This study assesses the accuracy of Spinning Enhanced Visible and Infrared Imager (SEVIRI) geostationary sensor-derived evapotranspiration (ET) estimates from Meteosat Second Generation (MSG) satellites across Europe. Evaluation encompasses seven dimensions: diurnal cycle, daily, intra-annual, inter-annual, ecosystem, and climate zone. Using in situ measurements from 54 eddy covariance (EC) sites spanning 2004 to 2018, SEVIRI actual ET products (diurnal and daily SEVIRI-ETa) and reference ET (daily SEVIRI-ET0) were examined. Results indicate varying accuracies based on diurnal and daily assessments, with seasonal fluctuations. Notably, SEVIRI-ETademonstrated better accuracy during summer and midday. Intra-annual accuracy for daily SEVIRI-ETashowed improvement during mid-year. Peat and grassland ecosystems exhibited higher accuracy than cropland ecosystems. SEVIRI-ET0mirrored similar patterns with the highest accuracy in crop ecosystems. Overall, the SEVIRI-ET products showed stable accuracy trends across different spatial domains, effectively capturing both inter-annual and spatial variations. This study comprehensively evaluates SEVIRI diurnal and daily ET products in Europe, offering insights for optimized product selection.

The paper gives a brief overview of the methods used to determine soil moisture using sentinel products, emphasizing the integration of different approaches. First, the importance of sentinel products for observing microwave backscatter is emphasized, highlighting their unique characteristics. It then discusses the current state of the art in soil moisture retrieval, which includes short-term change detection, physically based scattering models and AI-based methods. The challenges and benefits of each method are systematically explored, providing a comprehensive perspective. Finally, potential future research directions are outlined, highlighting the complex role of prevailing vegetation conditions and the promising prospects for integrating machine learning into multi-SAR missions. Overall, the paper provides a thorough foundation for understanding, refining, and advancing soil moisture detection methods.

A joint approach for estimating vertically continuous soil moisture profiles by combining P-band SAR polarimetry with soil hydrological modeling is proposed. The approach compares the decomposed soil scattering component from remotely sensed P-band SAR observations of NASA’s Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission with an ensemble of simulated counterparts based on the hydrological model HYDRUS-1D and the soil scattering model multi-layer small perturbation method (SPM). From the best fit between remote sensing and soil modeling, the most probable soil moisture profile can be retrieved. Estimated soil moisture profiles at individual monitoring stations across the U.S. are compared to in situ measurements, as well as the European ReAnalysis (ERA5) land and AirMOSS L4 products. Pearson’s coefficient of determination between estimated and auxiliary products prove the overall feasibility of the proposed method with respective R2of 0.92, 0.95, and 0.87.

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) data.Our study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation period.We find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

The retrieval of (almost) daily soil moisture from C-band Sentinel-1 Synthetic Aperture Radar (SAR) records is still influenced by incidence angle effects and vegetation dynamics. In this study we present a method to reduce the effects of both methods on the alpha approximation methods, a time series approach assuming changes in backscattering signals between two consecutive observations are related to a change in soil moisture. By implementing a Fourier series for incidence angle normalization and a linear regression to co-polarized backscatter for a vegetation adaption, the alpha approximation method has been extended to gain high temporal resolution soil moisture time series. The approach was tested in the Rur catchment, Germany and the Apulian Tavoliere, Italy.

Unmanned Aircraft Systems (UAS) are being used more often in agriculture to provide estimations of important metrics such as biomass because of the potential for improved temporal and spatial resolutions. More recently LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Being an active sensor, LiDAR signals are capable of penetrating through the vegetation canopy providing more information on plant structure. Commonly, LiDAR data is used to derive only height information. However, newer studies have shown the retrieval of additional information from the spatial distribution and intensity of LiDAR signals. This study takes a unique look at combining these types of informative products, that are particular to LiDAR, for making biomass estimation with winter wheat.

As unmanned aircraft systems (UAS) remote sensing technology has advanced, providing unprecedented resolution, crop status at the individual plant level has become popular. Often plant detection is performed using high resolution RGB cameras that utilize algorithms and machine learning methods centered around trained pixel patterns of object textures. Similar methods with UAS LiDAR are not as explored considering their more recent UAS adaptation and the significantly larger price tag. Methods that have been created center around individual tree detection using crown delineations utilizing the height information and local maximum filtering. This study explores if this methodology can be used in a similar way for crops such as maize.

