Temporal speckle reduction allows 1-km resolution global mapping at ~ 15 day interval. A global waterbody map was derived using the preliminary SAR data from SMAP for a one-month period of May 2015. Transient features such as a flooded dry lake are correctly identified. Comparison with the 500m-resolution static database (MOD44W derived with mainly Shuttle Radar data of 2000) was made, because MOD44W is currently used to correct for the effect of waterbody extent in the SMAP's soil moisture algorithm. Over a 500km x 500km area in Ethiopia in Africa, the two maps agree over an area of 2050 km2. SMAP reports a smaller water area by 732 km2, which appears to correspond to the shrunk extents of lakes. Soil moisture retrieval also supports this attribution. SMAP produces additional 252-km2 area of water, which may as well be the recent inundation as well as false alarms. The false alarms are associated with the radar speckle and the intrinsic errors in the classification algorithm.
By treating MOD44W as a reference in this region, the probability of detection (POD) is 0.73, the false alarm rate is 0.11, the Jaccard distance is 0.32 (away from the perfect similarity of zero), and the Brier score is 0.004 (0 is the best match; the near-zero value may increase when the radar speckle error is incorporated into SMAP's detection probability). These statistics indicate that the two databases differ, partly because MOD44W is outdated as evidenced by the errors in the soil moisture retrieval. The categorical statistics were derived over about 40 waterbody targets in the world.
On going investigations are as follows. The speckle effects will be further reduced through spatial-domain analysis. The mixed-pixel cases of partial water and land will be identified using initial guess of NRCS of the two surfaces. Cross-validation with high-resolution optical data such as the Landsat8 data will help confirm the SMAP's classification. We will explore L-band capability to penetrate vegetation and clouds to monitor the underlying surface.
Kim, S.B., J. Oullette, J. J. van Zyl, and J. T. Johnson, Dual-copolarized approach to detect surface water extent using L-land radar, IEEE Trans. Geosci. Remote Sens, revision, 2015.