Radiometric and biophysical measures of global vegetation from multi-dimensional MODIS data Ramakrishna Nemani NTSG

Acknowledgements: University of Arizona Boston University NTSG Alfredo Huete Ranga Myneni Joe Glassy Kamel Didan Y. Knyazikhhin Petr Votava Tomoaki Miura Y. Zhgang Hiroki Yoshioka Y. Tian Laerte Ferreira Xiang Gao Karim Batchily

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, m 2 /m 2 ) FPAR (Fraction of incident PAR that is absorbed)

Vegetation Indices are ‘robust’ spectral transformations of two or more bands designed to enhance the ‘vegetation signal’ and allow for reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations. VEGETATION INDICES

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

Spatio-temporal vegetation dynamics

1999 Onset of Greenness

Departure from Average Maps from the Wildland Fire Assessment System Departure from Average maps relate current year vegetative greenness to average vegetative greenness for the same time of year.

Leaf Area Index (LAI) Fraction of intercepted photosynthetically active radiation (FPAR)

Global Leaf Area Index derived from Pathfinder NDVI and NDVI-LAI relationships

Global FPAR derived from Pathfinder NDVI and NDVI-FPAR relationships

Relating transpiration and photosynthesis to NDVI, 1988

Spectral reflectance of leaves Theoretical basis for spectral vegetation indices :

SVI Formulations Simple Ratio = NIR/Red Normalized Difference = (NIR-Red)/(NIR+Red) Vegetation Index Advantages: simple Disadvantages: residual influences of atmosphere, background and viewing geometry

Atmospheric Influences on Spectral Response Functions Path Radiance Sunlight Skylight Reflected Energy Total Radiance Atmosphere influences are not the same for Red and NIR Water vapor absorption Scattering by aerosols

Wavelength in Micrometers TM 4 Band 6 : ReflectanceReflectance Background Influences Vegetation Dry Soil Wet Soil

Angular dependence

VI Equations Enhanced Vegetation Index: -where  is atmospherically-corrected, surface reflectances, L is the canopy background adjustment, G is a gain factor, and C 1, C 2 are coefficients for atmospheric resistance.

MODIS Standard Vegetation Index Products Products KThe MODIS Products include 2 Vegetation Indices (NDVI, EVI) and QA produced at 16-day and monthly intervals at 250m/ 500m, 1km, and 25km resolutions KThe narrower ‘red’ MODIS band provides increased chlorophyll sensitivity (band 1), KThe narrower ‘NIR’ MODIS band avoids water vapor absorption (band 2) KUse of the blue channel in the EVI provides aerosol resistance

Dotted lines indicate AVHRR bands 1 RED 2 NIR

Normalizing the VIs to nadir values

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)

MODIS-VI Compositing Scheme Flow Diagram

Global NDVI at 500 m DOY

500m NDVI subset DOY Tapajós

MOD13A1 QA 500m

1km EVI Time Series 1km NDVI Time Series South America

1 km VI’s Tapajós ‘Forest’ NDVIEVI NDVI EVI

MODIS & AVHRR NDVI Comparisons

Dotted lines indicate AVHRR bands 1 RED 2 NIR AVHRR & MODIS Red and NIR bands

White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water

White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water

SUMMARY Both indices were robust and performed well in global vegetation monitoring and analysis The improved spectral and spatial resolutions of MODIS offer the potential for improved change detection / land use and conversion studies,

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

Applications of FPAR and LAI FPAR and LAI are useful variables which help describe: –canopy structure –radiation absorption –vegetative productivity –seasonal boundaries, phenological state –global carbon cycling

MODIS Terrestrial Productivity Remote Sensing Inputs Model Land Cover FPAR LAI NPP = GPP - Respiration Outputs Weekly and Annual Productivity Daily Weather (Tmin, Tmax, Rnet)

Leaf Area Index (LAI) Fraction of intercepted photosynthetically active radiation (FPAR) Functional relations

0.70 NDVI Need for a more robust approach

FPAR, LAI Algorithmic Approach Two-tier algorithmic approach: LUT based approach using spectral as well as angular observations simple VI based backup

Controlling factors: Leaf optical properties (refl,tran,abs) Canopy structure Background reflectance Sun-sensor geometry Leaf area

Controlling factors: Leaf optical properties (refl,tran,abs) Canopy structure Background reflectance Sun-sensor geometry Leaf area White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water

White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water 0.70 NDVI

Controlling factors: Leaf optical properties (refl,tran,abs) Canopy structure Background reflectance Sun-sensor geometry Leaf area

The LUT contains entries at one critical wavelength only, and certain other non-wavelength dependent constants; thus, as the algorithm ingests 2 band data or 4 band data or even 7 band data, the size of the LUT is the same! Leaf Spectral reflectance is characterized for 6 biomes at 152 points. RSAC figure

Wavelength in Micrometers TM MSS Band 6 : ReflectanceReflectance Vegetation Jarosite Kaolinite Dry Soil Wet Soil Background parameterization (25 types)

since the main algorithm is physically based, sun and view angle changes are treated as SOURCES of information rather than NOISE and thus aid in LAI/FPAR retrievals

LAI is defined as: LAI = g * LAI o LAI o is mean LAI of a plant g is canopy cover, which controls both total LAI as well as background contribution

THE LUT Contains: for each biome (6) leaf albedo at one wavelength coefficients to compute albedo any wavelength coefficients to compute BRF coefficients to compute effective background reflectance sun-sensor geometry intervals number of LAI intervals LAI saturation point

THE LUT Key features: energy conservation ability to ingest multiple wavelengths allows the use of uncertainities angular data as a source of information

FPAR, LAI Algorithm Inputs –Aggregated and atmospherically corrected 1km surface reflectances from channels {1..6}, and their uncertainities; currently only 1,2 {VIS,NIR} are used. –Land cover classification (IGBP translated to 6- class biome scheme; new 6-class coming. –Ancillary data: Radiative Transfer model lookup tables, epsilon

White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water Controlling factors: Background reflectance Sun-sensor geometry Leaf area

FPAR, LAI Algorithm Outputs a distribution of LAI and FPAR, and NOT a single value! The mean of the distribution and its standard deviation are reported, thus providing an error/uncertainity estimate of its own. LAI Frequency

When does LUT approach fail? Land cover mixtures

Effect of changing Epsilon

Leaf Area Index (LAI) Fraction of intercepted photosynthetically active radiation (FPAR) SATURATION

Deriving LAI/FPAR at 250m resolution! Need land cover at 250m Blue band is at 500m

SUMMARY -physically based approach -use of angular data (e.g. MISR synergism) -realizing a distribution of LAIs rather than one LAI -ability to change the LUT for other sensors -VI based backup