Brecht Martens

Copula-Based Downscaling of Coarse-Scale Soil Moisture Observations With Implicit Bias Correction

Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Données SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soil moisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the variable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soil moisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation applications, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.

Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin

AbstractThe Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. There...

Estimating Effective Roughness Parameters of the L-MEB Model for Soil Moisture Retrieval Using Passive Microwave Observations From SMAPVEX12

Despite the continuing efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the leaf area index of the vegetation. These results indicate that the roughness parameters are compensating for both roughness and vegetation effects. It is also shown, using a leave-one-out cross validation, that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the usefulness of the roughness parameterization, the performance of the model is compared to more traditional roughness formulations. Results indicate that the soil moisture retrieval error can be reduced to 0.054 m3/m3 if the roughness formulation proposed in this study is implemented in the soil moisture retrieval algorithm.

Global climatic drivers of vegetation based on wavelet analysis

Cross-correlations provide a useful technique to analyse the similarities between vegetation indices and time series of climatic variables. However, correlation analyses are not sufficient to unveil changes in the co-variability of vegetation and climate as a function of time or for different temporal scales. Here, we introduce the use of wavelet coherence to evaluate the relationship between vegetation and its climate drivers, aiming to reveal how this relation has changed globally during the period 1984–2007. We diagnose vegetation through the use of Normalised Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Vegetation Optical Depth (VOD), while precipitation, air temperature and incoming radiation are considered as separate climatic drivers. Our results indicate that the wavelet coherence analysis can be used to disentangle the contrasting response of global ecosystems to their climatic environment. Our global maps of mean wavelet coherence, align with literature-reported areas of preferential water and energy stress. Based on these global maps, some areas of interest are selected for which a detailed spectral analysis of the time series is performed. Initial results indicate a clear discrepancy in the climatic response of different vegetation diagnostics over grasslands and woody regions.