Alexander Loew

Derivation of surface soil moisture from ENVISAT ASAR wide swath and image mode data in agricultural areas

Water and energy fluxes at the interface between the land surface and atmosphere are strongly dependent on surface soil moisture content, which is highly variable in space and time. It has been shown in numerous studies that microwave remote sensing can provide spatially distributed patterns of surface soil moisture. In order to use remote-sensing-derived soil moisture information for practical applications as, for example, flood forecasting and water balance modeling in mesoscale areas, frequent large-area coverage is a prerequisite. New sensor generations such as ENVISAT Advanced Synthetic Aperture Radar (ASAR) or RADARSAT allow for image acquisitions in different imaging modes and geometries. Imaging modes with the capability of large-area coverage, such as the Wide Swath Mode of ENVISAT ASAR, are of special interest for practical applications in this context. This paper presents a semiempirical soil moisture inversion scheme for ENVISAT ASAR data. Different land cover types as well as mixed-image pixels are taken into account in the soil moisture retrieval process. The inversion results are validated against in situ measurements, and a sensitivity analysis of the model is conducted.

Globally Gridded Satellite observations for climate studies

Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them that no central archive of geostationary data for all international satellites exists, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multisatellite climate studies. The International Satellite Cloud Climatology Project (ISCCP) set the stage for overcoming these issues by archiving a subset of the full-resolution geostationary data at ~10-km resolution at 3-hourly intervals since 1983. Recent efforts at NOAAs National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad ...

Precipitation from Space: Advancing Earth System Science

Advances to space-based observing systems and data processing techniques have made precipitation datasets quickly and easily available via various data portals and widely used in Earth sciences. The increasingly lengthy time span of space-based precipitation data records has enabled cross-discipline investigations and applications that would otherwise not be possible, revealing discoveries related to hydrological and land processes, climate, atmospheric composition, and ocean freshwater budget and proving a vital element in addressing societal issues. The purpose of this article is to demonstrate how the availability and continuity of precipitation data records from recent and upcoming space missions is transforming the ways that scientific and societal issues are addressed, in ways that would not be otherwise possible.

First Results of SMOS Soil Moisture Validation in the Upper Danube Catchment

With the Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2009, global measurements of L-band microwave emissions and processed “soil moisture” products at a fine time resolution are available. They may, after validation, lead to quantitative maps of global soil moisture dynamics. This paper presents a first validation of the SMOS “soil moisture” product delivered by the European Space Agency in the upper Danube catchment (southern Germany). Processing of the SMOS “soil moisture” product and the methodology to compare it with in situ and model data are described. The in situ data were taken from May to mid-July 2010 in a small and homogeneous area within the catchment, while the modeled time series spans from April to October 2010 for the whole catchment. The comparisons exhibit a dry bias of the SMOS data of about 0.2 m3·m-3 with respect to in situ measurements. Throughout the catchment, the SMOS data product shows a dry bias between 0.11 and 0.3 m3·m-3 when compared to modeled soil moisture. Correlation coefficients between both data were found to be mostly below 0.3. Radio-frequency interference (RFI) over Europe appears to be the main problem in obtaining valuable information from the SMOS soil moisture product over this region. RFI is not adequately captured by current methods for filtering and flagging. Nevertheless, some improvements of these results might be achievable through refinements of the soil moisture modeling as well as through improvements to the processors used to generate the SMOS soil moisture product.

Brightness Temperature and Soil Moisture Validation at Different Scales During the SMOS Validation Campaign in the Rur and Erft Catchments, Germany

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

A review of spatial downscaling of satellite remotely sensed soil moisture

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

Evaluation of satellite and reanalysis soil moisture products over Southwest China using ground-based measurements

Long-term global satellite and reanalysis soil moisture products have been available for several years. In this study, in situ soil moisture measurements from 2008 to 2012 over Southwest China are used to evaluate the accuracy of four satellite-based products and one reanalysis soil moisture product. These products are the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), the Advanced Scatterometer (ASCAT), the Soil Moisture and Ocean Salinity (SMOS), the European Space Agency’s Climate Change Initiative soil moisture (CCI SM), and the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim). The evaluation of soil moisture absolute values and anomalies shows that all the products can capture the temporal dynamics of in situ soil moisture well. For AMSR-E and SMOS, larger errors occur, which are likely due to the severe effects of radio frequency interference (RFI) over the test region. In general, the ERA-Interim (R = 0.782, ubRMSD = 0.035 m3/m3) and CCI SM (R = 0.723, ubRMSD = 0.046 m3/m3) perform the best compared to the other products. The accuracy levels obtained are comparable to validation results from other regions. Therefore, local hydrological applications and water resource management will benefit from the long-term ERA-Interim and CCI SM soil moisture products.

