pine (Pinus italica) in Italy, by using multi-frequency microwave radiometers, are described. Measurements were performed at L, C, X, Ku and Ka bands at different incidence angles, both in H and V polarizations, by using microwave radiometers mounted on an hydraulic boom. The sensitivity of L-band emission to woody volume was confirmed, although the effect of soil moisture is significant, especially at low values of forest biomass. Measurements carried out in upward direction gave the possibility of separating the contributions of crowns, trunks and soil and, by using a simplified model based on the radiative transfer theory, measuring consequently the forest transmissivity at different frequencies.

Modeling the Multifrequency Emission of Broadleaf Forests and Their Components

This paper shows a model study about the emissivity of forests. Model outputs are compared with multifrequency airborne measurements carried out over five broadleaf forests in Italy. Two flights took place, in summer 1999 and winter 2002. Available ground truth included important variables, such as biomass, tree density, and average trunk diameter. This data set, in conjunction with allometric equations and information taken from the literature, is used to give inputs to the model. A general agreement between simulated and measured data is observed at L-, C-, and X-bands. The same model is used to investigate the sensitivity of forest emissivity to soil moisture, woody volume, and average diameter. As expected, a moderate effect of soil moisture is observed only at L-band and for forests with a lower woody volume. At L-band, the model predicts a general increase of emissivity with woody volume but indicates that also the trunk diameter exerts an important influence, since it is a variable which controls several geometrical properties. These results allow us to single out the influence of soil moisture, woody volume, and geometrical properties at L-band. The increase of emissivity with frequency, observed in experimental data, is interpreted by means of electromagnetic considerations about branch scattering.

The Intercomparison of X-Band SAR Images from COSMO‑SkyMed and TerraSAR-X Satellites: Case Studies

The analysis of experimental data collected by X-band SAR of COSMO-SkyMed (CSK®) and TerraSAR-X (TSX) images on the same surface types has shown significant differences in the signal level of the two sensors. In order to investigate the possibility of combining data from the two instruments, a study was carried out by comparing images collected with similar orbital and sensor parameters (e.g., incidence angle, polarization, look angle) at approximately the same date on two Italian agricultural test sites. Several homogenous agricultural fields within the observed area common to the two sensors were selected. Some forest plots have also been considered and used as a reference target). Direct comparisons were then performed between CSK and TSX images in different acquisition modes. The analysis carried out on the agricultural fields showed that, in general, the backscattering coefficient is higher in TSX Stripmap images with respect to CSK-Himage (about 3 dB), while CSK-Ping Pong data showed values lower than TSX of about 4.8 dB. Finally, a difference in backscattering of about 2.5 dB was pointed out between CSK-Himage and Ping-Pong images on agricultural fields. These results, achieved on bare soils, have also been compared with simulations performed by using the Advanced Integral Equation Model (AIEM).

The contribution of multitemporal SAR data in assessing hydrological parameters

The sensitivity of radar backscattering to the principal hydrological parameters, such as vegetation biomass, soil moisture, and surface roughness, is discussed. Results obtained by using multifrequency synthetic aperture radar (SAR) data measured by the Jet Propulsion Laboratory Airborne Synthetic Aperture Radar, Spaceborne Imaging Radar-C, and European Remote Sensing 1/2 sensors are summarized. The sensitivity of L- and C-bands to spatial variations of plant and soil parameters is masked by the presence of surface roughness, which in turn affects the radar signal. However, from the observation of data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture and vegetation biomass is found to be significant, since the effects of spatial variations are smoothed. On the other hand, the sensitivity to surface roughness becomes appreciable when multitemporal data are averaged in time, thus reducing the effects of temporal moisture variations.

The Potential of COSMO-SkyMed SAR Images in Monitoring Snow Cover Characteristics

Monitoring of snow cover is crucial to the study of global climate changes for water resource management, as well as for flood and avalanche risk prevention. The sensitivity to snow characteristics of X-band backscattering of COSMO-SkyMed mission has been analyzed in the framework of experimental and model activities. X-band data have been found to contribute to the retrieval of the snow water equivalent (SWE), provided that the snow cover is characterized by a snow depth (SD) of roughly 60-70 cm (SWE >; 100-150 mm) and with relatively large crystal dimensions. Subsequently, an algorithm for retrieving SD or SWE has been developed and tested with experimental data collected on several ground stations.

Monitoring Snow Characteristics With Ground-Based Multifrequency Microwave Radiometry

Long-term microwave and infrared radiometric measurements of snowpack were carried out with ground-based sensors in winter 2006-2007 and 2007-2008, together with conventional measurements of snow-cover profiles. The first experiment focused on the behavior of snow emission during the destructive and constructive metamorphisms. The second involved a correlation analysis of the small fluctuations related to diurnal solar cycle in order to obtain the time delay of microwave brightness temperatures Tb with respect to the snow surface temperature. From this analysis, it was possible to estimate an effective (weighed average) temperature and the thickness of the layer that mostly contributed to microwave emission at 19 and 37 GHz. The ratio of the brightness temperature to the effective temperature can be assumed to be an equivalent emissivity of the snowpack. Data collected in both years have been compared with simulations carried out using the advanced Institute of Applied Physics (IFAC) Radiative Advanced Dry Snow Emission (IRIDE) model driven by data collected on ground. The model is based on the advanced integral equation method to represent soil, coupled to a layer of dry snow whose electromagnetic properties are described by the dense medium radiative transfer theory with quasi-crystalline approximation applied to a medium (air) filled with sticky particles. Simulations performed by using ground data as inputs to the model have been found to be well in agreement with experimental data. Moreover, the comparison of model simulations with experimental data allowed one to understand some peculiar characteristics of microwave emission from the snowpack related to its physical conditions.