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Microwave Vegetation Indices and VWC in NAFE’06

Microwave Vegetation Indices and VWC in NAFE’06. T. J. Jackson 1 , J. Shi 2 , and J. Tao 3. 1 USDA ARS Hydrology and Remote Sensing Lab, Maryland, USA 2 University of California at Santa Barbara, Santa Barbara, CA 3 Beijing Normal University, Beijing, China. Introduction: MVI Motivation.

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Microwave Vegetation Indices and VWC in NAFE’06

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  1. Microwave Vegetation Indices and VWC in NAFE’06 T. J. Jackson1, J. Shi2, and J. Tao3 1 USDA ARS Hydrology and Remote Sensing Lab, Maryland, USA 2 University of California at Santa Barbara, Santa Barbara, CA 3 Beijing Normal University, Beijing, China

  2. Introduction: MVI Motivation • Soil moisture retrieval using microwave remote sensing requires a correction for vegetation effects. • One approach is to use vegetation water content (VWC). • VWC is usually estimated from vegetation indices (NDVI, NDWI) derived from optical satellite sensors. • Issues • Atmospheric effects • Primarily responsive to the leafy part of the vegetation • Merging multiple data sources in real time

  3. MVI Approach • Basis: The effect of vegetation on surface emissivity (soil moisture) varies with microwave frequency. • Can this fact be used to derive information on both the leafy and woody parts of the vegetation canopy, especially if combined with traditional indices? • Objective: Evaluate the possibility of monitoring vegetation, in particular VWC, using microwave vegetation indices (MVIs).

  4. Outline • RT model • Simulation datasets and computation of indices • AMSR-E • Qualitative evaluation of global and seasonal patterns • Comparison to other MW indices • Preliminary quantitative verification of VWC estimates during NAFE’06

  5. Microwave RT Model (t-w model) Three component emission model: Full canopy* Symbols p= polarization f=frequency v=vegetation s=soil surf=soil surface q=angle w=single scattering albedo g=optical depth=exp(t*sec(q)) TB=brightness temperature e=emissivity T=temperature t=optical depth 1 2 3 *At the satellite footprint scale, the fraction of vegetation cover needs to be considered, the following presentation is for 100% cover. Here it is assumed that incidence angle is constant.

  6. Radiative Transfer Equation (rearranged) Vegetation Emission Component Vegetation Attenuation Component

  7. Radiative Transfer Equation (rearranged) • Implies that the measured brightness temperature at a given frequency (f) and polarization (p) can be linearly related to the soil surface emissivity. • Bare soil: TB~esurfTsoil • Dense canopy: TB~Tveg • Both the slope and intercept are functions of the vegetation fractional cover, temperature and other physical properties including biomass, water content, and characteristics of the scatter size, shape and orientation of vegetation canopy. • The RT equation has too many unknowns for solution without some assumptions. Vegetation Emission Component Vegetation Attenuation Component

  8. Frequency Dependence of the Surface Emission • If the surface component behaves in a predictable manner with freq., it will be possible to reduce the number of unknowns when using multifrequency observations • Characteristics of bare surface emission at the different AMSR-Efrequencies were evaluated using a simulated surface emission database for the sensor parameters • Advanced Integral Equation Model (AIEM) • Frequencies: 6.925, 10.65, and 18.7 GHz, V and H, and q=55 • Soil moistures (2% to 44%, 2% interval); surface roughness parameters, root mean square heights (0.25 cm to 3 cm, 0.25 cm interval) and correlation lengths (2.5 cm to 30 cm, 2.5 cm interval). • 2,904 simulated emissivities for each frequency and polarization. • Examined the relationships of frequency pairs

  9. Characteristics of Surface Emissivity at Different Frequencies/Polarizations • Surface emissivity increases with frequency due to the frequency dependence of the dielectric properties of water • Surface emissivities at two adjacent AMSR-E frequencies are correlated for all soil moisture and surface roughness conditions • They can be described by a linear function and are polarization independent..leading to: Emissivity Emissivity

  10. Predict Surface Emissivity at One Frequency from Another Frequency for a Specific Sensor Configuration • A general linear model was fit to the simulated data set • This yielded the following results for the AMSR-E channels considered • This result is key to the vegetation index analysis: it allows us to minimize the effects of the surface

  11. Derivation of the MVIs • Start with the t-w model and re-arrange terms • Specify the data source (AMSR-E) • Reduce dimensionality by establishing the frequency dependence of surface emissivity equations and calibrate these relationships using numerical simulation • Further reduce dimensionality by assuming that at satellite scales the VE and VA terms are independent of polarization • Re-arranging terms results in

