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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