Unmanned Aircraft Systems (UAS), with the ability to fly close to the ground and under clouds, make it possible to collect data at unprecedented spatial and temporal resolutions. LiDAR systems are more commonly being used on UAS platforms as these sensors become smaller and more accessible. Within the field of precision farming, UAS LiDAR is often used for height calculations that takes advantage of its ability to penetrate through the canopy to the ground but the rate at which these signals pass through can provide important metrics on crop structure and (vegetation) density. These can be related to well known (or classically used) vegetation indices such as Leaf Area Index (LAI) which often plays a major contributor in monitoring plant health and predicting crop yield. This study exploits UAS LiDAR advantages and investigates its ability to estimate LAI for crops such as winter wheat. It was found that LiDAR LAI spatial patterns were consistent with other forms of data while providing estimates similar to ground measurements.

A method for estimating complex soil permittivity (or moisture) and penetration depth based on SAR decomposition is presented. By combining model- and eigen-based decomposition techniques, SAR observations are separated into single scattering components (from soil & vegetation). The proposed method incorporates a multi-layer rough surface scattering model to simulate the soil scattering contribution. From the decomposed soil scattering component, permittivity and thus penetration depth can be estimated from SAR observations. Results are presented for the AirMOSS campaign within the MOISST site, OK, USA. As first results, a median value of 16.44 + 2.02 for the complex permittivity was estimated from the 19 P-band data takes at the SoilSCAPE in situ station in Canton, OK, USA. Overall, the retrieval results for the real part of the complex permittivity are similar to in situ values at 30 cm soil depth, with differences in respective median values of 3.44. The median of penetration depths at the Canton site is 23.91 cm,

The upcoming launch of the L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) will enable multi-frequency SAR observations when combined with existing C-band satellite missions (e.g., Sentinel-1). Due to the different penetration depths of the SAR signals, multi-frequency SAR offers great potential for field-scale agricultural monitoring and the estimation of soil and plant parameters. The SARSense campaign, conducted between June and August 2019 at the Selhausen agricultural test site near Jülich, Germany, has yielded a comprehensive dataset that includes both air- and space-borne C- and L-band SAR data, extensive in-situ field measurements of soil and plant parameters as well as unmanned aerial systems (UAS)-based multispectral and thermal infrared measurements and cosmic neutron sensing observations. The study provides both, an insight into the strengths and limitations of the acquired dataset as well as an analysis of the different behaviour of C- and L-band backscattering on changing soil moisture and plant parameters for taproot crops and cereals.

Droughts in recent years increased tree mortality in Europe. Especially the spruce, with its shallow root system, can not endure extreme events and loses resilience to infestations such as the bark beetle. We developed a simple method to transfer official forest statistics of a reference year to larger areas to locate affected spruce stands and retrieve actual statistics of a target year by implementing Sentinel-2 data. Google Earth Engine (GEE) was used to implement the approach and to investigate the impact of elevation, as a key environmental factor, on spatial patterns of spruce mortality.

Wildfire is an important process that affects nature environment and living organisms. In this study, we applied a causal network discovery method called PCMCI which performs in a PC condition selection stage to select relevant conditions and a MCI conditional independent test stage to control false positive rate, to detect casual relationships and time lags between wildfire burned area and weather/drought and vegetation conditions. The results show that for grassland, weather and aridity conditions are dominant indicators to BA (burned area). For shrub land, FWI (fire weather index) is dominant. For sparsely vegetated land cover, which is water or fuel limited region, vegetation growth and health conditions are dominant. For broad leaf forests, radiation is the most important indicator. While for needle leaf forests, temperature is dominant.

The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup has been established to coordinate the development of standardized validation across the satellite-derived products from different platforms, sensors, and algorithms with reference measurements from the in situ networks. Soil moisture exhibits a high variability in space that challenges the in situ validation. One of the main drivers for this variability is the characteristic heterogeneity in the soil texture. By the machine learning methods using the soil profile measurements and the remotely sensed predictors, spatially continuous maps of basic soil properties such as soil texture and bulk density are available. Those can be used to estimate soil moisture variability within a satellite product grid cell, here exemplarily shown for the Soil Moisture Active Passive (SMAP) 36-km product. The soil moisture standard deviation is described as a function of the mean soil moisture, whereby the approach needs the mean and standard deviation of the hydraulic parameters as input. The resulting global data set helps identifying the number of in situ stations necessary to validate the coarse soil moisture products. For most SMAP grid cells, three to four stations are adequate to estimate the mean soil moisture for validation; however, also regions were identified where 80 stations are necessary.

