GCOM-C/SGLI
Leaf Area Index (LAI)/ fraction of Absorbed Photosynthetically Active
Radiation (fAPAR) product
Algorithm Theoretical Basis Document (ATBD)
22 June, 2020 (v2.001)

Kobayashi, T., Ono, Y., Kobayashi, H., Yang, W.
JAXA/EORC,

1. Introduction
This is the Algorithm Theoretical Basis Document (ATBD) described for the algorithm ofGCOM-C LAI/FAPAR product (Algorithm ID: T2B).
Leaf Area Index (LAI) is defined as one half of the total green leaf area per unit groundsurface area. GCOM-C LAI is estimated differently for overstory and understory vegetation
because LAI for overstory vegetation and understory vegetation (like trees and grasses)
differs in seasonal variation and ecosystem function. The unit of the LAI is m2/m2. The
fraction of Absorbed Photosynthetically Active Radiation (fAPAR) is defined as theproportion of the effectively absorbed solar radiation by green leaves in thephotosynthetically active wavelengths (the spectral region from 400 to 700 nm). GCOM-C
fAPAR corresponds to the instantaneous fAPAR at the observation time of GCOM-Csatellite.

2. Algorithm of LAI/fAPAR estimation
2.1 Theoretical description
LAI/fAPAR are estimated based on the Look-Up Tables (LUTs) showing therelationships between the SGLI multi-angle atmospherically-corrected surface
reflectance data and the LAI or fAPAR [1]. The LUTs are produced using a radiativetransfer simulator, the Forest Light Environmental Simulator (FLiES) [2], to fit with thefield-observed reference data collected from literatures. The FLiES simulates radiativetransfers in the forests and grasslands based on the Monte Carlo ray tracing method.
LAI/fAPAR were estimated by the multi-angle SGLI data to optimize the LUTs. Figure
1 shows the processing flow.

Figure 1. Processing flow
2.2 LUTs
The LUTs are produced for 7 kinds of land cover types: open needle-leaved forest,
closed needle-leaved forest, open broadleaved forest, closed broadleaved forest, tropicalbroadleaved forest, paddy field and grassland/shrubland/cropland. The
reflectance/transmittance of canopy leaves and understory vegetation, the reflectance ofstems and soil, and virtual forest landscape scenarios are needed as the input parameters
for the FLiES. The input parameters are collected from literatures [3-8]. The virtual forest
landscape scenarios, which consist of the total number of trees, the geometric shapes oftrees, the positions of trees, and the heights of trees, are generated by an empiricalmodel [9].
The multi-angle SGLI data needs to agree with the top of canopy bidirectionalreflectance factor simulated by FLiES using input parameters and virtual forest
landscape scenarios. The input parameters and forest landscape scenarios are selected sothat the top of canopy bidirectional reflectance factor can agree with the multi-angle
SGLI data. The consistency between them are investigated at several sites where thefield-observed reference LAI data are available. The field-observed reference LAI data
are collected from literatures. Each LUT is produced for each sun and satellite
geometries. Figure 2 shows the flow for creating LUTs.

Figure 2. Flow for creating LUT
2.3 LAI/fAPAR retrieval
LAI/fAPAR for overstory vegetation and Normalized Difference Vegetation Index(NDVI) for understory vegetation in forests are estimated using LUTs. NDVI forunderstory vegetation in forests are converted into apparent understory LAI by thefollowing equation,

LAIu = 6.7913*NDVIu4-4.2145*NDVIu3-0.1439*NDVIu2+2.2167*NDVIu-0.324(LAIu = 0.0, if NDVIu < 0.152),
where LAIu is the apparent LAI for understory vegetation, and NDVIu is the NDVI forunderstory vegetation. The equation is estimated as the soil type is fixed.
fAPAR for understory vegetation in forests are estimated from understory LAI by theequation,fAPARu =(1 – fAPARo - REDrefl) * fAPAR0

fAPAR0= -0.0071* LAIu 4 + 0.0795* LAIu 3 - 0.3515* LAIu 2 + 0.8125* LAIu +0.0105
where fAPARu is the LAI for understory vegetation, fAPARo is the fAPAR for overstory
vegetation, fAPAR0 is the fAPAR for understory vegetation when there is no overstory
vegetation, and REDrefl is the reflectance of overstory vegetation for red region. The
equation is estimated as the soil type is fixed and the reflection by overstory vegetation
approximately equals REDrefl.
LAI/fAPAR are retrieved for each pixel from LUTs and SGLI data. The LUT used foreach pixel is decided using “Base map (described in 2.4)”. The multi LUTs are exploredfor LAI/fAPAR retrieval in the 2nd version, because the land cover types are not always
correctly assigned by “Base map”. LAI/fAPAR for urban/ water/ permanent snow andice/ bare areas are assigned as “no data”, though LAI/fAPAR in bare areas at “Basemap” are also retrieved in the 2nd version.
A) Retrieved LAI for 1st version
The following value is retrieved as LAI.
LAI: Overstory LAI (LAI for overstory vegetation) for forests assigned by“Base map”, and Total LAI (LAI for overstory and understory vegetation) for
non-forests assigned by “Base map”. Forests and non-forests do not mean
the true land cover types, but the land cover types assigned by “Base map”.
For example, overstory LAI is estimated for the grasslands when the land
cover type was assigned as forests by “Base map”.

