October 28, 2004C Pools from EOS MISR & MODIS1 Carbon Pools in Desert Grasslands from EOS: First Meeting Jornada Experimental Range, Las Cruces, NM, October.

October 28, 2004 C Pools from EOS MISR & MODIS 1

Carbon Pools in Desert Grasslands from EOS: First MeetingJornada Experimental Range, Las Cruces, NM, October 28, 2004

8:30 a.m. Welcome, Statement of Objectives

8:45 a.m. Summary of Activities: Milestones & Results

9:45 a.m. Perspectives on RS for Ecological Modeling

10:15 a.m. Discussion on Goals and Approaches

10:45 a.m. Coffee

11:00 a.m. Discussion on Goals and Approaches (continued)

12:00 a.m. Lunch

2:00 p.m. Remote Sensing Techniques: MISR

4:00 p.m. ...or before if possible: End.

Agenda


The overall objective stated in the proposal was to validate a new approach to parameterizing the CENTURY model for the arid and semiarid grasslands of the southwestern US (and beyond) by exploiting the unique information content of multi-angular remote sensing data from the EOS MISR and MODIS sensors.

Overall Objective


Our new overall objective is to validate a new approach to deriving parameters for driving biogeochemical cycling models for the arid and semiarid grasslands of the southwestern US (and beyond) by exploiting the unique information content of multi-angular remote sensing data from the EOS MISR and MODIS sensors.

Overall Objective


• We want to combine MISR and MODIS data to exploit: 1. physical &| semi-empirical measures from the RPV and Li-Ross models; 2. structural measures from CR models and ANIX; and 3. spectral measures of canopy vigor (SVIs). These are highly complementary.

• We are not currently able to include process modeling as a result of the recent budget changes -- but we will strengthen the validation aspects of our work and continue to seek funding for the modeling.

Approach


• to assess the ability of kernel-driven and EMRPV BRDF models to yield parameters useful in precision community type mapping

• to assess the ability of these models to yield information useful in determining bare soil:grass:shrub proportions

• to assess the ability of geometric-optical models and multi-angle metrics to yield useful canopy structure information.

Specific Objectives


And…

• to evaluate the minimum acquisition period required for the construction of adequate multi-angle data sets for model inversions over SW rangelands. This is predicated on the need to augment the angular sampling of either sensor as well as increase the number of observations.

Specific Objectives


N.B. these objectives are no longer immediate:

• to create a map of above and belowground C pools for arid shrub-grasslands of S. NM, S.E. AZ and S.W. TX as predicted by CENTURY.

• to assess the reduction in uncertainty in C pool estimates using an areally-weighted species-specific parameterization of CENTURY based on well-defined vegetation community types and the proportions of grasses, shrubs and bare soil.

Specific Objectives


Summary of Activities: Milestones & Preliminary Results (Chopping/Su)

Timeline of Main Events

Dec '03: Proposal accepted.Mar '04: Search for post-doc initiated.Jun '04: Funds received.Jun '04: Dr. Lihong Su selected from a field of 11.Jul '04: Lihong appointed as Research Associate.Jul '04: Data acquisition/programming…Aug '04: ARS funding now deemed infeasible.Oct '04:Still no funds, so no CENTURY post-doc.

We have to revert back to the original remote sensing scope suggested by the panel (OK’d by GG on 10/13).



EOS Data Processing: Technical Overview

HDF-EOS MODIS Observations (MOD09-- 250 m ISIN)

HDF-EOS MISR Cloud Mask (RCCM -- 1,100 m)

HDF-EOS MISR Aerosols(17,600 m)

HDF-EOS MISR Angles (GEOM -- 17,600 m)

HDF-EOS MISR Observations(MI1B2T, includes QC -- 275 m )

HDF-EOS MODIS Angles(MODPTQKM -- 1,000 m ISIN)

HDF-EOS MODIS QC(MODGST -- 1,000 m ISIN)

Physical Structure (FVC, radius/height, gap, fiPAR, LAI)

Screened surface bi-directional reflectance estimates accumulated over a 9-day period. Max. # observations possible for RED wavelength = 27 (9 x MODIS/Terra + 9 x MISR and eventually + 9 from MODIS/Aqua).Plus other MISR channels at nadir.

