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Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land.

Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land

Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land

Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land

A modification of the Barcelona Expert Center (BEC) algorithm to downscale the Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) to 300 m spatial resolution is presented. It maintains the same functional relationship as the currently implemented version but employs the following inputs: SMOS brightness temperature (TB) and SM (25 km), European Center for Medium Weather Forecast (ECMWF) skin temperature (9 km), and Sentinel 3 Normalized Difference Vegetation Index (NDVI, 300 m).The performance of the downscaled SMOS SM at 300 m is analyzed by means of a temporal validation with in-situ observations from the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) and the Continuous Soil Moisture and Temperature Ground-based Observation Network (RSMN) during the year 2021. No significant differences in correlation, unbiased root mean square difference (ubRMSD) and bias are obtained over both networks compared to the 25 km and 1 km SM products, suggesting the BEC downscaling algorithm could work at hundreds of meters and result in a similar SM accuracy

The Federated Satellite Systems/ 3 Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S 3 ) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3 Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard 3 Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3 Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3 Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results

This technical note describes the Soil Moisture and Ocean Salinity (SMOS) soil moisture products freely distributed in netCDF format by the Barcelona Expert Center (BEC) on Remote Sensing. The products can be visualized by means of a web map service. The data files can be accessed and downloaded through a secure ftp (sftp) server, after registration as a user on our website: https://bec.icm.csic.es

A model for monitoring agricultural drought (SIMAGRI) has been developed in Brazil. This model is based on gridded precipitation product, real evapotranspiration calculated from vegetation index data (as proposed by [1]), and soil water storage. The soil water storage is derived from the estimation of field capacity and wilting point using pedo-transfer functions (PTFs). The SIMAGRI model suggest that the soil moisture influence is unquestionably a quantitative indicator of drought. In addition, using this model, it is possible to monitor drought episodes in agricultural regions of Brazil, especially over the Northeast, where vulnerability to drought is the highest in the country due to the prevalence of rain fed agricultural practice and frequent droughts

Incidence angle diversity of space-borne L-band radiometers needs to be taken into account for a consistent estimation of surface soil moisture (SM). In this study, the Land Parameter Retrieval Model (LPRM) is applied to SMOS brightness temperatures to calibrate the effective scattering albedo (w) and the soil roughness (h 1 ) parameter against ERA5-land SM. The analysis is carried out for SMOS data at three different incidence angles ( 32.5±5∘, 42.5±5∘ and 52.5±5∘ ) focusing in 2016 on the three main land cover types of the Iberian Peninsula according to the Climate Change Initiative (agricultural, forest and grassland). The parameterization shows an increasing trend of w and h 1 with rise of incidence angle. The SM retrieval have been evaluated with in situ SM measurements of the REMEDHUS network on rainfed crop fields. Both compare well at the three incidence angles, obtaining high correlations (0.81-0.85), an ubRMSE around 0.04 m 3 m −3 and low bias (0-0.015 m 3 m −3)

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (TB) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat—designed to provide TB data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 TB and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models—covering thin ice up to 0.6 m and full-range thickness—were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45 ºN). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth

Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature

The novel Correlated Triple Collocation (CTC) analysis allows to assess three different data sources of similar spatial resolutions, but with two of them being correlated. In this study, the CTC was applied to estimate the unbiased random errors of the global soil moisture (SM) data provided by two L-band satellite missions —the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP)— and one numerical model—the ERA5-Land. The three existing SMOS SM products distributed by different research institutions were also analyzed. Preliminary results revealed that errors of SMOS and SMAP SM are correlated, with correlations of ∼0.5-0.6. Thus, only ERA5-Land can be considered as independent. The lowest error was obtained for SMAP (0.025 m 3 m −3 ), followed by ERA5-Land (0.036 m 3 m −3 ). Among the SMOS SM, SMOS-IC had the lowest error (0.046 m 3 m −3 ), SMOS-BEC showed an intermediate value (0.048 m 3 m −3 ), and SMOS-CATDS had the highest error (0.055 m 3 m −3 )

