Fabio Fascetti

Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

Abstract The reliable estimation of hydrological variables in space and time is of fundamental importance in operational hydrology to improve the flood predictions and hydrological cycle description. Nowadays remotely sensed data can offer a chance to improve hydrological models especially in environments with scarce ground based data. The aim of this work is to update the state variables of a physically based, distributed and continuous hydrological model using four different satellite-derived data (three soil moisture products and a land surface temperature measurement) and one soil moisture analysis to evaluate, even with a non optimal technique, the impact on the hydrological cycle. The experiments were carried out for a small catchment, in the northern part of Italy, for the period July 2012–June 2013. The products were pre-processed according to their own characteristics and then they were assimilated into the model using a simple nudging technique. The benefits on the model predictions of discharge were tested against observations. The analysis showed a general improvement of the model discharge predictions, even with a simple assimilation technique, for all the assimilation experiments; the Nash–Sutcliffe model efficiency coefficient was increased from 0.6 (relative to the model without assimilation) to 0.7, moreover, errors on discharge were reduced up to the 10%. An added value to the model was found in the rainfall season (autumn): all the assimilation experiments reduced the errors up to the 20%. This demonstrated that discharge prediction of a distributed hydrological model, which works at fine scale resolution in a small basin, can be improved with the assimilation of coarse-scale satellite-derived data.

Analysis of two years of ASCAT- and SMOS-derived soil moisture estimates over Europe and North Africa

Abstract More than two years of soil moisture data derived from the Advanced SCATterometer (ASCAT) and from the Soil Moisture and Ocean Salinity (SMOS) radiometer are analysed and compared. The comparison has been performed within the framework of an activity aiming at validating the EUMETSAT Hydrology Satellite Application Facility (H-SAF) soil moisture product derived from ASCAT. The available database covers a large part of the SMOS mission lifetime (2010, 2011 and partially 2012) and both Europe and North Africa are considered. A specific strategy has been set up in order to enable the comparison between products representing a volumetric soil moisture content, as those derived from SMOS, and a relative saturation index, as those derived from ASCAT. Results demonstrate that the two products show a fairly good degree of correlation. Their consistency has some dependence on season, geographical zone and surface land cover. Additional factors, such as spatial property features, are also preliminary investigated.

A comparison of ASCAT and SMOS soil moisture retrievals over Europe and Northern Africa from 2010 to 2013

Abstract A comparison between ASCAT/H-SAF and SMOS soil moisture products was performed in the frame of the EUMETSAT H-SAF project. The analysis was extended to the whole H-SAF region of interest, including Europe and North Africa, and the period between January 2010 and November 2013 was considered. Since SMOS and ASCAT soil moisture data are expressed in terms of absolute and relative values, respectively, different approaches were adopted to scale ASCAT data to use the same volumetric soil moisture unit. Effects of land cover, quality index filtering, season and geographical area on the matching between the two products were also analyzed. The two satellite retrievals were also compared with other independent datasets, namely the NCEP/NCAR volumetric soil moisture content reanalysis developed by NOAA and the ERA-Interim/Land soil moisture produced by ECMWF. In situ data, available through the International Soil Moisture Network, were also considered as benchmark. The results turned out to be influenced by the way ASCAT data was scaled. Correlation between the two products exceeded 0.6, while the root mean square difference did not decrease below 8%. ASCAT generally showed a fairly good degree of correlation with ERA, while, as expected considering the different kinds of measurement, the discrepancies with respect to local in situ data were large for both satellite products.

Quadruple Collocation Analysis for Soil Moisture Product Assessment

For validating remotely sensed products, the triple collocation (TC) is often adopted, which is able to retrieve the independent error variances of three systems observing the same target parameter. In this letter, three years of soil moisture data derived from the Advanced SCATterometer (ASCAT) aboard the MetOp satellite and the Soil Moisture and Ocean Salinity (SMOS) radiometer are analyzed and compared with the ERA Interim/Land model outputs and the ground measurements available from the International Soil Moisture Network. As we have four sources, a novel quadruple collocation (QC) approach is developed, which is more precise than TC since it uses the sources jointly. The results of QC show that the ERA model has the lowest error variance, while ground measurements are likely to be affected by the difficulty to represent a mean soil moisture within the satellite field of view by a limited number of stations. Moreover, the ASCAT retrievals outperform the SMOS ones if only anomalies with respect to the seasonal trend are considered, while the opposite occurs when the whole dynamic of soil moisture variation is considered.

Sinergistic use of radar and optical data for agricultural data products assimilation: A case study in Central Italy

The paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering.

Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

Abstract. A multitemporal algorithm, originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated to retrieve soil moisture from L-band radar data, such as those provided by the National Aeronautics and Space Administration Soil Moisture Active/Passive (SMAP) mission. This type of algorithm may deliver more accurate soil moisture maps that mitigate the effect of roughness and vegetation changes. Within the multitemporal inversion scheme based on the Bayesian maximum a posteriori probability (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model. The model calibration and validation tasks have been accomplished using the data collected during the SMAP validation experiment 12 spanning several soil conditions (pasture, wheat, corn, and soybean). The data have been used to update the forward model for bare soil scattering at L-band and to tune a simple vegetation scattering model considering two different classes of vegetation: those producing mainly single scattering effects and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction. The algorithm retrievals showed a root mean square difference (RMSD) around 5% over bare soil, soybean, and cornfields. As for wheat, a bias was observed; when removed, the RMSD went down from 7.7% to 5%.

Error Characterization of Soil Moisture Satellite Products: Retrieving Error Cross-Correlation Through Extended Quadruple Collocation

The triple collocation (TC) technique is being increasingly used to validate soil moisture retrievals derived from different systems, like satellites, hydrological models, or in situ probes. In recent years, several extensions of this method were proposed in order to evaluate the error standard deviations of more than three systems and to soften the TC hypothesis. In this paper, a novel extended quadruple collocation (E-QC) method is proposed, in order to consider the possibility of a cross correlation between product errors, identifying automatically the couple of error cross-correlated systems. The method is applicable even to a larger number of collocated datasets, although it may be unfeasible to collect them in practice. A synthetic experiment showed promising results, concluding that the E-QC is able to individuate (if any) the pair of systems with cross-correlated errors. It correctly compensates for the latter contribution and accurately retrieves error standard deviations of each system, otherwise biased if cross correlation is not taken into account. The E-QC was applied to soil moisture retrievals provided by satellite (SMOS, ASCAT, and SMAP), model (ERA Interim), and in situ probes (ISMN). The E-QC method identified the presence of error cross-correlation between the satellite products. This was also confirmed by analyzing the five datasets all together. E-QC showed fair performances of satellite products, especially of SMAP, although not as good as in case the presence of error correlation is not correctly taken into account.

Validation of remote sensing soil moisture products with a distributed continuous hydrological model

The reliable estimation of soil moisture in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays several satellite-derived soil moisture products are available and can offer a chance to improve hydrological model performances especially in environments with scarce ground based data. The goal of this work is to test the effects of the assimilation of different satellite soil moisture products in a distributed physically based hydrological model. Among the currently available different satellite platforms, four soil moisture products, from both the ASCAT scatterometer and the SMOS radiometer, have been assimilated using a Nudging scheme. The model has been applied to a test basin (area about 800 km2) located in Northern Italy for the period July 2012-June 2013.

Error characterization of SMOS, ASCAT, SMAP, ERA and ISMN soil moisture products: Automatic detection of cross-correlation error through extended quadruple collocation

The triple collocation (TC) technique is being increasingly used to validate soil moisture retrievals derived from different sensors. In recent years, several extensions of this method were proposed in order to evaluate the error standard deviations of more than three systems. As the number of datasets grows the TC fundamental hypothesis, i.e. the absence of cross-correlation between the system errors, could be violated. In this paper, an Extended Quadruple Collocation (E-QC) is proposed to consider the presence of the error cross-correlation between soil moisture products, identifying automatically the couple of cross-correlated systems. The method is applied to soil moisture retrievals provided by satellite (SMOS, ASCAT, SMAP), model (ERAInterim) and in situ probes (ISMN). The method identified the presence of error cross-correlation between the satellite products and was able to correctly retrieve the system errors, otherwise biased if cross-correlation is not taken into account.

An assessment of SMOS version 6.20 products through Triple and Quadruple Collocation techniques considering ASCAT, ERA/Interim LAND, ISMNand SMAP soil moisture data

In this work, two remotely sensed soil moisture data sets, derived by the Advaced SCATterometer (ASCAT) and the Soil Moisture Ocean Salinity (SMOS), have been compared with the soil moisture provided by the ERA/Interim Land data sets and measured by the in situ probes belonging to the International Soil Moisture Network (ISMN). The Triple Collocation (TC) represents a very useful tool for validating remotely sensed products; in this work, since four sources have been considered, a Quadruple Collocation (QC) approach has been also applied in order to jointly estimate the error standard deviation of the four sources making reference to a common scale as for its magnitude. Both Europe and North Africa were considered during a period starting from June, 2010 to May, 2014. Moreover, the preliminary results of a TC analysis between SMOS, ASCAT and SMAP (Soil Moisture Active/Passive) soil moisture products are shown for the same region of interest considering a period between April and December, 2015.