Floods continue to affect millions of the global population annually. SAR-based methods are one of the most reliable tools for mapping floods expeditiously. Among these are Bayesian flood mapping methods that rely on conditional and prior probability formulations to make labeling decisions. Recent work demonstrated a globally applicable Height Above the Nearest Drainage (HAND)-based prior probability function to improve Bayesian flood mapping. However, limitations were identified due to the input DEM. In this contribution, we assess the performance of three (near-)globally available HAND datasets as input to this function. Compared to the HAND dataset used in the previous study, the MERIT and Deltares HAND datasets were derived from improved SRTM DEMs and finer detailed drainage networks. We hypothesize that these finer-resolution HAND datasets can potentially improve probabilistic flood mapping further. Thus, we compare the flood mapping performance using the baseline SRTM-derived, MERIT, and Deltares HAND data as priors on (the original) six study sites for both flooded and non-flooded scenarios. Our results show similar performance in the flooded scenarios using the MERIT and Deltares HAND datasets. The MERIT dataset shows slightly better performance among the three. However, an increase in False Positive Rates was apparent in non-flooded scenarios attributed to smaller drainages in the new datasets tested. These results suggest caution in applying the HAND prior method with HAND datasets derived from drainage networks with small upstream contributing areas.

Soil moisture retrievals from the Advanced Scatterometer (ASCAT) have so far relied on the assumption that soil backscatter increases monotonically with soil moisture content. However, under dry soil conditions, discontinuities in the soil profile caused by the presence of stones, rocks, or distinct soil layers may disturb this relation, causing backscatter to decrease with increasing soil wetness. As of yet, subsurface scattering is a poorly understood phenomenon and some of its manifestations on ASCAT soil moisture retrievals have in the past been wrongly attributed to topographic effects or changes in soil surface roughness and vegetation. Therefore, this study aims at mapping subsurface scattering effects on a global scale, explore their dependency on land surface characteristics, and describe the impacts on ASCAT soil moisture retrievals. The results obtained with one statistical and two physically based indicators show that the subsurface scattering is not only widespread in desert regions but also in more humid climates with a dry season. Along with the dryness of the soil, the presence of coarse fragments in the soil profile and sparse vegetation cover are important factors that favor its occurrence. The impact on ASCAT soil moisture retrievals is severe, making subsurface scattering the most significant source of unaccounted errors in the current version of the ASCAT soil moisture data as provided by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management. Users of the product are recommended to mask soil moisture data affected by subsurface scattering effects using the indicators and masks developed in this study.

Due to the complexity of urban environments, the synthetic aperture radar (SAR) based mapping of floodwater is impacted by different factors such as water depth, building orientation and the density of built-up areas. Several studies have proven that both SAR multitemporal intensity and interferometric SAR (InSAR) coherence data acquired in VV and VH polarizations support the urban flood mapping. We propose a deep learning (DL) based method using dual-polarization Sentinel-1 multitemporal intensity and coherence data combined with prior information to map floodwater in urbanized areas. The proposed method aims at mapping flooded areas in urbanized regions and bare soils/sparsely vegetated areas within the entire frame of a Sentinel-1 image. In this paper, our method is evaluated for the Houston (US) urban flood event in 2017 via a qualitative and quantitative comparison with two established DL models. The proposed method has the lowest number of false alarms in flooded urban areas, indicating that the prior information from the probabilistic urban mask is valuable.

Rainfall deficits that develop into socio-economic droughts have triggered major humanitarian crises in recent years, and are expected to become even more frequent as a result of climate change. Earth observation methods are very useful for drought hazard assessment as they are available globally at high spatiotemporal resolutions. In this study, we analyze the potential to track rainfall deficits through the water cycle using satellite-based datasets, with a focus on the added value of satellite-derived soil moisture (SM). For the country of Senegal in the western Sahel, we show that SM closes the gap between rainfall deficits and negative impacts on vegetation, and that including SM in drought hazard assessment allows for an early warning of a developing food crisis. These findings support the development of novel drought risk financing mechanisms, which are much faster than traditional indemnity-based models.

Due to the complexity of backscattering mechanisms in built-up areas, the synthetic aperture radar (SAR)-based mapping of floodwater in urban areas remains challenging. Open areas affected by flooding have low backscatter due to the specular reflection of calm water surfaces. Floodwater within built-up areas leads to double-bounce effects, the complexity of which depends on the configuration of floodwater concerning the facades of the surrounding buildings. Hence, it has been shown that the analysis of interferometric SAR coherence reduces the underdetection of floods in urbanized areas. Moreover, the high potential of deep convolutional neural networks for advancing SAR-based flood mapping is widely acknowledged. Therefore, we introduce an urban-aware U-Net model using dual-polarization Sentinel-1 multitemporal intensity and coherence data to map the extent of flooding in urban environments. It usesa prioriinformation (i.e., an SAR-derived probabilistic urban mask) in the proposed urban-aware module, consisting of channel-wise attention and urban-aware normalization submodules to calibrate features and improve the final predictions. In this study, Sentinel-1 single-look complex data acquired over four study sites from three continents have been considered. The qualitative evaluation and quantitative analysis have been carried out using six urban flood cases. A comparison with previous methods reveals a significant enhancement in the accuracy of urban flood mapping: the F1 score of flooded urban increased from 0.3 to 0.6 with few false alarms in urban area using our method. Experimental results indicate that the proposed model trained with limited datasets has strong potential for near-real-time urban flood mapping.

The new, systematic Global Flood Monitoring (GFM) product of the Copernicus Emergency Management Service will provide a continuous monitoring of floods worldwide by immediately processing and analysing all incoming S-1 Interferometric Wide Swath data and making use of the data cube approach enabling a high product timeliness and the implementation of flood mapping algorithms that require data-driven model training. It integrates three independently developed flood mapping algorithms to improve the robustness and accuracy of the flood and water extent maps and to build a high degree of redundancy into the service.

Due to the similarity of the radar backscatter in flooded and unflooded conditions over particular areas, it is not possible to carry out a comprehensive SAR-based flood mapping at large scale. In this paper, an additional information layer derived from Sentinel-l time series data, called Exclusion map (EX-map), is introduced. Its aim is to enhance and complement the results of automatic change detection-based flood mapping methods. The EX-map aims at delineating areas where observed variations of SAR backscatter do not allow detecting the appearance of floodwater. The EX-map is mainly composed of the following land cover classes: topographic shadow/layover, double bounce and smooth tarmac in urban areas, arid areas, dense vegetation and permanent water bodies. The method is evaluated over six study sites across the globe and tested for different flood events. The EX-map not only increases the classification accuracy of change detection-based flood maps derived from Sentinel-l data from 95.92% to 97.02%, but also enables a better interpretation of any SAR-based floodwater map.

