Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth's surface soil moisture (SSM): the ESA's Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA's Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth's surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30-40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active-passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation. Keywords: soil moisture; moisture variability; temporal dynamics; moisture patterns; spatial disaggregation; Soil Moisture Active Passive (SMAP); Soil Moisure and Ocean Salinity (SMOS); REMEDHUSSobre la continuidad de las misiones satelitales debanda L. Nuevos paradigmas en productos y aplicaciones, grant numbers ESP2017-89463-C3-2-R (UPC part) andESP2017-89463-C3-1-R (ICM part)Unidad de Excelencia María de Maeztu MDM-2016-060

Assessment of Root Zone Soil Moisture Estimations from SMAP, SMOS and MODIS Observations

[EN]In this study, six satellite-based root zone soil moisture (RZSM) estimates from March 2015 to December 2016 were evaluated both temporally and spatially. The first two were the Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) L4 RZSM products. The other four were obtained through the Soil Water Index (SWI) approach, which embedded surface soil moisture (SSM). The SMOS-Barcelona Expert Center (BEC) L4 SSM product and the apparent thermal inertia (ATI)-derived SSM from the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used as SSM datasets. In the temporal analysis, the RZSM estimates were compared to in situ RZSM from 14 stations of the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS). Regarding the spatial assessment, the resulting RZSM maps of the Iberian Peninsula were compared between them. All RZSM values followed the temporal evolution of the ground-based measurements well, although SMOS and MODIS showed underestimation while SMAP displayed overestimation. The good results obtained from MODIS ATI are notable, notwithstanding they were not estimated through microwave radiometry. A very high agreement was found in terms of spatial patterns for the whole Iberian Peninsula except for the extreme north area, which is dominated by high mountains and dense forests

Estimation de l'humidité du sol à haute résolution spatio-temporelle : une nouvelle approche basée sur la synergie des observations micro-ondes actives/passives et optiques/thermiques

