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

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

L-Band Vegetation optical depth and effective scattering albedo estimation from SMAP

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. Attenuation, as represented by vegetation optical depth (VOD), is a potentially useful ecological indicator. The NASA Soil Moisture Active Passive (SMAP) mission carries significant potential for VOD estimates because of its radio frequency interference mitigation efforts and because the L-band signal penetrates deeper into the vegetation canopy than the higher frequency bands used for many previous VOD retrievals. In this study, we apply the multi-temporal dual-channel retrieval algorithm (MT-DCA) to derive global VOD, soil moisture, and effective scattering albedo estimates from SMAP Backus-Gilbert enhanced brightness temperatures posted on a 9 km grid and with three day revisit time. SMAP VOD values from the MT-DCA follow expected global distributions and are shown to be highly correlated with canopy height. They are also broadly similar in magnitude (though not always in seasonal amplitude) to European Space Agency Soil Moisture and Ocean Salinity (SMOS) VOD. The SMOS VOD values are based on angular brightness temperature information while the SMAP measurements are at a constant incidence angle, requiring an alternate approach to VOD retrieval presented in this study. Globally, albedo values tend to be high over regions with heterogeneous land cover types. The estimated effective scattering albedo values are generally higher than those used in previous soil moisture estimation algorithms and linked to biome classifications. MT-DCA retrievals of soil moisture show only small random differences with soil moisture retrievals from the Baseline SMAP algorithm, which uses a prior estimate of VOD based on land cover and optical data. However, significant biases exist between the two datasets. The soil moisture biases follow the pattern of differences between the MT-DCA retrieved and Baseline-assigned VOD values

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk

An initial assessment of SMAP soil moisture retrievals using high-resolution model simulations and in situ observations

At the end of its first year of operation, we compare soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission to simulations from a land surface model with meteorological forcing downscaled from observations/reanalysis and in situ observations from sparse monitoring networks within continental United States (CONUS). The radar failure limits the duration of comparisons for the active and combined products (~3 months). Nevertheless, the passive product compares very well against in situ observations over CONUS. On average, SMAP compares to the in situ data even better than the land surface model and provides significant added value on top of the model and thus good potential for data assimilation. At large scale, SMAP is in good agreement with the model in most of CONUS with less-than-expected degradation over mountainous areas. Lower correlation between SMAP and the model is seen in the forested east CONUS and significantly lower over the Canadian boreal forests.United States. National Aeronautics and Space Administration (NNX14AH92G)United States. National Aeronautics and Space Administration (NNX13AI44G

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

Impact of day/night time land surface temperature in soil moisture disaggregation algorithms

Since its launch in 2009, the ESA’s SMOS mission is providing global soil moisture (SM) maps at ~40 km, using the first L-band microwave radiometer on space. Its spatial resolution meets the needs of global applications, but prevents the use of the data in regional or local applications, which require higher spatial resolutions (~1-10 km). SM disaggregation algorithms based generally on the land surface temperature (LST) and vegetation indices have been developed to bridge this gap. This study analyzes the SM-LST relationship at a variety of LST acquisition times and its influence on SM disaggregation algorithms. Two years of in situ and satellite data over the central part of the river Duero basin and the Iberian Peninsula are used. In situ results show a strong anticorrelation of SM to daily maximum LST (R˜-0.5 to -0.8). This is confirmed with SMOS SM and MODIS LST Terra/Aqua at day time-overpasses (R˜-0.4 to -0.7). Better statistics are obtained when using MODIS LST day (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.06 m3 /m3 ) than LST night (R˜0.45 to 0.80; ubRMSD˜0.04 to 0.07 m3 /m3 ) in the SM disaggregation. An averaged ensemble of day and night MODIS LST Terra/Aqua disaggregated SM estimates also leads to robust statistics (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.07 m3 /m3 ) with a coverage improvement of ~10-20 %.Peer ReviewedPostprint (published version

The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;value&lt;/sub&gt; data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results

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