MODIS Vegetation Indices (MOD13)
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MODIS Vegetation Indices (MOD13)

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Remote sensing –Beyond images ...

Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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MODIS Vegetation Indices (MOD13) MODIS Vegetation Indices (MOD13) Presentation Transcript

  • MODIS Vegetation Indices (MOD13) Ramón Solano-Barajas rsolano@ucol.mx Laboratorio de Geomática, Universidad de Colima Coquimatlán, Col. Mexico 28400
  • WHY TO CARE ABOUT VIS? • VIs are estimates of the APAR of plants hence correlated with photosynthetic capacity (Sellers 1985, Myneni 1995) • • • Then • Better understanding of carbon cycle and sink X VI over time ~ GPP Related to vegetation “greeness” and “health” Allow time series analysis (growing season analysis)
  • BIOPHYSICAL BASIS • VIs are a function of the optical and physical properties of leaves, canopy structure, soil background, and atmosphere load. • VIs are typically based on the absorption of blue and red light by chlorophyll, and reflection of near-infrared light by the leaves’s structure
  • CHALLENGES • Observing vegetation from space has several challenges, mainly due to atmospheric and soil effects • Additional issues may be introduced by aggregating techniques (e.g. MVC) and observation geometry (BRDF effects)
  • VIS FORMULATION • Several forms of VIs have been proposed over time. Original goals were to estimate vegetation status and biomass (Kriegler 1969 , Miller 1972, Rouse 1974, Tucker 1977). Now many applications • Each VI instance try to address a specific issue, such as atmospheric or soil effects • Examples: • • • • NDVI - Normalized Difference VI ARVI - Atmospheric Resistant VI SAVI - Soil Adjusted VI EVI - Enhanced VI N DV I = N IR R N IR + R N IR RB N IR + RB RB = R (B R) ARV I = EV I = G · N IR N IR + C1 · R R C2 · B + 1
  • MODIS VI (MOD13) • The MODIS VI product (MOD13) is a chain of VI products at varying spatial (250m, 1km, 0.05 degree) and temporal (16-day, monthly) resolutions • It contains both NDVI and EVI indices: NDVI as the continuity index (+30 yr data). Simple, wellknown, APAR-correlated. Shows “saturation” and atmospheric and soil issues • • • EVI as an enhanced index, addressing some NDVI concerns (although at some “costs”) A 2-band EVI (no blue band) is used as backup algorithm under anomalous areas
  • MODIS VI (MOD13) Product Family •MOD13Q1: 16 d, 250 m, tiled •MOD13A1: 16 d, 500 m, tiled •MOD13A2: 16 d, 1 km, tiled •MOD13A3: monthly, 1 km, tiled •MOD13C1: 16 d, 0.05 deg (CMG) •MOD13C2: monthly, 0.05 deg (CMG) MODIS Sinusoidal 10x10 deg tiles Tile h09v05
  • MOD13 AGGREGATION • MODIS VI products rely on the upstream daily Surface Reflectance (SR, MOD09) series • The VI algorithms temporally composite the SR to generate the VI products. • The 1-km VI product first aggregates 250- and 500m pixel sizes to 1 km. • The CMG products are generated through spatial averaging of the 1-km versions • Monthly products are time-weighed averages of their 16-day versions
  • MOD13 PRODUCT CHAIN L2G( Surface(flectance( Surface( Reflectance( MOD09GHK MOD09GQK MOD09GST MOD09GAD MOD0PTHKM MODPTQKM ComposiBng( MODPRAGG( MOD13(Q1/A1(( 250/500m(16day( Aggregated(1km( Surface( Reflectance( ComposiBng( MOD13C1( 0.05deg(16day( SpaBal(Averaging( MOD13A2( 1km(16day( Temporal( Averaging( MOD13C2( 0.05deg(Monthly( Temporal( Averaging( MOD13A3( 1km(Monthly(
  • MOD13 AGG ALGORITHM • MODIS VI algorithm applies a filter to the data based on quality, cloud, and viewing geometry • Contaminated and extreme off-nadir data are considered lower quality. A cloud-free, nadir view pixel with no residual atmospheric contamination represents the best quality pixel. • Compositing VI technique uses a Constrained View angle - Maximum Value Composite (CV-MVC) • It uses a simple MVC when no enough data exist
  • MOD13 AGG ALGORITHM !*%&8(>$(7;(6%AD<( !"#$%&'()'&*%+&'( A9%5'@( 3AL'D(P<(?AL'D(%+%D<@A@( O>( O>( =>9?"*'(SADD( E%D"'("@A+5(( FA@*>#A&(%E'#%5'( +(%E%ADK( 3AL'D@(M(7( +(5>>6( G4(3AL'D@( M(-( N'@( N'@( !'D'&*(P'@*( ?AL'D("@A+5( 0Q=( !'D'&*(P'@*( ?AL'D("@A+5(=QR 0Q=( T9%5'(A+*'5#%*'6( ?AL'D(P<(?AL'D( /'@"DB+5( &>9?>@A*'(BD'( Figure 3: MODIS VI Compositing algorithm data flow.
