Radar remote sensing is beneficial for retrieval of hydrological information such as soil moisture and flood extents due to the strong influence of water on the radar signal. The proper monitoring and analysis of such temporally dynamic phenomena requires dense time series data. Radar time series data is also useful for mitigating uncertainties in individual images, e.g. for the mapping of permanent water bodies. This chapter reviews capabilities, potentials and challenges of spaceborne radar time series data for the mapping of permanent water bodies, the monitoring of floods, and the retrieval of soil moisture content. The focus is put on the Lower Mekong Basin (LMB) in Southeast Asia. Two thirds of the LMB’s population of 60 million people live directly from agriculture and fisheries. The Mekong River's resources are under pressure among others from an increasing population, intensified agriculture, and the expansion of hydropower. A thorough understanding of water resources in the LMB is therefore crucial to the sustainable development in the region. The chapter provides an outline of radar remote sensing for retrieval of hydrological information as well as an overview of the relevant operational capabilities of radar missions. A map of permanent water bodies of the entire Lower Mekong Basin derived from a time series of ENVISAT Advanced Synthetic Aperture Radar (ASAR) data is presented. Potentials and challenges of flood monitoring with SAR are illustrated with ASAR imagery showing the evolution of the floods that occurred around Tonle Sap Lake in Cambodia in 2011. Finally, the spatial and temporal dynamics of soil moisture across the LMB are analysed with the use of 14 years of scatterometer time series data acquired by the ERS-1, ERS-2, Metop-A and Metop-B satellites. The average seasonal soil moisture cycle was computed at the sub-catchment level. An anomaly analysis of the temporal soil moisture dynamics revealed large inter-annual variability across the Lower Mekong Basin.

Upcoming remote sensing systems onboard satellites will generate unprecedented volumes of spatial data, hence challenging processing facilities in terms of storage and processing capacities. Thus, an efficient handling of remote sensing data is of vital importance, demanding a well-suited definition of spatial grids for the data׳s storage and manipulation. For high-resolution image data, regular grids defined by map projections have been identified as practicable, cognisant of their drawbacks due to geometric distortions. To this end, we defined a new metric named grid oversampling factor (GOF) that estimates local data oversampling appearing during projection of generic satellite images to a regular raster grid. Based on common map projections, we defined sets of spatial grids optimised to minimise data oversampling. Moreover, they ensure that data undersampling cannot occur at any location. From the resulting GOF-values we concluded that equidistant projections are most suitable, with a global mean oversampling of 2% when using a system of seven continental grids (introduced under the name Equi7 Grid). Opposed to previous studies that suggested equal-area projections, we recommend the Plate Carrée, the Equidistant Conic and the Equidistant Azimuthal projection for global, hemispherical and continental grids, respectively.

A surface soil moisture (SSM) product at a 1-km spatial resolution derived from the Envisat Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode data was evaluated over the entire African continent using coarse spatial resolution SSM acquisitions from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Noah land surface model from the Global Land Data Assimilation System (GLDAS-NOAH). The evaluation was performed in terms of relative soil moisture values (%), as well as anomalies from the seasonal cycle. Considering the high radiometric noise of the ASAR GM data, the SSM product exhibits a good ability (Pearson correlation coefficient (R) = ~0.6 for relative soil moisture values and root mean square difference (RMSD) = 11% when averaged to 5-km resolution) to monitor temporal soil moisture variability in regions with low to medium density vegetation and yearly rainfall >250 mm. The findings agree with previous evaluation studies performed over Australia and further strengthen the understanding of the quality of the ASAR GM SSM product and its potential for data assimilation. Problems identified in the ASAR GM algorithm over arid regions were explained by azimuthal effects. Diverse backscatter behavior over different soil types was identified. The insights gained about the quality of the data were used to establish a reliable masking of the existing ASAR GM SSM product and the identification of areas where further research is needed for the future Sentinel-1-derived SSM products.

Flood detection and inundation mapping are amongst the most important applications for remote-sensing data. Space-borne radar systems, synthetic aperture radar (SAR) in particular, and its application for waterbody mapping have recently been subject to research in many publications. Although very good results have been achieved with such data, in some cases automatic waterbody classification based on SAR data is not feasible. Factors influencing the applicability are, e.g., local environmental conditions, roughening of water surfaces due to wind, or the satellite observation geometry. In this study, a measure for the usability of SAR imagery for flood mapping was investigated. Additionally, a method for permanent waterbody mapping was introduced. The study is based on Envisat ASAR wide swath mode (150 m spatial resolution) data of the Mekong River Basin. For the usability measure, the concept of 'high-contrast tiles' was established, which allows an a priori estimation of the expected accuracy of a waterbody classifier. The SAR-based permanent waterbody map was used for the validation of the approach. It was found that, for the test site, the new SAR usability measure allows the identification of unsuitable scenes with a certainty of more than 90%. The method is expected to be very useful for near-real-time flood mapping applications where human interaction is neither desired nor feasible when large regions and large data volumes are considered.

