We model the sea foam emissivity at frequencies from 1 to 89 GHz. This model is part of the work done by an international science team to develop a radiative transfer model of reference quality for the ocean surface emissivity from L band to infrared frequencies. A study of the sensitivity to different foam properties (foam layer thickness and upper limit of the foam void fraction) guided the effort to tune the foam emissivity model by frequency and polarization. The results show that the differences between simulated and observed brightness temperatures decrease when using the tuned foam model.

Surface water storage is an essential component of the hydrological cycle. Remote sensing offers valuable tools for monitoring both surface water extent from satellite images and water levels from radar altimetry. Combining both information, we were able to estimate the variations of surface water extent and storage in the Lower Mekong Basin from 2000 to 2020. Signatures of the extreme climatic events - floods from 2000 to 2002, of 2011, drought of 2015 clearly appear on both extent and storage. The mean amplitude of these variables shows a strong decrease when comparing the periods of 2000–2010 and 2011–2020. Between these two periods, a large reduction of the annual average number of days with the presence of floods can be observed in most of the Lower Mekong Basin, except around the Tonle Sap (Cambodia) and in some parts of the delta.

Vegetation optical depth (VOD) is a remotely sensed indicator characterizing the opacity of the vegetation layer. This study focuses on the behaviour of L-band VOD (L-VOD) retrieval algorithm over seasonally inundated areas, as previous observations have shown an unexpected decline in VOD during floods. The signal emitted by a mixed scene composed of soil and standing water was simulated, leading to an overestimation of the retrieved soil moisture (SM) and an underestimation of the retrieved L- VOD, typically by ~ 1 0% over flooded forests and up to 100% over flooded grasslands. We evaluated the induced underestimation of aboveground biomass (AGB) by 15/20 Mg ha-1 in the largest seasonal wetlands, which can represent more than 50% of the actual AGB of the savanna wetland, and up to higher values during exceptional years. Surface water seasonality needs to be taken into account in passive microwave retrieval algorithms to better estimate the global biomass.

Passive microwave remotely sensed data are increasingly used for the estimation of earth system parameters with application for precision agriculture or meteorology. They are almost impervious to clouds and can be used to provide long term global observations of Earth surface temperatures, soil moisture, etc. However the effective sampling depth of the soil is not constant and this leads to dubious results over arid areas. To account for this phenomenon, in this study we analyse the relationship between the soil geological characteristics such as porosity, sand/clay/silt fractions, and its thermal and electrical properties. Indeed the brightness temperatures measured at different frequencies depend on the effective temperature and emissivity that is a related to the sampling depth and the soil temperature profile. The soil temperature profile can be estimated based on the diffusion equation that relies on the thermal properties of the soil, and the microwave sampling depth is related to the dielectric permittivity of the material. The early results shown here show how we can use soil characteristics maps, and relate them to differences in brightness temperatures at the GMI frequencies between 10 and 89 GHz over the Sahara desert and to different penetration depths at these frequencies.

This study presents an analysis of radar signature at Ku-band for incidences ranging from 0° to 10° over the major bioclimatic zones, soil and vegetation types encountered in West-Africa, using data from Jason-3 and SWIM. Time-series of radar responses were built over the following environments: stone and sand deserts, Sahelian savannah and floodplain, flooded and non-flooded equatorial forests. Deserts and non-flooded equatorial forest exhibit almost constant responses, decreasing as the incidence angle increases. Similar seasonal variations of the backscattering coefficient between the dry and the wet season are observed at nadir for Jason-3 and SWIM with a decrease in dry season level and amplitude with the increase of the incidence angle. Backscattering at Ku-band can be related to soil roughness, vegetation cover and soil wetness.

Sea ice concentration, sea ice thickness, and snow depth over sea ice are important physical parameters for modeling the cryosphere. We present here preliminary results of a method to combine passive radiometer, altimeter, and scatterometer, and to evidence their synergy. The benefit of merging the data from several instruments at level 1 is emphasized, through the use of a classification methodology. This work is performed in the framework of the preparation of the future Copernicus missions CIMR and CRISTAL, that will fly at the same time as ASCAT on board MetOp-SG.

