FRM4SM: SMOS validation strategy and uncertainty mapping

Abstract

All passive microwave satellite sensors are characterized by their low spatial resolution of several hundreds of kilometers square, and the satellite, Soil Moisture and Ocean Salinity (SMOS), is one of those. Quantifying the accuracy of the derived geophysical parameters is challenging due to the difference of spatiotemporal resolution with the in-situ measurements, assumed to be the reference. Actually, the representativeness of the in-situ measurement is within a range of a few square meters around the in-situ device with an hourly sampling, while satellite only revisit at a given time one of several days apart (revisit). Besides, ground measurements also have their own limitations and inaccuracies.

In this context, the ESA’s project Fiducial Reference Measurement for Soil Moisture (FRM4SM) is investigating, among other activities, the validation strategy of SMOS soil moisture estimation as well as the uncertainties assessment. The objectives of the present study are twofold: 1) to evaluate the SMOS accuracy requirement of 0.04m3/m3 on specified committed areas and 2) to define the uncertainties elsewhere regarding the geophysical surface conditions.

The method proposed here to fulfill these objectives is to assess a global validation of SMOS, then to define the relation between geophysical descriptors and the validation statistical scores. First, a global validation process is computed by using the International Soil Moisture Network (ISMN) database as reference. This global database gathers data from more than 2800 harmonized soil moisture in-situ stations supported by ESA and the Vienna University of Technology (TUWIEN). Then, sensitivity analyses are performed to characterize the influence of probe configuration and the geophysical characteristics of the SMOS footprint. Finally, maps are computed with several geophysical condition thresholds corresponding to a specific range of uncertainties.

The validation chain used here is composed of 3 main steps: the masking/filtering step for the two databases, the spatiotemporal collocating step, and finally the computation of statistical scores to compare the two data (R, SDD, bias). In the first step, Radio Frequency Interference (RFI) are filtered in order to remove observations contaminated by anthropogenic emissions. In the second step, the spatial collocation, the nearest SMOS pixel (DGG node) is attributed to each probe location. Concerning the temporal collocation, for each SMOS value, the nearest in-situ value is attributed (within a limit of 30 min). Then, finally, the third step compares the SMOS and in-situ time-series through the statistical scores.

The first validation benchmark of SMOS soil moisture considers the whole ISMN database, with more than 5500 probes and 1600 SMOS pixels. Overall, it shows a global agreement between SMOS and the whole ISMN soil moisture database (R=0.462, SDD=0.087m3/m3 and bias=-0.069m3/m3) and confirms the method consistency. However, those performances have been computed without any assumption on the probe depth, technologies, or location. The analysis of the score through the probe-depth point of view shows a worsening of the performance as depth increases. The correlation results show an improvement higher than 0.1 when considering only the probes within the first 10cm. As a result, the rest of the analysis only considers the probes within the first 10cm (45% of the whole validation results).

This presentation will show maps that are derived from our analysis to geographically represent a range of expected uncertainties considering specific surface conditions. To derive these maps, SMOS auxiliary databases were used. These auxiliary databases describe the landcover (IGBP classification) and soil texture (SoilGrid) in terms of vegetation, topography, water presence, sand and clay content of the soil, bulk density, etc. Globally, the scores show an improvement of the performance with a minimization of forest, topography, water, and ice in the footprint. Concerning the soil parameters, the scores improve when the footprint is sandier, has less clay, and has a high bulk-density. This strategy can be applied to other datasets, such as SMAP and AMSRE.