GCOM-C Biomass product
GCOM-C Vegetation Roughness Index product
Algorithm Theoretical Basis Document

Version 2.0

Koji Kajiwara, Yusaku Ono, Yoshiaki Honda
GCOM PI#103

2015.03.31

1. Algorithm Outline
This document is the Algorithm Theoretical Basis Document (ATBD) for
GCOM-C/SGLI Above Ground Biomass (AGB) product and Vegetation Roughness
Index (VRI) product. It's explained by this single document about two products
because the two product generation process stage is just different in last productgeneration processing, and the most part of the process to source data is common.

1.1 Above Ground Biomass (AGB)
AGB is estimated using two sets of the red and near-infrared channel data observed
from nadir and slant viewing direction by SGLI sensor.
The empirical estimation equation using the parameters which are obtained by achange of category area ratio, that are canopy in a sunny place, shadowed canopy,forest floor in a sunny place, shadowed forest floor, trunk in a sunny place andshadowed trunk, in the sensor IFOV.

These ratios define BRF and changed bysun-target-sensor geometry on SGLI observation condition.

1.2 Vegetation Roughness Index (VRI)
VRI is the index that expresses the degree of the unevenness changes in spatial
distribution of canopy density of vegetation. When VRI has large value, the target
vegetated area has large magnitude of unevenness. VRI also calculated from two setsof the red and near-infrared channel data observed from nadir and slant viewing
direction by SGLI sensor.
In the AGB and VRI product generation process, the nadir and slant view of thepixels in the corresponding ground position is used in simultaneously. Since SGLI
can observe nadir and slant direction in along track, cloud / aerosol screening will
be applied to the region of spatially continuous state in atmospheric condition.
However, since the use of data from different observation path when using MODIS
like sensor to verify the performance of the algorithm, the number of pixels after thescreening is extremely low in spatially continuous region, the number of available
pixels is also very less to. Currently, the performance test results have somelimitations.

2. Theoretical Description
2.1 Above Ground Biomass: AGBIO and VRI
2.1.1 Basic concept
Figure 1 shows schematic diagram of the transition of the reflectance in the case ofobserving the same vegetation region from nadir and slant direction by the red andnear-infrared channel. Change in the category area ratio of the field of view due tothe canopy structure, causes a change indicated here. However transition directionin the two-dimensional space is different by canopy structure.

Figure 1. Transition of the red-NIR reflectance on nadir and off- nadir observation.

Here, P1 is the norm of vector connecting the position of nadir observationreflectance and the origin. P2 is the norm of vector indicating the reflectancetransitions along with sensor view angle change. P3 shows the angle between red
axis and P2. Parameter P1 relates to total brightness including shadow effect in thefield of view. P2 has close relation to reflectance change caused by canopy structure
and P3 indicates apparent vegetation cover ratio, which has similar meaning ofNDVI. Using these parameter, biomass can be estimated the following empiricalequation.

# P2 & 3
AGB = C⋅ %+1( ⋅ P3
$ P1 'C is determined for each canopy type based on the ground measurement, which is a
proportionality constant that depends on the type of vegetation. However, obtained
value of the same forest target P1, P2, P3 is varied with the solar zenith angle and€relative azimuth angle at the time of satellite observations. Therefore, AGB
estimation is performed with obtained parameters (P1, P2, P3) from simulated
Bi-directional Reflectance Factor (BRF) on fixed Sun-Target-Sensor-Geometry(STSG).
2.1.2 Bi-Directional Reflectance SimulatorThe simulator BiRS (Bi-Directional Reflectance Simulator) for estimating thereflectance to be used in this manner to calculate P1, P2, P3 was developed. BiRS
uses Liner Mixture Model (LMM) as BRF model. That, the sensor observed BRF
that is assumed could be expressed by the following equation.
!

𝐵𝑅𝐹! 𝜃! , 𝜑! , 𝜃! , 𝜑! =𝐴! 𝜃! , 𝜑! , 𝜃! , 𝜑! ∙ 𝜌!,!
!!!

𝜃! ∶   Solar  Zenith  Angle

𝐴  :  Category  cover  ratio  𝜑!   :  Solar  Azimuth  Angle𝜌  :  Apparent  category  reflectance  𝜃!   :  Sensor  Zenith  Angle

𝜆  :  Wave  length  𝜑!   :  Sensor  Azimuth  Angle𝑖  :  Category  number  In above equation, the parameter A indicated category cover ratio in IFOV. In thi
context, "category" is meant a ground configuration having different apparent
spectral reflectance, such as illuminated canopy, shadowed canopy, illuminatedstem or branch, shadowed stem or branch, illuminated soil, shadowed soil. Category
area ratio varies with the viewing direction of the sensor, and its manner of change
depends on the structure of the forest.
Category reflectance to be provided to the BiRS can be estimated using the LMM as
shown from the BRF and the obtained category area ratio polygonal observed in fieldmeasurements shown in Figure 2.