Microwave cloud polarized observations have shown the potential to improve precipitation retrievals since they are linked to the orientation and shape of ice habits. Stratiform clouds show larger brightness temperature (TB) polarization differences (PDs), defined as the vertically polarized TB (TBV) minus the horizontally polarized TB (TBH), with ~10 K PD values at 89 GHz due to the presence of horizontally aligned snowflakes, while convective regions show smaller PD signals, as graupel and/or hail in the updraft tend to become randomly oriented. The launch of the global precipitation measurement (GPM) microwave imager (GMI) has extended the availability of microwave polarized observations to higher frequencies (166 GHz) in the tropics and midlatitudes, previously only available up to 89 GHz. This study analyzes one year of GMI observations to explore further the previously reported stable relationship between the PD and the observed TBs at 89 and 166 GHz, respectively. The latitudinal and seasonal variability is analyzed to propose a cloud scattering polarization parameterization of the PD-TB relationship, capable of reconstructing the PD signal from simulated TBs. Given that operational radiative transfer (RT) models do not currently simulate the cloud polarized signals, this is an alternative and simple solution to exploit the large number of cloud polarized observations available. The atmospheric radiative transfer simulator (ARTS) is coupled with the weather research and forecasting (WRF) model, in order to apply the proposed parameterization to the RT simulated TBs and hence infer the corresponding PD values, which show to reproduce the observed GMI PDs well.

A microwave emissivity retrieval is applied to five years of global precipitation measurement (GPM) microwave imager (GMI) observations over land and sea ice. The emissivities are colocated with GPM's dual-frequency precipitation radar (DPR) surface backscatter measurements in clear-sky conditions. The emissivity-backscatter database is used to characterize surfaces within the GPM orbit for precipitation retrieval algorithms and other applications. The full 10-166-GHz emissivity vector is retrieved using optimal estimation. Since GMI includes water vapor sounding channels, retrieval of the atmospheric and surface states are performed simultaneously. Using the MERRA2 reanalysis as the a priori atmospheric state and with proper characterization of its error, we are able to effectively screen for cloud- and precipitation-affected emissivities. Comparisons with colocated CloudSat data show that this GMI-based screen is able to detect precipitation that DPR alone does not; however, about 10% of precipitation occurrence from CloudSat is still undetected by GMI. The unsupervised Kohonen classification technique was then applied to multiyear monthly 0.25° gridded mean retrieved emissivities and backscatter distinctly for snow-free, snow-covered, and sea ice surfaces in order to classify surfaces based on both active and passive microwave characteristics. The classes correspond to vegetation coverage and type, inundation zones, soil composition, and terrain roughness. Snow and sea ice surfaces show clear seasonal cycles representing the increase in snow and ice spatial extent and reduction in the spring. Applications toward GPM precipitation retrieval algorithms and sensitivity to accumulated rain and snowfall are also explored.

A set of Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) data assimilation (DA) experiments are presented. The SMOS soil moisture dataset used in this study was produced training a neural network (NN) using SMOS brightness temperatures as input and ECMWF H-TESSEL SM fields as reference for the training. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. SMOS NN SM DA experiments were compared to Advanced Scat-terometer (ASCAT) SM DA. In both cases, experiments with and without 2 metre air temperature and relative humidity DA are discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives similar results to the model open loop with no assimilation. The effect of the soil moisture analysis on the Numerical Weather Prediction (NWP) was evaluated using the analysed surface fields to perform atmospheric forecast experiments. In the Northern Hemisphere both with ASCAT and SMOS, the experiments using 2m air temperature and relative humidity improve the forecast in April-September. SMOS alone has a significant positive effect in July-September. Maps of the forecast skill with respect to the open loop experiment show that SMOS improves the forecast in North America and to a lesser extent in Northern Asia for up to 72 hours.

Global soil moisture estimates at fine spatial resolutions is necessary for many applications. However, current spaceborne instruments have coarse resolution. In this study, we develop a new Artificial Neural Network (ANN) based disaggregation algorithm to downscale soil moisture observations from Soil Moisture Active Passive (SMAP) mission to a fine resolution of ~2km using ancillary data from visible/infrared frequencies. We use soil moisture estimates from SMAP at two different spatial resolutions to train the downscaling algorithm. Results show that ANN can successfully capture the complex relationship between the soil moisture estimates at two different spatial resolutions using the ancillary data provided.