This work presents the first scientific results over land from the Flexible Microwave Payload −2 (FMPL-2), onboard the FSSCat mission. FMPL-2 is composed of an L-band microwave radiometer and a Global Navigation Satellite System - Reflectometer (GNSS-R). Two separate ANNs models are trained using the first three months of collected data of both observations, with the objective to retrieve global soil moisture maps. The first network addresses the coarsely-resolved FMPL-2 antenna footprint in a downscaling approach. Predicted values resulted in good agreement with those obtain from the SMAP mission, with an error smaller than 9.6%, and a bias smaller than 0.001 m 3 /m 3 . The second network is implemented to estimate soil moisture exclusively on GNSS-R data. In this second case, the combination of multiple GNSS-R measurements in a single track allows to retrieve soil moisture data with an error standard deviation with respect to SMAP lower than 0.056 m 3 /m 3 , with a bias smaller than 0.0007 m 3 /m 3

FSSCat, the “Federated Satellite Systems/ 3 Cat-5” mission was the winner of the 2017 ESA S^3 (Sentinel Small Satellite) Challenge and overall winner of the Copernicus Masters competition. FSSCat consists of two 6 unit cubesats carrying on board UPC's Flexible Microwave Payload - 2 (FMPL-2), an L-band microwave radiometer and GNSS-Reflectometer implemented in a software defined radio, and Cosine's HyperScout-2 visible and near infrared + thermal infrared hyperspectral imager, enhanced with PhiSat-1, a on board Artificial intelligence experiment for cloud detection. Both spacecrafts include optical and UHF inter-satellite links technology demonstrators, provided by Golbriak Space and UPC, respectively. This paper describes the mission, and the main scientific results of the FMPL-2 obtained during the first three months of the mission, notably the sea ice concentration and thickness, and the downscaled soil moisture products over the Northern hemisphere

Knowledge about sea ice concentration and extent in polar regions is of great interest both for economic interests, and as a proxy of the climate change. Retrieved maps are based on data from microwave radiometers, which are currently provided by large satellite missions. Nowadays, CubeSats have proven to be a cost-effective alternative. Due to their low cost, they can be launched in large constellations to obtain high spatial coverage and daily revisit. This study presents a neural network approach to generate sea ice concentration and sea ice extension maps using the L-band microwave radiometer, and the GNSS-Reflectometer data from the FMPL-2 instrument onboard 3 Cat-5/A, one of the two CubeSats of the FSSCat mission. The results obtained during the first 2 months of the mission are presented

Unaffected by the atmospheric conditions and solar illumination, L-band emission and scattering are sensitive to vegetation water content and can be used to estimate crop yield. However, for rice which has an inundated period during its growing cycle, the current methods do not work due to the water under the crops. In this paper, we propose to use Global Navigation Satellite System Reflectometry (GNSS-R) signals to find how the water in rice field influence vegetation optical depth (VOD) which had been recently used to estimate the crop yield. Soil moisture (SM) and VOD in Thailand rice fields are compared to signal to noise ratio (SNR) from CYGNSS. Good correlation among them has been found. Results indicate that GNSS-R signals can be used to flag the presence of water and develop an adapted VOD algorithm that can be used to improve the estimation of rice yields

A comprehensive understanding of temporal variability of root-zone and surface soil moisture (SM) and the relationship with the underlying soil characteristics is of great importance in hydrological and agricultural applications. For the last ten years, global and frequent satellite SM observations have been available to investigate SM dynamics. However, validating remote sensing retrievals against in-situ observations based on the comparison of collocated SM time series is complicated. While satellite retrievals are approximated from inversion models over an area, in-situ measurements are determined at point-scale. This usually produces different SM dynamic ranges and biases in the corresponding time series. Moreover, the influence of soil properties and meteorological conditions can cause SM time series obtained from indirect remote sensing techniques and direct in-situ observations to be non-linearly related. Dynamic Time Warping (DTW) is a dynamic programming technique, capable of coping with temporal distortions by aiming for finding the optimal match between time series. In this study, DTW was used to provide a time lag evolution as a continuous dissimilarity measure comprising the main temporal variability features of two time series. The DTW technique was applied to SM time series from the Soil Moisture and Ocean Salinity mission (SMOS) L4 product developed at Barcelona Expert Center (BEC) with in-situ measurements at top- and subsoil-representative depth levels, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW parameters were customized to the particular input time series to obtain a robust and meaningful time lag. Seasonal differences in SM dynamics were analyzed in a clustering approach by investigating the link between SM time series and SM-regime-related parameters including precipitation and categorical features such as soil type and land use. Since the technique resolves the non