In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape A and orientation distribution ψ of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities (|SHH|2, |SHV|2, and |SVV|2) combined with a two-parameter (APand ψ) discrete scatterer model of vegetation. The objective is to understand whether this confined set of observations contains enough information to estimate the two vegetation structure parameters from the L-band radar signals. In order to disentangle soil and vegetation scattering influences on these signals and ultimately perform a vegetation only retrieval of vegetation shape A and orientation distribution ψ, we use the subpixel spatial heterogeneity expressed by the covariation of co- and cross-polarized backscatter ΓPP-PQof the neighboring cells and assume it is indicative for the amount of a vegetation-only co-to-cross-polarized backscatter ratio μPP-PQ. The ratio-based retrieval approach enables a relative (no absolute backscatter) estimation of the vegetation structure parameters which is more robust compared to retrievals with absolute terms. The application of the developed algorithm on global L-band Soil Moisture Active Passive (SMAP) radar data acquired from April to July 2015 indicates the potential and limitations of estimating these two parameters when no fully polarimetric data are available. A focus study on six different regions of interest, spanning land cover from barren land to tropical rainforest, shows a steady increase in orientation distribution toward randomly oriented volumes and a continuous decrease in shape arriving at dipoles for tropical vegetation. A comparison with independent data sets of vegetation height and above-ground biomass confirms this consistent and meaningful retrieval of APand ψ. The retrieved shapes and orientation distributions represent the main vegetation elements matching the literature results from model-based decompositions of fully polarimetric L-band data at the SMAP spatial resolution. Based on our findings, APand ψ can be directly applied for parameterizing the vegetation scattering component of model-based polarimetric decompositions. This should facilitate decomposition into ground and vegetation scattering components and improve the retrieval of soil parameters (moisture and roughness) under vegetation.

In summer 2019 the SARSense campaign was held in Jülich, Germany, to provide insights into the potentials and specifications of the ESA Copernicus candidate mission ROSE-L (Radar Observation System for Europe). ROSE-L will consist of two satellites that carry a polarimetric L-band SAR. Since the L-band signal can penetrate through many natural materials such as vegetation, dry snow and ice, the mission will provide additional information that cannot be gathered by the Copernicus Sentinel-1 C-band SAR mission. The overall objective of the SARSense 2019 campaign is to analyze the mission design concerning its potential for agricultural monitoring services including target applications such as soil moisture monitoring, irrigation management, crop type discrimination, food security and precision farming. The SARSense in situ measurements of soil moisture, soil temperature, vegetation properties, UAS-based multispectral and thermal mapping, as well as the airborne SAR observations are presented as well as strategies for soil moisture retrieval and first analysis.

Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell.

Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvantages. In order to combine active and passive measurements, their covariations with respect to soil moisture need to be known. The covariation is dependent on how the active and passive microwaves interact with vegetation canopy and soil surface. In this paper, we introduce a physics-based model for the covariation of active and passive microwaves over soil surfaces with vegetation cover. The analytical form for a covariation function is derived which depends on the scattering and absorption of microwaves by soil and vegetation with different orientations, structures, and water contents. The main finding is that the covariation function β is related to the roughness and vegetation losses in the two measurements. An increase in soil roughness or in vegetation cover leads to less negative values of β, which is pronounced for dense and moist vegetation. Both the soil and vegetation components introduce a polarization dependence of β that is caused by polarization-induced differences in soil scattering and oriented plant structures. The forward modeled covariations are plotted together with statistically derived covariation estimates from two months of global active and passive L-band observations of the Soil Moisture Active Passive mission. The physically modeled and statistically derived estimates of covariation are comparable in magnitude and scale.