This study investigates the performance of the TU Wien soil moisture retrieval (TUW-SMR) algorithm by adapting the strength of the vegetation correction. The semiempirical change detection method TUW-SMR exploits the multiangle backscatter observations from spaceborne fan-beam scatterometer systems in order to derive surface soil moisture information expressed in the degree of saturation. The vegetation parameterization of TUW-SMR is controlled by the dry and wet crossover angles that are used to determine the dry and wet backscatter reference. Backscatter observations from the Advanced Scatterometer (ASCAT) are used to produce four soil moisture data sets based on different dry and wet crossover angles describing: 1) a static, respectively, no vegetation correction; 2) the currently used seasonal vegetation correction; 3) a stronger seasonal vegetation correction; and 4) a spatially variable seasonal vegetation correction with the stronger vegetation correction over vegetated areas and no vegetation correction over bare land. All four ASCAT soil moisture data sets are evaluated against soil moisture estimates from GLDAS-2.1 Noah land surface model and the European Space Agency (ESA) climate change initiative (CCI) Passive v04.5 soil moisture product using the triple collocation method and traditional correlation analysis. The results show that the spatially variable vegetation correction overall improves soil moisture estimates in both more densely vegetated areas, e.g., in large parts of North America and Europe, and more sparsely vegetated, e.g., Western Africa. Nonetheless, the experiment also provides insight into challenging retrieval conditions where the TUW-SMR fails to take all relevant backscatter processes into account, e.g., wetlands and bare soils with subsurface scattering.

The knowledge of the distribution of crop types is of great importance to numerous applications at regional to global scales. Different techniques, including microwave remote sensing methods, have been developed for automatized, accurate crop mapping, however, the discrimination of crops with similar morphology and phenology remains a challenge. In this study, we investigate how to distinguish wheat and barley fields by applying statistical methods and a long-short term memory network to backscatter observed by the C-band SAR instrument onboard the Sentinel-1 satellite.

This article presents the results of a laboratory investigation to explain anomalously high soil moisture estimates observed in retrievals from SAR and scatterometer backscatter, affecting extensive areas of the world associated with arid climates. High-resolution C-band tomographic profiling was applied in experiments to understand the mechanisms underlying these anomalous retrievals. The imagery captured unique high-resolution profiles of the variations in the vertical backscattering patterns through a sandy soil with moisture change. The relative strengths of the surface and subsurface returns were dependent upon both soil moisture and soil structure, incidence-angle, and polarization. Copolarized returns could be dominated by both surface and subsurface returns at times, whereas crosspolarized returns were strongly associated with subsurface features. The work confirms suspicions that anomalous moisture estimates can arise from the presence of subsurface features. Diversity in polarization and incidence angle may provide sufficient diagnostics to flag and correct these erroneous estimates, allowing their incorporation into global soil moisture products.

Earth observation researchers use specialised computing services for satellite image processing offered by various data backends. The source of data is often the same, for example Sentinel-2 satellites operated by Copernicus, but the way how data is pre-processed, corrected, updated, and later analysed may differ among the backends. Backends often lack mechanisms for data versioning, for example, data corrections are not tracked. Furthermore, an evolving software stack used for data processing remains a black box to researchers. Researchers have no means to identify why executions of the same code deliver different results. This hinders reproducibility of earth observation experiments. In this paper, we present how infrastructure of existing earth observation data backends can be modified to support reproducibility. The proposed extensions are based on recommendations of the Research Data Alliance regarding data identification and the VFramework for automated process provenance documentation. We implemented these extensions at the Earth Observation Data Centre, a partner in the openEO consortium. We evaluated the solution on a variety of usage scenarios, providing also performance and storage measures to evaluate the impact of the modifications. The results indicate reproducibility can be supported with minimal performance and storage overhead.

The main objective of this study is to introduce and evaluate a SAR-based flood mapping algorithm enabling the automatic generation of a large-scale flood record from the ENVISAT ASAR data archive. The flood mapping algorithm is based on a change detection approach and requires an automatic selection of optimal reference images. The flood mapping algorithm is applied to selected pairs of images to sequentially generate a record of flood extent maps. False alarms caused by water-like areas are reduced using auxiliary data sources such as the Height Above Nearest Drainage (HAND) index derived from topography data. The proposed method is applied to several ENVISAT WS ASAR datasets acquired over the UK and results are validated with a flood extent map derived from aerial photography. Results presented in this paper demonstrate the effectiveness of the methodology.

Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR's high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1's inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service.

Satellite observations from active and passive microwave sensors are a valuable means to measure surface soil moisture at the global scale. More than a dozen historical and currently active missions have been used for this purpose, together spanning a period from 1978 to present. Within the Climate Change Initiative (CCI) of the European Space Agency (ESA), these separate missions are systematically combined into homogenized Climate Data Records (CDRs). Currently, the operational production and the near-real-time updating of these CDRs is being transferred to the EU Copernicus Climate Changes Services (C3S). In this study, we provide an overview of the characteristics of the most recent ESA CCI and C3S soil moisture products. In particular, we focus on the new merging procedure, which optimally weighs the individual missions based on their uncertainties estimated with triple collocation analysis.

A number of satellite missions have the capability to provide surface soil moisture information at a range of spatial and temporal scales. However, the validation of such products heavily relies on point measurements from permanent stations, which may or may not be representative of the larger scale soil moisture conditions. Hence, methods need to be developed that allow the sampling of surface soil moisture on the ground across large scales over a reasonably short time scale, in order to capture the spatial variability within a footprint, or to provide spatially sufficiently large data sets to validate high-resolution products, be they at their native resolution or downscaled. In this study, two field-scale ground sampling techniques, namely stationary and roving Cosmic Ray Probes, are compared against a high-resolution satellite product. The data are compared for their temporal performance as well as with a focus on capturing the correct spatial variability. The challenge is the inherently different sensing depth of the various technologies. It is shown that this may largely be overcome through scaling the products.

We propose a method for merging soil moisture retrievals from spaceborne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from using triple collocation analysis (TCA). In regions where TCA is deemed unreliable, we use correlation significance levels ($p$-values) as indicator for retrieval quality to decide whether to use active data only, passive data only, or an unweighted average. We apply the proposed merging scheme to active retrievals from advanced scatterometer and passive retrievals from the Advanced Microwave Scanning Radiometer—Earth Observing System using Global Land Data Assimilation System-Noah to complement the triplet required for TCA. The merged time series is evaluated against soil moisture estimates from ERA-Interim/Land andin situmeasurements from the International Soil Moisture Network using the European Space Agency’s (ESA’s) current Climate Change Initiative—Soil Moisture (ESA CCI SM) product version v02.3 as benchmark merging scheme. Results show that the$p$-value classification provides a robust basis for decisions regarding using either active or passive data alone, or an unweighted average in cases where relative weights cannot be estimated reliably, and that the weights estimated from TCA in almost all cases outperform the ternary decision upon which the ESA CCI SM v02.3 is based. The proposed method forms the basis for the new ESA CCI SM product version v03.x and higher.

For Earth observation data to be useful for a wide range of land surface applications, a kilometer or finer resolution is required. Unfortunately, passive microwave observations at low microwave frequencies (1-10 GHz), already providing important information on soil moisture and vegetation dynamics, are generally only available at a resolution of tens of kilometers. This letter presents a new downscaling method relating L-band radiometer and C-band radar observations for downscaling purposes. The data were obtained from two extensive airborne field experiments across a 80 000-km2catchment in south-eastern Australia and coinciding Envisat Advanced Synthetic Aperture Radar acquisitions, performed during the Austral summer and spring of 2010. The novel approach of this study is in the downscaling of coarse-scale emissivities as observed by the radiometer with a new interpretation of the change detection methodology for the radar signal to relate the spatiotemporal changes of those two types of observations at 1 km. It is shown that, for most land surface conditions, a good spatial representation at high resolution is achieved, without considering land surface specific parameterizations, which is promising for using very high resolution radar data from the Sentinel-1 platform for downscaling of passive microwave data from current missions, such as National Aeronautics and Space Administration's Soil Moisture Active Passive and European Space Agency's SMOS.

