# Presentation: 669711

## BIOPHYSA PHYSICALLY-BASED CONTINUOUS FIELDS ALGORITHMEcosystem, Climate and Carbon Models

- FORREST G. HALL, FRED HUEMMRICH
- Joint Center for Earth Systems Technology (JCET)
- NASA's Goddard Space Flight Center
- Greenbelt, MD 20771
- Derek Peddle
- Lethbridge University
- Alberta, Canada
- David Landis
- SSAI
- Lanham, MD

**Notes:**
This is going to be a very brief update on our team’s work over the past year on our MODIS Project,  to develop a Physically based continuous fields algorithm to provide  vegetation biophysical structural and optical parameters for ecosystem, carbon and water cycling investigations. BIOPHYS is a Canopy radiative transfer based algorithm for inferring vegetation state vectors from multiangle modis observations of surface reflectance.  The BIOPHYS team consists of me, Fred Huemmrich David Landis of Goddard, and Derek Peddle, a former student, then post doc, now of Lethbridge University in Canada.

## 2005 Activities

- 2005 our first full year of activity
- Initial test and development using helicopter MMR (landsat 5 radiometer)
- SNF 1983 data sets, 3 solar zenith angles
- Development of Landsat BIOPHYS
- Processing of selected Landsat scenes
- Biophysical structure and succession
- Preliminary validation
- Initiation of MODIS development and testing

**Notes:**
We have been at this for  just over a year.  Last year at the MODIS team meeting we reported some very preliminary results of the intiial test and development of BIOPHYS using Landsat.  We began with Landsat because first, BIOPHYS is generally applicable to any sensor.  Second, landsat provides us the opportunity to use a high-resolution sensor capable of resolving test sites with detailed measurements.  So far we have used data from a field experiment I co-lead along with Dan Botkin and Bob Macdonald with in 1983 while I was at the Johnson Space Center that was conducted over the superior national forest in Minnesota as well as BOREAS, a much more recent field experiment in the boreal forest which some of you may be more familiar with.

We have developed Landsat BIOPHYS to the point where we have processed multiple Landsat frames as a part of the LEDAPS project, have produced biophysical parameter maps and are beginning to validate them.  Last, but not lease, we have begun the initial development and testing of the MODIS version of BIOPHYS.     

## Satellites

- (spectral, angular, temporal)
- si (Ωi)

- CANOPY REFLECTANCE MODEL (LUTS)
- {si}= (LOP,BOP,Cg,LAIC,LAIb,CC)
- Satellite Measurement (L2)
- {si±∆ si}i i bands, angles, dates
- Table Lookup
- {LOP,BOP,Cg,LAIC,LAIb,CC }k  {si±∆ si }i
- Compute Parameter Statistics
- <{LOP,BOP, Cg ,LAIC,LAIb, CC}>
- Ecosystem Parameters
- , Fpar, gc, Zo, Pv,NPv=
- F(<LOP,BOP, Cg,LAIC,LAIb, CC>)

- Biophys Framework

- Numerical, Non-analytic

- BIOPHYS CONCEPT

**Notes:**
The BIOPHYS concept and framework is the same, whether we are working with the Landsat, MODIS, MISR or what ever sensor.  We use canopy radiative transfer models to generate and place into a LUT the BRDF as a function of the biophysical parameters (e.g. canopy structural and optical properties, stand structural properties and background reflectance.  For a set of satellite measurements, which can be for multiple view and illumination geometries, we use a retrieval algorithm to find those values of biophysical parameters whose computed reflectance falls within the measured reflectance value plus or minus a prespecified tolerance.  The tolerance may be set to reflect measurement errors arising from sensor signal to noise, calibration, atmospheric correction and so forth.  For the majority of cases, we find that multiple solutions are retrieved, that is those solutions which lie in the noise “neighborhood” of the measurement.  We have chosen to use the average of these solutions to represent the “correct” solution, and the standard deviation of those solutions to represent the precision of the estimates, for example, leaf and branch optical properties, crown geometry, leaf and branch area index, and canopy cover.

What we retrieve in the original pass are only the parameters related to reflectance through the radiative transfer models.  These parameters are in turn used in a second model to compute  higher level parameters such as albedo, fraction of absorbed photosynthetically active radiation, canopy conductance, roughness length etc.

## 1

- Band 1

- Band 2

- Band 3

**Canopy Reflectance Models**

- Zap!

- Spectral
- Space

- BIOPHYS uses CRMs to
- map spectral space
- Into biooptical
- parameter space.