FSSCAT és una innovadora missió espacial d’observació de la Terra proposada per l’UPC NanoSatLab a l'ESA Sentinel Small Satellite Challenge, guanyadora d’aquest premi i també guanyadora global de la Copernicus Masters Competition al 2017. FSSCAT consisteix en dos cubesats de 6 unitats cadascun "federats" en suport dels serveis de medi ambient marí i terrestre de Copernicus, que proporciona mapes d'humitat del sòl i mapes d'extensió i gruix del gel marí. El “challenge” no era només la complexitat de la missió, sinó el seu desenvolupament en un temps rècord. El primer dels satèl·lits (3Cat-5/A) porta una càrrega útil de microones doble: reflectòmetre de senyals de navegació i radiòmetre de microones en banda L amb detecció / mitigació d’interferències de radiofreqüència. El segon dels satèl·lits (3Cat-5/B) porta una càrrega útil òptica híper-espectral. A més, ambos satèl·lits porten un enllaç òptic i un de radio-freqüència per testejar conceptes avançats de federació de satèl·lits i d'altres sensors a terra. FFSCAT va ser llençat el 3 de Setembre de 2020 en el VEGA SSMS PoC, i en menys d'un mes les plataformes i les càrregues útils ja estaven comissionades i va començar la producció de dades. En aquesta xerrada es presentarà el concepte de la missió FSSCAT, el disseny de la càrrega útil de microones, l'estació de recepció de dades ubicada a l'observatori del Montsec, els algorismes per a la obtenció de mapes d'humitat, extensió i gruix de gel, i l'experiment de federació de satèl·lits, tots ells desenvolupats per alumnes de doctorat

The main objectives of the WP3 are: 1) to identify the potential for retrieving additional variables from EO data linked to the state of the Polar Regions that are required for assimilation into models and forecasts of meteorological and environmental processes and their variations and 2) to assess the capabilities of future satellite missions (with special focus on the Copernicus Expansion Missions) for environmental monitoring and for providing data for integration/assimilation into modelling/forecast products, considering different In situ and airborne field measurement scenarios. To achieve these objectives we have prepared a comprehensive review of the current status of remotely sensed parameters acquired over Polar Regions and compared them with the products provided by the Copernicus service to identify current data gaps. Besides, an assessment of future satellite missions (in particular the HPCMs) has been performed, in terms of their benefit for environmental monitoring and their integration/assimilation in modelling/forecast products. We have also identified possible synergies between parameters obtained from different satellite missions to enhance the information content of specific data products considering the end-users requirements. Finally, we have identified the limitations of the currently assimilated variables as well as the potential of new variables that are relevant for assimilation into models for simulations and forecasts of conditions in the Polar Regions. Moreover, a concept for a move forward on data assimilation is presented

This technical note describes the Soil Moisture and Ocean Salinity (SMOS) soil moisture products freely distributed in netCDF format by the Barcelona Expert Center (BEC). The soil moisture products can be visualized by means of a web map service. The data files can be accessed and downloaded through a secure ftp (sftp) server, after registration as user in our website: http://bec.icm.csic.es

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land

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 scal

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 scal, Sobre 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-2016-0600