Empirical Surface Metrics(iso, geo, vol; k, ; ANIX)

EMRPV, Li-Ross, ANIX,

NDAX

GORT/SGM/other non-linear

model

1st level classification (Community Types [on soils])

2nd level classification (condition)

C Pools = AGC + BGC

Comm.Type subdivisions

PROCESSING


Merge the observations from the 9 cameras for one orbit (IDL)

Estimate surface reflectance from TOA radiance (IDL/C/6S)

HDF ==> TOC reflectance for working region (ISIN ==> UTM w/MODIS Reprojection Tool)HDF-EOS MODIS Observations

(MOD09-- 250 m ISIN)









Collate observations, angles, and screen by QC on an orbit (IDL: -- WIP)

Accumulate observations from multiple orbits (9 days) (IDL: -- WIP)

HDF ==> TOA radiance, mask for cloud (IDL: SOM ==> UTM)

Combine MISR and MODIS data for each 9-day period


Merge 9 cameras observations on one orbit (IDL)

Estimate surface reflectance from TOA radiance (IDL, C, Fortran: 6S)

ISIN ==> UTM for working region(MODIS Reprojection Tool)HDF-EOS MODIS Observations

(MOD09-- 250 m ISIN)









Collate observations, angles, and screen them by QC on an orbit (IDL: -- WIP)

Accumulate observations on multiple orbits (9 days) (IDL: -- WIP)

Obtain TOA radiance after cloud mask& navigate (IDL: SOM ==> UTM)

Collect multiple orbits during a period (C)

Combine MISR & MODIS during a period( C -WIP)




Lihong -- anything to add here?

Note: last 2 slides are the same material organized / colored differently!


Summary of Activities: Milestones & Preliminary Results (Chopping/Su)BRDF / CR Model Inversion (ongoing work)

We have been investigating the potential for using a model based on geometric-optics (GO) to retrieve information on shrub cover, density, size and shape, initially using 4 x 631nm (red) multi-angle reflectance images @25m from CHRIS on Proba. Principles:

_ _ 2r2r

__

__ __

__||

hh||

||

2b2b

||__

__



How does a GO model respond to very heterogeneous canopies?

-- are GO principles violated in this case?-- do GO models operate on mean parameter values?



Can GO models work for very heterogeneous canopies which have a highly variable and bright understorey?



Can GO models work for very heterogeneous canopies which have a highly variable and bright understorey, on different soils?



GO models have been demonstrated as useful tools for forested environments but are more challenging in arid environments: here, the magnitude and anisotropy of the remotely-sensed signal is dominated by the "background" comprised of varying proportions of exposed soil, grasses, litter and forbs.

How to obtain the background BRDF in order to isolate the effects of the larger canopy elements?



The first approach taken was by setting the large plant parameters estimated from high resolution imagery and inverting the SGM for the background (represented by the 4-parameter Walthall model). This was done for a wide range of conditions and a linear relation between near-nadir 631 nm reflectance and the four Walthall model coefficients was obtained. This relation was used on inverting the SGM for large canopy parameters over the entire imaged area (September 28, 2003 scene).



25 m CHRIS FCC @ 24° Back (748 nm, 631 nm, 530 nm)

25 m CHRIS 631 nm reflectance (56°Fwd, 40°Back, 24°Back)

A look at some CHRIS imagery from 08/22/03 (SZA=26°



The 25 m CHRIS 631

nm reflectance @4

viewing angles Note:

low SZA for 08/22/03.

0°

18 0 °

90 °

December 28 2003

August 22 2003

30 June 2003 12/28/03

08 /22/03

06 /30/03

60

4 0

2 0

0

Zenith angle (°)

3 0 °

6 0 °

150°

120°

21 0°

24 0°

300°

330°



IKONOS pan image 05/23/01 over the transition zone showing the 7 points selected for contrasting over- and understorey configurations



IKONOS chips showing locations selected to obtain Walthall:1





















Another lookat the IKONOS pan image 05/23/01 over the transition zone



25 m CHRIS 631 nm reflectance @ nadir viewing

0.20

0.25

0.30

0.35

0.40

-60-40-200 204060View Zenith Angle (°)

BRF (631 nm)

#6#7#3#5#4#2#1

ground (transition)

Walthall model scaled with near-nadir brightness (SZA=26° VZA=24° RAA=33°) for the seven test plots (solid lines). Ground-measured sand BRDF at the transition site (dotted line).