Les capteurs micro-ondes passifs SMOS et SMAP fournissent des données d'humidité du sol (SM) à une résolution d'environ 40 km avec un intervalle de 2 à 3 jours à l' échelle mondiale et une profondeur de détection de 0 à 5 cm. Ces données sont très pertinentes pour les applications cli- matiques et météorologiques. Cependant, pour les applications à échelle régionales (l'hydrologie) ou locales (l'agriculture), des données de SM à une haute résolution spatiale (typiquement 100 m ou plus fine) seraient nécessaires. Les données collectées par les capteurs optiques/thermiques et les radars peuvent fournir des indicateurs de SM à haute résolution spatiale, mais ces deux approches alternatives ont des limites. En particulier, les données optiques/thermiques ne sont pas disponibles sous les nuages et sous les couverts végétaux. Quant aux données radar, elles sont sensibles à la rugosité du sol et à la structure de la végétation, qui sont tous deux difficiles à caractériser depuis l'espace. De plus, la résolution temporelle de ces données est d'environ 6 jours. Dans ce contexte, la ligne directrice de la thèse est de proposer une nouvelle approche qui combine pour la première fois des capteurs passifs micro-ondes, optiques/thermiques et actifs micro-ondes (radar) pour estimer SM sur de grandes étendues à une résolution de 100 m chaque jour. Notre hypothèse est d'abord de nous appuyer sur une méthode de désagrégation existante (DISPATCH) des données SMOS/SMAP pour atteindre la résolution cible obtenue par les radars. A l'origine, DISPATCH est basé sur l'efficacité d' évaporation du sol (SEE) estimée sur des pixels partiellement végétalisés à partir de données optiques/thermiques (généralement MODIS) de température de surface et de couverture végétale à résolution de 1 km. Les données désagrégées de SM sont ensuite combinées avec une méthode d'inversion de SM basée sur les données radar afin d'exploiter les capacités de détection des radars Sentinel-1. Enfin, les capacités de l'assimilation des donnés satellitaires de SM dans un modèle de bilan hydrique du sol sont évaluées en termes de prédiction de SM à une résolution de 100 m et à une échelle temporelle quotidienne.Dans une première étape, l'algorithme DISPATCH est amélioré par rapport à sa version actuelle, principalement 1) en étendant son applicabilité aux pixels optiques entièrement végétalisés en utilisant l'indice de sécheresse de la végétation basé sur la température et un produit de couverture végétale amélioré, et 2) en augmentant la résolution de désagrégation de 1 km à 100 m en utilisant les données optiques/thermiques de Landsat (en plus de MODIS). Le produit de SM désagrégé à la résolution de 100 m est validé avec des mesures in situ collectées sur des zones irriguées au Maroc, indiquant une corrélation spatiale quotidienne variant de 0,5 à 0,9. Dans un deuxième étape, un nouvel algorithme est construit en développant une synergie entre les données DISPATCH et radar à 100 m de résolution. En pratique, le produit SM issu de DISPATCH les jours de ciel clair est d'abord utilisé pour calibrer un modèle de transfert radiatif radar en mode direct. Ensuite, le modèle de transfert radiatif radar ainsi calibré est utilisé en mode inverse pour estimer SM à la résolution spatio-temporelle de Sentinel-1. Sur les sites de validation, les résultats indiquent une corrélation entre les mesures satellitaires et in situ, de l'ordre de 0,66 à 0,81 pour un indice de végétation inférieur à 0,6. Dans une troisième et dernière étape, une méthode d'assimilation optimale est utilisée pour interpoler dans le temps les données de SM à la résolution de 100 m. La dynamique du produit SM dérivé de l'assimilation de SM DISPATCH à 100 m de résolution est cohérente avec les événements d'irrigation. Cette approche peut être facilement appliquée sur de grandes zones, en considérant que toutes les données (télédétection et météorologique) requises en entrée sont disponibles à l' échelle globale.SMOS and SMAP passive microwave sensors provide soil moisture (SM) data at 40 km resolution every 2-3 days globally, with a 0-5 cm sensing depth relevant for climatic and meteorological applications. However, SM data would be required at a higher (typically 100 m or finer) spatial resolution for many other regional (hydrology) or local (agriculture) applications. Optical/thermal and radar sensors can be used for retrieving SM proxies at such high spatial resolution, but both techniques have limitations. In particular, optical/thermal data are not available under clouds and under plant canopies. Moreover, radar data are sensitive to soil roughness and vegetation structure, which are challenging to characterize from outer space, and have a repeat cycle of at least six days, limiting the observations' temporal frequency. In this context, the leading principle of the thesis is to propose a new approach that combines passive microwave, optical/thermal, and active microwave (radar) sensors for the first time to retrieve SM data at 100 m resolution on a daily temporal scale. Our assumption is first to rely on an existing disaggregation method (DISPATCH) of SMOS/SMAP SM data to meet the target resolution achieved by radars. DISPATCH is originally based on the soil evaporative efficiency (SEE) retrieved over partially vegetated pixels from 1 km resolution optical/thermal (typically MODIS) surface temperature and vegetation cover data. The disaggregated SM data is then combined with a radar-based SM retrieval method to exploit the sensing capabilities of the Sentinel-1 radars. Finally, the efficacy of the assimilation of satellite-based SM data in a soil water balance model is assessed in terms of SM predictions at the 100 m resolution and daily temporal scale. As a first step, the DISPATCH algorithm is improved from its current version by mainly 1) extending its applicability to fully vegetated optical pixels using the temperature vegetation dryness index and an enhanced vegetation cover product, and 2) increasing the targeted downscaling resolution from 1 km to 100 m using Landsat (in addition to MODIS) optical/thermal data. The 100 m resolution disaggregated SM product is validated with in situ measurements collected over irrigated areas in Morocco, showing a daily spatial correlation in the range of 0.5-0.9. As a second step, a new algorithm is built on a synergy between DISPATCH and radar 100 m resolution data. In practice, the DISPATCH SM product available on clear sky days is first used to calibrate a radar radiative transfer model in the direct mode. Then the calibrated radar radia- tive transfer model is used in the inverse mode to estimate SM at the spatio-temporal resolution of Sentinel-1. Results indicate a positive correlation between satellite and in situ measurements in the range of 0.66 to 0.81 for a vegetation index lower than 0.6. As a third and final step, an optimal assimilation method is used to interpolate 100 m resolution SM data in time. The assimilation exercise is undertaken over irrigated crop fields in Spain. The analyzed SM product derived from the assimilation of 100 m resolution DISPATCH SM is consistent with irrigation events. This approach can be readily applied over large areas, given that all the required input (remote sensing and meteorological) data are available globally