Soil moisture is an essential climate variable which plays a crucial role linking the Earth’s water, energy and carbon cycles between the land and the atmosphere. Although it represents a small percentage of all the water on Earth, it provides key information about evaporation, transpiration, infiltration and runoff. As a result, accurate knowledge of soil moisture is crucial for both global and local applications. Currently there are two operating missions, SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive), launched by ESA and NASA, which are specifically devoted to measure the (surface) soil moisture. Their passive microwave instruments operate at L-band, which is favorable for measuring soil moisture due to the high sensitivity of the microwave signal to the soil dielectric constant at this frequency. The Copernicus Imaging Microwave Radiometer (CIMR) high priority candidate mission is planned to have an L-band radiometer on-board and could provide continuation of the observational data stream. Accurate high-resolution soil moisture maps are needed to fulfill a growing number of application tasks e.g., prevention of wildfires [1][2], early-detection of forest decline [3], monitoring the evolution of insect pests [4]. To downscale soil moisture from the coarse-scale radiometer resolution to a finer one, downscaling algorithms typically use additional observations at fine scales. A wide variety of downscaling algorithms exists [5]. They may use observations from different sensors, use different ancillary data or rely on different physical assumptions. The performance of these algorithms depends on the physics they are based on, and on the information at fine scale that they use, which may also depend on season, climate and land cover. This makes a direct comparison difficult, since their performance is intrinsically time and region dependent, and the algorithm set-up will therefore reflect into the results. The baseline downscaling, [1] D. Chaparro, M. Piles, M. Vall-Llossera, A. Camps, “Surface moisture and temperature trends anticipate drought conditions linked to wildfire activity in the Iberian Peninsula,” European Journal of Remote Sensing, vol. 49, issue 1, pp. 955-971, 2016. [2] D. Chaparro, M. Vall-llossera, M. Piles, A. Camps, C. Rüdiger and R. Riera-Tatché, "Predicting the Extent of Wildfires Using Remotely Sensed Soil Moisture and Temperature Trends," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2818-2829, June 2016. [3] D. Chaparro, J. Vayreda, M. Vall-llossera, M. Banqué, M. Piles, A. Camps, J. Martínez-Vilalta, “The role of climatic anomalies and soil moisture in the decline of drought-prone forests,”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, issue 2, pp. 503-514, 2017. [4] M. J. Escorihuela, O. Merlin, V. Stefan, G. Moyano, O. A. Eweys, M. Zribi, ... & S. Ghaout. “SMOS based high resolution soil moisture estimates for Desert locust preventive. [5] J. Peng, A. Loew, O. Merlin, N. Verhoest, “A review of spatial downscaling of satellite remotely sensed soil moisture,” Reviews of Geophysics, 10.1002/2016RG000543, March 2017. [6] N. Das, R.S. Dunbar, “Level 2 SMAP/Sentinel Active/Passive Soil Moisture Product Specification Document,” Jet Propulsion Laboratory, D-56548, August 2017. [7] M. Piles, N. Sánchez, M. Vall-llossera, A. Camps, J. Martínez-Fernández, J. Martínez, V. González-Gambau, “A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula,” IEEE Journal of Sel. Topics in Applied Earth Obs. and Remote Sens., vol. 7, no. 9, pp. 3845-3857, 2014. [8] G. Portal et al., "A Spatially Consistent Downscaling Approach for SMOS Using an Adaptive Moving Window," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 6, pp. 1883-1894, June 2018