Combined active-passive remote sensing has the potential for capturing the relative advantage of each sensing approach in geophysical retrievals. One cornerstone of combined active-passive microwave sensing is the modeling of the covariation of active and passive signals, which arise from equivalent sensitivities of both sensor types to changes in geophysical properties. In this research contribution, we propose a physics-based active-passive combination of active and passive microwave observations based on Kirchhoff's law of energy conservation. This allows establishing a physics-based forward model as well as a fully data-driven, single-pass retrieval methodology for active-passive microwave covariation. The forward model and the retrieval approach are adaptable to different sensor characteristics (incidence angle, frequency & polarization). The theoretical (forward model) as well as applied (retrieval method) physics-based covariation framework is tested with SMAP (LL) and SMAP/Sentinel-1 (LC) data to reveal potentials and constraints for active-passive microwave sensing. As a result of the conducted study, a linear functional relationship between active and passive microwave observations (e.g. assumed for the SMAP mission) is confirmed, if higher-order scattering can be omitted.

L-Band microwave radiometry over land mainly focuses on observations of horizontally (H) and vertically (V) polarized brightness temperatures. However, it has been demonstrated that measurements of the full Stokes vector [1] are sensitive to additional environmental properties, e.g. azimuthal plant row orientation. Furthermore, model simulations show that also a smooth surface with a periodic permittivity pattern can cause azimuthal dependencies of the Stokes parameters. The objective of this paper is to present fully polarimetric Lband measurement results from observations of a striped wood and styrodur target, when rotated 360° in small steps. Measurement results of a striped soil and open water target are also reported. Finally, measurements are compared to model simulations, with very good agreement within the validity range of the model (stripe thickness <; λ/2).

Vegetation optical depth and scattering albedo are crucial parameters within the widely used τ-ω model for passive microwave remote sensing of vegetation and soil. A multi-sensor data integration approach using ICESat lidar vegetation heights and SMAP radar as well as radiometer data enables a direct retrieval of the two parameters on a physics-derived basis. The crucial step within the retrieval methodology is the calculus of the vegetation scattering coefficient KS, where one exact and three approximated solutions are provided. It is shown that, when using the assumption of a randomly oriented volume, the backscatter measurements of the radar provide a sufficient first order estimate and subsequently lead to effective estimates of vegetation optical depth and scattering albedo acquired with the novel multi-sensor approach.

After failure of the SMAP L-band radar, its substitution by the Sentinel-1A/B C-band instruments for combined active-passive retrieval of soil moisture demands an algorithm update for this dual-frequency (L/C) case. In order to account for the different frequencies and acquisition geometries of the two sensor types, the microwave covariation, being the fundamental building block of the moisture retrieval algorithm, is modeled using a fully physics-based approach. Moreover, a data-driven, single-pass retrieval methodology for dual-frequency microwave covariation is proposed and tested on SMAP and Sentinel-1 data. The retrieval is also physics-based, incidence angle independent and therefore globally applicable without any empirical or statistical calibration.

Low resolution satellite products are often validated by point-scale in-situ soil moisture measurements from TDR/FDR devices, where the scale mismatch limits the quality of validation efforts in heterogeneous regions. In Cosmic Ray Neutron Probes (CRNP) provide area-average soil moisture within a 150-250 m radius footprint, so that they are able to fill the scale gap between both systems. In this study we evaluate differences and communalities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). CRNPs within the Rur catchment in Germany have been selected for comparison. Standard validation scores identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs.

The NASA Soil Moisture Active Passive (SMAP) mission aims at producing high-resolution (9 km) global maps of surface soil moisture based on L-band radar and radiometer measurements. In this study, a physically-based retrieval of the active-passive covariation parameter β from one active-passive (single-pass) SMAP acquisition couple is proposed, circumventing empirical time-series regressions. The key to single-pass retrieval of β is the vegetation correction of the backscatter signal. This can be achieved by use of the measured cross-polarized backscatter signal and parameters appropriately describing the structure of the vegetation volume. These parameters can be derived from the observed Γ-parameters of the SMAP baseline algorithm enabling a fully SMAP data-driven, single-pass estimation of the covariation parameter β without any auxiliary information. Moreover, vegetation structural parameters, indicative of preferential vegetation shape and orientation, are retrieved using the observed Γ-parameters.