In microwave remote sensing of the Earth's surface, the satellite signal holds information on both soil moisture and vegetation. This necessitates a correction for vegetation effects when retrieving soil moisture. This paper assesses the strengths and weaknesses of the existing vegetation correction as part of the Vienna University of Technology (TU-Wien) method for soil moisture retrieval from coarse-scale active microwave observations. In this method, vegetation is based on a multiyear climatology of backscatter variations related to phenology. To assess the plausibility of the correction method, we first convert the correction terms for retrievals from the Advanced Scatterometer (ASCAT) into estimates of vegetation optical depth τausing a water-cloud model. The spatial and temporal behaviors of the newly developed τaare compared with the optical depth retrieved from passive microwave observations with the land parameter retrieval model τp. Spatial patterns correspond well, although low values for τaare found over boreal forests. Temporal correlation between the two products is high (R = 0.5), although negative correlations are observed in drylands. This comparison shows that τaand thus the vegetation correction method are sensitive to vegetation dynamics. Effects of the vegetation correction on soil moisture retrievals are investigated by comparing retrieved soil moisture before and after applying the correction term to modeled soil moisture. The vegetation correction increases the quality of the soil moisture product. In areas of high interannual variability in vegetation dynamics, we observed a negative impact of the vegetation correction on the soil moisture, with a decrease in correlation up to 0.4. It emphasizes the need for a dynamic vegetation correction in areas with high interannual variability.

In this work the possibilities of combining modelled (GEOtop, Hydrological model) and remotely sensed (ENVISAT ASAR WS) soil moisture content (SMC) values were investigated introducing a novel approach for data fusion on a product level. Data fusion was performed through the definition of a correction term for the modelled SMC dataset. For the determination of this term machine learning (Support Vector Regression) was used. As a reference dataset in-situ SMC measurements were considered. The benefit of the proposed method was successfully shown as R2between modelled and measured SMC values was improved from 0.11 to 0.61.

An Artificial Neural Network (ANN) algorithm for the Soil Moisture Content (SMC) retrieval from the C-band EPS-SG SCA scatterometer, which will replace the Metop ASCAT, was implemented and tested with real data and model simulations. The main aim of this activity was in understanding the potential of VH channel, which inclusion on the mid-beam antenna of EPS-SG SCA is currently being considered, for improving the retrieval accuracy respect to the existing SMC product derived from ASCAT.

Performances of two global satellite-based surface soil moisture (SSM) retrievals with respect to model-based SSM derived from the MERRA (Modern-Era Retrospective analysis for Research and Applications) rea-nalysis were explored in this paper: (i) Soil Moisture and Ocean Salinity (SMOS; passive) Level-3 SSM (SMOSL3) and (ii) the Advanced Scatterometer (ASCAT; active) SSM. Temporal correlation was used to investigate the performance of SMOSL3 and ASCAT SSM products during the period 05/2010–2012 on a global basis. Both SMOSL3 and ASCAT (slightly better) captured well (R>0.70) the long-term variability of the modelled SSM, particularly, over the Indian subcontinent, the Great Plains of North America, and the Sahel. However, ASCAT had negative correlations in arid regions, in particular across the Sahara and the Arabian Peninsula. This may be due to complex scattering mechanisms over very dry surfaces. To explore the land cover dependence of the analyzed statistical indicators, the global correlation results were averaged per biome extracted from a global map of biomes. In general, SMOSL3 and ASCAT performances behaved differently from one biome to another. For SMOSL3, the highest average correlation was observed over “tropical semi-arid” (R = ∼ 0.5) and “temperate semi-arid” biomes, whereas for ASCAT, the highest correlations were observed over “tropical semi-arid” (R = ∼ 0.7) and “tropical humid” biomes. The poorest agreement for both SMOSL3 and ASCAT was generally found over “tundra” and “desert temperate” biomes, particularly for ASCAT. This study showed that the performance of both SMOSL3 and ASCAT is highly dependent on vegetation. We also showed that both of them provide complementary information on SSM, which implies a potential for data fusion which would be pertinent for the ESA climate change initiative (CCI).

In this study we evaluate five different retrieval algorithms, applied on MetOp-A ASCAT backscatter data, in their ability to retrieve soil moisture on a global scale. Correlation and triple collocation analysis are performed using in situ and land surface model data as a reference. Results do not clearly identify one best algorithm. We therefore conclude that future work should focus on the exploitation of the strengths and weaknesses of different modelling approaches in a synergetic way rather than trying to find one model that suits every possible situation.

Validation of soil moisture observations from remote sensing platforms is quickly becoming a routine task as operational soil moisture products continue to be developed. Despite this, there are no agreed upon validation procedures or open source implementations of common tasks and methods. This makes the reproduction of scientific results difficult, especially since it is not yet common that software is include in publications. The presented work aims to improve this situation by showing an open source toolbox that makes it easy to perform common validation tasks. This toolbox is also used as the backend of a web application that aims to simplify the comparison of different validation methods.

For the first time, airborne passive microwave data were collected at 1 km resolution over parts of Central Australia coinciding with spaceborne active data, allowing a comparison of such data sets acquired at medium (1 km) spatial resolution. L-band airborne passive microwave scenes were compared with C-band scenes and temporal parameters from the Advanced Synthetic Aperture Radar. It was found that the radar-returned signal, as well as the “sensitivity” and “correlation” parameters derived from the long time-series of the ASAR GM data, is similar to spatial patterns in the passive microwave data, suggesting that similar physical interactions are underlying both data sets, especially across heterogeneous landscapes. Comparable patterns found over the dry Lake Eyre salt bed (r2= 0.37) suggest that very high-resolution C-band radar data may be used to describe subpixel heterogeneity within coarse resolution radiometer data, such as the future Soil Moisture Active Passive mission.

The analysis of multitemporal synthetic aperture radar (SAR) images requires normalization to a common incidence angle when the images are acquired in varying view geometry. The dependence of radar backscatter on the incidence angle is known to vary with vegetation cover and will therefore change throughout the year. This letter tries to quantify the effect of neglecting this seasonal variability in an empirical incidence angle normalization for a region under Mediterranean climatic conditions. A methodology is presented to assess, at monthly intervals, the seasonal variability of the angular dependence of ASAR Wide Swath (WS) backscatter over Calabria, Italy. It is observed that the angular dependence of backscatter strongly fluctuates temporally, depending on the land cover. The angular dependence of ASAR WS backscatter has a similar seasonal behavior as that of the Advanced Scatterometer for large parts of the study site when both are resampled to a common spatial and temporal resolution. It is found that errors that are larger than the radiometric accuracy of the sensor may be introduced when this temporal variability is ignored in an angular normalization.

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.

The assimilation of satellite soil moisture data into rainfall-runoff modelling represents an important issue not only for research purposes but also for hydrological application addressing flood forecasting. Notwithstanding the large effort made in the last three decades, only few studies demonstrated a benefit deriving from the use of satellite soil moisture data in hydrology. This matter can be ascribed to the differences in the quality of the assimilated data, in the climatic conditions and in the data assimilation techniques that have been adopted. Based on that, this study compares different satellite soil moisture products in different catchments worldwide to shed light about the more suitable products and climatic conditions that should be employed for improving runoff prediction. The results reveal that the employed soil moisture products can be conveniently used to improve runoff prediction. However, reliability differs according to the climatic region and the accuracy of satellite retrievals.