The European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite specifically dedicated to measuring soil moisture (SM) [1]. SMOS was launched in November 2009 and is still in orbit, providing an unprecedented record of L-band brightness temperature (TB) observations at tens of km of spatial resolution (~40 km) with a 3-day revisit time. Since its launch, SMOS has exhibited a successful performance, fulfilling the scientific requirements in terms of both SM and ocean salinity [2]. SMOS as well as the Soil Moisture Active Passive (SMAP) mission have allowed obtaining the most ever accurate SM measurements at global scale [3]. The operational continuity of L-band observations after SMOS and SMAP is now being proposed via the Copernicus Microwave Imaging Radiometer (CIMR) high priority candidate mission [4]. The latest release (v650) of the ESA’s SMOS Level 2 (L2) Soil Moisture User Data Product (SMUDP) has several improvements on the L2 processor [5]. SMOS L2 SMUDP is generated for each orbit at a 15 km Icosahedral Snyder Equal Area (ISEA) 4H9 grid. Due to that, SMOS Level 3 (L3) SM products, which are a composite map of all orbits within the day at a 25 km Equal Area Scalable Earth (EASE)-2 grid, are preferred by research community in some cases to avoid the manipulation difficulties of ISEA. Nevertheless, all SMOS L2 and L3 SM products, and even a higher spatial resolution Level 4 (L4) SM product [6], have demonstrated to be useful for a wide range of scientific and operational applications up to date, being used in predictive hydrological and atmospheric models [7, 8], to monitor flood and drought events [9, 10], to predict wildfire risks [11] and to estimate root zone SM [12]. Nowadays, there are three different available daily SMOS L3 SM products. The first one is a composite of binned data generated by the Centre Aval de Traitement des Données SMOS (CATDS). The SMOS-CATDS L3 SM product is retrieved using a multi-orbit algo, References [1] Kerr, Y.K.; Waldteufel, P.; Wigneron, J.P.; Delwart, S.; Cabot, F.; Boutin, J.; Escorihuela, M.J.; Font, J.; Reul, N.; Gruhier, C.; et al. (2010) “The SMOS Mission: New Tool for Monitoring Key Elements of the Global Water Cycle”, Proceedings of IEEE, 98: 666-687. [2] Mecklenburg, S.; Drusch, M.; Kaleschke, L.; Rodriguez-Fernandez, N.; Reul, N.; Kerr, Y.H.; Font, J.; Martin-Neira, M.; Oliva, R.; Daganzo-Eusebio, E.; et al. (2016) “ESA's Soil Moisture and Ocean Salinity mission: From science to operational applications”, Remote Sensing of Environment, 180: 3-18. [3] Kerr, Y.H.; Al-Yaari, A.; Rodríguez-Fernández, N.; Parrens, M.; Molero, B.; Leroux, D.; Bircher, S.; Mahmoodi, A.; Mialon, A.; Richaume, P.; et al. (2016) “Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation”, Remote Sensing of Environment, 180: 40-63. [4] Donlon, C.J. (2018) “Copernicus Imaging Microwave Radiometer (CIMR). Mission Requirements Document”, Technical report ESA-EOPSM-CIMR-MRD-3236, revision 1.5, Mission Science Division, European Space Agency (ESA), Noordwijk, Netherlands. [5] ESA (2017) “Read-me-first note for the release of the SMOS Level 2 Soil Moisture data products”, Technical report, Expert Support Laboratory (ESL) Level 2 Soil Moisture and Array Systems Computing Inc. [6] Portal, G.; Vall-llossera, M.; Piles, M.; Camps, A.; Chaparro, D.; Pablos, M.; Rossato, L. (2018) “A spatially consistent downscaling approach for SMOS using an adaptive moving window”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6): 1883-1894.. [7] Ridler, M.E.; Madsen, H.; Stisen, S.; Bircher, S.; Fensholt, R. (2014) “Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark”, Water Resources Research, 50: 8962-8981. [8] Leroux, D.J.; Pellarin, T.; Vischel, T.; Cohard, J.M.; Gascon, T.; Gibon, F.; Mialon, A.; Galle, S.; Peugeot

This technical note describes the SMOS land products freely distributed in netCDF format by the BEC (http://bec.icm.csic.es). The maps can be visualized by means of a web map service. The data files can be accessed and downloaded through sFTP, after registration as use

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 m/m) than LST night (R≈0.45 to 0.80; ubRMSD≈0.04 to 0.07 m/m) 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 m/m) with a coverage improvement of~10-20 %