- si (Ωi, Bi) =(LOP, BOP,Cg, h/w, LAIC, LAIb, CC)
- si (Ωi, Bi) = Spectral Space
- Ωi = view and illumination geometry
- Biooptical parameters  Bi
- 	LOP&BOP= leaf&background optical properties
- Cg = Canopy geometry
- LAIC = Crown leaf area index
- LAIb = Branch leaf area index
- CC = fractional crown cover

## Then...

- Biophysical
- Parameter 1

- Biophysical Parameter 2

- Biophysical Parameter3

- Carbon/Water/Energy
- Transport Relations
- Map Biooptical Parameters
- Into model Parameters, for example

- Model Parameters
- canopy albedo
- Fpar = Fraction of absorbed
-          photosynthetically active
-          radiation
- gc = bulk canopy conductance
- Pv,NPv Fraction of photosynthetically
-        and non-photosynthetically
-        active vegetation
- LAI = Canopy leaf area index

## BIOPHYS Landsat Processing Program:Creating the “Averaged” Table

**BIOPHYS Landsat Processing Program:****Creating the “Averaged” Table**

- SELECT avg(green_lai),stddev(green_lai),
- avg(branch_lai),stddev(branch_lai),
- avg(fract_canopy_cover),stddev(fract_canopy_cover),
- avg(crown_h_w_ratio),stddev(crown_h_w_ratio)
- FROM boreal_gs_test2 WHERE
- (landsat_b2_refl_sza35 BETWEEN 1 AND 100) AND
- (landsat_b3_refl_sza35 BETWEEN 201 AND 300) AND
- (landsat_b4_refl_sza35 BETWEEN 1001 AND 1100);

**Sample SQL Query**

**New “Averaged” Reflectance LUT ASCII File**

_**Create the Original LUT**_
- The original data was created using the GeoSail model. The biophysical parameters (Green LAI, Branch LAI, Fractional Canopy Cover, & Crown H/W Ratio) were varied sequentially as input to the model. A reflectance for various SZA’s (30 to 70, in 5 steps) and Landsat Bands (1 to 4) was then output from the model.

_**Create the “Averaged” LUT**_
- The original MySQL table was then processed using a C program that runs a series of SQL commands and saves the results to a data file. These commands used SQL to create averages and standard deviations for the biophysical parameters where the reflectance for bands 2, 3, and 4 were all within preset binned values (bin size was 100, which is a 1% tolerance). It also creates “%counts” for the 4 cover types (aspen, spruce, ojp, yjp).This processing took about 48 hours, but only had to be done once.

_**Keep the “Averaged” LUT As an ASCII File**_
- The resulting ASCII data file (with 15,000 records) was then used by the image-processing program (next slide).

_**Load the Original LUT into MySQL**_
- The GeoSail data file (with 2.5 million records) was loaded into a MySQL table. Then all values were turned into 2-byte integers to match the Landsat file format: reflectances x 10,000 and  4 biophysical parameters x 100.

**Notes:**
In our original tests we have used the Geosail model developed by Fred to compute the look up tables with millions of records.  We have previously validated this model over both the superior national forest and boreas and it works very well.  We used an existing retrieval algorithm MySQL to retrieve the solutions.  From these we generate a new LUT with just a few thousand records consisting of the average of the non-unique solutions and the error statistics.  The LUT of averages and MySQL powers the operational algorithm which takes just a few seconds to process an entire Landsat frame.  The really nice thing about this set up is that the raw LUT provides us the ability, prior to any processing to investigate the accuracy of the retrievals as a function of the view and illumination geometry.  In other words, we know before hand just how well the algorithm will work under various circumstances.  Thus, in addition to the biophysical parameters, we also retrieve the error structure of the estimates.

## Initial Test Landsat (MMR), vis, nir  0.01)16 Black Spruce sites3 solar angles (3 dates)1983 Superior National Forest, Minnesota

**Notes:**
This table shows the site characteristics of 16 black spruce sites we used to test Landsat BIOPHYS.  The tables contains the ground measurements we made of several of the parameters retrieved by BIOPHYS    LAI values range from very low, 0.4 to a high of 3.6.  As Steve Running once said to me, this is a poor excuse for a forest.

## SNF Landsat Analyses: Results

- Landsat data acquired at multiple solar view angles improved retrieval precision.

- Mean of non-unique solutions appear to converge on actual mean

**Notes:**
Here is a quick summary of the results.  Results I presented last year.  They show first how information gained from the BRDF really increases the accuracy of the retrievals.  This plot shows how the precision of the estimates decreases as additional landsat acquistions are added to the retrieval.  The black dots and red dots are the number of retrieved records and associated standard deviation of the retrievals with one acquisition, the blue dots and red circles with two acquisitions and the green with three.  They also show that the mean of the non-unique solutions in the measurement noise neighborhood  appear to converge to the actual mean.  Finally, even with a single landsat acquisition, the retrieved LAI agrees well with the measured, and note that the precision of the retrievals is about the same as the precision of the ground measurements. 

