Soil Moisture and Ocean Salinity (SMOS) L1c brightness temperature and L2 optical depth data are analysed with a coupled land surface (PROMET) and radiative transfer model (L-MEB). The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and land surface temperature conditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in southern Germany. The brightness temperature root-mean-squared errors are between 6 K and 9 K. The L-MEB parameterisation is considered appropriate under local conditions even though it might possibly be further optimised. SMOS L1c brightness temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band brightness temperature data. Only low to fair correlations are found for this comparison (R between 0.1–0.41). SMOS L1c brightness temperature data do not show the expected seasonal behaviour and are positively biased. It is concluded that RFI is responsible for a considerable part of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the brightness temperature data from the lower SMOS look angles and the horizontal polarisation are less reliable. This information could be used to improve the brightness temperature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (R = 0.65) even though the two variables are considered independent in the study area. The value of coupled models as a tool for the analysis of passive microwave remote-sensing data is demonstrated by extending this SMOS data analysis from a few days during a field campaign to a longer term comparison.

In the course of Soil Moisture and Ocean Salinity (SMOS) mission calibration and validation activities, a ground based L-band radiometer ELBARA II was situated at the test site Puch in Southern Germany in the Upper Danube Catchment. The experiment is described and the different data sets acquired are presented. The L-band microwave emission of the biosphere (L-MEB) model that is also used in the SMOS L2 soil moisture algorithm is used to simulate the microwave emission of a winter oilseed rape field in Puch that was also observed by the radiometer. As there is a lack of a rape parameterization for L-MEB the SMOS default parameters for crops are used in a first step which does not lead to satisfying modeling results. Therefore, a new parameterization for L-MEB is developed that allows us to model the microwave emission of a winter oilseed rape field at the test site with better results. The soil moisture retrieval performance of the new parameterization is assessed in different retrieval configurations and the results are discussed. To allow satisfying results, the periods before and after winter have to be modeled with different parameter sets as the vegetation behavior is very different during these two development stages. With the new parameterization it is possible to retrieve soil moisture from multiangular brightness temperature data with a root mean squared error around 0.045–0.051 m³/m³ in a two parameter retrieval with soil moisture and roughness parameter H_r as free parameters.

Keywords: M4Land, land management classification system, Sentinel, canopy reflectance model SLC, crop growth model PROMET To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2’s continuous and manifold data stream.

As passive microwave remote sensing data is sensitive to the surface soil moisture, recent satellite mission concepts, like the Soil Moisture and Ocean Salinity (SMOS) mission, to measure the surface soil moisture content with L-band passive microwave sensors will provide global soil moisture information with a high temporal resolution of about 1-3 days. In contrast, the spatial resolution used for the retrieval of the soil moisture from SMOS images will be rather coarse, in the order of tens of kilometres. The provided soil moisture information is therefore integrated over a large area that may be composed of different land covers and soils. Uncertainties of the soil moisture retrieval depend on the accuracy and spatial resolution of ancillary data, like land use information data, which is necessary for the retrieval. The sensitivity of the retrieval to different land cover information datasets is investigated, using a coupled SVAT-radiative transfer-model comparing the standard land use map used within the SMOS Level 2 soil moisture processor with a well established map for the study area. The used approach shows big differences of the retrieval in some parts of the study area while in others the retrieved soil moisture is nearly the same with both land cover maps used.