[EN] This article summarizes some of the activities in which Jordi Font, research professor and head of the Department of Physical and Technological Oceanography, Institut de Ciències del Mar (CSIC, Spanish National Research Council) in Barcelona, has been involved as co-Principal Investigator for Ocean Salinity of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Mission from the perspective of the Remote Sensing Lab at the Universitat Politècnica de Catalunya. We have probably left out some of his many contributions to salinity remote sensing, but we hope that this review will give an idea of the importance of his work. We focus on the following issues: 1) the new accurate measurements of the sea water dielectric constant, 2) the WISE and EuroSTARRS field experiments that helped to define the geophysical model function relating brightness temperature to sea state, 3) the FROG 2003 field experiment that helped to understand the emission of sea foam, 4) GNSS-R techniques for improving sea surface salinity retrieval, 5) instrument characterization campaigns, and 6) the operational implementation of the Processing Centre of Levels 3 and 4 at the SMOS Barcelona Expert Centre, [ES] Este artículo resume algunas de las actividades en las que Jordi Font, profesor de investigación y jefe del Departamento de Física y Tecnología Oceanográfica, del Institut de Ciències del Mar (CSIC) en Barcelona, ha estado desarrollando como co-Investigador Principal de la parte de la misión SMOS de la ESA, una misión Earth Explorer, desde la perspectiva del Remote Sensing Lab, de la Universitat Politècnica de Catalunya. Seguramente, estamos olvidando algunas de sus muchas contribuciones a la teledetección de la salinidad, pero esperamos que esta revisión dé una idea de la importancia de su trabajo. Este artículo se focaliza en los siguientes puntos: 1) las medidas de alta calidad de la constante dieléctrica del agua marina, 2) las campañas de medidas WISE y EuroSTARRS que ayudaron a la definición del modelo geofísico relacionando la temperatura de brillo con el estado del mar, 3) la campaña de medidas FROG 2003 que ayudó a entender la emisión de la espuma marina 4) presentación de las técnicas de GNSS-R para la mejora de la recuperación de la salinidad superficial 5) campañas para la caracterización del instrumento y 6) la implantación del centro de procesado operacional de niveles 3 y 4 en el SMOS Barcelona Expert Centre

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ≈− 0.6 to −0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ≈− 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ≈− 0.5 to −0.7; satellite R ≈− 0.4 to −0.7) indicating SM–LST coupling, than in winter (in situ R ≈ +0.3; satellite R ≈− 0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ∼0.12 m 3 /m 3 , stable over the study period and consistent between

The Dome-C region, in the East Antarctic Plateau, is regarded as an ideal natural laboratory for calibration/validation of space-borne microwave radiometers. At L-band, the thermal stability of this region has been confirmed by several experimental campaigns. However, its use as an independent external calibration target has recently been questioned due to some spatial inhomogeneities and seasonal effects revealed in the brightness temperatures (TB) acquired in this area. This paper shows the observed relationship, from exploratory research, between the Antarctic ice thickness spatial variations and the measured Aquarius TB changes. A 3-months no-daylight period during the Austral winter has been analyzed. Four transects have been defined over East Antarctica covering areas with different ice thickness variations and ranges. The theoretical L-band penetration depth has been estimated to understand the possible contributions to the measured signal. A good agreement has been observed between Aquarius TB and ice thickness variations over the whole Antarctica, with correlations of ∼0.6-0.7. The two variables show a linear trend with slopes of ∼8.3-9.5 K/km. No correlation has been observed with the subglacial bedrock. The maximum L-band penetration depth has been estimated to be ∼1-1.5 km. Results are therefore consistent: the spatial variations found on Aquarius TB are not related to the emissivity of the bedrock, which lies deeper. This study provides evidence that new L-band satellite observations could contribute to further our understanding of Antarctic geophysical processes. Key Points: Relationship between Antarctic ice thickness and observed Aquarius TB. Maximum L-band penetration depth over Antarctic ice.Possible influence of subglacial lakes on Aquarius TB © 2015. American Geophysical Union. All Rights Reserved

