Algorithm Theoretical Based Document (ATBD) - CESBIO

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SO-TN-ESL-SM-GS-0001 Issue 1.a Date: 31/08/2006 SMOS level 2 processor Soil moisture <strong>ATBD</strong> CODE Plain Notation Nota tion meaning t WEF_A WEF A WEF approximation u WEF_SIZE Size (km) of the DFFG working area (WA DFFG ) u WVC Water vapor atmospheric content v,o X_SWATH Dwell line abscissa 2 ALGORITHM OVERVIEW 2.1 Background information Passive microwave radiometry has been used for some years both at ground level and in airborne and space borne experiments. At high frequencies it has now reached a significant level of maturity, especially for atmospheric retrievals. At low frequency and in particular at L band, most of the background lies with ground experiments (BARC, PORTOS 91, 93, PAMIR, MIRAS 99, Avignon 01, SMOSREX, Bordeaux 04,…..) with a few space-borne campaigns, mainly in the US (using PBMR, ESTAR, PALS) such as SGPnn, HAPEX SAHEL, SMEXmm, Eurostarrs[7-14]. From these experiments, models representing emission from soil and vegetation were elaborated and somewhat validated [4, 15- 19]. There is thus now a consensus on the models and limitations, although a certain level of empiricism in the different approaches is still present [20-24]. The step to SMOS data is, however, still significant. The challenges will be mainly with large pixels including a variety of targets (water, crops, fallow layer urban/roads etc mixed) with potential caveats, not always well understood and /or modelled (RFI, topography…). Finally it should also be said that in, many field experiments, the targets were rather pure, which hardly happens over land surfaces in real life. For instance, under natural vegetation, a layer of litter (dead matter) may develop, giving way to very specific signals as a function of the litter moisture content. Such factors imply a good part of humility as to the validity range of existing algorithms as they were very often developed and tested in specific conditions. Currently known facts are as follows: • Retrieval of soil moisture over bare soil with low vegetation should be easy but, • Snow is a very tricky target, as snow conditions may evolve very quickly with drastic changes in the signal • Bare dry soil has behaviour that is not well understood/modelled • Frozen soil behaves as dry soil • Forest emission and attenuation are mostly correlated with “branches” water content (not the “leaves” water content). Under dense forests no SM will be retrieved. • Urban areas are yet to be modelled • Water bodies will have to be taken into account, including seasonal effects and fractional coverage • Topography will reduce signal quality until no retrievals are possible • Litter, when substantial, can appreciably modify soil emission • Surface roughness at SMOS scale appears to be relatively small and a function of soil moisture • Sun-glint might not be negligible • … It may be noted that the list consists mainly of limitations 2.2 Selected approach The basis for the approach taken here lies with the results of an ESA Study on soil moisture retrieval for SMOS [25-27]. The principle is to find the best suited set of soil moisture (SM) and vegetation characteristics by minimizing the differences between modelled direct and measured brightness temperature (TB) data. Other potential methods could have been . 17
SO-TN-ESL-SM-GS-0001 Issue 1.a Date: 31/08/2006 SMOS level 2 processor Soil moisture <strong>ATBD</strong> • Direct retrieval, if there was a direct and unique relationship between SM and TB, which is not the case and not feasible anyway because of the heterogeneous characteristics of the pixels. • Empirical / statistical approaches (see[25-27]) where a regression is built between SM and TBs • Neural network approaches[25, 28, 29] The main issue with statistical and neural network approaches is that in the SMOS case it will require measurements, and can only be implemented some time after launch. A simple inter-comparison table is presented below (Table 5). Table 5: Statistical modelling vs. Physical modelling Method Advantages Disadvantages Quickness Robustness Simplicity Empirical statistical Iterative forward models using physical Close to the physics Easy to upgrade Provide theoretical uncertainty Opaque Need a learning data base every time it is upgraded Requires real data (hence after launch in our case) Clumsy for variable range of incidence angles (i.e. SMOS conditions) Limited validity range/area depending on training areas and conditions Heavy Strong demand on auxiliary data Limited by the availability of reliable direct models! We understand that ESA might want to have all the placeholders defined so that some time after launch (at least 3 months after the end of the commissioning phase), a statistical / NN approach might be implemented. It is, however, clear that the efficiency of the statistical approach will depend on available reliable data, which is per se a challenge. The baseline is thus an iterative approach. 2.3 General layout 2.3.1 Layman description In the iterative approach, one essentially aims at minimizing a cost function through minimizing the sum of squared weighted differences between measured and modelled brightness temperature (TB) data, for a variety of incidence angles. This is achieved by finding the best suited set of the parameters which drive the direct TB model, e.g. soil moisture (SM) and vegetation characteristics. Despite the simplicity of this principle, the main reason for the complexity of the algorithm is that SMOS "pixels" which contribute the radiometric signal are rather large areas, and therefore strongly inhomogeneous; moreover the exact description of pixels, given by a weighting function which expresses the directional pattern of the SMOS interferometric radiometer, depends on the incidence angle. The goal is to retrieve soil moisture over fairly large and thus inhomogeneous pixels. The retrieval is carried out at nodes of a fixed Earth surface grid. The first step will be to assess the input data quality (at each node) and filter out all unwanted data (outside the spatial mask requirement, L1c data quality flags etc). Auxiliary data is then ingested and in particular time varying data and data having an impact on the SMOS product (meteorological data, vegetation opacity). Afterwards, the retrieval process per se can be initiated. This cannot be done blindly as the direct model will be dependent upon surface characteristics (snow is different from vegetated soil and water for instance). It is thus necessary to first assess what is the dominant 1 land use of a node. For this an average weighing function (MEAN_WEF) which takes into account the “antenna” pattern is run over the high resolution land use map to assess the dominant cover type. This is used to drive the decision tree which, steps by steps selects the type of model to be used as per surface conditions. 1 Dominant for the well behaved node (i.e., with normal land use). When the majority of the surface is occupied by a target of no direct interest for soil moisture (e.g., water), “dominant” applies to the complementary part of the node . 18
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