Modeling microwave emission from snow covered soil

Il ciclo idrologico rappresenta l’insieme di tutti i fenomeni legati alla circolazione e alla conservazione dell’acqua sulla Terra. Il monitoraggio su scala globale dei fattori che concorrono a produrre e modificare tale ciclo (umidità del terreno, copertura vegetale, estensione e caratteristiche del manto nevoso) risulta di estrema importanza per lo studio del clima e dei cambiamenti globali. Inoltre, l’osservazione sistematica di queste grandezze è importante per prevedere condizioni di rischio da alluvioni, frane e valanghe come pure fare stime delle risorse idriche. In questo contesto Il telerilevamento da satellite gioca un ruolo fondamentale per le sue caratteristiche di osservazioni continuative di tutto globo terrestre. I sensori a microonde permettono poi di effettuare misure indipendentemente dall’illuminazione solare e anche in condizioni meteorologiche avverse. I processi idrologici, ed in particolare quelli della criosfera (la porzione di superficie terrestre in cui l’acqua è presente in forma solida), sono fra quelli che meglio si possono investigare analizzando la radiazione elettromagnetica emessa o diffusa. Mediante l’utilizzo di modelli elettromagnetici che permettono di simulare l’emissione e lo scattering da superfici naturali è possibile interpretare le misure elettromagnetiche ed effettuare l’estrazione di quelle grandezze che caratterizzano i suoli e la loro copertura. In questo lavoro di dottorato si è affrontato il problema della modellistica a microonde dei terreni coperti da neve, sia asciutta che umida. Dopo aver preso in considerazione i modelli analitici maggiormente utilizzati per simulare diffusione ed emissione a microonde dei suoli nudi e coperti da neve si è proceduto allo sviluppo e implementazione di due modelli di emissività. Il primo, basato sulla teoria delle fluttuazioni forti, è atto a descrivere il comportamento di un manto nevoso umido. Il secondo, basato sull’accoppiamento del modello di scattering superficiale AIEM (Advanced Integral Equation Method) con la teoria del trasferimento radiativo nei mezzi densi, è volto allo studio di uno strato di neve asciutta sovrastante un suolo rugoso. Tali modelli tengono conto degli effetti coerenti presenti nell’emissione del manto nevoso e non inclusi nella teoria del trasporto radiativo classico. Entrambi i codici sono stati validati con datasets numerici e sperimentali in parte derivati da archivi ed in parte ottenuti nel contesto di questo lavoro che ha previsto quindi anche una fase sperimentale. Quest’ultima è stata condotta con misure radiometriche multifrequenza su un’area di test situata sulle Alpi orientali. Le simulazioni ottenute con questi modelli e le conseguenti analisi hanno permesso di individuare la sensibilità della temperatura di brillanza ai parametri di interesse (spessore, equivalente in acqua e umidità del manto nevoso) in funzione di diverse configurazioni osservative (frequenza, polarizzazione ed angolo di incidenza). Questo ha consentito di migliorare la comprensione dei meccanismi di emissione dalle superfici innevate e di individuare le migliori condizioni osservative per un sistema di telerilevamento terrestre

Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X- and Ku-Bands

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient R ≃ 0.77 between estimated and target SD, a root-mean-square error (RMSE) ≃ 13 cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with R = 0.74, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount of in situ data required for training the algorithm and of extending the algorithm exportability to other test sites

Microwave Radiometry at Frequencies From 500 to 1400 MHz: An Emerging Technology for Earth Observations

icrowave radiometry has provided valuable spaceborne observations of Earth’s geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observ- ing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsur- face at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500–1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under veg- etation canopies. However, the absence of significant spectrum re- served for passive microwave measurements in the 500–1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500–1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities

UWBRAD 500-2000 MHz brightness temperature measurements during the ISSIUMAX campaign in Terra Nova Bay, Antarctica

The dataset contains 500-2000 MHz brightness temperature measurements gathered by the Ultra-Wideband Software Defined Microwave Radiometer (UWBRAD) during the Ice Sheet and Sea Ice Airborne Microwave eXperiment (ISSIUMAX) in Terra Nova Bay, Antarctica. UWBRAD was operated from a DHC-6 Twin Otter during the XXXVIII Italian Antarctic Expedition. The campaign collected the first ultrawideband brightness temperature measurements in Antarctica over different targets including sea ice, ice sheets, glaciers, ice shelves, and land. The dataset was collected on 24-25 November 2018 in the coastal region surrounding Mario Zucchelli Station (-74.694807°, 164.113268°). The UWBRAD microwave radiometer measures nadiral circularly polarized brightness temperatures over the entire 500–2000 MHz range using multiple frequency channels. Since this frequency range is not a protected portion of the spectrum, the measurements can be affected by anthropogenic RFI. To address this issue, UWBRAD includes RFI detection and filtering methods to filter out artificial signals received by the radiometer. A full description of the instrument can be found in Andrews et al, 2022 (DOI: 10.1109/TGRS.2021.3090945). The published dataset is in ascii format and consists of geolocated nadiral brightness temperature measurements collected over 12 sub-bands whose central frequencies are 560, 660, 820, 900, 1180, 1240, 1370, 1500, 1630, 1740, 1860, and 1950 MHz. Only measurements with a viewing angle within 5 deg with respect to nadir are reported