Mapping Soil Moisture from Remotely Sensed and In-situ Data with Statistical Methods

Soil moisture is an important factor for accurate prediction of agricultural productivity and rainfall runoff with hydrological models. Remote sensing satellites such as Soil Moisture Active Passive (SMAP) offer synoptic views of soil moisture distribution at a regional-to-global scale. To use the soil moisture product from these satellites, however, requires a downscaling of the data from an usually large instantaneous field of view (i.e. 36 km) to the watershed analysis scales ranging from 30 m to 1 km. In addition, validation of the soil moisture products using the ground station observations without an upscaling treatment would lead to cross-level fallacy. In the literature of geographical analysis, scale is one of the top research concens because of the needs for multi-source geospatial data fusion. This dissertation research introduced a multi-level soil moisture data assimilation and processing methodology framework based on spatial information theories. The research contains three sections: downscaling using machine learning and geographically weighted regression, upscaling ground network observation to calibrate satellite data, and spatial and temporal multi-scale data assimilation using spatio-temporal interpolation. (1) Soil moisture downscaling In the first section, a downscaling method is designed using 1-km geospatial data to obtain subpixel soil moisture from the 9-km soil moisture product of the SMAP satellite. The geospatial data includes normalized difference vegetation index (NDVI), land surface temperature (LST), gross primary productivity (GPP), topographical moisture index (TMI), with all resampled to 1-km resolution. The machine learning algorithm – random forest was used to create a prediction model of the soil moisture at a 1-km resolution. The 1-km soil moisture product was compared with the ground samples from the West Texas Mesonet (WTM) station data. The residual was then interpolated to compensate the unpredicted variability of the model. The entire process was based on the concept of regression kriging- where the regression was done by the random forest model. Results show that the downscaling approach was able to achieve better accuracy than the current statistical downscaling methods. (2) Station network data upscaling The Texas Soil Observation Network (TxSON) network was designed to test the feasibility of upscaling the in-situ data to match the scale of the SMAP data. I advanced the upscaling method by using the Voronoi polygons and block kriging with a Gaussian kernel aggregation. The upscaling algorithm was calibrated using different spatial aggregation parameters, such as the fishnet cell size and Gaussian kernel standard deviation. The use of the kriging can significantly reduce the spatial autocorrelation among the TxSON stations because of its declustering ability. The result proved the new upscaling method was better than the traditional ones. (3) Multi-scale data fusion in a spatio-temporal framework None of the current works for soil moisture statistical downscaling honors time and space equally. It is important, however, that the soil moisture products are consistent in both domains. In this section, the space-time kriging model for soil moisture downscaling and upscaling computation framework designed in the last two sections is implemented to create a spatio-temporal integrated solution to soil moisture multi-scale mapping. The present work has its novelty in using spatial statistics to reconcile the scale difference from satellite data and ground observations, and therefore proposes new theories and solutions for dealing with the modifiable areal unit problem (MAUP) incurred in soil moisture mapping from satellite and ground stations

A review of spatial downscaling of satellite remotely sensed soil moisture

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed

Synergistic optical and microwave remote sensing approaches for soil moisture mapping at high resolution

