A Study on Vegetation OpticalA Study on Vegetation Optical Depth Parameterization and its Impact onDepth Parameterization and its Impact on Passive Microwave Soil Moisture Retrievals Passive Microwave Soil Moisture Retrievals

Microwave technology is the most promising remote Microwave technology is the most promising remote

sensing method that permits truly quantitative estimates sensing method that permits truly quantitative estimates

of soil moisture using physically based expressions such of soil moisture using physically based expressions such

as radiative transfer models (Owe et al., 2001). as radiative transfer models (Owe et al., 2001). However, However,

different retrieval algorithms using the same tau-omega different retrieval algorithms using the same tau-omega

radiation transfer equation and the same satellite radiation transfer equation and the same satellite

observations have produced very different soil moisture observations have produced very different soil moisture

data products. One of the main causes is the handling of data products. One of the main causes is the handling of

the masking of microwave signals from top layer soil by the masking of microwave signals from top layer soil by

the spatially heterogeneous vegetation cover. Therefore, the spatially heterogeneous vegetation cover. Therefore,

it is critical to examine the estimation of vegetation optical it is critical to examine the estimation of vegetation optical

depth (VOD) for improving microwave soil moisture depth (VOD) for improving microwave soil moisture

retrievals. retrievals.

For the Single Channel Retrieval (SCR) algorithm For the Single Channel Retrieval (SCR) algorithm

(Jackson et al., 1993), land surface temperature is (Jackson et al., 1993), land surface temperature is

estimated from Ka-band passive microwave observations estimated from Ka-band passive microwave observations

(Holmes et al., 2009). VOD inversed from the soil moisture (Holmes et al., 2009). VOD inversed from the soil moisture

data of Global Land Data Assimilation System is used as a data of Global Land Data Assimilation System is used as a

reference VOD to examine the impact of three VOD reference VOD to examine the impact of three VOD

parameterization methods: the Microwave Vegetation parameterization methods: the Microwave Vegetation

Index (MVI) (Shi et al., 2008), the Normalized Vegetation Index (MVI) (Shi et al., 2008), the Normalized Vegetation

Difference Index (NDVI), and the Microwave Polarization Difference Index (NDVI), and the Microwave Polarization

Difference Index (MPDI) (Meesters et al., 2005). Six soil Difference Index (MPDI) (Meesters et al., 2005). Six soil

moisture measurement sites with relatively uniform land moisture measurement sites with relatively uniform land

cover are selected to derive a relationship between the cover are selected to derive a relationship between the

reference VOD to MVI or NDVI. The retrieved VOD from reference VOD to MVI or NDVI. The retrieved VOD from

MPDI is noted as VODMPDI is noted as VODmm. Finally the impact of each of the . Finally the impact of each of the

three VOD parameterization methods is examined with the three VOD parameterization methods is examined with the

AMSR-E observations using the single channel soil AMSR-E observations using the single channel soil

moisture retrieval algorithm for vegetation growing season.moisture retrieval algorithm for vegetation growing season.

GLOBAL PATTERNS OF NDVI AND MVI AND GLOBAL PATTERNS OF NDVI AND MVI AND CORRECTIONS WITH VODCORRECTIONS 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 VODDISCREPANCY EVALUATION OF VOD AND VODmm INVERSED BY GLDAS SOIL MOISTURE DATASETINVERSED BY GLDAS SOIL MOISTURE DATASET

23th April - 8th May, 2010 13th Aug - 28th Aug, 2010

Fig. 3. Global Difference between VOD and VODm

NDVINDVI parameterization, despite known limitations in parameterization, despite known limitations in

representing vegetation biomass or water content, can representing vegetation biomass or water content, can

provide relatively more accurate VOD estimates for soil provide relatively more accurate VOD estimates for soil

moisture retrieval using a 16-day composite. moisture retrieval using a 16-day composite.

MVIMVI integrated information at K-band can provide real- integrated information at K-band can provide real-

time parameterization of VOD, but exhibited a seasonal time parameterization of VOD, but exhibited a seasonal

variability only for short vegetation cover. Soil moisture variability only for short vegetation cover. Soil moisture

retrievals from MVI derived VOD was susceptible to the retrievals from MVI derived VOD was susceptible to the

land surface temperature change. land surface temperature change.

VODVODm m can provide relatively more reasonable soil can provide relatively more reasonable soil

moisture retrievals at global scale by partitioning surface moisture retrievals at global scale by partitioning surface

emission into its primary sources with iterations, but is emission into its primary sources with iterations, but is

strongly sensitive to land surface temperature biases. strongly sensitive to land surface temperature biases.

Assumption of polarization independence should be Assumption of polarization independence should be

deliberated in some region. deliberated in some region.

Fig. 7. VSM retrieval using NDVI (upper left), MVI (upper right), VODm (lower left) and corresponding

GLDAS result (lower right) in 20th Jun, 2011

GLOBAL RETRIEVAL RESULTSGLOBAL RETRIEVAL RESULTS

SUMMARYSUMMARY

Fig. 4. Mean Bias Rate of SM Retrievals using Parameterized VOD and VODm Impacted by surface Temperature Bias in

two SCAN sites.

INTRODUCTIONINTRODUCTION

Table 1. Summary of coefficient of variation of VOD, correlation coefficients, and difference between VOD and VODm

for different vegetation types in 2010

Land cover type CV Corr. Coeff. Corr. Coeff. Corr. Coeff. Difference Difference

VOD NDVI-VOD VOD-MVI VOD-VODm VOD-VODm VOD-VODm

Evergreen Needleleaf Forests (ENF) 0.13 0.21 0.07 0.36 -0.12 -0.14

Evergreen Broadleaf Forests (EBF) 0.12 0.18 -0.13 0.53 0.01 -0.02

Deciduous Needleleaf Forests (DNF) 0.14 0.02 0.20 0.27 -0.09 -0.21

Deciduous Broadleaf Forests (DBF) 0.16 0.53 -0.01 0.53 -0.06 -0.08

Mixed Forests (MF) 0.14 0.59 -0.09 0.43 -0.12 -0.08

Woodlands (WOD) 0.15 0.25 -0.19 0.55 -0.04 -0.13

Wooded Grasslands/Shrubs (WGS) 0.19 0.65 -0.41 0.92 -0.05 -0.05

Closed Bushlands or Shrublands (CBS) 0.18 0.41 -0.36 0.92 -0.04 -0.04

Open Shrublands (OS) 0.19 0.39 -0.19 0.92 -0.03 -0.03

Grasses (GRA) 0.19 0.39 0.10 0.53 -0.07 -0.10

Croplands (CRP) 0.20 0.54 -0.02 0.63 -0.10 -0.13

Lichens and Mosses (LM) 0.47 -0.53 0.20 0.18 0.14 -0.16

Bare (BAE) 0.51 0.02 -0.24 0.82 0.01 0.01

SOIL MOISTURE RETRIEVAL ERROR SOIL MOISTURE RETRIEVAL ERROR EVALUATION FOR THREE VOD APPROACHEVALUATION 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 VODSITE TIME SERIES OF VOD, NDVI, MVI AND VODmm

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

METHODOLOGYMETHODOLOGY

Fig. 2. Pixel-wise correlations of VOD & NDVI and VOD & MVI from April to October in 2010

REFERENCES: REFERENCES: Please contact with the authors for a list Please contact with the authors for a list of the references (Xin.Wang@noaa.gov or of the references (Xin.Wang@noaa.gov or Xiwu.Zhan@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 & Xiwu ZHAN, NOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MD. (Xin.Wang@noaa.gov or Xiwu.Zhan@noaa.gov)