Within the Climate Change Initiative of the European Space Agency a multi-satellite soil moisture product covering the period 1979-2010 was released. In this study we first assess its quality by comparing it with soil moisture from ground-based stations and several land surface model estimates. Secondly, the dynamics in the dataset were assessed using trend analysis and comparisons with ancillary data sets of precipitation and vegetation. Significant changes over time were found that largely correspond to changes in precipitation and vegetation vigorousness. However, the influence of changing observation density and data set quality over time need to be better understood for a more precise interpretation of the observed trends.

Soil moisture is a crucial variable for a large variety of applications with different requirements on the spatial and temporal resolution of the observations. Coarse-scale instruments can provide data operationally with a nearly-daily global coverage at a spatial resolution of several hundreds of square kilometers, whereas SAR instruments provide a spatial resolution of less than one hectare to about one square kilometer but with a revisit time varying from several days to several months. This study uses coarse-scale MetOp ASCAT data and higher resolution Envisat ASAR data taken in the GM mode and the WS mode together with in-situ measurements to demonstrate (i) the potential of Sentinel-1 to capture very local soil moisture variations and (ii) the expected impact of the significantly improved radiometric accuracy of Sentinel-1 compared to existing soil moisture missions.

This paper describes the design and characteristics of a new in situ soil moisture network in Austria, developed for the validation of spaceborne and modeled soil moisture products. The carefully chosen design of the network enables upscaling of the point measurements to the scale of a satellite footprint. As a first assessment soil moisture time series from an existing station are compared to both ASCAT and GLDAS surface soil moisture data. Results of this comparison show that both modeled and remotely sensed soil moisture corresponds with the in situ measurements. However, the overall higher soil moisture values for the in situ station, which are represented in the bias, emphasize the need for a carefully designed and calibrated soil moisture network.

Satellite observations from active and passive microwave sensors have been successfully used to infer the status of soil water at a global scale. The individual instruments that can be used for this purpose cover a period of more than 30 years. Combining the separate missions into a single homogenized dataset would provide a unique opportunity to study the dynamics of soil moisture over space and time. This study presents a recently developed method for combining global soil moisture datasets with different specifications into a merged data record. The approach profits from the advantages of the various products and retrieval techniques while explicitly addressing the uncertainties related to vegetation density. Initially, products from two active and four passive microwave mission were combined. The merged dataset was analyzed with respect to trends and connects with various climate modes.

With the increasing number of remotely sensed soil moisture products a need for their standardized evaluation becomes necessary. This work comprises of two major parts. First, it provides a brief overview on three advanced evaluation measures commonly used to evaluate soil moisture products. Second, it investigates the performance of the triple collocation (TC) and error propagation (EP) methods when these are used to evaluate the medium resolution ASAR GM soil moisture product. The study highlights the fact that each evaluation method has limitations and presumptions that may not always be fully achievable. It further demonstrates that a combination of only two evaluation methods can shed more light on the performance quality of the methods and may lead to an improved understanding of the error structures of the product.

Knowledge about the freeze/thaw state of the surface is of major importance for climate modelling, hydrology and numerous other applications. In this study, a freeze/thaw state detection algorithm using the ASCAT scatterometer is compared to Land Surface Temperature (LST) from MODIS as well as to a product derived from ENVISAT ASAR data. Good agreement with the LST product was found over the study area in Northern Siberia with disagreement below 22% for all 8-day periods of 2007. SAR derived surface status can, if sufficient sampling is available, provide similar results as with ASCAT but even with higher spatial detail.

Quality assessment methods are introduced through the production of the enhanced resolution soil moisture product. Derived model parameters for the retrieval of soil moisture estimates from ASPS ERS-2 backscatter data as well as the resulting soil moisture product itself, are evaluated by validation analysis. Statistical measure and visual comparison confirm the demand on high data quality of the model parameters. The enhanced resolution soil moisture product is validated against 181 in-situ stations. Correlations with in-situ data show the excellent performance of the product.

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.

Soil moisture is of high importance in permafrost regions. Within the DUE Permafrost project, adjustments to the 1 km Surface Soil Moisture (SSM) product, derived from ENVISAT ASAR Global Monitoring mode data, have been made to account for some of the conditions encountered at high latitudes. Soil moisture retrieval from SAR requires taking into account the presence of water bodies. This is challenging in regions of permafrost, as the majority of lakes in tundra environments are smaller than the spatial resolution of global and regional land cover datasets. A method to account for the presence of water bodies at and below the 1 km scale in support of SSM retrieval is presented. A high potential for transfer of the presented methodologies to the Sentinel-1 mission is apparent due to the consistency of measurements with ENVISAT ASAR.

Due to the strong contrast of the dielectric properties of dry and wet soil at microwave frequencies soil moisture can be retrieved on a global scale from active and passive microwave remote sensing instruments. Recent validation studies carried out over a number of in situ networks in Europe, the US and Australia have demonstrated that the soil moisture retrieval skill achieved with the Soil Moisture and Ocean Salinity (SMOS) mission and the Advanced Scatterometer (ASCAT) compares well in many regions of the world. But of course, there are also areas where the retrieved soil moisture values from these instruments do not match well. In this study global SMOS and ASCAT soil moisture data are compared with the aim to identify the areas of agreement and disagreement. The analysis confirms the findings of the previous studies that over most regions worldwide the temporal evolution of SMOS and ASCAT retrievals compare quite well. Notable exceptions are arid environments where the ASCAT retrievals fail to reproduce the real soil moisture trends, while the SMOS retrievals perform as expected. More work is required to understand the contrasting behavior of the ASCAT and SMOS soil moisture retrievals in these environments.

The Sentinel-1 mission is a polar-orbiting satellite constellation for the continuation of C-band Synthetic Aperture Radar (SAR) applications. Contrary to its predecessor instruments onboard of ENVISAT and RADARSAT, the Sentinel-1 satellites will be operated following a predefined and fixed baseline acquisition scenario. This will significantly facilitate the development of fully automatic processing chains for the generation of higher-level geophysical products and their uptake in applications. This paper gives an overview of the potential use of Sentinel-1 for land applications, discussing different land cover products (permanent water bodies, forest/non-forest, rice) and parameters of high relevance for hydrological monitoring (soil moisture, snow and freeze/thaw status, surface inundation).

The triple collocation technique, which retrieves the error variances of three sets of measurements of the same parameter, is applied to soil moisture records in central Spain: ASCAT remote sensing observations, REMEDHUS in-situ probes, and the ERA Interim model. The objective is the estimation of the temporal variability of the error of ASCAT. The three data sets have to be calibrated with respect to each other as they show different mean values and dynamic ranges. The time-variant estimation of both the error and the calibration parameters is shown to be very sensitive to the extents of the temporal windows used and the calibration procedure. Due to the temporal fluctuations of the calibration constants, artefacts such as seasonal variations and extreme values are introduced. This case study shows that the temporal analysis of the errors using the collocation technique can lead to spurious results when the data sets have to be referenced with respect to one another.

Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport, and vegetation productivity. Their characterization offers numerous possibilities for the improvement of our understanding of complex land-surface–atmosphere interactions. In this paper, soil moisture dynamics at the soil's surface (the first centimeters) and in its root zone (at depths down to 1 m) are investigated using$25 \times 25\ \hbox{km}^{2}$scale data (Advanced Scatterometer (ASCAT)/METorological OPerational (METOP) scatterometer), for a semiarid region in North Africa. Our study highlights the quality of the surface and root-zone soil moisture products, derived from ASCAT data recorded over a two-year period. Surface soil moisture tends to be highly variable because it is strongly influenced by atmospheric conditions (rain and evaporation). On the other hand, root-zone moisture is considerably less variable. A statistical drought-monitoring index, referred to as the “moisture anomaly index,” is derived from ASCAT and European Remote Sensing (ERS) time series. This index was tested with ERS and ASCAT products during the 1991–2010 study period. A strong correlation is found between the proposed index and the standardized precipitation index.

Nowadays, the availability of soil moisture estimates from satellite sensors offers a great chance to improve real-time flood forecasting through data assimilation. In this paper, two real data and two synthetic experiments have been carried out to assess the effects of assimilating soil moisture estimates into a two-layer rainfall-runoff model. By using the ensemble Kalman filter, both the surface- and root-zone soil moisture (RZSM) products derived by the Advanced SCATterometer (ASCAT) have been assimilated and the model performance on flood estimation is analyzed. RZSM estimates are obtained through the application of an exponential filter. Hourly rainfall-runoff observations for the period 1994-2010 collected in the Niccone catchment (137 km2), Central Italy, are employed as case study. The ASCAT soil moisture products are found to be in good agreement with the modeled soil moisture data for both the surface layer (correlation coefficient (R) of 0.78) and the root zone (R= 0.94). In the real data experiment, the assimilation of the RZSM product has a significant impact on runoff simulation that provides a clear improvement in the discharge modeling performance. On the other hand, the assimilation of the surface soil moisture product has a small effect. The same findings are also confirmed by the synthetic twin experiments. Even though the obtained results are model dependent and site specific, the possibility to efficiently employ coarse resolution satellite soil moisture products for improving flood prediction is proven, mainly if RZSM data are assimilated into the hydrological model.

Since December 2008, the European Organisation for the Exploitation of Meteorological Satellites has been operationally distributing a global 25-km surface soil moisture product derived from the Advanced Scatterometer (ASCAT) onboard the meteorological operational platform (METOP) satellite METOP-A. Soil moisture is retrieved by using the semiempirical change detection method originally developed by the Vienna University of Technology (TU Wien) for the Active Microwave Instrument (AMI) flown on the European Remote Sensing (ERS) satellites ERS-1 and ERS-2. With the launch of the first of the three Meteorological Operational Platforms (METOP-A) in October 2006, ASCAT onboard METOP-A inherits and continues the role of his predecessor AMI. The original soil moisture retrieval algorithm (TU Wien model) was expected to be almost directly applicable for ASCAT with only minor changes, since the configuration and technical design is similar to the ERS scatterometers. Since the TU Wien model requires a robust historic long-term reference of scattering parameters, the initial near real-time METOP ASCAT soil moisture product had to rely on the model parameters derived from over 15 years of ERS-1/2. However, the combination of ASCAT backscatter measurements and ERS-1/2 historic long-term reference introduced some artifacts in the soil moisture product. The objectives of this paper were to analyze and investigate the impact of the ERS-1/2 historic long-term reference on the soil moisture retrieval. An error model has been developed to quantify the effects of the two main error sources: differences in spatial resolution and absolute calibration. The results of the study show that a simple model is able to describe the artifacts in the initial near real-time METOP ASCAT soil moisture product, which frequently occur in areas characterized by sharp backscatter contrasts. The expected overestimation of soil moisture using ERS-1/2 model parameters due to a calibration bias between AMI and ASCAT could be modeled as well.

Information on soil surface state is valuable for many applications such as climate studies and monitoring of permafrost regions. C-band scatterometer data indicate good potential to deliver information on surface freeze/thaw. Variation in state or amount of water contained in the soil causes significant alteration of dielectric properties of the soil which is markedly observable in scatterometer backscattered signal. A threshold-analysis method is developed to derive a set of parameters to be used in evaluating the normalized backscatter measurements through decision trees and anomaly detection modules for determination of freeze/thaw conditions. The model parameters are extracted from two years (2007-2008) backscatter data from ASCAT scatterometer onboard Metop satellite collocated with ECMWF ReAnalysis (ERA-Interim) soil temperature. Backscatter measurements are flagged as indicator of frozen/unfrozen surface, and snowmelt or existing water on the surface. The output product, so-called surface state flag (SSF), compares well with two modeled soil temperature data sets as well as the air temperature measurements from synoptic meteorological stations across the northern hemisphere. The SSF time series are also validated with soil temperature data available at four in situ observation sites in Siberian and Alaska regions showing the overall accuracy of about 80% to 90%.

A novel sensor fusion algorithm for retrieving the freeze/thaw (f/t) state from scatterometer data is presented: It is based on a probabilistic model, which is a variant of the Hidden Markov model, and it computes the probability that the landscape is frozen, thawed, or thawing for each day. By combining Ku- and C-band scatterometer data, the distinct backscattering properties of snow, soil, and vegetation at the two radar bands are exploited. The parameters that are necessary for inferring the f/t state are estimated in an unsupervised fashion, i.e., no training data are required. Comparison with model and in situ temperature data in a test area in Siberia/northern China indicates that the approach yields promising results (typical accuracies exceeding 90%); difficulties are encountered over bare rock and areas where large fluctuations in soil moisture are common. These limitations turn out to be closely linked to the inherent assumptions of the probabilistic model.

Soil moisture is an import parameter for high latitude research focusing on carbon exchange and permafrost issues. This paper reviews requirements, constrains and possibilities of satellite derived soil moisture data for high latitude applications. Major points are freezing and thawing, and landscape heterogeneity. Special focus is on data derived from ENVISAT ASAR. The different ScanSAR modes (wide swath and global monitoring mode) can be used to address various issues. Sensitivity over tundra at this wavelength is similar to semi-arid regions in mediterranean and subtropic climates.

A time-efficient solution to estimate the error of satellite surface soil moisture from the land parameter retrieval model is presented. The errors are estimated using an analytical solution for soil moisture retrievals from this radiative-transfer-based model that derives soil moisture from low-frequency passive microwave observations. The error estimate is based on a basic error propagation equation which uses the partial derivatives of the radiative transfer equation and estimated errors for each individual input parameter. Results similar to those of the Monte Carlo approach show that the developed time-efficient methodology could substitute computationally intensive methods. This procedure is therefore a welcome solution for near-real-time data assimilation studies where both the soil moisture product and error estimate are needed. The developed method is applied to the C-, X-, and Ku-bands of the Aqua/Advanced Microwave Scanning Radiometer for Earth Observing System sensor to study differences in errors between frequencies.