Aplicat embargament des de la data de defensa fins al dia 1 d'octubre de 2022Soil moisture is an essential climate variable that plays a crucial role linking the Earth’s water, energy, and carbon cycles. It is responsible for the water exchange between the Earth’s surface and the atmosphere, and provides key information about soil evaporation, plant transpiration, and the allocation of precipitation into runoff, surface flow and infiltration. Therefore, an accurate estimation of soil moisture is needed to enhance our current climate and meteorological forecasting skills, and to improve our current understanding of the hydrological cycle and its extremes (e.g., droughts and floods). L-band Microwave passive and active sensors have been used during the last decades to estimate soil moisture, since there is a strong relationship between this variable and the soil dielectric properties. Currently, there are two operational L-band missions specifically devoted to globally measure soil moisture: the ESA’s Soil Moisture and the Ocean Salinity (SMOS), launched in November 2009; and the NASA’s Soil Moisture Active Passive (SMAP), launched in January 2015. The spatial resolution of the SMOS and SMAP radiometers, in the order of tens of kilometers (~40 km), is adequate for global applications. However, to fulfill the needs of a growing number of applications at local or regional scale, higher spatial detail (< 1 km) is required. To bridge this gap and improve the spatial resolution of the soil moisture maps, a variety of spatial enhancement or spatial (sub-pixel) disaggregation approaches have been proposed. This Ph.D. Thesis focuses on the study of the Earth’s surface soil moisture from remotely sensed observations. This work includes the implementation of several soil moisture retrieval techniques and the development, implementation, validation and comparison of different spatial enhancement or downscaling techniques, applied at local, regional, and continental scale. To meet these objectives, synergies between several active/passive microwave sensors (SMOS, SMAP and Sentinel-1) and optical/thermal sensors (MODIS) have been explored. The results are presented as follows: - Spatially consistent downscaling approach for SMOS using an adaptive moving window A passive microwave/optical downscaling algorithm for SMOS is proposed to obtain fine-scale soil moisture maps (1 km) from the native resolution (~40 km) of the instrument. This algorithm introduces the concept of a shape-adaptive window as a central improvement of the disaggregation technique presented by Piles et al. (2014), allowing its application at continental scales. - Assessment of multi-scale SMOS and SMAP soil moisture products across the Iberian Peninsula The temporal and spatial characteristics of SMOS and SMAP soil moisture products at coarse- and fine-scales are assessed in order to learn about their distinct features and the rationale behind them, tracing back to the physical assumptions they are based upon. - Impact of incidence angle diversity on soil moisture retrievals at coarse and fine scales An incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of radiative transfer effective parameters single scattering albedo and soil roughness has been carried out, highlighting the importance of such parameterization to accurately estimate soil moisture at coarse-resolution. Then, these parameterizations are used to examine the potential application of a physically-based active-passive downscaling approach to upcoming microwave missions, namely CIMR, ROSE-L and Sentinel-1 Next Generation. Soil moisture maps obtained for the Iberian Peninsula at the three different angles, and at coarse and fine scales are inter-compared using in situ measurements and model data as benchmarks.La humedad del suelo es una variable climática esencial que juega un papel crucial en la relación de los ciclos del agua, la energía y el carbono de la Tierra. Es responsable del intercambio de agua entre la superficie de