Vegetation Indices Radiometric measures of the amount, structure, and condition of vegetation, Precise monitoring tool phenology inter-annual variations Serve as intermediaries in the assessment of various biophysical parameters green cover, biomass, leaf area index (LAI), fAPAR chlorophyll conc.

APPLICATIONS Indicators of seasonal and inter-annual variations in vegetation (phenology) Change detection studies (human/ climate) Tool for monitoring and mapping vegetation Serve as intermediaries is the assessment of various biophysical parameters: leaf area index (LAI), % green cover, biomass, FPAR, land cover classification

Relating transpiration and photosynthesis to NDVI, 1988

Testing limits of interpretation for NDVI, 1988

Relating canopy processes to NDVI, 1988

Atmospheric Influences on Spectral Response Functions Total Radiance Path Radiance Sunlight Water vapor absorption Scattering by aerosols Reflected Energy Skylight Total energy incident at the surface is comprised of direct and skylight energy. Total energy at the sensor will be a function of direct reflectance from the surface (accounting for skylight), and the Path Radiance. Atmosphere influences are not the same for Red and NIR

FPAR (Fraction of absorbed PAR): BIOPHYSICAL MEASURES Leaf Area Index (m2/m2): FPAR (Fraction of absorbed PAR): Incident Radiation Ground Leaf PAR absorption (radiometric) Leaf Area (structural)

Theoretical basis for spectral vegetation indices:

Aerosol Correction from Collection 1 to Collection 4 Collection 4 (Current re-processing) No aerosol correction

Annual Cloudcover Percentage for MODIS Figure: MODIS 2003 NPP QC outputs showing regional impacts of cloud/aerosol effects on optical/IR satellite retrievals. Unlike microwave remote sensing, optical remote sensing is strongly constrained by atmospheric conditions and solar illumination. Tropical forests of South America and Southeast Asia, for example, are largely obscured by frequent cloud cover, smoke and other atmospheric aerosols. At high latitudes reduced solar illumination, enhanced shadowing and atmospheric aerosols from boreal fire activity significantly degrade optical remote sensing retrievals as well. Data processing techniques such as spatial and temporal compositing can partially mitigate these effects, but at the price of decreased spatial and temporal fidelity. New remote sensing methods are needed that integrate synergistic information from multiple sensors for improved information extraction and global monitoring capabilities. For example, microwave remote sensing provides day-night and all-weather monitoring capabilities, as well as sensitivity to landscape structure and moisture conditions. Integration of these data with optical remote sensing based estimates of vegetation photosynthetic properties would improve global assessment and monitoring of soil-vegetation interactions with the atmosphere that could benefit regional weather forecast accuracies. The wide array of current and planned sensors and integrated satellite platforms such as those available under NASA EOS and future NPOESS programs offer unprecedented coverage of the globe and rich opportunities to exploit synergy among different sensors for improved biospheric monitoring.

Persistent clouds

Compositing Algorithm Provide cloud-free VI product over set temporal intervals, Reduce atmosphere variability & contamination Minimize BRDF effects due to view and sun angle geometry variations Depict and reconstruct phenological variations Accurately discriminate inter-annual variations in vegetation. Physical and semi-empirical BRDF models Maximum VI (MVC) or constrained VI (CMVC)

Normalized Difference Vegetation Index (NDVI) The NDVI is a normalized ratio of the NIR and red bands, NDVI is a functionally equivalent to and is a non-linear transform of the simple ratio. 3

Enhanced Vegetation Index (EVI) rNIR - rred EVI = L + rNIR + C1 rred + C2 rblue L = canopy background adjustment, C1 and C2 are for aerosol correction and feedback L=1, C1 and C2 are 6 and 7.5 G = gain factor of 2.5 Reduces both atmosphere and canopy background contamination. Increased sensitivity at high biomass levels (less saturation) Linear *G

1 km VI’s Tapajós ‘Forest’ Day 113 - 128 NDVI EVI 1 km VI’s Tapajós ‘Forest’ Day 113 - 128 EVI NDVI

NDVI - Soil Sensitivity 1 0.9 LAI=2.7 0.8 0.7 LAI=1.6 0.6 0.5 NDVI LAI=1.1 0.4 0.3 0.2 LAI=0.5 0.1 Soil 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Soil Albedo

Soil Adjusted Vegetation Index SAVI isolines overlap vegetation isolines over a wide range of LAI values. SAVI becomes insensitive to soil noise within this range of LAI The perfect range depends on the choice of the ‘L’ value in the SAVI formulation.