The impact of measurement incidence angle (θ) on the accuracy of radar-based surface soil moisture (Θs) retrievals is largely unknown due to discrepancies in theoretical backscatter models as well as limitations in the availability of sufficientlyextensive ground-based Θsobservations for validation. Here, we apply a data assimilation-based evaluation technique for remotely-sensed Θsretrievals that does not require groundbased soil moisture observations to examine the sensitivity of skill in surface Θsretrievals to variations in θ. Application of the evaluation approach to the TU-Wien European Remote Sensing (ERS) scatterometer Θsdata set over regional-scale (~10002 km2) domains in the Southern Great Plains (SGP) and Southeastern (SE) regions of the United States indicate a relative reduction in correlation-based skill of 23% to 30% for Θsretrievals obtained from far-field (θ > 50°) ERS observations relative to Θsestimates obtained at θsretrieval noise predictions made using the TU-Wien ERS Water Retrieval Package 5 (WARP5) backscatter model. However, over moderate vegetation cover in the SE domain, the coupling of a bare soil backscatter model with a "vegetation water cloud" canopy model is shown to overestimate the impact of θ on Θsretrieval skill.

Due to the high temporal sampling rate of ASAR Global Monitoring mode, it has an application potential for analyzing seasonal changes in permafrost environment. The objective of the study is to develop a robust method for monitoring freeze/thaw cycles beyond threshold approaches. In order to use ASAR GM time-series for analyzing freeze/thaw states, a least square fitting of piecewise step function is introduced in this paper. An experiment result for Siberian permafrost area near Yakutsk illustrates that it can be a promising approach in monitoring permafrost ecosystems.

Since December 2008 the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has been disseminating global 25 km ASCAT surface soil moisture data in near real-time (within 135 minutes after sensing) over its broadcast system EUMETCast. The ASCAT surface soil moisture product is thus the first truly operational satellite soil moisture product that may be used for Numerical Weather Prediction (NWP), flood forecasting and other time-critical applications. In this paper we provide information about the status of the ASCAT Level 2 soil moisture processor, review first published validation and application studies and discuss plans for further improvements.

The impact of measurement incidence angle (θ) on the accuracy of radar-based surface soil-moisture (Θs) retrievals is largely unknown due to discrepancies in theoretical backscatter models as well as limitations in the availability of sufficiently extensive ground-based Θsobservations for validation. Here, we apply a data-assimilation-based evaluation technique for remotely sensed Θsretrievals that does not require ground-based soil-moisture observations to examine the sensitivity of skill in surface Θsretrievals to variations in θ. Past results with the evaluation approach have shown that it is capable of detecting relative variations in the anomaly correlation coefficient between remotely sensed Θsretrievals and ground-truth soil-moisture measurements. Application of the evaluation approach to the Vienna University of Technology (TU Wien) European Remote Sensing (ERS) scatterometer Θsdata set over regional-scale ( ~ 10002km2) domains in the Southern Great Plains and southeastern (SE) regions of the U.S. indicate a relative reduction in correlation-based skill of 23% to 30% for Θsretrievals obtained from far-field (θ>50°) ERS observations relative to Θsestimates obtained at θsretrieval noise predictions made using the TU-Wien ERS Water Retrieval Package 5 backscatter model. However, over moderate vegetation cover in the SE domain, the coupling of a bare soil backscatter model with a “vegetation water cloud” canopy model is shown to overestimate the impact of θ on Θsretrieval skill.

The Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode offers an opportunity for global soil moisture (SM) monitoring at much finer spatial resolution than that provided by the currently operational Advanced Microwave Scanning Radiometer for the Earth Observing System and future planned missions such as Soil Moisture and Ocean Salinity and Soil Moisture Active Passive. Considering the difficulties in modeling the complex soil-vegetation scattering mechanisms and the great need of ancillary data for microwave backscatter SM inversion, algorithms based on temporal change are currently the best method to examine SM variability. This paper evaluates the spatial sensitivity of the ASAR GM surface SM product derived using the temporal change detection methodology developed by the Vienna University of Technology. This evaluation is made for an area in southeastern Australia using data from the National Airborne Field Experiment 2005. The spatial evaluation is made using three different types of SM data (station, field, and airborne) across several different scales (1-25 km). Results confirmed the expected better agreement when using point (Rstation= 0.75) data as compared to spatial (RPLMR, 1 km= 0.4) data. While the aircraft-ASAR GM correlation values at 1-km resolution were low, they significantly improved when averaged to 5 km (RPLMR, 5 km= 0.67) or coarser. Consequently, this assessment shows the ASAR GM potential for monitoring SM when averaged to a spatial resolution of at least 5 km.

To address the needs of the hydrological community for medium resolution soil moisture dataset an approach developed at the TU WIEN for the coarse resolution ERS/METOP datasets has been transferred to medium resolution SAR data. This work was performed within the ESA Tiger Innovator project SHARE and introduces an operational soil moisture monitoring service for the region of the Southern African Development Community (SADC) and Australia. The data from the ASAR onboard ENVISAT operating in Global Mode (GM) with 1 km spatial resolution were implemented for the dataset generation that provides twice weekly measurements and captures highly variable soil moisture patterns. Several validation and application studies were summarized in this paper that demonstrated the ability of ASAR Global Mode (GM) Soil Moisture for global soil moisture monitoring. The dataset can be accessed via http://www.ipf.tuwien.ac.at/radar/share/.

The ability of the active microwave remote sensing to complement existing optical vegetation indices has been explored by variety of studies. To demonstrate these complementarities, we investigate synergies between the slope parameter from the ERS scatterometer (ERS-SCAT) and the Normalized Difference Vegetation Index (NDVI). While the NDVI is strongly linked to the absorption of photosynthetically active radiation (PAR), the active microwave signal has the ability to penetrate partially a vegetation canopy, bouncing back from its stalks and stems and so returning a signal (i.e. backscatter) that is influenced by the canopy structure. While this interaction is rather complicated and not fully understood, this study suggests the ability of the ERS-SCAT slope parameter to differentiate between varieties of structural vegetation properties and complement so to existing optical vegetation indices.

The scatterometers onboard the European Remote Sensing satellites (ERS-1 & ERS-2) and the METeorological OPerational satellite (METOP) have been shown to be useful for surface soil moisture retrieval using the so-called TU-Wien change detection method. This paper presents an improved soil moisture retrieval algorithm based on the existing TU-Wien method but with new parameterization as well as a series of modifications. The new algorithm, WAter Retrieval Package 5 (WARP5), copes with some limitations identified in the earlier method WARP4 and provides the possibility of migrating soil moisture retrieval from ERS-SCAT to METOP-ASCAT data. The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters. Cross-comparisons of WARP4 and WARP5 data sets with the Oklahoma Mesonetinsituobservations and also with European Centre of Medium Range Weather Forecast (ECMWF) ReAnalysis (ERA-Interim) global modeled data show that the new algorithm has a better performance and effectively corrects retrieval errors in certain areas.

The Advanced Synthetic Aperture Radar (ASAR) onboard of the satellite ENVISAT can be operated in global monitoring (GM) mode. ASAR GM mode has delivered the first global multiyear C-band backscatter data set in HH polarization at a spatial resolution of 1 km. This paper investigates if ASAR GM can be used for retrieving soil moisture using a change detection approach over large regions. A method previously developed for the European Remote Sensing (ERS) scatterometer is adapted for use with ASAR GM and tested over Oklahoma, USA. The ASAR-GM-derived relative soil moisture index is compared to 50-km ERS soil moisture data and pointlikeinsitumeasurements from the Oklahoma MESONET. Even though the scale gap from ASAR GM to theinsitumeasurements is less pronounced than in the case of the ERS scatterometer, the correlation for ASAR against theinsitumeasurements is, in general, somewhat weaker than for the ERS scatterometer. The analysis suggests that this is mainly due to the much higher noise level of ASAR GM compared to the ERS scatterometer. Therefore, some spatial averaging to 3-10 km is recommended to reduce the noise of the ASAR GM soil moisture images. Nevertheless, the study demonstrates that ASAR GM allows resolving spatial details in the soil moisture patterns not observable in the ERS scatterometer measurements while still retaining the basic capability of the ERS scatterometer to capture temporal trends over large areas.