la Tierra y la atmósfera, y proporciona información crucial sobre la evaporación del suelo, la transpiración de las plantas y la distribución de la precipitación en escorrentía, flujo superficial e infiltración. Por lo tanto, es necesaria una estimación precisa de la humedad del suelo para mejorar las predicciones climáticas y meteorológicas, y comprender mejor el ciclo hidrológico y sus extremos (v.g., sequías e inundaciones). Los sensores pasivos y activos en banda L se han usado durante las últimas décadas para estimar la humedad del suelo debido a la relación directa que existe entre esta variable y las propiedades dieléctricas del suelo. Actualmente, hay dos misiones operativas en banda L específicamente dedicadas a medir la humedad del suelo a escala global: la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA, lanzada en noviembre de 2009; y la misión Soil Moisture Active Passive (SMAP) de la NASA, lanzada en enero de 2015. La resolución espacial de los radiómetros SMOS y SMAP, del orden de unas decenas de kilómetros (~40 km), es adecuada para aplicaciones a escala global. Sin embargo, para satisfacer las necesidades de un número creciente de aplicaciones a escala local o regional, se requiere más detalle espacial (<1 km). Para solventar esta limitación y mejorar la resolución espacial de los mapas de humedad, se han propuesto diferentes técnicas de mejora o desagregación espacial. Esta Tesis se centra en el estudio de la humedad de la superficie terrestre a partir de datos obtenidos a través de teledetección. Este trabajo incluye la implementación de distintos algoritmos de recuperación de la humedad del suelo y el desarrollo, implementación, validación y comparación de distintas técnicas de desagregación, aplicadas a escala local, regional y continental. Para cumplir estos objetivos, se han explorado sinergias entre diferentes sensores de microondas activos/pasivos (SMOS, SMAP y Sentinel-1) y sensores ópticos/térmicos. Los resultados se presentan de la siguiente manera: - Técnica de desagregación espacialmente consistente, basada en una ventana móvil adaptativa, aplicada a los datos SMOS Se propone un algoritmo de desagregación del píxel basado en datos obtenidos de medidas radiométricas de microondas en banda L y datos ópticos, para mejorar la resolución espacial de los mapas de humedad del suelo desde la resolución nativa del instrumento (~40 km) hasta resoluciones de 1 km. El algoritmo introduce el concepto de una ventana de contorno adaptativo, como mejora principal sobre la técnica de desagregación presentada en Piles et al. (2014), permitiendo su implementación a escala continental. - Análisis multiescalar de productos de humedad del suelo SMAP y SMOS sobre la Península Ibérica Se han evaluado las características temporales y espaciales de distintos productos de humedad del suelo SMOS y SMAP, a baja y a alta resolución, para conocer sus características distintivas y comprender las razones de sus diferencias. Para ello, ha sido necesario rastrear los supuestos físicos en los que se basan. - Impacto del ángulo de incidencia en la recuperación de la humedad del suelo a baja y a alta resolución Se ha llevado a cabo una calibración adaptada al ángulo de incidencia (32.5°, 42.5° y 52.5°) de los parámetros efectivos, albedo de dispersión simple y rugosidad del suelo, descritos en el modelo de transferencia radiativa � − �, incidiendo en la importancia de esta parametrización para estimar la humedad del suelo de forma precisa a baja resolución. El resultado de las mismas se ha utilizado para estudiar la potencial aplicación de un algoritmo activo/pasivo de desagregación basado en la física para las próximas misiones de microondas, llamadas CIMR, ROSE-L y Sentinel-1 Next Generation. Los mapas de humedad recuperados a los tres ángulos de incidencia, tanto a baja como a alta resolución, se han obtenido para la Península Ibérica y se han comparado entre ellos usando como referencia mediciones de humedad in situ.Postprint (published version

