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

…The Algorithm Weighted average scheme

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:

Long term stability

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.

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.

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

VI - FPAR Relationships NDVI EVI

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

MIXED PIXEL ISSUES 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

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

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