The strong relationship between soil moisture content and the soil dielectric constant offers a direct way of measuring soil moisture with microwave sensors. Global coarse-resolution soil moisture datasets are currently retrieved from active microwave spaceborne instruments (AMI onboard ERS and ASCAT on Metop-A) and in the near future from dedicated passive microwave satellite sensors (SMOS). This article summarizes recent soil moisture research activities and briefly discusses the strategies for validation and cross-comparison of remotely sensed soil moisture datasets. Special attention is given to the first validation of the soil moisture data from the ASCAT instrument on Metop-A in relation to the absolute and relative calibrations of the instrument. A first assessment of the quality of the ASCAT surface soil moisture is given by studies of stable targets and the spatial and temporal extent of recent extreme drought and rainfall events.

Triple collocation error estimation is a powerful tool to simultaneously estimate the error structure and calibrate a set of independent observations. In this study, we use this technique to estimate the errors of a passive microwave (TRMM-TMI) derived, an active microwave (ERS-2 scatterometer) derived and a modelled (ERA-Interim reanalysis) soil moisture data sets.

Current global satellite scatterometer-based soil moisture retrieval algorithms do not take soil characteristics into account. In this paper, the characteristic time length of the soil water index has been calibrated for ten sampling frequencies and for different soil conductivity associated with 12 soil texture classes. The calibration experiment was independently performed from satellite observations. The reference soil moisture data set was created with a 1-D water-flow model and by making use of precipitation measurements. The soil water index was simulated by applying the algorithm to the modeled soil moisture of the upper few centimeters. The resulting optimized characteristic time lengths$T$increase with longer sampling periods. For instance, a$T$of 7 days was found for sandy soil when a sampling period of 1 day was applied, whereas an optimized$T$-value of 18 days was found for a sampling period of 10 days. A maximum rmse improvement of 0.5% vol. can be expected when using the calibrated$T$-values instead of$T = 20$. The soil water index and the differentiated$T$-values were applied to European Remote Sensing (ERS) satellite scatterometer data and were validated againstin situsoil moisture measurements. The results obtained using calibrated$T$-values and$T = 20$did not differ ($r = 0.39$,$\hbox{rmse} = 5.4 \%$vol.) and can be explained by the averaged sampling period of 4–5 days. The soil water index obtained with current operational microwave sensors [Advanced Wind Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer—Earth Observation System] and future sensors (Soil Moisture and Ocean Salinity and Soil Moisture Active Passive) should benefit from soil texture differentiation, as they can record on a daily basis either individually or synergistically using several sensors. The proposed differentiated characteristic time length enables the continuation of the soil water index of sensors with varying sampling periods (e.g., ERS-ASCAT).

Spatially continuous soil moisture information is on high demand globally. A database which is available from active microwave data (ERS scatterometer, C-band, 50 km) has been assessed over two large basins (Mackenzie and Lena) in northern latitudes. This information was combined with snowmelt patterns which can be derived based on diurnal thaw-refreeze of the snow cover from a further scatterometer (Quikscat, Ku-band, 25 km). Relative soil water information has been averaged over each basin and compared to discharge measurements for the summer periods 1996-2000. A correlation (logarithmic function) of 0.78 is observed for the Lena basin. The Mackenzie is much more complex and thus the relationship is less obvious. This is also reflected in the snowmelt data. Whereas 80% of basin area undergoes melting at the same time in the Lena basin in 2000, at maximum 40% can be observed for the Mackenzie during the same year. Maximum runoff in the Lena basin is observed when snowmelt ceases for the entire basin. This is delayed by three weeks for the Mackenzie although increased runoff can be observed already after 70% of the basin is snow-free.

In this study we assess the impact of different land cover types on the azimuthal noise of backscatter signal using multi-year ERS-scatterometer data. Results indicate a strong response of the azimuthal noise level to the different land cover types like rainforests, lakes, rivers, floodplains, coastal areas, permanent snow or ice, urban areas, and deserts as well as topography. Complex topography with high standard deviation in heights, ridge-shaped features oblique to the satellite track on the surface, and water-contaminated areas are the main causes of the high azimuthal noise in scatterometer measurements. The azimuthal noise fluctuations generally show a minimum over rain forests and maximum over sand deserts. Changes in the level of azimuthal noise of backscatter signal clearly reflect changes in land cover or surface roughness.

Studies of the Earth's land surface involving scatterometers are becoming an increasingly important application field of microwave remote sensing. Similarly to scatterometer observations of ocean waves, the backscattering coefficient (sigma0) response of land surfaces depends on both the incidence and azimuth angle under which the observations are made. In order to retrieve geophysical parameters from scatterometer data, it is necessary to account for azimuthal-modulation effects of the backscattered signal. In the present study, this paper localizes the regions affected by a strong azimuthal signal dependence when observed with the European Remote Sensing Satellite Scatterometer and the SeaWinds Scatterometer on QuikSCAT (QSCAT). The possible physical reasons for the azimuthal effects, relating the very detailed QSCAT azimuthal response to the spatial orientation of special topographic features and land cover within the sensor footprint, were then discussed. Different methods for normalizing the backscattering coefficient with respect of observation azimuth angle were also proposed and evaluated. First, the mean local incidence angle of the sensor footprint using the shuttle radar topography mission digital elevation model (DEM) were modeled and concluded that the resolution of the DEM is too coarse to characterize most of the observed azimuthal effects. A more effective way of normalizing the backscatter with respect to azimuth is then found to be by using historical backscatter observations to statistically determine the expected backscatter at each observation azimuth and incidence angle as well as time of the year. The efficiency of this method is limited to the availability of past measurements for each location on the Earth

ENVISAT's ASAR Global Monitoring mode offers exciting capabilities for monitoring highly dynamic processes of the hydrosphere. Based on examples from different ecoregions (Africa, Siberia, Spain) we highlight the potential of this sensor system for monitoring wetlands and soil moisture. The hydrosphere is often called the sphere as it includes all the earth's water that is found in the oceans, streams, lakes, the soil, groundwater, and in the air. Water is continually cycled between these various reservoirs through processes such as precipitation, melting, runoff, infiltration, and evaporation. Particularly these exchange processes and the highly dynamic water reservoirs (soil moisture, snow, streams, sea-ice) hold the key to our understanding of how the hydrosphere will react to global warming and increasing human pressure on the environment. Effective monitoring of these highly dynamic processes should therefore be a priority in earth observation. Unfortunately, we currently lack earth observation techniques which allow daily monitoring of the hydrosphere at a high spatial resolution ( 1000 km), but are severely hampered by cloud cover and illumination conditions. Therefore, the effective temporal sampling interval is highly irregular, necessitating multi-temporal composting over time periods of one week or longer. Microwave systems are not restricted in this respect and, in addition, exhibit a high sensitivity to water. So while in principle they appear to be ideally suited, system design limitations have so far prevented widespread use. Microwave radiometers and scatterometers are characterized by short repeat intervals ( 20 km). Therefore their use is limited to large-scale applications. Synthetic Aperture Radars (SARs) offer a high spatial resolution (< 100 m) but have so far been characterized by short duty cycles (acquisition time per orbit) and short swath width (< 100 km). Therefore, large-scale operational monitoring at acceptable time intervals has been out of question. ENVISAT offers now for the first time ScanSAR capabilities with a duty cycle of up to 100 %. However a relatively short swath width (405 km) and a large number of competing imaging modes results in sub-optimal temporal sampling intervals. Still, ENVISAT's ASAR Global Monitoring mode is well suited to explore the potential of ScanSAR techniques for monitoring the hydrosphere at scales compatible with AVHRR, MERIS or MODIS.