Radar bistatic configurations for soil moisture retrieval: A simulation study

The possible contribution of bistatic radar measurements for bare soil moisture retrieval is investigated in this paper. A simulation study based on well-established electromagnetic models of rough surface scattering (both coherent and incoherent components) has been accomplished for this purpose. The retrieval accuracy has been evaluated by using both the Cramer-Rao lower bound and the error variance of a linear regression estimator, thus considering slightly different assumptions on retrieval conditions. Both methods have allowed us to identify the optimal system configurations in terms of observation directions, polarizations, and frequency. This identification has been carried out for single-polarization and multipolarization receivers and for the case in which bistatic measurements are complemented by monostatic ones, which are expected to be available through already-existing spaceborne synthetic aperture radars. The optimal systems have first been singled out by considering a Gaussian autocorrelation function (ACF) and a constant value of correlation length. Successively, the simulations for an exponential ACF and a variable correlation length have been analyzed, demonstrating that the results substantially remain the same. The comparison between the soil moisture estimation accuracy yielded by the optimal configurations and that provided by the standard monostatic radar has shown that a significant improvement in the quality of retrieval can be achieved by complementing bistatic and monostatic measurements. © 2008 IEEE

On the coherent and non coherent components of bare and vegetated terrain bistatic scattering: Modelling the GNSS-R signal over land

The work presented in this paper has been carried out with the aim of interpreting the data of a GNSS Reflectometer (GNSS-R) over land. The problem involves the analysis of bistatic scattering of the incoming signal collected around the specular direction. This requires to model the coherent component associated to the mean surface but at the same time the diffuse incoherent component due to roughness at wavelength scale. In presence of vegetation, both components will be affected, the former mainly because of the canopy attenuation and the latter for the combined effect of attenuation and volume scattering. The paper reviews the problem and presents the approach followed to develop a simulator of GNSS-R data over land, aiming to support potential applications of GNSS-R for soil moisture and biomass retrieval

Bistatic radar with large baseline for bio-geophysical parameter retrieval

This work aims at defining applications, products and user requirements, as well as the hardware and ground processing design of a companion satellite mission which shall carry aboard a 'passive' radar working in tandem with the Argentinian L-band radar developed by CONAE and denoted as SAOCOM. The primary objective (i.e., science driver) of the SAOCOM companion satellite mission (SAOCOM-CS) is forest tomography, which will be carried out by exploiting small baselines between active and passive systems (order of km) changing with time. Conversely, this paper summarizes the investigation carried out for different bistatic radar configurations that are characterized by much larger spatial baselines (up to hundreds of km) and bistatic angles with very large components both in azimuth and in elevation. Soil moisture and vegetation biomass retrieval takes advantage from the combined exploitation of monostatic and bistatic measurements. The retrieval ambiguity related to target azimuthal anisotropy could also be reduced by bistatic observations, like in the case of the ocean surface. The bistatic system shall collect data with suitable directions and polarizations. The expected performances of a multistatic system have been predicted using electromagnetic model

Soil moisture estimation over flat lands in the Argentinian Pampas region using Sentinel-1A data and non-parametric methods

A procedure for soil moisture (SM) estimation over flat lands in the Argentinian Pampas region, using the water balance equation that considers SM to be the result of water inflows and outflows to the soil system, is presented. In recent years, remotely sensed data with Synthetic Aperture Radar (SAR) and radiometer sensors have been used to develop different methodologies to obtain SM maps. Thus, a variety of methodologies with different levels of complexity are available nowadays. These models require soil information such as soil physical properties and mineral composition, not readily available in Argentina and many other remote areas of the world. The procedure presented in this paper takes into account water input and output processes of the soil system and represents them with different hydro-environmental variables and SAR data. The water balance equation was solved with Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) statistical models, fed with readily available data over Comisión Nacional de Actividades Espaciales (CONAE) core site located in Cordoba province, Argentina. The resulting models were obtained with precipitation (PP), air temperature (T a ) and relative humidity (RH) observations and with SAR data from the Sentinel-1A satellite mission. The accuracy of the model estimates represents 10% of the observed measured values of SM and is in line with state of the art algorithms. Results suggest that any model can be used with similar precision, since they show similar errors, although the MLR method allows analyzing and quantifying the errors introduced by the variables.Fil: García, Gabriel Agustin. Universidad Nacional del Litoral. Facultad de Ingenieria y Ciencias Hidricas. Centro de Estudios Hidro-ambientales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Venturini, Virginia. Universidad Nacional del Litoral. Facultad de Ingenieria y Ciencias Hidricas. Centro de Estudios Hidro-ambientales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Brogioni, Marco. Consiglio Nazionale delle Ricerche. Istituto di Fisica Applicata “N. Carrara”; ItaliaFil: Walker, Elisabet. Universidad Nacional del Litoral. Facultad de Ingenieria y Ciencias Hidricas. Centro de Estudios Hidro-ambientales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rodríguez, Leticia. Universidad Nacional del Litoral. Facultad de Ingenieria y Ciencias Hidricas. Centro de Estudios Hidro-ambientales; Argentin