The Dome-C region, in the East Antarctic Plateau, has been used for calibration/validation of satellite microwave radiometers since the 1970's. However, its use as an independent external target has been recently questioned due to some spatial inhomogeneities found in L-band airborne and satellite observations. This work evidences the influence of the Antarctic ice thickness spatial variations on the measured SMOS and Aquarius brightness temperatures (TB). The possible effects of subglacial water and bedrock on the acquired radiometric signals have also been analyzed. A 3-months no-daylight period during the Austral winter has been selected. Four transects over East Antarctica have been defined to study the spatial variations. A good agreement between SMOS and Aquarius TB changes and ice thickness variations over the whole Antarctica has been observed, obtaining linear correlations of 0.6-0.7 and slopes of 8.6-9.5 K/km. The subglacial lakes may affect the vertical physical temperature profile and/or the dielectric properties of the ice layers above. As expected, the subglacial bedrock is not contributing to the measured TB, since the maximum estimated L-band penetration depth is ~1-1.5 km

Surface Soil Moisture (SSM) affects the soil surface energy balance and thus affects the Land Surface Temperature (LST), and viceversa. Currently, LST and SSM are remotely sensed using TIR sensors and L-band radiometers, respectively. The NASA's Terra/Aqua missions provide full coverage of LST measurements under clear sky conditions using MODIS. The ESA's SMOS mission is the first satellite providing frequent SSM and ocean salinity observations at global scale. In this paper, a sensitivity study about the relationship of the LST and SSM is performed using in-situ measurements from the REMEDHUS network and spaceborne observations from MODIS and SMOS. Results show that the correlation between SSM and LST (both in-situ and remotely sensed) is highest using the daily maximum LST. This could help improving SSM algorithms and deriving new SSM products at higher resolution from the synergy of microwave and TIR observations

Passive microwave remote sensing at L-band is considered to be the most suitable technique to measure soil moisture and ocean salinity. These two variables are needed as inputs of predictive models, to improve climate and weather forecast, and to increase our knowledge of the water cycle. Nowadays, there are two space missions providing frequent and global observations of moisture and salinity of the Earth's surface with L-band radiometers on-board. The first one is the ESA's SMOS satellite, launched on November 2, 2009, which carries a two-dimensional, multi-angular, and full-polarimetric synthetic aperture radiometer. The second one is the NASA/CONAE's Aquarius/SAC-D mission, launched on June 10, 2011, which includes three beam push-broom real aperture radiometers. The objective of this work is to compare SMOS and Aquarius brightness temperatures and verify the continuity and consistency of the data over the entire dynamic range of observations. This is paramount if data from both radiometers are used for any long term enviromental, meteorological, hydrological, or climatological studies. The inter-comparison approach proposed is based on the study of 1 year of measurements over key target regions selected as representative of land, ice, and sea surfaces. The level of linearity, the correlation, and the differences between the observations of the two radiometers are analyzed. Results show a higher linear correlation between SMOS and Aquarius brightness temperatures over land than over sea. A seasonal effect and spatial inhomogeneities are observed over ice, at the Dome-C region. In all targets, better agreement is found in horizontal than in vertical polarization. Also, the correlation is higher at higher incidence angles. These differences indicate that there is a non-linear effect between the two instruments, not only a bias. © 2008-2012 IEEE

The European Space Agency (ESA) launched the Soil Moisture and Ocean Salinity (SMOS) satellite on November 2, 2009, to globally monitoring surface soil moisture over the landmasses and salinity over the oceans. Its single payload is the first two-dimensional Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), an instrument that provides multi-angular L-band measurements in the field of view (from 0† to 65°) and has full-polarimetric capabilities (measures H, V and HV polarizations). The next satellite devoted to global sea surface salinity monitoring is the Aquarius/SAC-D, which was launched on June 9, 2011, by a collaboration between the US National Aeronautics and Space Administration (NASA) and the Argentinian Comisión Nacional de Actividades Espaciales (CONAE). It includes three beam push-broom L-band real aperture radiometers and real aperture scatterometers, both full-polarimetric and feeded by the same reflector at three incidence angles (28.7°, 37.8° and 45.6° for the inner, the middle and the outer beams)