The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications. In this study we evaluated the quality and spatiotemporal variability of the soil moisture product retrieved from C-band scatterometers data across the LMB sub-catchments. The soil moisture retrieval algorithm showed reasonable performance in most areas of the LMB with the exception of a few sub-catchments in the eastern parts of Laos, where the land cover is characterized by dense vegetation. The best performance of the retrieval algorithm was obtained in agricultural regions. Comparison of the available in situ evaporation data in the LMB and the Basin Water Index (BWI), an indicator of the basin soil moisture condition, showed significant negative correlations up to R = −0.85. The inter-annual variation of the calculated BWI was also found corresponding to the reported extreme hydro-meteorological events in the Mekong region. The retrieved soil moisture data show high correlation (up to R = 0.92) with monthly anomalies of precipitation in non-irrigated regions. In general, the seasonal variability of soil moisture in the LMB was well captured by the retrieval method. The results of analysis also showed significant correlation between El Niño events and the monthly BWI anomaly measurements particularly for the month May with the maximum correlation of R = 0.88.

The high data throughput of spaceborne Synthetic Aperture Radar (SAR) instruments introduces stringent requirements on the end-to-end system, from the on-board storage capacity and data downlink to processing power and archiving capacity in the ground segment. An efficient raw data compression scheme on-board the satellite is therefore required to maximise the return on investment for SAR missions. The next generation of European SAR satellites, the Sentinel-1 mission, will employ the Flexible Dynamic Block Adaptive Quantization (FDBAQ) compression scheme. In order to validate the implementation of the FDBAQ, expected radar reflectivity levels for the global land surface were needed.The paper presents the development of the Global Backscatter Model (GBM) which has been used to simulate realistic radar reflectivity acquisitions in support of performance validation of the Sentinel-1 FDBAQ implementation. The GBM consists of a parameter database and SAR image simulation software. The model parameter database characterises C-band radar reflectivity as a function of local incidence angle with a resolution of 1 km for 85% of the global land surface. The parameter database was derived from a global multi-temporal coverage of ENVISAT Advanced Synthetic Aperture Radar (ASAR) data. The SAR image simulation software uses the parameter database together with orbit propagation and swath determination algorithms in order to account for the instrument acquisition geometry and the location specific backscattering characteristics. The model has the potential to simulate acquisitions for other SAR instruments. Furthermore, the parameter database could be a valuable source of information for global land cover studies. (C) 2012 Elsevier Inc. All rights reserved.

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.

The applicability of radar data for monitoring the seasonal changes in permafrost thaw lake surface extent on the Yamal Peninsula is investigated. Data from the European Space Agency’s ENVISAT Advanced Synthetic Aperture Radar (ASAR) operating in wide swath mode are used to map water bodies by applying simple threshold classification algorithms. A change detection analysis of lake surface extent shows that there are inundation variations between seasons. These preliminary results allow for the identification of summer drainage of certain lakes. However, due to the sensor-related limitations, the reason for this seasonal lake drainage pattern cannot be established. We assume that the interplay between the lakes and rivers should be taken into account for a complete understanding of lake dynamics. This paper hopes to communicate that ENVISAT ASAR WS data can be successfully used in the first instance to identify hotspots of lake change.

The forthcoming two-satellite GMES Sentinel-1 constellation is expected to render systematic surface soil moisture retrieval at 1 km resolution using C-band SAR data possible for the first time fromspace. Owing to the constellation's foreseen coverage over the Sentinel-1 Land Masses acquisition region-global approximately every six days, nearly daily over Europe and Canada depending on latitude-in the high spatial and radiometric resolution Interferometric Wide Swath (IW) mode, the Sentinel-1 mission shows high potential for global monitoring of surface soil moisture by means of fully automatic retrieval techniques. This paper presents the potential for providing such a service systematically over Land Masses and in near real time using a change detection approach, concluding that such a service is-subject to the mission operating as foreseen-expected to be technically feasible. The work presented in this paper was carried out as a feasibility study within the framework of the ESA-funded GMES Sentinel-1 Soil Moisture Algorithm Development (S1-SMAD) project.

Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH4). The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the global scale, but the resolution is rather coarse (>25 km). The purpose of the present study is to assess the capability of active microwave sensors to derive inundation dynamics for use in land surface and climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR) operating in C-band since, among microwave systems, it has comparably high spatial resolution and data availability, and long-term continuity is expected.C-band data from ENVISAT ASAR (Advanced SAR) operating in wide swath mode (150 m resolution) were investigated and an automated detection procedure for deriving open water fraction has been developed. More than 4000 samples (single acquisitions tiled onto 0.5° grid cells) have been analysed for July and August in 2007 and 2008 for a study region in Western Siberia. Simple classification algorithms were applied and found to be robust when the water surface was smooth. Modification of input parameters results in differences below 1 % open water fraction. The major issue to address was the frequent occurrence of waves due to wind and precipitation, which reduces the separability of the water class from other land cover classes. Statistical measures of the backscatter distribution were applied in order to retrieve suitable classification data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution was used. On average only 40 % of acquisitions allow a separation of the open water class. Although satellite data are available every 2–3 days over the Western Siberian study region, the irregular acquisition intervals and periods of unsuitable weather suggest that an update interval of 10 days is more realistic for this domain. SAR data availability is currently limited. Future satellite missions, however, which aim for operational services (such as Sentinel-1 with its C-band SAR instrument), may provide the basis for inundation monitoring for land surface and climate modelling applications.

