1. A Study on Vegetation OpticalDepth Parameterization and its Impact on Passive Microwave Soil Moisture Retrievals A Study on Vegetation Optical Depth Parameterization and its Impact on Passive Microwave Soil Moisture Retrievals Microwave technology is the most promising remote sensing method that permits truly quantitative estimates of soil moisture using physically based expressions such as radiative transfer models (Owe et al., 2001). However, different retrieval algorithms using the same tau-omega radiation transfer equation and the same satellite observations have produced very different soil moisture data products. One of the main causes is the handling of the masking of microwave signals from top layer soil by the spatially heterogeneous vegetation cover. Therefore, it is critical to examine the estimation of vegetation optical depth (VOD) for improving microwave soil moisture retrievals. For the Single Channel Retrieval (SCR) algorithm (Jackson et al., 1993), land surface temperature is estimated from Ka-band passive microwave observations (Holmes et al., 2009). VOD inversed from the soil moisture data of Global Land Data Assimilation System is used as a reference VOD to examine the impact of three VOD parameterization methods: the Microwave Vegetation Index (MVI) (Shi et al., 2008), the Normalized Vegetation Difference Index (NDVI), and the Microwave Polarization Difference Index (MPDI) (Meesters et al., 2005). Six soil moisture measurement sites with relatively uniform land cover are selected to derive a relationship between the reference VOD to MVI or NDVI. The retrieved VOD from MPDI is noted as VOD m. Finally the impact of each of the three VOD parameterization methods is examined with the AMSR-E observations using the single channel soil moisture retrieval algorithm for vegetation growing season. GLOBAL PATTERNS OF NDVI AND MVI AND CORRECTIONS WITH VOD Fig. 1. Global 16-day mean NDVI and MVI in April and August 23th April - 8th May, 2010 13th Aug - 28th Aug, 2010 DISCREPANCY EVALUATION OF VOD AND VOD m INVERSED BY GLDAS SOIL MOISTURE DATASET 23th April - 8th May, 2010 13th Aug - 28th Aug, 2010 Fig. 3. Global Difference between VOD and VOD m NDVI parameterization, despite known limitations in representing vegetation biomass or water content, can provide relatively more accurate VOD estimates for soil moisture retrieval using a 16-day composite. MVI integrated information at K-band can provide real- time parameterization of VOD, but exhibited a seasonal variability only for short vegetation cover. Soil moisture retrievals from MVI derived VOD was susceptible to the land surface temperature change. VOD m can provide relatively more reasonable soil moisture retrievals at global scale by partitioning surface emission into its primary sources with iterations, but is strongly sensitive to land surface temperature biases. Assumption of polarization independence should be deliberated in some region. Fig. 7. VSM retrieval using NDVI (upper left), MVI (upper right), VOD m (lower left) and corresponding GLDAS result (lower right) in 20th Jun, 2011 GLOBAL RETRIEVAL RESULTS SUMMARY Fig. 4. Mean Bias Rate of SM Retrievals using Parameterized VOD and VODm Impacted by surface Temperature Bias in two SCAN sites. INTRODUCTION Table 1. Summary of coefficient of variation of VOD, correlation coefficients, and difference between VOD and VOD m for different vegetation types in 2010 Land cover type CVCorr. Coeff. Difference VODNDVI-VODVOD-MVIVOD-VODm Evergreen Needleleaf Forests (ENF)0.130.210.070.36-0.12-0.14 Evergreen Broadleaf Forests (EBF)0.120.18-0.130.530.01-0.02 Deciduous Needleleaf Forests (DNF)0.140.020.200.27-0.09-0.21 Deciduous Broadleaf Forests (DBF)0.160.53-0.010.53-0.06-0.08 Mixed Forests (MF)0.140.59-0.090.43-0.12-0.08 Woodlands (WOD)0.150.25-0.190.55-0.04-0.13 Wooded Grasslands/Shrubs (WGS)0.190.65-0.410.92-0.05 Closed Bushlands or Shrublands (CBS)0.180.41-0.360.92-0.04 Open Shrublands (OS)0.190.39-0.190.92-0.03 Grasses (GRA)0.190.390.100.53-0.07-0.10 Croplands (CRP)0.200.54-0.020.63-0.10-0.13 Lichens and Mosses (LM)0.47-0.530.200.180.14-0.16 Bare (BAE)0.510.02-0.240.820.01 SOIL MOISTURE RETRIEVAL ERROR EVALUATION FOR THREE VOD APPROACH Fig. 6. Scatter plots of soil moisture situ-measurement and retrieval using three VOD approach from 1th Jun to 31th Sep. SITE TIME SERIES OF VOD, NDVI, MVI AND VOD m Fig. 5. 8-day moving average time series of VOD, MVI, VODm and 16-day NDVI product in six soil moisture observation: four SCAN sites in U.S. and two SMONYS in Tibet-Plateau with relatively uniform land cover during 1th Jun. to 31th Oct METHODOLOGY Fig. 2. Pixel-wise correlations of VOD & NDVI and VOD & MVI from April to October in 2010 REFERENCES: Please contact with the authors for a list of the references (Xin.Wang@noaa.gov or Xiwu.Zhan@noaa.gov). Xin.Wang@noaa.gov Xiwu.Zhan@noaa.govXin.Wang@noaa.gov Xiwu.Zhan@noaa.gov Xin WANG & Xiwu ZHAN, NOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MD. (Xin.Wang@noaa.gov or Xiwu.Zhan@noaa.gov) Xin.Wang@noaa.govXiwu.Zhan@noaa.govXin.Wang@noaa.govXiwu.Zhan@noaa.gov