The objective of the NASA Soil Moisture Active Passive (SMAP) mission is to provide global measurements of soil moisture and freeze/thaw states. SMAP integrates L-band radar and radiometer instruments as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments are a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer and the active L-band system F-SAR of DLR were flown simultaneously on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e., the main land cover types and all experimental monitoring sites. Here, we used the obtained data sets as a test bed for the analysis of three active-passive fusion techniques: 1) estimation of soil moisture by passive sensor data and subsequent disaggregation by active sensor backscatter data; 2) disaggregation of passive microwave brightness temperature by active microwave backscatter and subsequent inversion to soil moisture; and 3) fusion of two single-source soil moisture products from radar and radiometer. Results indicate that the regression parameters β are dependent on the radar vegetation index. The best performance was obtained by the fusion of radiometer brightness temperatures and radar backscatter, which was able to reach the same accuracy as single-source coarse-scale radiometer soil moisture retrieval but on a higher spatial resolution.

The NASA Soil Moisture Active Passive (SMAP) mission is designed to produce high-resolution (9 km) global mapping of surface soil moisture based on L-band radar and radiometer measurements. The multi-scale measurements are combined using time-series of active passive microwave data to retrieve the statistical regression parameters (α, β) from successive overpasses. In this study, we introduce a physically-based forward model as well as data-based retrieval of the β-parameter. The forward model stems from analyses of the scattering and loss terms occurring during bare and vegetated soil scattering/emission and allows a physically-based modelling of the β-parameter. This provides possibilities to analyze the different influences of soil roughness as well as vegetation structure and moisture on the β-parameter under different environmental conditions. In addition, a physically-based retrieval of β from one active-passive SMAP acquisition couple is proposed, circumventing lengthy time-series regressions. The key operation enabling a single-pass retrieval of the β-parameter is the vegetation correction of the backscatter signal. This can be achieved by use of the measured cross-polarized backscatter signal together with an appropriate polarimetric vegetation volume model.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture and its freeze/thaw state. The SMAP measurement approach is to integrate L-band radar and radiometer as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments will be a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were flown on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e. all types of land cover and experimental monitoring sites. The obtained data sets are used as a test-bed for the analysis of existing and development of new active-passive fusion techniques.

Time series of Leaf Area Index (LAI) is of utmost importance for various disciplines of bio- and geosciences where satellite remote sensing makes LAI estimation possible for large areas. Remote sensing LAI, validated against in situ LAI (LAIinsitu), is used as base for calculating LAI for large areas e.g. on catchment scale. Various vegetation indices (NDVI, SAVI, both with and without substituting the red band with red-edge band) were applied for better estimates of LAIrapideye. SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) present same correlation between remote sensing based predicted LAI (LAIrapideye) against LAIinsituin winter wheat fields. Both NDVI and SAVI with red-edge band showed improved correlation of remote sensing based VI and in situ measurements. Prior to vegetation indices calculation, radiometric normalization was applied to the time series of RapidEye data. To test the impact of radiometric normalization for calculating vegetation indices on a time series of satellite images, pre- and post-radiometric normalization LAIrapideyewas compared. More precise and high resolution estimation of LAI for large areas is of vital importance for improving evapotranspiration and soil moisture.

Correct parameterization of radiative transfer models is very important for high accuracy soil moisture retrievals from spaceborne L-band passive microwave sensors, such as the ESA Soil Moisture and Ocean Salinity (SMOS) Mission. In order to investigate the characteristics of radiative transfer parameters such as vegetation opacity and soil surface roughness, a dual state and parameter update data assimilation system has been developed. By assimilating SMOS brightness temperatures into the L-band Microwave Emission of the Biosphere (L-MEB) model, respective parameters can be estimated. The data assimilation system makes use of a temporal smoothing algorithm based on the Sampling Importance Resampling Particle Filter. The new approach, namely the Sampling Importance Resampling Particle Smoother, makes use of a particle weighting function valid not for a single time step, but for a specific time period. The resulting parameters estimated are more stable throughout the whole period under investigation, where the possibility to vary with time is still given. This is important to cover the seasonality of e.g. vegetation parameters.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009 and delivers now brightness temperature and soil moisture products over terrestrial areas on a regular three-day basis. In 2010, several airborne campaigns were conducted to validate the SMOS products with microwave emission radiometers at L-band (1.4 GHz). In this paper, we present results from measurements performed in the Rur and Erft catchments in May and June 2010. The measurement sites were situated in the very west of Germany close to the borders to Belgium and The Netherlands. We developed an approach to validate spatial and temporal SMOS brightness temperature products. An area-wide brightness temperature reference was generated by using an area-wide modeling of top soil moisture and soil temperature with the WaSiM-ETH model and radiative transfer calculation based on the L-band Microwave Emission of the Biosphere model. Measurements of the airborne L-band sensors EMIRAD and HUT-2D on-board a Skyvan aircraft as well as ground-based mobile measurements performed with the truck mounted JÜLBARA L-band radiometer were analyzed for calibration of the simulated brightness temperature reference. Radiative transfer parameters were estimated by a data assimilation approach. By this versatile reference data set, it is possible to validate the spaceborne brightness temperature and soil moisture data obtained from SMOS. However, comparisons with SMOS observations for the campaign period indicate severe differences between simulated and observed SMOS data.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture by fused active and passive L-band microwave measurements. With an airborne campaign conducted in the Rur catchment, Germany, in 2012, an active and passive L-band microwave data set is generated. The passive Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were installed on the DLR Dornier DO 228 aircraft. Despite of the differences between the airborne and the future spaceborne sensors, the data set will serve as a test bed for the analysis of existing and development of enhanced future active/passive data fusion techniques. Moreover, the derivation of (vegetation) parameters for the fusion approaches is planned.