Plant's photosynthetic activity and transpiration are constrained by the amount of water available to them through roots (i.e. root zone soil moisture) as well as nutrient and atmospheric conditions. Therefore, to better understand the response of plants to different stress conditions, knowledge of root zone soil moisture is essential. However, current global satellites dedicated to soil moisture monitoring are limited to L-band frequencies that have a low (<; 5cm) penetration depth. In this study, we implement a new root zone soil moisture retrieval algorithm that takes advantage of multi-frequency microwave observations to infer root soil moisture from L-band measurements and inspired by plant hydraulics. The algorithm is a statistical retrieval that uses a set of target data to train an artificial neural network. Results of applying the retrieval algorithm to one year of observations along with future validation measures is presented.

This paper discusses a methodology to construct a synthetic dataset using realistic geophysical data and the L-MEB model to compute synthetic brightness temperatures (Tb's) and to train a Neural Network (NN) for global retrievals of soil moisture (SM). The trained NNs are applied to real Tb's measured by the Soil Moisture and Ocean Salinity (SMOS) satellite (L-MEB NN). The objective is twofold. First, to compare and provide feedback to the operational algorithm. Second, to evaluate this approach in the context of pre-launch algorithm development. The performance of the L-MEB NN dataset was evaluated by comparing with time series of in situ measurements in North America. The correlation, standard deviation and bias of NN SM and in situ SM are similar to those obtained with the SMOS L3 SM product and with ECMWF models. The L-MEB NN dataset was also compared globally to the SMOS Level 3 SM and SM from ECMWF models. The L-MEB NN dataset is in general wetter than SMOS L3 SM, closer to ECMWF models. Some possible reasons are briefly discussed.

Backscattering in Ku-band (13 GHz) over continental surfaces is analyzed, with the Tropical Rainfall Measurement Mission/Precipitation Radar instruments (incidence angles from 0° to 18°), along with observations from the Topex-Poseidon nadir-looking altimeter and the QuikSCAT scatterometer (incidence angles around 50°). The signals from the three instruments are very consistent. The backscattering tends to decrease with increasing vegetation density, as expected, making it possible to classify vegetation density with active microwaves. Over the northern African desert, a very large spatial variability of the backscattering is observed, with both surface and volume scatterings contributing to the signals. The use of multiangle observations does help characterizing the desert types, but in some areas, the ambiguity of the signals is still unexplained. The French-Chinese joint mission “Chinese-French Oceanic SATellite” will carry two active microwave instruments with a large range of incidence angles, from 0° to 50°. We show that the combined use of observations at low and high incidence angles adds information, particularly over desert surfaces.

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures,$T_{b} $'s) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using$T_{b} $'s measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ∼10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ∼5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated againstin situmeasurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models.

A methodology to retrieve soil moisture (SM) from multiinstrument remote sensing data is presented. The method uses a Neural Network (NN) to find the statistical relationship linking the input data to a reference SM dataset. The input data is composed of passive microwaves (L-band SMOS brightness temperatures), active microwaves (C-band ASCAT backscattering coefficients), and visible and infrared observations by MODIS. The reference SM data used to train the NN are ECMWF model predictions or SMOS L3 SM. After determining the best configuration of input data to retrieve SM using a NN, the NN soil moisture product is evaluated with respect to other global SM products and with respect to in situ measurements. The NN is able to capture the spatial and temporal dynamics of SM, and the SM computed with NNs compares well with the other SM datasets.

Uncertainties in the retrievals of microwave land-surface emissivities are quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including the Special Sensor Microwave Imager, the Tropical Rainfall Measuring Mission Microwave Imager, and the Advanced Microwave Scanning Radiometer for Earth Observing System, are studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land-surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally, these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1%-4% (3-12 K) over desert and 1%-7% (3-20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5%-2% (2-6 K). In particular, at 85.5/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are most likely caused by rain/cloud contamination, which can lead to random errors up to 10-17 K under the most severe conditions.