Figure 3 shows schematic diagram of BiRS. While BiRS has been developed by Onoet al., in 2010, it has been modified to use canopy structure model in this project.
Modified BiRS consists of following two parts: 1) Forest structure modeling partwhich calculates the category area ratio in IFOV by performing the ray tracing witha crown shape. 2) Reflectance simulator part calculates reflectance as the BRF frommixed category reflectance with Linear Mixture Model.

Figure 2. Obtain apparent category reflectance from in-situ observation.

Figure 3. Schematic diagram of BiRS.

Forest structure model create virtual forest characterized by following parametersof mean individual tree distance, trees position, average height, average crowndiameter, average crown depth, average diameter at breast height, that are to forma normal distribution. Against the virtual forest that has been generated here, theratio of category area on any of STSGs which are obtained by ray tracing andcalculating BRF by giving a set of category reflectance.

Figure 4. Schematic diagram for AGB estimation.

2.1.2 Canopy structure model
In AGB estimation schemes, BiRS to use the canopy model in order to convert the SGLI
observed value to a fixed geometry. The canopy model used following parameter , tree
the height,

crown diameter , crown depth and the tree standing position in order toform a virtual forest. Canopy consists of individual branches surrounding the trunk,crown shape is formed by giving an overall shape as their aggregates. Figure 5 shows anoverview of this Canopy model to be used in the BiRS. The other parameters mentioned
above, the number of branches of the branch layer, a tree canopy structure isdetermined by specifying the distribution of the angle to the horizontal plane of thebranch layer. For each branch it is possible to specify the details of leaf, and also eachbranch layer is determined position in the vertical direction of each branch by normal
randomize, single tree as shown in Figure 5 of shape is completed. Finally it is possible

to configure the virtual forest arbitrary shape as shown in Figure 6 by giving theparameters such as the position of the trees.

Figure 5. Canopy model used for modified BiRS.

Figure 6. Examples of virtual forest formed by using the canopy model

2.1.3 Performance of modified BiRS
In order to test the performance of modified BiRS using canopy structure model,
utilizing in-situ measured tree parameter and MODIS observing STSG to thesimulator. Figure 7 shows tree parameter obtained from point cloud obtained byradio controlled helicopter (UAV) equipped LIDAR system. Tree position, crowndiameter, tree height of each tree was used to form the virtual forest for simulatesparticular reflectance on MODIS observing geometry. Figure 8 shows a virtual
forest that was constructed using the in-situ measured parameters.

Figure 7. Tree parameter obtained by UAV equipped LIDAR system

Figure 8. Virtual forest constructed using the in-situ parameters
This modeled forest was used to simulate the reflectance of MODIS. The result of thesimulation is shown in Figure 9. A portion surrounded by a pink square is the result ofthe term that has conducted a field observation. Change in reflectance of the apparent
reflectance by STSG can be seen that are well reproduced.

Figure 9. Simulation result of modified BiRS for MODIS observed reflectance.

2.1.4 Performance of AGB estimation methodology using in-situ data
As an example, time series biomass estimation results for larch forest are shown infigure 10. These biomass values are estimated by using Digital Surface Model (DSM) of
the canopy, and BRF that were acquired from UAV observation. Four blue dots indicate
the time series values, within 6 years, on Yatsugatake larch forest test site locatedYamanashi prefecture. Red dot indicates the results for another larch forest test site
located near mt. Fuji. Based on this result, it can be said that even in different forest, iftree type is the same, proposed method is valid to estimate Above Ground Biomass.

Figure 10. AGB estimation result using in-situ data

2.1.5 Processing flow
Figure 11 shows the processing flow of AGBIO product. AGBIO uses 1km atmosphericcorrected products of following channels, VN8(nadir observed red), VN11(nadirobserved NIR), P1(slant observed red), P2(slant observed NIR), as the source data.
Since algorithm performance test should be used MODIS sensor data before GCOM-C1
launch, green dotted line surrounded part is added to system. This part is for selection
valid nadir/slant sensor view conditions. For the SGLI sensor data, after checking
aerosol/cloud flag, channel reflectance product data will directly input to reflectanceconversion process. The process for reflectance conversion, consist of tree parts, 1)
referencing land cover data that has information of vegetation type for particularlocation, select the lookup table for the corresponding vegetation type. 2) Obtaining
Sensor geometry from ancillary data of SGLI and calculate STSG using the DSM/Slopedata of target area. 3) Searching lookup table to obtain a set of reflectance of eachchannel.
After reflectance conversion process, VRI is calculated using unit C value (=1). Finally,
AGB is calculated with applying corresponded C value of the vegetation type.

Figure 11. Processing flow of AGBIO product

Since AGBIO algorithm uses the active vegetation canopy reflectance, it cannot
estimate the winter season’s data. Because of this reason, NDVI mask process will be
performed before estimation. Figure 12 indicates the QA flag of AGBIO product. WhenNDVI low flag is set, AGBIO and VRI algorithm will not be performed and set the 0value to the flag for algorithm performed (LSB of QA flag).

Figure12. QA flag definition for AGBIO