ASCAT and SMOS soil moisture products were validated for the year 2010 in the Rur and Erft catchments in the west of Germany. In situ data of three test sites of the TERENO initiative were used to calibrate the hydrological model WaSiM-ETH, which was applied to generate a soil moisture reference for the whole study area. Comparison of SMOS soil moisture with the reference displayed a high dry bias and low to moderate correlations. ASCAT soil moisture showed higher correlations and no bias. A temporal stability analysis exhibits low stability of the SMOS data and higher stability of ASCAT data. Generally, the performance of ASCAT is well, while there are still some problems with soil moisture retrieval and RFI in the SMOS soil moisture product.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been launched in November 2009 and delivers now brightness temperature and soil moisture products over terrestrial areas on a regular three day basis. In 2010 several airborne campaigns were conducted to validate the SMOS products with microwave emission radiometers at L-band (1.4 GHz). In this paper we present the activities performed in the Rur and Erft catchment, which is situated in the very west of Germany close to the borders to Belgium and The Netherlands. Measurements of the L-band sensors EMIRAD and HUT-2D on board a Skyvan aircraft as well as ground- based mobile measurements with the JULBARA radiometer mounted on a truck are analyzed in a qualitative comparison for different crop stands. These data can be used for validation of the SMOS sensor by giving valuable information about parameters for the radiative transfer modeling.

ESA provides operational routines to calculate the SMOS Level-2 product soil moisture from the radiometer Tb. But, the radiative transfer from measured Tb into soil moisture is influenced by several conditions such as soil surface roughness and vegetation opacity, which are parameterized in a general way. These cannot be easily measured at the scale of SMOS observation. The absolute values of these parameters for different land surfaces are uncertain, and the degree of this uncertainty is unknown as well. In addition, recent studies found that SMOS overestimates Tb. In this paper, we present a method to enhance the accuracy of the SMOS soil moisture product by parameter estimation using a data assimilation technique (Sampling Importance Resampling Particle Filter SIR-PF) with in-situ soil moisture observations. Therefore, we performed a synthetic study to analyze the ability of the system to track the temporal evolution of parameters such as vegetation opacity and soil surface roughness. To generate a soil moisture and soil temperature reference the hydrological model HYDRUS-1D was used. Based on this, the L-band Microwave Emission of the Biosphere (L-MEB) forward model was run and perturbed according to the measurement accuracy of MIRAS to simulate the SMOS Tb observations. L-MEB was integrated into a data assimilation framework using the SIR-PF, which is able to concurrently update L- MEB states and parameters. In addition, we investigate the ability of the proposed approach to account for the SMOS observation bias by introducing a bias factor in L-MEB. The overall advantage of the proposed sequential approach is its ability to be integrated into the operational near real time processing of the Level-2 product. The objectives of this study are: (i) to retrieve radiative transfer parameters and their temporal changes and (ii) to account for a bias in SMOS measurements.

In this paper we present calibration and validation activities of European Space Agency's SMOS mission, which utilize airborne interferomentric L-band radiometer system HUT-2D of the Aalto University. During spring 2010 the instrument was used to measure three SMOS validation target areas, one in Denmark and two in Germany. We present these areas shortly, and describe the airborne activities. We show some exemplary measurements of the radiometer system and demonstrate the studies using the data.