Passive microwave (PMW) satellite-based precipitation over land algorithms rely on physical models to define the most appropriate channel combinations to use in the retrieval, yet typically require considerable empirical adaptation of the model for use with the satellite measurements. Although low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes since the low-frequency methods are limited to the highly variable land surface background, whose radiometric contribution is substantial and can vary more than the contribution of the rain signal. Thus, emission techniques are generally useless over the majority of the Earth's surface. As a first step toward advancing to globally useful physical retrieval schemes, an intercomparison project was organized to determine the accuracy and variability of several emissivity retrieval schemes. A three-year period (July 2004-June 2007) over different targets with varying surface characteristics was developed. The PMW radiometer data used includes the Special Sensor Microwave Imagers, SSMI Sounder, Advanced Microwave Scanning Radiometer (AMSR-E), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Sounding Units, and Microwave Humidity Sounder, along with land surface model emissivity estimates. Results from three specific targets in North America were examined. While there are notable discrepancies among the estimates, similar seasonal trends and associated variability were noted. Because of differences in the treatment surface temperature in the various techniques, it was found that comparing the product of temperature and emissivity yielded more insight than when comparing the emissivity alone. This product is the major contribution to the overall signal measured by PMW sensors and, if it can be properly retrieved, will improve the utility of emission techniques for over land precipitation retrievals. As a more rigorous means of comparison, these emissivity time series were analyzed jointly with precipitation data sets, to examine the emissivity response immediately following rain events. The results demonstrate that while the emissivity structure can be fairly well characterized for certain surface types, there are other more complex surfaces where the underlying variability is more than can be captured with the PMW channels. The implications for Global Precipitation Measurement-era algorithms suggest that physical retrievals are feasible over vegetated land during the warm seasons.

Sub-millimetre remote sensing instruments can provide critical information on cirrus clouds and an alternative way of measuring precipitation with a much smaller antenna than those which microwave sensors currently use. Two satellite concepts CIWSIR and GOMAS were proposed as ESA Earth Explorer missions; these were not funded, however they were recommended for an aircraft demonstrator. ESA studies have been performed to identify the optimum instrument and platform to demonstrate these satellite concepts. This paper reports on one of these preparatory activities; the design of a sub-millimetre wave airborne demonstrator for both ice cloud and precipitation observations which will be able to prove the feasibility of the scientific principles of both satellite missions. The paper will describe the derivation of the demonstrator requirements, consideration of the available platform and instrument options, the design of the selected concept, performance prediction and the outline of a proof of concept flight campaign. It will present the outcome of the study which describes a demonstrator design based upon the new Met Office International Sub-Millimetre Airborne Radiometer (ISMAR).

Land surface emissivities have been calculated for Tropical Rainfall Measuring Mission (TRMM) Microwave Instrument (TMI), Special Sensor Microwave/Imager (SSM/I), and Advanced Microwave Sounder Unit-A conditions, for two months (July 2002 and January 2003) over the globe at the European Centre for Medium-Range Weather Forecasts, directly from satellite observations. From this data set, a parameterization of the microwave emissivities that account for frequency, incidence angle, and polarization dependences is proposed. It is anchored to climatological monthly mean maps of the emissivities at 19, 37, and 85 GHz, which are calculated from SSM/I. For each location and time of the year, it provides realistic first-guess estimates of the microwave emissivities from 19 to 100 GHz, for all scanning conditions. The results are compared to radiative transfer model estimates. The new estimates provide rms errors that are usually within 0.02, with the noticeable exception of snow-covered regions where the high spatial and temporal variabilities of the emissivity signatures are difficult to capture.

The 14 papers in this special issue focus on remote sensing and modeling of surface properties. The issue is devoted to the modeling and retrieval of surface parameters from satellite measurements, and the techniques used to assimilate surface-sensitive channels in numerical prediction models (NWPs).

For future remote sensing applications the potential of the millimeter wavelength range for precipitation observations from geostationary orbits is investigated. Therefore, a database consisting of hydrometeor profiles from various mid-latitude precipitation cases over Europe and corresponding simulated brightness temperatures at 18 microwave frequencies was built using the cloud resolving model Meso-NH and the radiative transfer model micro wave model. The information content of the database was investigated by applying simple statistical methods, as well as developing first-order retrieval approaches. The results show that, particularly for snow and graupel, the total column content can be retrieved accurately with relative errors smaller than 25% in dominantly stratiform precipitation cases over land and ocean surfaces. The performance for rain-water path is similar to the one for graupel and snow in light precipitation cases. For the cases with higher precipitation amounts, the relative errors for rain-water path are larger particularly over land. The same behavior can be seen in the surface rain rate retrieval with the difference that the relative errors are doubled in comparison to the rain-water path. Algorithms with reduced number of frequencies show that window channels at higher frequencies are important for the surface rain rate retrieval because these are sensitive to the scattering in the ice phase related to the rain below. For the frozen hydrometeor retrieval, good results can be achieved by retrieval algorithms based only on frequencies at 150 GHz and above which are suitable for geostationary applications due to their reduced demands concerning the antenna size.