Understory NDVI: NDVI of understory vegetation for forests assigned by“Base map”. Understory NDVI is assigned as “0” for non-forests.

B) Retrieved LAI for 2nd version
The following value is retrieved as LAI.
 LAI: Total LAI (LAI for overstory and understory vegetation).
Overstory LAI: LAI for overstory vegetation.

※ “Understory_LAI” = “LAI” – “Overstory_LAI”.
※ “LAI for forest” in the 1st version = “Overstory LAI” in the 2nd version. “LAIfor grassland” in the 1st version = “LAI” in the 2nd version.
2.4 Base map
GlobCover2009 V2.3 [10,11] and GLCNMO2008 [12] are used for the “Base map”, inwhich “paddy field” assigned by GLCNMO2008 is overlaid on GlobCover2009 V2.3.
The LUTs used for estimating LAI/fAPAR are assigned using this “Base map” (landcover codes are shown in Table 1). The LUTs used for each land cover code are listedbelow.
A) LUT for open needle-leaved forest✓

LC1, LC4B) LUT for closed needle-leaved forest✓LC3C) LUT for open broadleaved forest✓

LC2, LC5, LC17D) LUT for closed broadleaved forest✓LC6, LC7

E) LUT for tropical broadleaved forest✓LC11F) LUT for paddy field✓

LC12, LC24

G) LUT for grassland/shrubland/cropland✓

LC8, LC9, LC10, LC13, LC14, LC15, LC16

H) Other land cover types (LAI/fAPAR were assigned as “no data”)✓

LC18, LC19, LC20, LC21, LC22, LC23

The multi-types of LUTs were used for each pixel in the ver. 2 products. The LUTs
used for each land cover code are listed below.
A) LUT for open needle-leaved forest✓

LC1, LC2, LC3, LC4, LC5, LC6, LC7, LC8, LC9, LC10

B) LUT for closed needle-leaved forest✓

LC3, LC4, LC6, LC7

C) LUT for open broadleaved forest✓

LC1, LC2, LC3, LC4, LC5, LC6, LC7, LC8, LC9, LC10D) LUT for closed broadleaved forest✓

LC3, LC4, LC6, LC7

E) LUT for tropical broadleaved forest✓LC11F) LUT for paddy field✓

LC8, LC9, LC10, LC12, LC13, LC14, LC15, LC16, LC17, LC18, LC24

G) LUT for grassland/shrubland/cropland✓

LC1, LC2, LC8, LC9, LC10, LC12, LC13, LC14, LC15, LC16, LC17, LC18,LC24

H) Other land cover types (LAI/fAPAR were assigned as “no data”)✓LC19, LC20, LC21, LC22, LC23

Table 1 Land cover code used in this document
CodeLegends

LC1Open (15-40%) needle-leaved deciduous or evergreen forest (>5m)

LC2

Mosaic forest or shrubland (50-70%) / grassland (20-50%)

LC3

Closed (>40%) needle-leaved evergreen forest (>5m)

LC4

Closed to open (>15%) mixed broadleaved and needle-leaved forest (>5m)

LC5Open (15-40%) broadleaved deciduous forest/woodland (>5m)

LC6

Closed (>40%) broadleaved deciduous forest (>5m)

LC7

Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water

LC8

Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)

LC9

Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)

LC10

Mosaic grassland (50-70%) / forest or shrubland (20-50%)

LC11

Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)

LC12

Post-flooding or irrigated croplands (or aquatic)

LC13

Rainfed croplands

LC14

Closed to open (>15%) (broadleaved or needle-leaved, evergreen or deciduous) shrubland (<5m)

LC15

Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)

LC16

Closed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh,
brackish or saline water

LC17

Sparse (<15%) vegetation

LC18

Bare areas

LC19

Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or
brackish water

LC20

Artificial surfaces and associated areas (Urban areas >50%)

LC21

Water bodies

LC22

Permanent snow and ice

LC23

No data (burnt areas, clouds,…)

LC24

Paddy field assigned by GLCNMO2008

2.5 Input data
The SGLI atmospherically collected land surface reflectance (RSRF) data and geometry
data (solar zenith angle, solar azimuth angle, satellite zenith angle and satellite azimuthangle) are used as inputs. The past 7-days of SGLI data are also used to get the data for
multi-angle direction. In ver. 1 products, NDVIs for nadir direction (calculated fromVN08 and VN11) and for slant direction (calculated from PI01 and PI02) are used forretrieval. The NDVI only for nadir direction is used for the backup algorithm, when theLAI/fAPAR can not be retrieved. In ver. 2 products, the reflectance for nadir direction(VN08 and VN11) and the reflectance for slant direction (PI01 and PI02) are used for
LAI/fAPAR retrieval in forests, though the NDVI is used for non-forests because nonforests with low LAI is highly affected by soils.
3. Information of the provided layers and QA flag
Four layers are provided for each daily LAI/fAPAR product(GC1SG1_yyyymmddD01D_Thhvv_L2SG_LAI_Q_2000.h5).
A) For the 1st version: LAI, Understory_NDVI, FAPAR, QA_flagB) For the 2nd version: LAI, Overstory_LAI, FAPAR, QA_flag
3.1 QA flag
The explanation of QA_flag is shown in Table 2.
Table 2 Bit specification of QA flag
Bit

Level-3
maskDescription

Meaning0

no data

Any of input SGLI data has error value

1land/water

0: water > 50% of the pixel
1: land > 50% of the pixel

2

mixed with land/water

0: pure land or pure water
1: The pixel is mixed with land and water

3

cloud

1: The pixel was assigned as cloud4

probably cloud

1: The pixel was assigned as probably cloud5

snow or ice

1: The pixel was assigned as snow or ice

6

cloud shadow

1: The pixel was assigned as cloud shadow

mask

7

The condition of sensor
zenith angle is not good

0: good
1: sensor zenith angle for nadir direction > 40°orsensor zenith angle for slant direction < 40°

mask

land cover typeThe land cover type assigned by “Base map”

quality level

00: good quality
10: insufficient retrieval
01: large variance
11: poor inputs

13

Value could not be
retrieved

1: LAI/fAPAR coulld not be retrieved14

pol cloud or high-tau

1: The pixel was assigned as pol cloud or high-tau

mask

15

backup algorithm1: Backup algorithm was applied for the retrieval

mask

8-1011-12

3.1.1 The land cover types assigned by “Base map” [bit 08-10]
The LUTs used for the LAI/fAPAR retrieval can be known for each pixel from theinformation described in 2.4 and this bit.

For the 1st versionA) [bit 08-10: 000]
✓B)
✓LC1, LC4
[bit 08-10: 100]
LC3C)

[bit 08-10: 010]✓

LC2, LC5, LC17D) [bit 08-10: 110]
✓LC6, LC7

E) [bit 08-10: 001]
✓LC11F) [bit 08-10: 101]
✓LC12, LC24

G) [bit 08-10: 011]
✓H)✓

LC8, LC9, LC10, LC13, LC14, LC15, LC16
[bit 08-10: 111]
LC18, LC19, LC20, LC21, LC22, LC23

For the 2nd versionA) [bit 08-10: 000]
✓LC1, LC2

B) [bit 08-10: 100]✓

LC3, LC4C) [bit 08-10: 010]
✓LC5D) [bit 08-10: 110]✓

LC6, LC7

E) [bit 08-10: 001]
✓LC11F) [bit 08-10: 101]
✓LC8, LC9, LC10G) [bit 08-10: 011]✓

LC12, LC13, LC14, LC15, LC16, LC17, LC18, LC24

H) [bit 08-10: 111]
✓LC19, LC20, LC21, LC22, LC23

3.1.2 Quality level assigned [bit 11-12]A) [bit 11-12: 00]

LAI/fAPAR were reasonably retrieved.
B) [bit 11-12: 10]
LAI/fAPAR were retrieved from the insufficient number of values.
C) [bit 11-12: 01]
LAI/fAPAR were retrieved with high variation.
D) [bit 11-12: 11]
LAI/fAPAR were retrieved from the poor input data.
4. Validation plan
The accuracy of the retrieved LAI/fAPAR is assessed using the in-situ observation data
which will be collected at several sites on global. The validation will be held mainly ongrasslands and forests. The data from other satellite products will also be used for intercomparison.
5. Known issues⚫

“LAI” is sometimes incorrectly retrieved at the grasslands with low LAI (<1.0 [m2/m2]).

⚫

The product has the tendency of overestimating LAI especially at the needle-leavedforests.

⚫

“Overstory LAI” does not become “0” at croplands or grasslands with dense vegetation.

⚫

“LAI” is underestimated in the winter season.

⚫

The accuracy of “LAI” is low at the snow-covered areas.

⚫

“LAI” is sometimes incorrectly estimated where the land cover was incorrectly assignedin base maps.

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