SAVI Soil Albedo 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Soil LAI=0.5 LAI=1.1 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Soil Albedo

Inter-relationships among Spectral VI’s VI’s behave similarly to each other under a ‘constant’ set of conditions, If soils are not varying then NDVI and SAVI are well correlated, If aerosol differences are minimal, then similarly there is minimal difference between EVI and SAVI.

NDVI & EVI Relationships MODIS Data (2000-2001) RT-Model

EVI & SAVI Relationships MODIS Data (2000-2001) RT-Model EVI

VI - FPAR Relationships NDVI EVI

…The Algorithm Weighted average scheme

Long term stability

Global NDVI at 500 m DOY 113-128

MODIS Standard Vegetation Index Products Products The MODIS Products include 2 Vegetation Indices (NDVI, EVI) and QA produced at 16-day and monthly intervals at 250m/ 500m, 1km, and 25km resolutions The narrower ‘red’ MODIS band provides increased chlorophyll sensitivity (band 1), The narrower ‘NIR’ MODIS band avoids water vapor absorption (band 2) Use of the blue channel in the EVI provides aerosol resistance The at launch MOD13 algorithm will allow the individual processing of two vegetation indices at different spatial and temporal resolution. The Level 3 HDF filespec will therefore be split in 5 files/products (MOD_PR13A, MOD_PR13B, MOD_PR13C, MOD_PR13D, MOD_PR13E) that each have commonalities with respect to spatial and spectral resolutions. The standard DAAC production run will process the NDVI at 250 m resolution for 16-day intervals. The enhanced VI (EVI) and NDVI will both be produced at 1km and CMG and both 16 days and monthly intervals. The output products will have datafields for the NDVI and EVI with corresponding QA, reflectance data, angular information and spatial statistics (mean and std of each VI and for the CMG scales.

Radiometric Measures Vegetation Indices SR (Simple Ratio), MSR (Modified SR) SAVI (Soil Adjusted VI), MSAVI, ARVI, GEMI NDVI (Normalized Difference Vegetation Index) EVI (Enhanced Vegetation Index) Biophysical Measures Leaf Area Index (Area of leaves per unit ground area, m2/m2) FPAR (Fraction of incident PAR that is absorbed)

VI Equations SVI Formulations Enhanced Vegetation Index: Simple Ratio = NIR/Red Normalized Difference = (NIR-Red)/(NIR+Red) Enhanced Vegetation Index: -where r is atmospherically-corrected, surface reflectances, L is the canopy background adjustment, G is a gain factor, and C1 , C2 are coefficients for atmospheric resistance.

Annual average EVI Amazon vegetation seasonal analyses and land conversion effects on biologic activity.

1 km VI’s Tapajós ‘Forest’ Day 113 - 128 NDVI EVI 1 km VI’s Tapajós ‘Forest’ Day 113 - 128 EVI NDVI

Vegetation seasonality in the Brazilian ‘Cerrado’ Region

Histograms of VI’s at 250 m, 500 m, and 1 km resolutions NDVI EVI South America (August 12 to August 27, 2000)

Analyses, Uncertainty & Validation Heterogeneous surfaces (scaling issues) Dynamic Range Tropical forests & clouds Arid/ semiarid regions Snow - vegetation problems Cloud shadows

Scaling issues & heterogenous surfaces Tropical forest conversion (Ikonos) Brasília – CCD/CBERS-1 CB2 - Minas Gerais - nov/03

100% 75% 50% 25% 75% 50% 25% 25% crop

Southwest Megadrought Analysis per land cover type (MODIS 3-year)

Inland water bodies (Caspian Sea) Traced to over/under corrected surface reflectance over water bodies Hypersensitivity of NDVI to the proportional relation between Red & NIR

Spatial issues (Blocky retrieval) Traced to Aerosol correction

Snow-Vegetation Surfaces Snow has Red > NIR causing numerator of VI’s (NDVI, SAVI, EVI) to become negative (or decrease in case of mixed pixels). Snow also has Blue > Red causing denominator of EVI equation to decrease and, at times, become negative

Inter-relationships among biophysical products (VI, LAI/FPAR) Global FPAR 2004 Global LAI 2004

MODIS Phenology Logic

10/08/2002 UNBC Seminar

MOD12Q2: Global Vegetation Phenology From Mark Friedl, Boston Univ. First global products for vegetation phenology based on MODIS EVI data released for 2001-2004 Identifies key transition dates in growing season Onset EVI increase Onset EVI maximum Available globally 2001-2004 Legend provides julian date for Onset EVI decrease Onset EVI minimum