  • MOD13 CONTENTS (SDS) Science&Data&Set& Units& XYZm%16%days%NDVI% NDVI% Data& type& int16% XYZm%16%days%EVI% EVI% int16% XYZm%16%days%VI%Quality%detailed%QA% XYZm%16%days%red%reflectance%(Band%1)% XYZm%16%days%NIR%reflectance%(Band%2)% XYZm%16%days%blue%reflectance%(Band%3)% XYZm%16%days%MIR%reflectance%(Band%7)% XYZm%16%days%view%zenith%angle% Bits% Reflectance% Reflectance% Reflectance% Reflectance% Degree% uint16% int16% int16% int16% int16% int16% XYZm%16%days%sun%zenith%angle% Degree% int16% XYZm%16%days%relative%azimuth%angle% Degree% int16% XYZm%16%days%composite%day%of%the%year% XYZm%16%days%pixel%reliability%summary%QA% Day%of%year% Rank% int16% int8% ! Valid& Range& 32000,% 10000% 32000,% 10000% 0,%65534% 0,%10000% 0,%10000% 0,%10000% 0,%10000% 39000,% 9000% 39000,% 9000% 33600,% 3600% 1,%366% 0,%3% Scale& factor& 0.0001% 0.0001% NA% 0.0001% 0.0001% 0.0001% 0.0001% 0.01% 0.01% 0.1% NA% NA%
  • MOD13 CONTENTS (SDS) Science&Data&Set& Units& XYZm%16%days%NDVI% NDVI% Data& type& int16% XYZm%16%days%EVI% EVI% int16% XYZm%16%days%VI%Quality%detailed%QA% XYZm%16%days%red%reflectance%(Band%1)% XYZm%16%days%NIR%reflectance%(Band%2)% XYZm%16%days%blue%reflectance%(Band%3)% XYZm%16%days%MIR%reflectance%(Band%7)% XYZm%16%days%view%zenith%angle% Bits% Reflectance% Reflectance% Reflectance% Reflectance% Degree% uint16% int16% int16% int16% int16% int16% XYZm%16%days%sun%zenith%angle% Degree% int16% XYZm%16%days%relative%azimuth%angle% Degree% int16% XYZm%16%days%composite%day%of%the%year% XYZm%16%days%pixel%reliability%summary%QA% Day%of%year% Rank% int16% int8% ! Valid& Range& 32000,% 10000% 32000,% 10000% 0,%65534% 0,%10000% 0,%10000% 0,%10000% 0,%10000% 39000,% 9000% 39000,% 9000% 33600,% 3600% 1,%366% 0,%3% Scale& factor& 0.0001% 0.0001% NA% 0.0001% 0.0001% 0.0001% 0.0001% 0.01% 0.01% 0.1% NA% NA%
  • MOD13 QA SCHEME Product Level SDS Level Field Level MOD13&QA& SUMMARY&QA&SDS& DETAILED&QA&SDS&& 9&Fields&(16&bits)& VI&USEFULNESS&Field&2& (BITS&2A5)&
  • MOD13 QUALITY ASSURANCE MOD13&QA& SUMMARY&QA&SDS& DETAILED&QA&SDS&& 9&Fields&(16&bits)& VI&USEFULNESS&Field&2& (BITS&2A5)& Rank%Key% 61% 0% 1% 2% 3% Summary%QA% Fill/No%Data% Good%Data% Marginal%data% Snow/Ice% Cloudy% Description% Not%Processed% Use%with%confidence% Useful,%but%look%at%other%QA%information% Target%covered%with%snow/ice% Target%not%visible,%covered%with%cloud% ! MOD13Q1/A1 Pixel Reliability SDS