Not sure if the scaling is accurate…



Inversion results for the points selected to obtain Walthall: good.

a measured by counting 1 m pixels in the May 23, 2001 IKONOS panchromatic image.b kG, kC, kZ+t are the fractions of viewed and sunlit background and crown and shaded components in the IFOV, respectively.

Plot#Shrub Cover (%)

Measureda ModeledkGb kCb KZ+tb

1 5.9 6.1 0.93 0.03 0.04

2 5.5 4.8 0.94 0.02 0.04

3 10.9 11.6 0.81 0.06 0.14

4 5.1 8.6 0.88 0.04 0.08

5 12.2 8.3 0.87 0.04 0.09

6 13.0 12.8 0.77 0.06 0.16

7 15.1 11.9 0.80 0.06 0.14



Results (vs. > 100 random point samples from QuickBird and IKONOS): ….poor! Potential Reasons:

1. The scale of the observation: 25 m may be too small -- it may violate the principles of GO models which assume a Poisson distribution.

2. Walthall model terms are not orthogonal -- this makes it difficult to obtain consistent relations with nadir brightness by setting large canopy parameters and inverting for Walthall (also: too many coefficients?).

3. The SGM may be inadequate (Goel: 35% error in k?)4. The CHRIS data or processing may be flawed.



Soon we will be able to test the last one (CHRIS data flawed or inadequate) by using MISR and MODIS data. The # and range of the angular sampling are better.

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

QuickTime™ and aYUV420 codec decompressor




QuickTime™ and aPhoto - JPEG decompressor


Field photograph @ nadir (January 2004)


Perspectives on RS for Ecological Modeling (Peters)

Perspectives on RS for Ecological Modeling (Peters); Perspectives on RS for Ecological Modeling (Peters);Perspectives on RS for Ecological Modeling (Peters); Perspectives on RS for Ecological Modeling (Peters);Perspectives on RS for Ecological Modeling (Peters); Perspectives on RS for Ecological Modeling (Peters);Perspectives on RS for Ecological Modeling (Peters); Perspectives on RS for Ecological Modeling (Peters);Perspectives on RS for Ecological Modeling (Peters); Perspectives on RS for Ecological Modeling (Peters);Perspectives on RS for Ecological Modeling (Peters); Perspectives on RS for Ecological Modeling (Peters);


Discussion on Goals and Approaches

temporal element (when to sample? transitions/stages of maturity?)

what to measure, with respect to our goal?

hard / soft, or hard + soft classification?

spatial scales and BRDF / CR modeling

solutions to CR modeling background problems

validation plan


Carbon Pools in Desert Grasslands from EOS-- First Meeting --

Jornada Experimental Range, Las Cruces, NMOctober 28, 2004



Canopy Reflectance Modeling (with CHRIS/Proba)

HDF-EOS MODIS Observations (MOD09-- 250 m ISIN)

HDF-EOS MISR Cloud Mask (RCCM -- 17600 m)

HDF-EOS MISR Aerosols(xxx -- 17600 m)

HDF-EOS MISR Angles (GEOM -- 17600 m)


HDF-EOS MODIS Angles(MODPTQKM -- 1000 m ISIN)

HDF-EOS MODIS QC(MODxxx -- 1000 m ISIN)


• SAVED: We are taking a mechanistic approach to estimating spatial distributions of C pools in the arid and semi-arid SW using multi-angle data from MISR & MODIS.

• Our approach is based on the premise that there is unique information in the BRDF as sampled by these EOS instruments which complements spectral measures to aid mapping objectives.

• We want to combine MISR and MODIS data to exploit: 1. semi-empirical measures from RPV and Li-Ross models; 2. structural measures from CR models and ANIX; and 3. spectral measures of canopy vigor (SVIs). These are highly complementary.

• We are not currently able to include process modeling as a result of the recent budget changes -- we will strengthen the validation aspects of our work and continue to seek funding for the modeling.

Welcome, Statement of Objectives

October 28, 2004C Pools from EOS MISR & MODIS1 Carbon Pools in Desert Grasslands from EOS: First Meeting Jornada Experimental Range, Las Cruces, NM, October.

Documents

c pools

eos misr modis3

eos misr modis6

eos misr modis5

eos misr modis2

eos misr modis7

eos misr modis4

eos misr modis1 carbon