HISTORICAL AND FORECASTED KENTUCKY SPECIFIC SLOPE STABILITY ANALYSES USING REMOTELY RETRIEVED HYDROLOGIC AND GEOMORPHOLOGIC DATA

Hazard analyses of rainfall-induced landslides have typically been observed to experience a lack of inclusion of measurements of soil moisture within a given soil layer at a site of interest. Soil moisture is a hydromechanical variable capable of both strength gains and reductions within soil systems. However, in situ monitoring of soil moisture at every site of interest is an unfeasible goal. Therefore, spatiotemporal estimates of soil moisture that are representative of in-situ conditions are required for use in subsequent landslide hazard analyses. This study brings together various techniques for the acquisition, modeling, and forecasting of spatiotemporal retrievals of soil moisture across areas of Eastern Kentucky for use in hazard analyses. These techniques include: A novel approach for determination of satellite-based soil moisture retrieval correction factors for use in acquisition of low orbit-based soil moisture retrievals in site-specific analyses, unique spatiotemporal modeling of soil moisture at various depths within the soil layer through assimilation of satellite-based and land surface modeled soil moisture estimates, and the development of a novel workflow to effectively provide 7-day forecasts of soil moisture for use in subsequent forecasting of landslide hazards. Soil moisture retrieved through the previous approaches was implemented within landslide hazard and susceptibility analyses across known rainfall-induced landslides within Eastern Kentucky. Investigated analyses were conducted through a coupling of spatial soil moisture retrievals with that of site-specific geomorphologic data. These analyses proved capable in the detection of incipient failure conditions indicative of landslide occurrence over these known investigated slides. These soil moisture-based analyses show that inclusion of soil moisture, as hydromechanical variable, yields a more capable hazard analysis approach. Additionally, these analyses serve as a means to gain a better understanding of the coupled hydro-mechanical behavior associated with the initiation of rainfall-induced landslides

Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large résolution spatiale (25-50 km) n’en font pas un outil adéquat pour des applications hydrologiques à une échelle locale telles que la modélisation et la prévision hydrologiques. Dans de nombreuses études, une désagrégation d’échelle de l’humidité du sol des produits satellites micro-ondes est faite puis validée avec des mesures in-situ. Toutefois, l’utilisation de ces données issues d’une désagrégation d’échelle n’a pas encore été pleinement étudiée pour des applications en hydrologie. Ainsi, l’objectif de cette thèse est de proposer une méthode de désagrégation d’échelle de l’humidité du sol issue de données satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) à différentes résolutions spatiales afin d’évaluer leur apport sur l’amélioration potentielle des modélisations et prévisions hydrologiques. À partir d’un modèle de forêt aléatoire, une désagrégation d’échelle de l’humidité du sol de SMAP l’amène de 36-km de résolution initialement à des produits finaux à 9-, 3- et 1-km de résolution. Les prédicteurs utilisés sont à haute résolution spatiale et de sources différentes telles que Sentinel-1A, MODIS et SRTM. L'humidité du sol issue de cette désagrégation d’échelle est ensuite assimilée dans un modèle hydrologique distribué à base physique pour tenter d’améliorer les sorties de débit. Ces expériences sont menées sur les bassins versants des rivières Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situés aux États-Unis. De plus, le modèle assimile aussi des données d’humidité du sol en profondeur issue d’une extrapolation verticale des données SMAP. Par ailleurs, les données d’humidité du sol SMAP et les mesures in-situ sont combinées par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilé dans le modèle hydrologique pour tenter d’améliorer la prévision hydrologique sur le bassin versant Au Saumon situé au Québec. Les résultats montrent que l'utilisation de l’humidité du sol à fine résolution spatiale issue de la désagrégation d’échelle améliore la représentation de la variabilité spatiale de l’humidité du sol. En effet, le produit à 1- km de résolution fournit plus de détails que les produits à 3- et 9-km ou que le produit SMAP de base à 36-km de résolution. De même, l’utilisation du produit de fusion SMAP/ in-situ améliore la qualité et la représentation spatiale de l’humidité du sol. Sur le bassin versant Susquehanna, la modélisation hydrologique s’améliore avec l’assimilation du produit de désagrégation d’échelle à 9-km, sans avoir recours à des résolutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la résolution spatiale la plus fine à 1- km qui offre les meilleurs résultats de modélisation hydrologique. L’assimilation de l’humidité du sol en profondeur issue de l’extrapolation verticale des données SMAP n’améliore que peu la qualité du modèle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon améliore la qualité de la prévision du débit, même si celle-ci n’est pas très significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices