Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France (original) (raw)
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Remote Sensing of …, 2011
Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. Active and passive microwave sensors are generally considered as the best technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT) on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soil moisture retrieval in the last years. However, due to differences in the spatial resolution, observation depths and measurement uncertainties, validation of satellite data with in situ observations and/or modelled data is not straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimations from the ASCAT and AMSR-E sensors is carried out by using observed and modelled soil moisture data over 17 sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data, products generated by implementing three different algorithms with AMSR-E data are considered: (i) the Land Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and Space Administration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University of Technology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approach root-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regression correction and Cumulative Density Function (CDF) matching, are employed to remove systematic differences between satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisture values (i.e., between 0 and 1) and anomalies from the climatological expectation. Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highest correlation with observed and modelled data is found using the LPRM algorithm. Considering relative soil moisture values for an~5 cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites in France and central Italy while similar results are obtained in all other regions. Specifically, the average correlation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT and AMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satellite products when exponential filtering and CDF matching approaches are applied. On the other hand, considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperforms all the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soil moisture products was found to decrease with increasing vegetation density and to be in good accordance with previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustness over different regions in Europe, thereby highlighting advantages and shortcomings for the effective use of these data sets for operational applications such as flood forecasting and numerical weather prediction.
An EKF assimilation of AMSR-E soil moisture into the ISBA land surface scheme
Journal of Geophysical Research, 2009
1] An Extended Kalman Filter (EKF) for the assimilation of remotely sensed nearsurface soil moisture into the Interactions between Surface, Biosphere, and Atmosphere (ISBA) model is described. ISBA is the land surface scheme in Météo-France's Aire Limitée Adaptation Dynamique développement InterNational (ALADIN) Numerical Weather Prediction (NWP) model, and this work is directed toward providing initial conditions for NWP. The EKF is used to assimilate near-surface soil moisture observations retrieved from C-band Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures into ISBA. The EKF can translate near-surface soil moisture observations into useful increments to the root-zone soil moisture. If the observation and model soil moisture errors are equal, the Kalman gain for the root-zone soil moisture is typically 20-30%, resulting in a mean net monthly increment for July 2006 of 0.025 m 3 m À3 over ALADIN's European domain. To test the benefit of evolving the background error, the EKF is compared to a Simplified EKF (SEKF), in which the background errors at the time of the analysis are constant. While the Kalman gains for the EKF and SEKF are derived from different model processes, they produce similar soil moisture analyses. Despite this similarity, the EKF is recommended for future work where the extra computational expense can be afforded. The method used to rescale the nearsurface soil moisture data to the model climatology has a greater influence on the analysis than the error covariance evolution approach, highlighting the importance of developing appropriate methods for rescaling remotely sensed near-surface soil moisture data.
Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is -with the exception of arid environments -comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.
From Near-Surface to Root-Zone Soil Moisture Using Different Assimilation Techniques
Journal of Hydrometeorology, 2007
A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T , is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.
Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France
Hydrology and Earth System Sciences, 2011
This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cutoff version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cutoff. However, comparing the near-real time and delayed cutoff SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the rootzone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50 %), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.
European Journal of Remote Sensing, 2013
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.
Assimilation of Surface-and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling
… and Remote Sensing, …
Nowadays, the availability of soil moisture estimates from satellite sensors offers a great chance to improve real-time flood forecasting through data assimilation. In this paper, two real data and two synthetic experiments have been carried out to assess the effects of assimilating soil moisture estimates into a two-layer rainfall-runoff model. By using the ensemble Kalman filter, both the surface-and root-zone soil moisture (RZSM) products derived by the Advanced SCATterometer (ASCAT) have been assimilated and the model performance on flood estimation is analyzed. RZSM estimates are obtained through the application of an exponential filter. Hourly rainfall-runoff observations for the period 1994-2010 collected in the Niccone catchment (137 km 2 ), Central Italy, are employed as case study. The ASCAT soil moisture products are found to be in good agreement with the modeled soil moisture data for both the surface layer (correlation coefficient (R) of 0.78) and the root zone (R = 0.94). In the real data experiment, the assimilation of the RZSM product has a significant impact on runoff simulation that provides a clear improvement in the discharge modeling performance. On the other hand, the assimilation of the surface soil moisture product has a small effect. The same findings are also confirmed by the synthetic twin experiments. Even though the obtained results are model dependent and site specific, the possibility to efficiently employ coarse resolution satellite soil moisture products for improving flood prediction is proven, mainly if RZSM data are assimilated into the hydrological model.
Journal of Hyperspectral Remote Sensing, 2017
In recent years, there has been increasing interest in remote sensing the temporal dynamics of soil moisture contents in large agricultural areas, such as those located in the Cattinga biome of the Northeast Brazil (NEB). In this context, validation is critical for accurate and credible satellite-based products usage. The aim of this work is to present the results of the quality assessment of the Surface Soil Moisture (SSM) estimates derived from the microwave sensors on board of the Soil Moisture and Ocean Salinity (SMOS) satellite and the METOP satellite series. Dataset for both platforms are disseminated through the SMOS SSM and ASCAT-SWI operational products, respectively. SMOS SMM and ASCAT-SWI time series were compared to in situ SSM data taken in two sites from the Alagoan semiarid where the Caatinga biome is dominant from February 2012 to October 2013 at a bimonthly time scale. The Spearman's rho (r), Bias, and Root Mean Square Error (RMSE) were used as statistical metrics. Results revealed a poor performance for both products, but the SWI showed relatively good agreement in terms of trend when the soil moisture content in the upper layers was near to zero because of severe drought conditions. SWI could be useful for monitoring the variation of the SSM in rainfed crop areas of the Caatinga biome affected by severe droughts.
Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model
Journal of Irrigation and Drainage Engineering, 2017
Estimation of root-zone soil moisture (RZSM) at regional scales is a critical issue in surface hydrology that could be a great help for estimating evapotranspiration, erosion, runoff, and irrigation requirements, etc. A significant number of satellites [soil moisture and ocean salinity (SMOS), special sensor microwave imager (SSM/I), advanced microwave scanning radiometer-EOS (AMSR-E), tropical rainfall measuring mission/microwave imager (TRMM/TMI), etc.] retrieve surface soil moisture (SSM) using passive microwave remote sensing. This information can be used to derive RZSM using a new mathematical filter. In particular, the recently developed soil moisture analytical relationship (SMAR) can relate the surface soil moisture to the moisture of deeper layer using a relationship derived from a soil water balance equation where infiltration is estimated based on the relative fluctuations of soil moisture in the surface soil layer. In the present paper, the SMAR model is tested on two research databases in Africa and North America [African monsoon multidisciplinary analysis (AMMA) and soil climate analysis network (SCAN), respectively], where field measurements at different depths are available. Furthermore, the TRMM/ TMI Satellite is selected to retrieve the satellite SSM data of the studied regions using the land parameter retrieval model (LPRM). Both remotely sensed SSM and field measurements are used within the SMAR model to explore their ability in reproducing the RZSM and also to explore the existing difference in model parameterization moving from one dataset to the other. The SMAR model is applied using three different schemes: (1) with parameters calibrated using surface field measurements, (2) with parameters calibrated using remotely sensed SSM as input, and finally (3) using the remotely sensed SSM with the same parameters calibrated in Scheme 1. In all cases, SMAR parameters have been calibrated using a genetic algorithm optimizing the root-mean square error (RMSE) between SMAR prediction and measured RZSM. The results show that remotely sensed data may be coupled with the SMAR model to provide a good description of RZSM dynamics, but it requires a specific parameterization respect to Scheme 1. Nevertheless, it is surprising to observe that two of the four parameters of the model related to the soil texture are relatively stable moving from remote-sensed to field data.