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.

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 C-band scatterometer data have been demonstrated in many studies [1-9] to be valuable for monitoring of surface soil moisture using the so-called TU Wien change detection method [10-11]. High temporal sampling in all weather conditions, multi-viewing capability and availability of long-term measurements make the European C-band scatterometers excellent observation tools for soil moisture change detection. The observations of the ERS-1/2 scatterometers together with the new series of advanced scatterometers (ASCAT) onboard Metop satellites ensure long-term global observation (from 1991 until at least 2020). Soil moisture is recognized as an important component of the water cycle in hydrological and natural environmental processes. Information on surface and profile soil moisture is demanding for a wide range of applications concerning water supply, agriculture, weather forecasting, climate modeling, and etc. This study presents an in-depth evaluation of the soil moisture products retrieved from C-band scatterometer data (from 1991-2000 and 2007-2010) in regional scale and demonstrates application examples in lower Mekong Basin in Southeast Asia. The Mekong River is the longest river in Southeast Asia and is one of the ten longest rivers in the world. It rises in the Tibetan highlands and flows through six states and drains an area of 795,000 km². It crosses the southeast Chinese province of Yun-Nan, forming the border between Myanmar and Laos and in the lower reaches of a large part of the border between Laos and Thailand. It flows through Cambodia and into South Vietnam branched into several mouth-arms that make up the vast Mekong delta where it empties into the South China Sea. The climate of the Mekong region is influenced by the Southwest and Northeast monsoons. The tropical monsoonal regime in lower Mekong area generates a distinctly biseasonal pattern of wet and dry periods of more or less equal length. This results in an annual flood pulse and therefore a distinct seasonality in the annual hydrological cycle between a flood season and a low-flow season. The strong seasonal variations in rainfall leads to extreme conditions for the people of the lower Mekong Region: Large-scale and long-lasting floods alternating with periods of drought and water shortage. Floods and droughts can occur anywhere in the basin imposing large economic and social costs on the people [12]. Therefore monitoring of soil moisture conditions in Mekong basin is valuable for many hydrological and agricultural applications. The study includes a catchment-base noise analysis of soil moisture data in lower Mekong basin to identify the areas where soil moisture retrieval is robust and applicable. In general, the seasonal variability of soil moisture in Mekong basin is well captured by the retrieval method especially in agricultural areas. Comparison of the soil moisture data with topography and land cover classifications showed that the soil moisture noise increases in highlands with complex topography and in areas covered by very dense vegetation. It is found that the quality of soil moisture is strongly degraded during oversaturated soil situations particularly by large flooded events in delta region in Vietnam and Tonle Sap basin in Cambodia during the peak of the wet season. Furthermore, a catchment-base statistical analysis of the soil moisture data has been carried out to evaluate the relation between the Basin Water Index (BWI), an indicator of the basin soil moisture condition, with in-situ hydrometeorological measurements in different months of year. The results of analysis also showed significant correlations between Enso events and monthly BWI anomaly measurements.

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling.

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.

Abstract. Spatial information on inundation dynamics is expected to improve greenhouse gas estimates in climate models. Satellite data can provide land cover information from local to global scale. The detection capability for dynamics is however limited. Cloud cover and daylight independent methods are required for frequent updates. Suitable are therefore sensors which make use of microwaves. The purpose of the present study is to assess such data for determination of wetland dynamics from the viewpoint of use in climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR) operating in C-band due to, among microwave systems, comparably good spatial resolution and data availability. Continuity is also expected for such systems. Simple classification algorithms can be applied to detect open water in an automatised way allowing the processing of time series. Such approaches are robust when the water surface is smooth. C-band data from ENVISAT ASAR (Advanced SAR) operating in wide swath mode (150 m resolution) have been investigated for implementation of an automated detection procedure of open water fraction. More than 4000 samples (single acquisitions tiled into 0.5 degree grid cells) have been analysed for July/August 2007 and 2008. Modification of input parameters results in differences below 1 % open water fraction. The actual challenge is the frequent occurrence of waves due to wind and precipitation. This reduces the separability of the water class from other land cover. The possible update intervals for surface water extent are therefore decreased considerably. Statistical measures of the backscatter distribution can be applied in order to retrieve the for classification suitable data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution has been used for assessment. On average only 40 % of acquisitions allow a separation of the open water class. Satellite data are available every 2–3 days over the Western Siberian study region. With respect to the irregular acquisition intervals and varying length of unsuitable weather periods a minimum update interval of 10 days is suggested for the Northern Eurasian test case. Although SAR data availability is currently constraint future satellite missions which aim for operational services such as Sentinel-1 with its C-band SAR instrument may provide the basis for inundation monitoring in support of climate modelling.