  • MOD13 QUALITY ASSURANCE MOD13&QA& Bits% 051% %% %% %% 255% %% %% %% %% 657% 8% %% 9% 10% 11513% 14% 15% ! Parameter%Name% VI%Quality%(MODLAND%QA% Bits)% %% %% %% Value% 00% VI%Usefulness% %% %% %% %% Aerosol%Quantity% Adjacent%cloud%detected% %% Atmosphere%BRDF% Correction% Mixed%Clouds% Land/Water%Mask% Possible%snow/ice% Possible%shadow% 0000% :% 1101% 1110% 1111% 00% 0% 1% 0% VI%produced,%but%check%other%QA% Pixel%produced,%but%most%probably%cloudy% Pixel%not%produced%due%to%other%reasons%than% clouds% Highest%quality% :% Quality%so%low%that%it%is%not%useful% L1B%data%faulty% Not%useful%for%any%other%reason/not%processed% Climatology,%Low%5%High% No% Yes% No/Yes% 0% 000% 0% 0% No/Yes% Shallow%ocean,%Land,%inland%water,%etc% No/Yes% No% 01% 10% 11% Description% VI%produced%with%good%quality% MOD13Q1/A1 VI Quality detailed QA SDS SUMMARY&QA&SDS& DETAILED&QA&SDS&& 9&Fields&(16&bits)& VI&USEFULNESS&Field&2& (BITS&2A5)&
  • MOD13 QUALITY ASSURANCE MOD13&QA& Bits% 051% %% %% %% 255% %% %% %% %% 657% 8% %% 9% 10% 11513% 14% 15% ! Parameter%Name% VI%Quality%(MODLAND%QA% Bits)% %% %% %% Value% 00% VI%Usefulness% %% %% %% %% Aerosol%Quantity% Adjacent%cloud%detected% %% Atmosphere%BRDF% Correction% Mixed%Clouds% Land/Water%Mask% Possible%snow/ice% Possible%shadow% 0000% :% 1101% 1110% 1111% 00% 0% 1% 0% VI%produced,%but%check%other%QA% Pixel%produced,%but%most%probably%cloudy% Pixel%not%produced%due%to%other%reasons%than% clouds% Highest%quality% :% Quality%so%low%that%it%is%not%useful% L1B%data%faulty% Not%useful%for%any%other%reason/not%processed% Climatology,%Low%5%High% No% Yes% No/Yes% 0% 000% 0% 0% No/Yes% Shallow%ocean,%Land,%inland%water,%etc% No/Yes% No% 01% 10% 11% Description% VI%produced%with%good%quality% MOD13Q1/A1 VI Quality detailed QA SDS SUMMARY&QA&SDS& DETAILED&QA&SDS&& 9&Fields&(16&bits)& VI&USEFULNESS&Field&2& (BITS&2A5)&
  • MOD13 QUALITY ASSURANCE MOD13&QA& SUMMARY&QA&SDS& DETAILED&QA&SDS&& 9&Fields&(16&bits)& VI&USEFULNESS&Field&2& (BITS&2A5)& Parameter'Name' Aerosol'Quantity' Condition' If'aerosol'climatology'was'used'for'atmospheric' correction'(00)' If'aerosol'quantity'was'high'(11)' If'no'adjacency'correction'was'performed'(0)' '' Atmosphere'Adjacency' Correction' Atmosphere'BRDF'Correction' If'no'atmosphereEsurface'BRDF'coupled'correction'was' performed'(0)' Mixed'Clouds' If'there'possibly'existed'mixed'clouds'(1)' Shadow' If'there'possibly'existed'shadow'(1)' View'zenith'angle'(vz)' If''vz'>'40' Sun'zenith'angle'(sz)' If''sz'>'60' ! MOD13 Q1/A1 QA Detailed SDS > VI Usefulness Field (bits 2-5) Score' 2' 3' 1' 2' 3' 2' 1' 1'
  • MOD13 MONTHLY ALGORITHM • This algorithm uses all 16-day VI products which overlap within a calendar month. • Once all 16-day composites are collected, a weighing factor based on the degree of temporal overlap is applied to each input. • In assigning the pixel QA, a worst case scenario is used, whereby the pixel with the lowest quality determines the final pixel QA.
  • product ready for incorporation in MODIS Data Collection 5, scheduled for processing in June 2005. The VI CMG is a seamless 3600x7200 pixel data product with 12 layers, at approximately 544MB per composite period. This is a higher quality climate product useful in time series analyses of earth surface processes. It incorporates a QA (quality analyses) filter scheme that removes lower quality, cloud contaminated pixels in aggregating the 1 km pixels into the 0.05 degree CMG product. It also incorporates a data fill strategy, based on historic data records, to produce a continuous and reliable product for ready entry into biogeochemical, carbon, and growth models. With its very manageable size, the VI CMG can be used for many purposes, some of which are presented here. MOD13 CMG (0.05 DEG) ALGORITHM • • • The VI CMG series is a seamless global 3600x7200 pixel data product with 13 SDS It incorporates a QA filter scheme that removes lower quality pixels in aggregating the 1-km pixels into the 0.05-deg CMG product • If all pixels are cloudy, it uses historical time series values Processing Flow At most 36, 1km pixels depending on latitude. Inverse mapping / projection of input data to geographical Inv Map/Projection coordinates. Reflectances averaged VIs recomputed Dominant QA Standard deviations QA Filter Spatial Calc. >1 high quality pixel retained from QA filter Uses only clean pixels to compute final value Filters pixels that are cloudy, mixed clouds, fill, or missing in input. “Climatology Record” 23 Avg. Composites One clean average year Complete CMG Pixel at 0.05 deg MODISFigure 5: MOD13 CMG Processing flow. VI CMG Data Layers
  • MODIS VI PRODUCTION SCHEME • To ensure the best quality, the MODIS program is periodically updated to integrate the latests advances and user needs. • Upgrades are released as “Collections”. Current is Collection 5 (C5), based on a 16-day composite period (1 image each 16 days) • Terra and Aqua are interleaved, then a combined 8-day product is possible. Considerations required.
  • MODIS VI C5 MAIN CHANGES • Improvement of the Constrained View angle - Maximum Value Composite (CV - MVC) compositing method • • CV-MVC was modified to favor smaller view angles. MVC is used when all input days are cloudy • • Update of the EVI backup algorithm from SAVI to a 2–band EVI • Added the 0.05 deg CMG product series Added Composite day of the year and Pixel reliability output parameters
  • MODIS VI C5 APPLICATIONS Left: NDVI anomaly (Z units) for a given three-month period, computed from the aggregate1-km 16-day MOD13 product (9 yr); Right: corresponding aerosol load extracted from the companion QA metadata
  • MOD13 PERSPECTIVES: TOWARDS C6
  • MODIS VI C6 CONSIDERATIONS • Upgrades for next C6 include increasing time resolution to 8 days • Also desired is using a standard SR parameter (derived from the Atmosphere section) for VI • More studies on BRDF effects and the CV-MVC aggregating approach • Impacts of these changes on the performance of the MODIS VI product for C6 are unknown.
  • MODIS VI C6 STUDIES • We developed test data for studying proposed changes impacts • A revised aggregating algorithm to reduce BRDF effects was proposed (reducing large VZ angles)
  • MODIS VI C6 STUDIES WHY BRDF REVISITING? !"+!# !"*!# !"*!# !")!# !")!# !"(!# !"(!# !"# !",!# !"+!# !"# !",!# 123 !"'!# !"'!# !"&!# !"&!# !"%!# ./01# !"%!# !"$!# 201# !"$!# !"!!# ./01#2.3# 401#2.3# !"!!# -*!# -(!# -&!# -$!# $!# &!# !$%&#'()*%#+,%)-# (!# *!# -*!# -(!# -&!# -$!# $!# &!# !$%&#'()*%#+,%)-# (!# *!# F IGURE B.5: VIs computed from the MODIS ASRVN data set. Left panel shows the VIs VIs computed from the angle-corrected from derived from the actual MODIS geometry. Right panel shows the VIs computed(zenith-the normalized) ASRVN subset. Data correspond to the angle-corrected (zenith-normalized) ASRVN subset. Data correspond to the241-256 site, GSFC validation site, dates 2002 GSFC dates 2002 241-256
  • positive angles (forward scattering direction). The NIR band exhibited the largest bias, MODIS VI C6 STUDIES lastly the red band (0.0232 to 0.0418). In relative units, the NIR varied? WHY BRDF REVISITING ±19% from the with values ranging from 0.2511 to 0.3599, followed by the blue band (0 to 0.0374) and estimated unbiased (zenith) value, while the blue and red bands diverged ±77% and ±29% Should be constant independently of the view angle! respectively. '!" !"+!# !" !"'!# &#" !"*!# !" &!" !")!# !" %#" !"(!# !" !"&$# +,-# !"&!# ./+# !"%$# 012,# !"# !" !"# !",!# !"#$%&#'()$*#+,&$-.&+/$ '#" !"'$# !"#"$%&'$"()*+( !"(!# %!" !"'!# !" $#" !"&!# !" $!" !"%!# ./01# !" !"!$# #" !"$!# 201# !" !"!!# !" !"!!# !"%!# )*!# )$!# )'!# )%!# %!# '!# ,-".(&'/0"()1"/+( $!# *!# ()!" -*!# -(!# -&!# -$!# $!# &!# (!# (#!" (&!" ($!" $!" &!" #!" )!" !$%&#'()*%#+,%)-# 0'&1$*#+,&$-.&+/$ !" *!# F IGURE reflectance bands from the MODIS AS F IGURE 2.4. Effective BRDF effects onDifferential effects on B.5. VIs computed(left figure) BRDF effects on daily MODIS. individual MODISVI bands. Large impact and corresponding sun zenith angles (right figure),derived from the2002ASRVN forgeometry. Right p as derived from actual MODIS actual on VIs. Data correspond to the GSFC validation site, dates the 241-256 angle-corrected (zenith-normalized) ASRVN sub MODIS geometry. Data correspond to the GSFC site, dates 2002 241-256. dates 2002 241-256
  • 0 2000 127 -60 -40 MODIS VI C6 STUDIES BRDF RESULTS -20 0 20 40 60 20 40 60 6000 4000 0 2000 C6 NDVI (x10k) 8000 vz (deg) 60 -60 -40 -20 0 vz (deg) F IGURE B.8: Distribution of NDVI vs. acquisition angle for two different MODIS tiles. Distribution of NDVI vs. acquisition angle for an Amazonian MODIS tile (h12v09). Upper panelsLeft: NDVI C5; to NDVI C5 andC6. Both tiles correspond to NDVI C6. Left column correspond Right: proxy NDVI bottom panels to proxy date 2002-177. correspond to tile h10v05 and right column correspond to tile h12v09. Both tiles correspond to compositing date 2002-177.
  • 0 2000 127 -60 -40 MODIS VI C6 STUDIES BRDF RESULTS -20 0 20 40 60 20 40 60 6000 4000 0 2000 C6 NDVI (x10k) 8000 vz (deg) 60 -60 -40 -20 0 vz (deg) F IGURE B.8: Distribution of NDVI vs. acquisition angle for two different MODIS tiles. Distribution of NDVI vs. acquisition angle for an Amazonian MODIS tile (h12v09). Upper panelsLeft: NDVI C5; to NDVI C5 andC6. Both tiles correspond to NDVI C6. Left column correspond Right: proxy NDVI bottom panels to proxy date 2002-177. correspond to tile h10v05 and right column correspond to tile h12v09. Both tiles correspond to compositing date 2002-177.
