Ceres Scales Ground Validation Campaigns for Gerb. Assessment of the Valencia Anchor Station Capabilities

Proceedings del 3rd MSG RAO Workshop, celebrado el 15 de junio de 2006 en Helsinki, Finlandia.The Valencia Anchor Station (VAS) was set up by the University of Valencia at the natural region of UtielRequena Plateau in 2001. The plateau is a large and reasonably homogeneous area suitable for validation of low spatial resolution satellite data and products such as GERB's. In the framework of the EUMETSAT/ESA MSG-RAO Project no. 138 GIST Proposal for Calibration/Validation of SEVIRI and GERB, and of the Spanish Research Programme on Space Project SCALES (SEVIRI & GERB Cal/Val Area for Largescale Field ExperimentS), three GERB ground validation campaigns have so far been carried out at the VAS under different land surface conditions. CERES instruments onboard NASA EOS Aqua and Terra satellites, operating in PAPS (Programmable Azimuth Plane Scanning) mode, have generously provided additional SW and LW radiance measurements to support validation efforts. These have shown to be most valuable as intermediate validation step between ground measurements and the large GERB pixel size

Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index Over a Vineyard Dominated Mediterranean Environment

[EN] Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 missions. To ensure its utility in a wide range of scientific and operational applications, validation efforts are required. In the past, direct validation has been constrained by the need for costly airborne hyperspectral data acquisitions, due to the lack of freely available high spatial resolution imagery incorporating appropriate spectral bands. The Multispectral Instrument (MSI) on-board the Sentinel-2 missions now offers a promising alternative. We explored the synergetic use of MSI data for validation of the OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. Using empirical and machine learning techniques applied to MSI data, in situ measurements were upscaled to the moderate spatial resolution of the OTCI. An RMSECV of 0.09 g.m(-2) (NRMSECV = 20.93%) was achieved, highlighting the valuable information MSI data can provide when used in synergy with OLCI data for land product validation. Good agreement between the OTCI and upscaled in situ measurements was observed (r = 0.77, p < 0.01), providing increased confidence to users of the product over vineyard dominated Mediterranean environments.This work was supported in part by the European Space Agency and European Commission through the Sentinel-3 Mission Performance Centre.Brown, LA.; Dash, J.; Lidón, A.; Lopez-Baeza, E.; Dransfeld, S. (2019). Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index Over a Vineyard Dominated Mediterranean Environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(7):2244-2251. https://doi.org/10.1109/JSTARS.2019.28999982244225112

Validación a largo plazo de datos de nivel 3 de tierra de SMOS con medidas de ELBARA-II en la Valencia Anchor Station

Revista oficial de la Asociación Española de Teledetección[EN] The Soil Moisture and Ocean Salinity (SMOS) mission was launched on 2nd November 2009 with the objective of providing global estimations of soil moisture and sea salinity. The main activity of the Valencia Anchor Station (VAS) is currently to assist in a long-term validation of SMOS land products. This study focus on a level 3 SMOS data validation with in situ measurements carried out in the period 2010-2012 over the VAS. ELBARA-II radiometer is placed in the VAS area, observing a vineyard field considered as representative of a major proportion of an area of 50×50 km, enough to cover a SMOS footprint. Brightness temperatures (TB) acquired by ELBARA-II have been compared to those observed by SMOS at the same dates and time. They were also used for the L-MEB model inversion to retrieve soil moisture (SM), which later on have been compared to those provided by SMOS as level 3 data. A good correlation between both TB datasets was found, improving year by year, mainly due to the decrease of precipitations in the analyzed period and the mitigation of radio frequency interferences at L-band. The larger homogeneity of the radiometer footprint as compared to SMOS explains the higher variability of its TB. Periods of more intense precipitation (spring and autumn) also presented higher SM, which corroborates the consistency of SM retrieved from ELBARA-II’s observations. However, the results show that SMOS level 3 data underestimate SM as compared to ELBARA-II’s, probably due to the influence of the small soil fraction which is not cultivated in vineyards. SMOS estimations in descending orbit (6 pm) had better quality (higher correlation, lower RMSE and bias) than the ones in ascending orbit (6 am, when there is a higher soil moisture). Guardar / Salir Siguiente >[ES] La misión de SMOS (Soil Moisture and Ocean Salinity) se lanzó el 2 de Noviembre de 2009 con el objetivo de proporcionar datos de humedad del suelo y salinidad del mar. La principal actividad de la conocida como Valencia Anchor Station(VAS) es asistir en la validación a largo plazo de productos de suelo de SMOS. El presente estudio se centra en una validación de datos de nivel 3 de SMOS en la VAS con medidas in situ tomadas en el periodo 2010-2012. El radiómetro ELBARA-II está situado dentro de los confines de la VAS, observando un campo de viñedos que se con-sidera representativo de una gran proporción de un área de 50×50 km, suficiente para cubrir un footprint de SMOS. Las temperaturas de brillo (TB) adquiridas por ELBARA-II se compararon con las observadas por SMOS en las mismas fechas y horas. También se utilizó la inversión del modelo L-MEB con el fin de obtener humedades de suelo (SM) que, posteriormente, se compararon con datos de nivel 3 de SMOS. Se ha encontrado una buena correlación entre ambas series de TB, con mejoras año tras año, achacable fundamentalmente a la disminución de precipitaciones en el perio-do objeto de estudio y a la mitigación de las interferencias por radiofrecuencia en banda L. La mayor homogeneidad del footprintdel radiómetro ELBARA-II frente al de SMOS explica la mayor variabilidad de sus TB. Los periodos de preci-pitación más intensa (primavera y otoño) también son de mayor SM, lo que corrobora la consistencia de los resultados de SM simulados a través de las observaciones del radiómetro. Sin embargo, se debe resaltar una subestimación por parte de SMOS de los valores de SM respecto a los obtenidos por ELBARA-II, presumiblemente debido a la influencia que la pequeña fracción de suelo no destinado al cultivo de la vid tiene sobre SMOS. Las estimaciones por parte de SMOS en órbita descendente (6 p.m.) resultaron de mayor calidad (mayor correlación y menores RMSE y bias) que en órbita ascendente (6 a.m., momento de mayor humedad de suelo).This work is carried out within the framework of the project MIDAS-7/UVEG Productos y Aplicaciones Avanzados de SMOS y Futuras Misiones (Parte UVEG) from the Spanish Research Programme on Space, Spanish Ministry for Economy and Competitiveness.Fernandez-Moran, R.; Wigneron, JP.; López-Baeza, E.; Miernecki, M.; Salgado-Hernanz, P.; Coll, M.; Kerr, YH.... (2015). Towards a long-term dataset of ELBARA-II measurements assisting SMOS level-3 land product and algorithm validation at the Valencia Anchor Station. Revista de Teledetección. (43):55-62. doi:10.4995/raet.2015.2297.SWORD55624

Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain

The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physiohydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models

Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station

In the framework of Soil Moisture and Ocean Salinity (SMOS) Calibration/Validation (Cal/Val) activities, this study addresses the use of the PERSIANN-CCS&lt;sup&gt;1&lt;/sup&gt;database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50×50 km&lt;sup&gt;2&lt;/sup&gt;). The study focuses on the Valencia Anchor Station (VAS) experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe. &lt;br&gt;&lt;br&gt; A faithful representation of the soil moisture distribution at SMOS pixel scale (50×50 km&lt;sup&gt;2&lt;/sup&gt;) requires an accurate estimation of the amount and temporal/spatial distribution of precipitation. To quantify the gain of using the comprehensive PERSIANN database instead of sparsely distributed rain gauge measurements, comparisons between in situ observations and satellite rainfall data are done both at point and areal scale. An overestimation of the satellite rainfall amounts is observed in most of the cases (about 66%) but the precipitation occurrences are in general retrieved (about 67%). &lt;br&gt;&lt;br&gt; To simulate the high variability in space and time of surface soil moisture, a Soil Vegetation Atmosphere Transfer (SVAT) model – ISBA (Interactions between Soil Biosphere Atmosphere) is used. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil is depicted by a comparison between different soil moisture data. Point-like and spatialized simulated data using rain gauge observations or PERSIANN – CCS database as well as ground measurements are used. It is shown that a good adequacy is reached in most part of the year, the precipitation differences having less impact upon the simulated soil moisture. The behaviour of simulated surface soil moisture at SMOS scale is verified by the use of remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E). We show that the PERSIANN database provides useful information at temporal and spatial scales in the context of soil moisture retrieval. &lt;br&gt;&lt;br&gt; &lt;br&gt;&lt;br&gt; &lt;sup&gt;1&lt;/sup&gt;Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System – &lt;a href="http://chrs.web.uci.edu/persiann"target="_blank"&gt;http://chrs.web.uci.edu/persiann&lt;/a&gt

Calibration of soil roughness and vegetation parameters in the SMOS retrieval algorithm and validation at local and global scale

La humedad del suelo y SMOS La humedad del suelo es un elemento clave que nos permite conocer los flujos de agua y energía entre el suelo y la atmósfera. Es además un parámetro de interés en aplicaciones hidrológicas y agricultura (Brocca et al., 2010), meteorología (de Rosnay et al., 2013), agricultura y predicción de riesgos naturales. La humedad del suelo en superficie se define como la fracción de agua contenida en un volumen de suelo húmedo, considerando una capa superficial de suelo de unos pocos centímetros (WMO, https://www.wmo-sat.info/oscar/variables/view/149). Puede expresarse de forma gravimétrica o de forma volumétrica. En este estudio se utiliza la relación entre el volumen de agua y el volumen de suelo que la contiene (m3·m-3). Dependiendo de su composición, todo suelo absorbe una cierta cantidad de agua hasta llegar a su punto de saturación. Existe por tanto una relación directa entre la humedad del suelo y su capacidad de infiltración, así como los flujos de calor sensible y humedad de la atmósfera, variables con una gran influencia en los modelos atmosféricos. La humedad del suelo es habitualmente una variable de iniciación de los modelos numéricos de predicción del tiempo (NWP) que permite mejorar su fiabilidad. Una aplicación significativa de la humedad del suelo a escala global es la monitorización de sequías y déficit hídrico en las plantas. El crecimiento y buen estado de la vegetación se relaciona con la cantidad de agua disponible en las raíces de la planta (hasta 1-2 m de profundidad), y esta a su vez, con la humedad superficial del suelo. La productividad de una planta dependerá por tanto de su nivel de estrés hídrico, humedad del suelo y el riesgo de hielo. La medida de la humedad del suelo desde satélite es posible gracias a la sensibilidad de la temperatura de brillo emitida en banda L a la humedad presente en la capa más superficial del suelo (~ 0-3 cm) (Escorihuela et al., 2010; Njoku and Kong, 1977). Esta relación se debe a que la emisividad del suelo en microondas está relacionada con su constante dieléctrica, y esta a su vez con la humedad del suelo. El satélite SMOS (Soil Moisture Ocean Salinity) forma parte de la primera misión cuyo objetivo es la estimación de la humedad del suelo (Kerr et al., 2012) y salinidad del agua en la Tierra (Reul et al., 2014). Su lanzamiento se produjo en Noviembre de 2009 por parte de la Agencia Espacial Europea (ESA) y fue seguido por el lanzamiento en Enero de 2015 de la misión SMAP (Soil Moisture Active Passive) por parte de NASA (Administración Nacional de la Aeronáutica y del Espacio) (Entekhabi et al., 2010), cuyo objetivo principal es la estimación de la humedad del suelo a escala global. La misión SMOS fue un proyecto ideado por la ESA en colaboración con el CDTI (Centro para el Desarrollo Tecnológico Industrial) en España, y el CNES (Centre National d’Études Spatiales) en Francia. El satélite SMOS posee un radiómetro interferométrico en banda L (1400 - 1427 MHz) de doble polarización (Kerr et al., 2001) con una resolución espacial de aproximadamente 43 km. Este radiómetro proporciona medidas multi-angulares y en polarización completa de temperatura de brillo de la Tierra con un periodo de revisita de 3 días. SMOS proporciona no solo medidas de humedad de suelo, sino también de espesor óptico de la vegetación. Este último parámetro se relaciona con ciertas características tales como el contenido en agua de la vegetación o la estructura de la misma (Grant et al., 2016). El modelo L-MEB (L-band Emission of the Biosphere) es la base de los algoritmos de nivel 2 (L2) y 3 (L3) de SMOS (Kerr et al., 2012). En ambos algoritmos, los parámetros del modelo de transferencia radiativa (Mo et al., 1982) relativos a la rugosidad del suelo y la vegetación, se consideran invariables en el tiempo y su valor viene dado por el tipo de cobertura vegetal siguiendo la clasificación de ECOCLIMAP (Masson et al., 2003). Los productos de SMOS se dividen en varios niveles (del 1 al 4). El nivel 1 es el producto primario que corresponde a las medidas de temperatura de brillo realizadas por el radiómetro. Los niveles 2 y 3 ofrecen además del producto de temperatura de brillo, la humedad de suelo y espesor óptico de la vegetación, así como todos los datos auxiliares utilizados en el modelo. Los productos de nivel 2 y 3 están geo-referenciados y usan, respectivamente, la malla ISEA (Icosahedral Synder Equal Area), 4H9 (Talone et al., 2015) y EASE (Equal-Area Scalable Earth) 2.0 (Armstrong et al., 1997). El modelo L-MEB El modelo L-MEB (Wigneron et al., 2007) es la base de los algoritmos L2 y L3 de SMOS, en los cuales se estima la humedad del suelo y el espesor óptico de la vegetación a partir de las observaciones de satélite. L-MEB emplea datos multi-angulares de temperatura de brillo en polarización horizontal (H) y vertical (V) y un modelo iterativo que consiste en la minimización de una función de coste basada en la diferencia entre la temperatura de brillo observada y la simulada, para todos los ángulos disponibles. Esta función tiene también en cuenta la incertidumbre de los parámetros elegidos para su estimación (humedad del suelo y espesor óptico de la vegetación, en el caso de los algoritmos L2 y L3 de SMOS). L-MEB modela la emisión de la capa de suelo cubierta por vegetación, teniendo en cuenta las contribuciones del suelo, la vegetación y la radiación del cielo. El suelo se presenta como una superficie rugosa cubierta de vegetación. La temperatura de brillo simulada para un suelo cubierto de vegetación se expresa como suma de la emisión directa de la vegetación, la emisión del suelo atenuada por la capa vegetal y la emisión de la vegetación que es reflejada por el suelo y atenuada por la vegetación. La relación entre la humedad del suelo y la emisión del suelo vienen dadas por el modelo dieléctrico de Mironov et al. (2012) y las ecuaciones de Fresnel, donde la humedad del suelo y la constante dieléctrica del suelo están relacionadas con la reflectividad de una superficie plana. Los efectos de rugosidad del suelo se consideran mediante una aproximación semi-empírica, mientras que para la modelización de la vegetación se considera el modelo de transferencia radiativa τ-ω (Mo et al., 1982), donde τ es el espesor óptico de la vegetación y ω el albedo de dispersión simple de la vegetación. Parámetros de rugosidad del suelo y vegetación en L-MEB En banda L, la temperatura de brillo es muy sensible a la humedad del suelo, pero existen otros factores que perturban la señal y que deben tenerse en cuenta, tales como la temperatura del suelo y la vegetación (Wigneron et al., 2007), la textura, rugosidad del suelo (Wigneron et al., 2008) y la cubierta vegetal (Grant et al., 2007). El valor efectivo del albedo de dispersión simple tiene en cuenta los efectos de absorción y dispersión debidos a la cubierta vegetal (Kurum, 2013). En los algoritmos L2 y L3 de SMOS, el valor de es 0.06 ó 0.08 en bosques (Kerr et al., 2012) y cero en cubiertas vegetales de escasa vegetación. Este último valor está basado en el análisis de campañas de medidas en banda L (Wigneron et al., 2007) sobre ciertas áreas agrícolas y por lo tanto no es aplicable a todas las clases de vegetación. El estudio de a escala global es reducido y no existe un gran número de referencias al respecto. En el algoritmo de nivel 2 de SMAP, los valores de dependen del tipo de cobertura vegetal, variando de 0 a 0.08 (O’Neill et al., 2012), mientras que el producto de nivel 4 de SMAP proporciona, entre otros parámetros, estimaciones de a escala global (De Lannoy et al., 2014). Otro estudio que trata el parámetro a escala global es Konings et al. (2016), donde se muestra un mapa de valores de ω, con valores entre 0.02 y 0.04 para coberturas vegetales de escasa vegetación y ω = 0.03 – 0.06 en bosques. Por su parte, el estudio de Van Der Schalie et al. (2016) establece ω = 0.12 como el valor más representativo a escala global tras aplicar el algoritmo LPRM (Land Parameter Retrieval Model) sobre las observaciones de SMOS y comparando el resultado de humedad del suelo con diferentes modelos. Otros parámetros que caracterizan la vegetación en el algoritmo L-MEB son ttV and ttH. Estos parámetros cuantifican la influencia del ángulo de incidencia en el espesor óptico de la vegetación. Un estudio detallado de estos parámetros fue llevado a cabo por Schwank et al. (2012) en la Valencia Anchor Station demostrando que existen variaciones importantes en los valores de ttp (p = H, V) entre verano e invierno y también entre las polarizaciones vertical y horizontal. Sin embargo, a escala global estos parámetros son difíciles de estimar debido a la complejidad de los efectos del tronco de la planta, tallos, hojas y ramas, cuya orientación es altamente aleatoria. El valor de ttP en los algoritmos L2 y L3 de SMOS es invariable e igual a 1, suponiendo que la vegetación es isotrópica. Un valor de ttP > 1 o ttP < 1 supone asumir una distribución anisotrópica de la vegetación y conlleva, respectivamente, un incremento o un decremento de τ_P en función de θ. Para tener en cuenta los efectos de la rugosidad del suelo, los algoritmos L2 y L3 de SMOS incluyen cuatro parámetros (HR, QR, NRH and NRV) (Wigneron et al., 2007). El parámetro HR tiene en cuenta la disminución en la reflectividad del suelo debida a los efectos de rugosidad; QR parametriza los efectos de la polarización (mayor o menor influencia) y NRp (p = H, V) la dependencia de la reflectividad con el ángulo de incidencia. En ambos algoritmos (L2 and L3 de SMOS), el valor de QR se supone igual a cero de manera global, mientras que a NRH y NRV se les asignan los valores 2 y 0, respectivamente. Por su parte, los valores de HR vienen definidos en función de la clasificación de usos del suelo ECOCLIMAP, siendo HR = 0.3 en bosque y HR = 0.1 en el resto de suelos (Kerr et al., 2012). En el algoritmo de humedad del suelo de SMAP L2, el valor de HR es diferente según la clasificación IGBP (International Geosphere-Biosphere), mientras que NRp = 2 (p = H, V). A escala local, existen algunas referencias sobre el valor de los parámetros de rugosidad. Como ejemplo, el estudio de Wigneron et al. (2007) arroja valores de HR = 0.1 - 0.2 para cultivos de soja y trigo y ~ 0.7 para campos de maíz. En España, el estudio de Cano et al. (2010) estima el valor de HR ~ 0.35 sobre la vegetación de matorral mediterráneo, mientras que el parámetro QR se analiza en Lawrence et al. (2013), concluyendo que QR = 0 es un valor generalizable en ausencia de condiciones de rugosidad extremas. En lo que respecta a los parámetros NRH y NRV, Escorihuela et al. (2007) y Lawrence et al. (2013) proponen una diferencia de NRH – NRV ~ 2 para superficies de poco relieve y (~ 1 – 1.5) para suelos rugosos.The capability of L-band radiometry to monitor surface soil moisture (SM) at global scale has been analyzed in numerous studies, mostly in the framework of the Soil Moisture Ocean Salinity (SMOS) and near future SMAP (Soil Moisture Active Passive) space borne missions. While the soil moisture of the first centimeters of the soil surface (~3 cm) is strongly related to the Brightness Temperature (TB) measurements, other parameters must be accounted for in order to produce accurate estimations of SM. To retrieve SM from L-band radiometric observations, two significant effects have to be accounted for: soil roughness and vegetation. In the first part of this thesis, the effects of soil roughness on retrieved SM values were evaluated using in-situ observations acquired by the L-band ELBARA-II radiometer, over a vineyard field at the Valencia Anchor Station (VAS) site during the year 2013. In the SMOS algorithm, L-MEB (L-band Microwave Emission of the Biosphere) is the forward model. Different combinations of the values of the model parameters used to account for soil roughness effects (HR, QR, NRH and NRV) were evaluated. The evaluations were made by comparing in-situ measurements of SM (used here as a reference) against SM retrievals derived from tower-based ELBARA-II brightness temperatures. The general retrieval approach consists of the inversion of L-MEB. Two specific configurations were tested: the classical 2-Parameter (2-P) retrieval configuration [where SM and τ_NAD (vegetation optical depth at nadir) were retrieved] and a 3-Parameter (3-P) configuration, accounting for the additional effects of the vineyard vegetation structure. Using the 2-P configuration, it was found that setting NRP (P = H or V) equal to -1 produced the best SM estimations in terms of correlation and unbiased Root Mean Square Error (ubRMSE). The assumption NRV = NRH = -1 leads to a simplification in L-MEB, since the two parameters τ_NAD and HR can be grouped and retrieved as a single parameter (method defined here as the Simplified Retrieval Method (SRP)). A main advantage of the SRP method is that it is not necessary to calibrate the value of HR before performing SM retrievals. Using the 3-P configuration, improved results were obtained in the SM retrievals in terms of correlation and ubRMSE. Finally, it was found that the use of in-situ roughness measurements to calibrate the values of the roughness model parameters did not provide significant improvements in the SM retrievals in comparison with the SRP method. The second part of the thesis focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters in the SM retrieval at global scale. In the current SMOS Level 2 (L2) and Level 3 (L3) retrieval algorithms, low vegetated areas are parameterized by ω = 0 and HR = 0.1, whereas values of ω = 0.06 - 0.08 and HR = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, p = H, V) were tested. In addition, the inversion approach was simplified by considering the SMOS pixels as homogeneous instead of retrieving SM only over a fraction of the pixel (excluding forested areas), as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, HR = 0.4 and NRp = -1 (p = H, V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HR varied, respectively, in the range 0.08 - 0.12 and 0.1 - 0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics. This result is a key step in the calibration of the roughness and vegetation parameters of future versions of the operational SMOS retrieval algorithm. This result was also at the base of the development of the so-called SMOS-INRA-CESBIO (SMOS-IC) product. The SMOS-IC product provides daily values of the SM and τ_NAD parameters at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. SMOS-IC is much simpler and does not account for corrections associated to the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary data sets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison of the SMOS-IC and SMO3L3 products (V300) is presented in this thesis based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A 6 year (2010-2015) inter-comparison of the two SMOS products (SMOS-IC and SMOSL3 SM (V300)) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ_NAD, SMOS-IC was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and of the northern mid-latitudes. The SMOS-IC VOD product is now extensively used in applications analyzing the impact of droughts on vegetation carbon budgets/biomass at continental scales

Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations

The final publication is available at Springer via http://dx.doi.org/10.1016/j.rse.2016.02.048Earth Observation (EO) technology is today at a maturity level that allows deriving operational estimates of Surface Soil Moisture (SSM) from a variety of sensors; yet, such products are provided at present at a coarse spatial and/or temporal resolution, which restricts their use in local or regional scale studies and practical applications. Herein, a methodology to derive SSM estimates from space at previously unattained spatio-temporal resolutions is proposed. The method is based on a variant of thePeer ReviewedPostprint (author's final draft

Simulation de l'humidité du sol-température de brillance à partir des données in situ dans le cadre de la validation des produits SMOS, site test Valencia Anchor Station

Lancée en novembre 2009, l'objectif principal de la mission SMOS (Soil Moisture and Ocean Salinity) est de fournir une cartographie globale de l'humidité du sol avec une précision supérieure à 0.04 m3/m3 et avec une résolution spatiale comprise entre 35 km au nadir et 55 km (43 km en moyenne) en utilisant la radiométrie en bande L (1.4 GHz). La résolution spatiale des satellites tels que SMOS induit de nombreuses questions scientifiques qui nécessite un minimum de mesures de terrain représentatives de la zone vue par le capteur. C'est dans cette optique que la validation des données SMOS nécessite l'acquisition de nombreuses mesures sur une vaste zone. Dans ce sens, le site VAS (Valencia Anchor Station), défini par l'université de Valence (Espagne) en décembre 2001, a pour objectif principal de caractériser une surface dédiée à l'étalonnage et la validation des missions satellites d'observation de la terre. Dans le contexte de la validation des produits SMOS au-dessus des terres émergées, l'objectif principal de cette thèse est la génération de "Match-ups" sur la zone VAS (équivalente à un pixel SMOS), qui sont à comparer avec les données réelles SMOS. Les Match-ups sont des températures de brillance micro-ondes passives simulées en utilisant les variables et les caractéristiques de surface de la zone VAS. Ainsi, pour modéliser l'humidité du sol et la température de brillance associée, nous avons couplé un modèle SVAT (Soil-Vegetation-Atmosphere-Transfer) avec un modèle de transfert radiatif. Les processus hydrologiques sont simulés avec le modèle ISBA (Interactions between Soil Biosphere Atmosphere), alors que l'émission micro-ondes est simulée avec le modèle L-MEB (L-band Microwave Emission of the Biosphere) qui est utilisé dans l'algorithme d'inversion des humidités de surface à partir des données SMOS.Launched in November 2009, the main goal of the SMOS (Soil Moisture and Ocean Salinity) mission is to map global fields of surface soil moisture with an accuracy better than 0.04 m3/m3 land with a spatial resolution ranging from 35 km at nadir up to 55 km (43 km average) using L-band (1.4 GHz) radiometry. The large pixel size of satellite missions such as SMOS, introduces a number of scientific questions that requires a minimum of field measurements representative of the area seen by the sensor. Thus, the validation of SMOS data requires the maintenance of long term measurements over large areas. This is the role of Valencia Anchor Station (VAS), established by the University of Valencia in December 2001, whose principal objective is to characterize an area dedicated to the calibration and validation of Earth Observation missions. In the context of the validation of the SMOS products over land, the main objective of this thesis consists in accurately generating the Match-ups over the VAS area (equivalent to a SMOS pixel) to be compared with the SMOS real data. The Match-ups are defined as simulated passive microwave brightness temperatures using the surface variables as well as the characteristics of the VAS area. A coupled SVAT (Soil-Vegetation-Atmosphere-Transfer) - radiative transfer model was developed for modelling the soil moisture and the resulting microwave emissions. The hydrological processes are simulated with a SVAT model named ISBA (Interactions between Soil Biosphere Atmosphere), while the microwave emission is simulated using the L-MEB (L-band Microwave Emission of the Biosphere) model upon which the SMOS Level 2 processor is based

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

The objective of this study was to compare several approaches to soil moisture (SM) retrieval using L-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30-60). Based on a three year data set (2010-2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as 'Mattar' and 'Saleh'). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and Mattar) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the reference SM data set derived from the multi-angular observations (R2 around 0.90, RMSE varying between 0.035 and 0.056 m3m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the Saleh methods based on either bi-angular or bipolarization observations (R2 around 0.93, RMSE around 0.035 m3m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the 'reference' SM time series (R2 around 0.75, RMSE around 0.055 m3m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V