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Title:   Author  item
                                            Crow, Wadeitem
                                            CHEN, F. - Science Systems, Incitem
                                            COLLIANDER, ANDREAS - Jet Propulsion Laboratory
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 5/1/2015
                                    Publication  Date: N/A
                                    Citation: N/AInterpretive Summary:
                                Technical Abstract: The contrast in horizontal spatial support between ground-based soil moisture observations and satellite-derived soil moisture estimates represents a long-standing challenge for the validation of satellite soil moisture data products [Crow et al., 2014]. This challenge can be alleviated by limiting ground validation to areas characterized by sufficiently dense ground-based sampling. That is, areas where multiple point samples obtained within a single remotely-sensed footprint can be adequately interpolated to characterize a single footprint-scale (i.e., 10 to 40 km) geographic region. This approach forms the basis of the so-called “Core Site” validation approach adopted by the NASA Soil Moisture Active/Passive (SMAP) mission [Entekhabi et al., 2012]. However, most available ground-based soil moisture observations occur in networks characterized by much sparser spatial sampling – such that (at most) only one or two ground-based observations are available within a given satellite footprint. Validation metrics (e.g. root-mean-square error or linear correlations) derived via direct comparisons between satellite retrievals and sparse network observations will be spuriously degraded by the sharply contrasting horizontal spatial supports of the two observations. Therefore, in order to use sparse network observations in a satellite validation context, up-scaling strategies must be developed to correct validation metrics for the impact of this scale contrast.

Recently, so-called triple collocation procedures have been applied to this validation up-scaling challenge. These procedures are based on obtaining a triplet of surface soil moisture time series data from 1) sparse ground based observations, 2) a land surface model and 3) satellite retrievals. Assuming that each of these products can be linearly related to some (unknown) true soil moisture time series and contain mutually independent error, triple collocation can be used to estimate that statistical properties of each member of the soil moisture triple. In particular, Miralles et al. [2012] proposed using triple collocation (TC) to compensate for the inadequate spatial support of ground-based surface soil moisture retrievals obtained from sparse network observations. In response to this potential, a triple collocation validation tool has been formally integrated into the validation plan for SMAP surface soil moisture products. This presentation will review this plan and describe efforts to verify key assumptions that underlie the implementation of a triple collocation strategy.

Crow, W.T., A.A. Berg, M.H. Cosh, A. Loew, B.P. Mohanty, R. Panciera, P. de Rosnay, D. Ryu and J.P. Walker, "Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products," Reviews of Geophysics, 50, RG2002, 10.1029/2011RG000372, 2012.

Entekhabi, D., E. Njoku, P. O'Neill, K. Kellogg, W. Crow, W. Edelstein, J. Entin, S. Goodman, T. Jackson, J. Johnson, J. Kimball, J. Piepmeier, R. Koster, K. McDonald, M. Moghaddam, S. Moran, R. Reichle, J. C. Shi, M. Spencer, S. Thurman, L. Tsang and J. Van Zyl, "The Soil Moisture Active and Passive (SMAP) Mission," Proceedings of the IEEE, 98(5), 704-716, 10.1109/JPROC.2010.2043918, 2010.

Miralles, D.G., W.T. Crow and M.H. Cosh, "Estimating spatial sampling errors in coarse-scale soil moisture estimates derived from point-scale observations," Journal of Hydrometeorology, 11(6), 1404-1410, 10.1175/2010JHM1285.1, 2010.


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