To determine land surface emissivity from satellite microwave measurements, the surface is usually assumed to be specular. Questions about the validity of this approximation to estimate emissivity from nadir viewing radiometers were raised. This work aims to examine the validity of the specular assumption by evaluating errors induced when deriving emissivities from near-nadir measurements over snow-free areas. Brightness temperature simulations near nadir above both a specular and a Lambertian surface are compared. Errors on the retrieved emissivity introduced by the specular assumption are also quantified. The results show that the impact of the specular assumption when the surface is Lambertian is limited: less than 1% error in most atmospheric situations over natural snow-free surfaces.

Atmospheric parameter retrievals over land from Advanced Microwave Sounding Unit (AMSU) measurements, such as atmospheric temperature and moisture profiles, could be possible using a reliable estimate of the land emissivity. The land surface emissivities have been calculated using six months of data, for 30 beam positions (observation zenith angles from -58/spl deg/ to +58/spl deg/) and the 23.8-, 31.4-, 50.3-, 89-, and 150-GHz channels. The emissivity calculation covers a large area including Africa, Eurasia, and Eastern South America. The day-to-day variability of the emissivity is less than 2% in these channels. The angular and spectral dependence of the emissivity is studied. The obtained AMSU emissivities are in good agreement with the previously derived SSMI ones. The scan asymmetry problem has been evidenced for AMSU-A channels. And possible extrapolation of the emissivity from window channels to sounding ones has been successfully tested.

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

The sensitivities of a large range of satellite observations to snow characteristics are evaluated and compared, for a winter season, for the Northern hemisphere. Satellite measurements include passive (SSM/I emissivities) and active (ERS scatterometer) microwaves, along with visible reflectances (AVHRR). The satellite responses are systematically compared with in situ snow measurements at 493 stations, in North America and Eurasia. Passive microwaves at high frequency (85 GHz) are very sensitive to the presence snow on the ground, even for very low snow depth. None of the tested satellite measurements is well correlated with the snow depth, at a global scale, making snow depth retrieval from these observations difficult. Active microwave observations show a high sensitivity to the onset of snow melting.

Land surface emissivities at AMSU frequencies are estimated using collocated brightness temperatures, the International Satellite Cloud Climatology Project (ISCCP) data and the European Centre for Medium-Range Weather Forecasts (ECMWF) temperature-humidity profiles. The impact of some sources of errors is estimated. Angular dependence of the AMSU emissivities is examined. Preliminary AMSU emissivity maps are presented as well as comparison with SSMI derived emissivities and a microwave land emissivity model.

To retrieve temperature and humidity profiles from special sensor microwave/temperature (SSM/T) and advanced microwave sounding units (AMSU), it is important to quantify the contribution of the Earth surface emission. So far, no global estimates of the land surface emissivities are available at SSM/T and AMSU frequencies and scanning conditions. The land surface emissivities have been previously calculated for the globe from the SSM/I conical scanner between 19 and 85 GHz. To analyze the feasibility of deriving SSM/T and AMSU land surface emissivities from SSM/I emissivities, the spectral and angular variations of the emissivities are studied, with the help of ground-based measurements, models, and satellite estimates. Up to 100 GHz, for snow and ice free areas, the SSM/T and AMSU emissivities can be derived with useful accuracy from the SSM/I emissivities. The emissivities can be linearly interpolated in frequency. Based on ground-based emissivity measurements of various surface types, a simple model is proposed to estimate SSM/T and AMSU emissivities for all zenith angles knowing only the emissivities for the vertical and horizontal polarizations at 53/spl deg/ zenith angle. The method is tested on the SSM/T-2 91.655 GHz channels. The mean difference between the SSM/T-2 and SSM/I-derived emissivities is /spl les/0.01 for all zenith angles with a root mean squared (RMS) difference of /spl ap/0.02. Above 100 GHz, preliminary results are presented at 150 GHz based on SSM/T-2 observations and are compared with the very few estimations, available in the literature.