Towards Multidecadal Consistent Meteosat Surface Albedo Time Series

Monitoring of land surface albedo dynamics is important for the understanding of observed climate trends. Recently developed multidecadal surface albedo data products, derived from a series of geostationary satellite data, provide the opportunity to study long term surface albedo dynamics at the regional to global scale. Reliable estimates of temporal trends in surface albedo require carefully calibrated and homogenized long term satellite data records and derived products. The present paper investigates the long term consistency of a new surface albedo product derived from Meteosat First Generation (MFG) geostationary satellites for the time period 1982-2006. The temporal consistency of the data set is characterized. The analysis of the long term homogeneity reveals some discrepancies in the time series related to uncertainties in the characterization of the sensor spectral response of some of the MFG satellites. A method to compensate for uncertainties in the current data product is proposed and evaluated.

Integrative Assessment of Informal Settlements Using VHR Remote Sensing Data—The Delhi Case Study

In contrast to the last century, where more people used to live in rural areas, at present, more than half of the worlds population lives in urban settlements. Hence, the 21st century is the century of the cities and of urbanization. The rapid urbanization process experienced by the majority of developing countries during the last few decades has resulted in fundamental changes to the environment and to the social structure. In most of the megacities that have grown to unprecedented size, the pace of urbanization has far exceeded the growth of necessary infrastructure and services. In order to carry out the urban planning and development tasks necessary to improve the living conditions for the poorest world-wide, a detailed spatial data basis is required. Due to the high dynamics of megacities, traditional methods such as statistical analyses or fieldwork are limited to capture the urban process. Remote sensing provides the opportunity to monitor spatial patterns of urban structures with high spatial and temporal resolution. The present study investigates the potential to use very high-resolution (VHR) remote sensing data to identify urban structures and dynamics within Delhi, India. The paper presents a semi-automated, object-oriented classification approach which allows for the identification of informal settlements within the urban area. In order to provide indicators to identify socio-economic structures and their dynamics, the image classification results are embedded in an integrative analysis concept. Information on population and water related parameters are derived. This is understood to be a first step to the development of indicators which will help to identify and understand the different shapes, actors, and processes in megacities.

Uncertainty Assessment of the SMOS Validation in the Upper Danube Catchment

The validation of coarse-scale remote sensing products like SMOS (ESAs Soil Moisture and Ocean Salinity mission) L2 soil moisture or L1c brightness temperature data requires the maintenance of long-term soil moisture monitoring sites like the Upper Danube Catchment SMOS validation site situated in Southern Germany. An automatic framework has been built up to compare SMOS data against in situ measurements, land surface model simulations, and ancillary satellite data. The uncertainties of the different data sets used for SMOS validation are being assessed in this paper by comparing different microwave radiative transfer and land surface model results to measured soil moisture and brightness temperature data from local scale to SMOS scale. The mean observed uncertainties of the modeled soil moisture decrease from 0.094 m3 m-3 on the local scale to 0.040 m3 m-3 root mean squared error (RMSE) on the large scale. The RMSE of anomalies is 0.023 m3 m-3 on the large scale. The mean R2 increases from 0.6 on the local scale to 0.75 on the medium scale. The land surface model tends to underestimate soil moisture under wet conditions and has a smaller dynamical range than the measurements. The brightness temperature comparison leads to a RMSE around 12-16 K between microwave radiative transfer model and airborne measurements under varying soil moisture and vegetation conditions. The assessed data sets are considered reliable and robust enough to be able to provide a valuable contribution to SMOS validation activities.