We present and assess a diurnal difference indicator that is related directly to the seasonal freeze-thaw effects, focusing, in this paper, primarily on the onset of snowmelt and terrestrial thawing. In order to be able to provide a level of certainty with the indicator our approach is based upon the development, and application, of a noise model that accounts for instrument noise, speckle, spatial heterogeneity, "environmental" noise and the influence of azimuth angle at which the measurement was acquired

A method to determine planting dates in semi-arid regions is presented, based on Ku-band spaceborne scatterometer data. The planting date analysis was performed for Mali, a region with a broad range of vegetation cover with tropical forest in the south and desert in the north. The Ku-band data was acquired by the Sea Winds scatterometer onboard the QuikSCAT satellite during the time from January 2000 to December 2003. Climate data from meteorological stations was compared with scatterometer time series of data colocated from a circular area of a specific size. The comparison shows that the evolution of the backscatter signal is highly correlated with the vegetation cycle triggered in turn by the rain season. An accurate date for the onset of the growing season and therefore a basic planting date can be determined from noise-filtered backscatter time series using a simple threshold method. The temporal variations of the backscatter time series are mainly caused by vegetation growth and changes of surface soil moisture. An increased backscatter signal indicates therefore more and more sufficient growing conditions. For the estimation of the contribution of surface soil moisture, the backscatter was additionally compared within situdata from test sites within the Duero basin in Spain, covered by the soil moisture measurement network of the University of Salamanca. The comparison showed a significant influence of surface soil moisture on the microwave backscatter

The analysis of Earth observation data is normally undertaken within the spatial, or image, domain. With sensors obtaining highly frequent observations it is sometimes difficult to understand the temporal evolution and specific characteristics of the data if the analysis of the data is performed solely in the spatial domain. This paper provides an overview of the processing chain that has been developed to extract time series data sets consisting of backscattering coefficient /spl sigma//sup 0/ measurements from the SeaWinds sensor on NASA's QuikSCAT platform. The current global, operational, solution for land surfaces is outlined along with some typical processing times. Due to the volume of the orbital data sets from the SeaWinds sensor, emphasis is drawn upon the fact that within the operational processing chain robustness and stability of the processing environment is critical. The time series data sets are used to analyse the dynamics of freeze-thaw events across the Siberian biome. This work has been undertaken within the framework of the SIBERIA II project.

In this paper we will report the first results of our work carried out within the framework of the SIBERIA II project. This project has the aim to demonstrate the viability of full greenhouse gas (GHG) accounting using a set of multi-sensor Earth Observation instruments, detailed existing databases of field information and some of the worlds most advanced climate models. Freeze-thaw information is intended to be used for validation and input into the GHG models. Utilising the high temporal sampling of the SeaWinds sensor on QuikSCAT, along with a unique gridding and extraction method, from observational to analysis space, we present a new approach for freeze-thaw detection by time series analysis and evaluation of the temporal characteristics of the backscattered signal.

C- and K/sub u/-band scatterometer backscatter time series are analysed and compared to meteorological data for two biomes, the African Steppe and the Scandinavian Boreal Forest. Observed characteristics of large scale scattering are inferred and discussed.

Soil moisture is a key variable in a number of geophysical and ecological processes. Despite its importance, availability of information on soil moisture is limited. Only recently it could be demonstrated that low resolution radar data in combination with a change detection method can resolve this constraint. Experience gained in a number of successful pilot projects, lead to an initiative, setting up a global soil moisture archive.

Water is a critical resource in western Africa, and droughts are a serious threat to the population and the environment. A technique to retrieve soil moisture from ERS scatterometer data is applied over a large region covering southern Mali and Burkina Faso. How the method accounts for the wide range of climatic and physiographic conditions encountered in the study area is discussed. An analysis of monthly soil moisture maps covering six years of data shows that the climatic conditions are well reflected in the remotely sensed data.

The scatterometer flown onboard the European remote-sensing satellites ERS-1 and ERS-2 is a vertically polarized radar operating at 5.3 GHz (C-band) and has a spatial resolution of 50 km. In a number of studies, the sensitivity of the ERS scatterometer to vegetation has been demonstrated, but it is not yet clear which vegetation parameters are of primary importance to explain the ERS scatterometer signal. In this paper, the effects of land cover and seasonal vegetation development are investigated by comparing ERS scatterometer data with land cover information, normalized difference vegetation index (NDVI) data sets, and meteorological observations. As a study area, the Iberian Peninsula was chosen. The Iberian Peninsula is characterized by the Mediterranean climate that has a wet winter and a dry summer. This allows the authors to better differentiate the effects of the annual vegetation and precipitation cycle on the temporal evolution of the backscattering coefficient /spl sigma//spl deg/. It is shown that the ERS scatterometer has only limited capabilities for monitoring the vegetation development within a given year because most of the temporal variability of /spl sigma//spl deg/ is due to soil moisture changes. On the other hand, it might be of merit for vegetation discrimination on large scales (regional to global) because the percentage area of forests, bushes, and shrubs within one ERS scatterometer pixel is found to explain a significant part of the spatial variability of the signal.

The capability of the scatterometers onboard the European Remote Sensing Satellites (ERS-1 and ERS-2) for soil moisture retrieval is investigated. The ERS scatterometer consists of three antennas that illuminate the Earth's surface from three different viewing directions. This allows the authors to study the dependence of the backscattering coefficient /spl sigma//sup 0/ on the azimuth and the incidence angle. An analysis of ERS scatterometer data over the Canadian Prairie region shows that land surfaces are slightly anisotropic with respect to the azimuth angle. It is proposed to consider the azimuthal anisotropy as an additional error source to /spl sigma//sup 0/. The variation of /spl sigma//sup 0/ with the incidence angle was found to be linked to vegetation, but independent of soil moisture. Based on these observations, a method for the normalization of the backscattering coefficient with respect to the incidence angle is proposed. The normalized backscattering coefficient at an incidence angle of 40/spl deg/, /spl sigma//sup 0/(40), is sensitive to vegetation and, in the case of moderate vegetation (grassland to sparsely forested areas), to the soil moisture content. Soil moisture maps derived from ERS-1 scatterometer measurements are compared to maps representing conditions on annually cropped land showing agreement. Results suggest that, over the Canadian Prairies, estimates of the total water content in the soil profile might be possible with an accuracy of about 10% of field capacity if little or no rainfall has occurred for three days before radar image acquisition.