  12. Derivation of the MVIs • Starting with the t-w model and re-arrange terms • Specify the data source (AMSR-E) • Reduce dimensionality by establishing the frequency dependence of surface emissivity equations and calibrate these relationships using numerical simulation • Further reduce dimensionality by assuming that at satellite scales the VE and VA terms are independent of polarization • Re-arranging terms results in • B is the Microwave Vegetation Index (MVI) Ratio of Polarization Differences

  13. Derivation of the MVIs (2) • With the three AMSR-E low frequencies available, two MVIs can be derived; • Low frequency: 6.925, 10.65 GHz • High frequency: 10.65, 18.7 GHz

  14. Preliminary Qualitative Interpretation of the MVIs • B is affected by biomass, water content, and characteristics of the size, shape and the orientation of scatters in the vegetation canopy. • Qualitative evaluation of global patterns and seasonal patterns in specific regions. • Data set: 2003 AMSR-E and MODIS NDVI

  15. Global NDVI (April 2003) • NDVI features: • Source 16 day composites, average of two per month • Averaged up from 1 km to 25 km • Deserts, Tropical forests show expectedpatterns. • MVI products: • Calculated on a daily basis, median filtered over 5 days, and averaged over the same 16 day period of the NDVI products NDVI A(6.925,10.65) A(10.65,18.7) B(10.65,18.7) B(6.925,10.65)

  16. Global MVIs and NDVI (April 2003) • The higher frequency MVI shows increased vegetation effects • Somewhat similar to NDVI ; Differences in the tropics, northern mid latitudes NDVI A(6.925,10.65) A(10.65,18.7) B(6.925,10.65) B(10.65,18.7) • Low values of B indicate different pol. differences for the two freq., high values indicate similar differences. • Interpretation issues; atmosphere, non-vegetated, dense vegetation?

  17. Seasonal Patterns (April vs. July 2003) NDVI B(10.67, 18.7 GHz) April July • NDVI: Seasonal increases in the northern hemisphere and decreases in the south • B: Seasonal patterns are not as variable as NDVI and there are differences in the tropics, northern mid latitudes

  18. Quantitative Comparison to VWC: National Airborne Field Experiment 2006 (NAFE’06) • 29 Oct – 20 Nov 2006, the Murrumbidgee watershed in southeastern Australia. • Lat: 35.159S to 34.683S, Lon: 145.640W to 145.900W • Vegetation types: dry pasture, irrigated rice, irrigated wheat, irrigated pasture, dry wheat and fallow • Ground Data: • MSR spectral signatures for different land covers • Survey of land cover • Observed vegetation water content • Satellite Data: • Landsat 5 TM data (6 Oct, 7 Nov and 23 Nov) • AMSR-E L3 data (From 6 Oct to 23 Nov)

  19. NDWI VWC Products • Normalized Difference Water Index (NDWI) is sensitive to liquid water molecules in vegetation canopies. • A linear relationship was calibrated using the ground and satellite observations for specific land covers. • This is applied with landcover to each Landsat data set (Oct. 6, Nov. 7, and Nov. 23). • Daily VWC maps are derived on specific days by interpolation.

  20. Deriving MVIs Using AMSR-E Data • MVI were derived using daily brightness temperature data at 6.925 GHz, 10.65 GHz and 18.7 GHz from AMSR-E (25 km x 25 km gird data) . • Flags

  21. … NO. 1 NO. 2 NO. 1 NO. 2 NO. 3 NO. 4 NO. 3 NO. 4 Comparison Comparison of MVIs and VWC • Landsat based VWC values were averaged over 4 AMSR-E EASE-GRID pixels within the Yanco area • For each day with AMSR-E data, the MVIs of the 4 pixels were compared to VWC

  22. Results: Comparison of MVIs and VWC N=23 N=23

  23. Results: Comparison of MVIs and VWC • Each grid cell exhibits a good correlation between B and VWC, however, (3,4) are different than (1,2). • Using the individual regressions, the SEE for VWC is 0.087 kg/m2. • The different slopes are likely associated with landcover.

  24. Summary • Described a new Microwave Vegetation Index (MVI) that utilizes multifrequency observations • The MVI conveys somewhat different information than NDVI. The two may be complementary. • Evaluated the potential of the MVI in predicting vegetation water content in a case study. • Further development and evaluation is ongoing; global responses and additional VWC data sets.

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