  • 2000 125 0 MODIS VI C6 STUDIES BRDF RESULTS -60 -40 -20 0 20 40 60 20 40 60 -40 -20 0 20 40 8000 4000 2000 -40 -20 0 -60 -4 vz (deg) 6000 8000 F IGURE B.6: Distribution of EVI vs. acquisition angle f Upper angle for MODIS tile h10v05. Distribution of EVI vs. acquisition panels show EVI C5 and bottom panels show proxy tile h10v05, located in 2002-177. Left: EVI C5; Right: proxy EVI C6. Compositing date isSE US, and right column shows ti Amazonia. Both tiles correspond to compositing date 2002x10k) 8000 0 -60 60 vz (deg) 6000 C6 EVI (x10k) 2000 0 0 -60 x10k) 6000 8000 6000 4000 C6 EVI (x10k) 6000 4000 2000 C5 EVI (x10k) 8000 vz (deg)
  • 2000 125 0 MODIS VI C6 STUDIES BRDF RESULTS -60 -40 -20 0 20 40 60 20 40 60 -40 -20 0 20 40 8000 4000 2000 -40 -20 0 -60 -4 vz (deg) 6000 8000 F IGURE B.6: Distribution of EVI vs. acquisition angle f Upper angle for MODIS tile h10v05. Distribution of EVI vs. acquisition panels show EVI C5 and bottom panels show proxy tile h10v05, located in 2002-177. Left: EVI C5; Right: proxy EVI C6. Compositing date isSE US, and right column shows ti Amazonia. Both tiles correspond to compositing date 2002x10k) 8000 0 -60 60 vz (deg) 6000 C6 EVI (x10k) 2000 0 0 -60 x10k) 6000 8000 6000 4000 C6 EVI (x10k) 6000 4000 2000 C5 EVI (x10k) 8000 vz (deg)
  • MODIS VI C6 STUDIES FLUX GPP CORRELATION 10 16-day “C6” 40 50 16-day C5 R2 GPP vs EVI 0.70 0.65 4 8-day “C6” 1500 2000 2500 3000 3500 4000 0.55 0 GPP vs EVI C5 fit line R2 EVI C5 (16d) R2 EVI C6 (8d) R2 EVI C6 (16d) 0.60 2 GPP (gC m−2d−1) 0.75 6 0.80 8 0.85 0 MODIS footprint (km2) 20 30 4500 EVI C5 (x10k) F IGURE A.15. As in Figure A.13, but showing a better correlation for proxy 16-day C6 relative 16-day C5. Data Correlation - Kruger National Park, ZA (ZA-Kru) site. from Skukuzq recovers for 16-day “C6” re- composites
  • values, meaning that the growing season was detected a little sooner in C6 than in C5. The MODIS VI C6 STUDIES SEASONALITY EFFECTS length of the growing season consequently showed reduced values for the majority of sites, indicating slightly smaller season lengths for C6 than for C5. The EVI values at the peak of ● ● ● ● Start End Length Peak time EVI (10k−scaled) −600 −400 −200 −0.5 0 0.0 0.5 200 ● ● −1.5 −1.0 16−day period ● 400 1.0 ● ● SOS = Start of Season EOS = End of Season LOS = Length of Season 600 1.5 the season were smaller for C6 than for C5 for the majority of sites (Figure A.26) Peak val Green season F IGURE A.26. Distribution of mean difference values in seasonality parameters as derived from MOD13 C5 and proxy MOD13 C6 (differences were computed as C6 - C5). Parameters included are: date of start of green season (Start), end of season (End), length of season Small increase in SOS (Length), peak of season (Peak time), and EVI value at the peak of season (Peak val). Dates Small Peak values correspond to 16-day units as defined by the MOD13 compositing scheme. reduction on EOS and LOS and on peak EVI correspond to difference in 10k-scaled EVI units.
  • MODIS VI C6 STUDIES SOME CONCLUDING REMARKS • 8-day “C6” EVI showed reduced EVI-GPP correlation. Most likely cause is the reduction in the compositing period (i.e. less probability of clear days). • 16-day “C6” EVI algorithm restored EVI-GPP correlation to C5 levels • Some impact on seasonality parameters: slight to moderate reduction on EOS, LOS and peak EVI value
  • MODIS VI C6 STUDIES SOME CONCLUDING REMARKS • BRDF effects were evidenced on MODIS data using daily ASRVN reference data • BRDF effects act different on individual B, R and NIR bands, hence affecting VIs • EVI and NDVI are affected differently: EVI is more biased and in the opposite direction than NDVI • BRDF effects are also present on the standard MODIS VI product • Proxy C6 results showed reduced VZ angles than current C5, but it is also affected. Lack of good data?
  • MODIS VI C6 STUDIES SOME CONCLUDING REMARKS IMPORTANT • These studies were conducted using a proposed new aggregating algorithm for further reducing BRDF effects • Actual C6 algorithm to be implemented may be different for a number of reasons
  • THANK YOU Questions?