## BIOPHYS Landsat Processing Program: Using the “Averaged” LUT, Logic-flow Diagram

**BIOPHYS Landsat Processing Program:**** Using the “Averaged” LUT, Logic-flow Diagram**

**New “Averaged” Reflectance LUT ASCII file**

- repeat for each point in the 4 lines

- repeat for each line in the 3 bands

**Start**

**End**

**Output Files**

- Read in the file name and the SZA. Extract all records from the “averaged” ASCII LUT for that SZA (about 1600 records) and store them in a 4D array “the_table[r2][r3][r4][c]” within the program (the 3 major indexes are the 3 refl/100). This is the only time the program accesses the LUT file.

- Open the Landsat RAW files (bands 2, 3, & 4) and the QA file (if present).

- Open 13 output files to write the results to: AV and STDEV for each of the 4 biophysical parameters, NDVI, and 4 %cover files (aspen, spruce, ojp, yjp).

- Read line Y of QA & band 2,3,4.

- If no QA problem, use point X of line Y of each band (binned into values of 100, then divided by 100) as an index number into the stored array “the_table[r2][r3][r4]” and return the 1 unique array record that fits that combination of reflectances in those bands.

- Store the 12 values from the array (AV and STDEV for the 4 biophysical parameters, 4 %counts) into the same point in the 12 output lines. Computer NDVI.

- When all the values for this line Y are processed, write each of the 13 output lines to its open file.

- Close all 13 output files, and the 3 input files. This processing takes about 60 seconds total.

**Landsat Image Files in RAW binary format**

**crown_green_lai_av**
**RAW binary file**

**crown_green_lai_sd**
**RAW binary file**

**crown_branch_lai_av**
**RAW binary file**

**crown_branch_lai_sd**
**RAW binary file**

**fract_canopy_cover_av**
**RAW binary file**

**fract_canopy_cover_sd**
**RAW binary file**

**crown_h_w_ratio_av**
**RAW binary file**

**crown_h_w_ratio_sd**
**RAW binary file**

**%cover Aspen**
**RAW binary file**

**%cover Spruce**
**RAW binary file**

**%cover OJP**
**RAW binary file**

**%cover YJP**
**RAW binary file**

**Computed NDVI**
**RAW binary file**

- Check the QA bits for point X in line Y. If there is a problem, fill the LUT return array values with “-99”.

**Notes:**
With these encouraging results we then proceeded to build the BIOPHYS Landsat full frame processing algorithm.  I won’t drag you though this other than to say that the actual processing algorithm uses the averaged solutions in the LUT and prints these values and their standard deviations in an output image file.

## BIOPHYS Landsat Output:Canopy Green LAI Average

**BIOPHYS Landsat Output:****Canopy Green LAI Average**

- BOREAS Region (p37 r22)
- Aug 12, 2001, SZA 45°

**Notes:**
This image is the result.  This particular one is of the canopy green LAI.  Not the total LAI but the LAI per canopy.  The higher values in red are associated with aspen stands.  The very low values generally with fens and bogs which are covered mostly in shrubs.

## BIOPHYS Landsat Output:Canopy Green LAI St-Dev

**BIOPHYS Landsat Output:****Canopy Green LAI St-Dev**

- BOREAS Region (p37 r22)
- Aug 12, 2001, SZA 45°

## BIOPHYS Landsat Output:Fractional Canopy Cover Average (%)

**BIOPHYS Landsat Output:****Fractional Canopy Cover Average (%)**

- BOREAS Region (p37 r22)
- Aug 12, 2001, SZA 45°

## BIOPHYS Landsat Output:Canopy Branch LAI Average

**BIOPHYS Landsat Output:****Canopy Branch LAI Average**

- BOREAS Region (p37 r22)
- Aug 12, 2001, SZA 45°

## BIOPHYS Landsat Output:Aspen Cover (%)

**BIOPHYS Landsat Output:****Aspen Cover (%)**

- Note: this is actually the percent of retrieved solutions from the LUT that corresponded to this cover type.

- BOREAS Region (p37 r22)
- Aug 12, 2001, SZA 45°

## LEDAPS

- Activity

- Landsat 5
- Uncorrected

- Landsat 5
- Corrected

- Landsat 7
- Corrected

## BIOPHYS AND A BOREALCHRONOSEQUENCE

## INITIAL MODIS INVESTIGATIONS

- MODIS pixels extracted from acquisitions for 16 day period during August 2001 over BOREAS Black Spruce Site
- Atmospherically corrected using MODIS aerosol product and 6s.
**RESULTS**
- MODIS provided very limited sample of BRDF
- Retrieved parameter values sensitive to view & illumination geometry
- Number of non-unique solutions decreased rapidly with number of angles used in retrievals

## What’s Next

- Complete exploratory work
- Finalize RT Model (GOMS, GORT ...) selection.
- Build the LUTs
- Run MODIS BIOPHYS Algorithm over selected regions
- Evaluate  MODIS Products and Iterate
- PRODUCE PROVISIONAL DATA SETS WITH BIOPHYS OVER SELECTED STUDY REGIONS.
- PLACE PROVISIONAL DATA SETS ONLINE; INITIATE USER EVALUATION.