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Title:   Author  item
                                            Yilmaz, Mustafaitem
                                            Crow, Wadeitem
                                            Anderson, Marthaitem
                                            HAIN, C - National Oceanic & Atmospheric Administration (NOAA)
  Submitted to: BARC Poster Day
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 3/27/2012
                                    Publication  Date: 4/1/2012
                                    Citation: Yilmaz, M.T., Crow, W.T., Anderson, M.C., Hain, C. 2012. An objective methodology for merging satellite-and model-based soil moisture products. BARC Poster Day. Abs. 56. BARC Poster Day.Interpretive Summary:
                                Technical Abstract: Consistent estimates of soil moisture can be obtained in various ways; for example through remote sensing or through modeling of the land-surface water budget. However, these estimates are not perfect and each method has characteristic uncertainties. Therefore for some applications it may be desirable to merge independent realizations to obtain a more accurate estimate. Theoretically, the more independent data we merge the smaller the noisiness of the merged product becomes. Here, it is important to objectively weight the products based on their accuracy in order to minimize errors. However, current soil moisture merging techniques commonly applied in hydrological sciences are primarily driven by user-defined parameters. In this study, we propose an objective methodology that does not require any user-defined error parameters as input. We merge different soil moisture products in a least squares framework that relies on the error estimates of the products obtained by using the triple collocation technique. Specifically, we merge thermal remote sensing based soil moisture proxy retrievals from the Atmosphere Land Exchange Inversion energy balance model, NOAH land surface model (LSM) soil moisture simulations, and Land Parameter Retrieval Model soil moisture estimates based on microwave remote sensing observations. This merging framework is also able to provide estimates of uncertainty in the merged soil moisture product.

In this study we found the merged products to be partially successful in that it improved the parent products in isolation, although it has very similar performance with merged estimates using equal-weighting when they are compared to MESONET and SCAN in-situ observations. Given the small differences found between cross-correlations of the products, the similarity between these two merged estimates are attributed to the marginal skill differences that exist between ALEXI, NOAH, and LPRM based soil moisture estimates over CONUS. 

The methodology proposed here can potentially add value to the soil moisture products derived from the current and future soil moisture satellite missions (i.e, SMOS: Soil Moisture and Ocean Salinity; SMAP, Soil Moisture Active Passive) by optimally merging them with independent soil moisture estimates acquired from infrared observations and land surface models. Given that an improved soil moisture product is obtained through this methodology, the final merged estimate can be used as a new drought product on varying time-scales (i.e. weekly, monthly, seasonal). This new product can also improve the currently available existing drought products not only with the improved accuracy but also with having an uncertainty estimate of the drought product itself.


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