GCOM-C/SGLI
Leaf Area Index (LAI)/ Fraction of Absorbed
Photosynthetically Active Radiation (FAPAR) product
Algorithm Theoretical Basis Document (ATBD)
1 October, 2021 (v3.000)

Kobayashi, T.A), Ono, Y. B), Kobayashi, H. C), Yang, W. B)

A)

University of Tsukuba
B)C)

Chiba University

Japan Agency for Marine-Earth Science and Technology

Contents
1. Introduction ......................................................................................................... 2
2. Algorithm description .......................................................................................... 2
2.1

Theoretical description .................................................................................... 2

2.2

LUTs used for LAI/FAPAR retrieval .............................................................. 3

2.3

Base map .......................................................................................................... 4

2.4

LAI/FAPAR retrieval ....................................................................................... 5

3. Inputs and outputs ............................................................................................... 7
3.1

Input data ......................................................................................................... 7

3.2

Output data ...................................................................................................... 7

3.3

Information about the QA flag ........................................................................ 8

3.3.1 Land cover types assigned by the “base map” [bit 08-10] ......................... 9
3.3.2

Assigned quality level [bit 11-12] ............................................................. 11

4. Validation plan .................................................................................................... 11
5. Known issues ........................................................................................................ 13
References ................................................................................................................ 13

1

1. Introduction
This document is an Algorithm Theoretical Basis Document (ATBD) describing thealgorithm of GCOM-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 separately for overstory and understory vegetation
because LAI of overstory vegetation and understory vegetation differs in seasonal variationand ecosystem function. The unit of the LAI is m2/m2.
The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is defined as the
proportion of solar radiation effectively absorbed by green leaves in the photosynthetically
active wavelengths (the spectral region from 400 to 700 nm). GCOM-C FAPAR
corresponds to white-sky FAPAR, or FAPAR by diffuse radiation by green vegetation.
2. Algorithm description
2.1 Theoretical description
The methodology for retrieving LAI and FAPAR is based on the work by Ono et al [1].
LAI and FAPAR are estimated based on the Look-Up Tables (LUTs) that show the
relationship between the SGLI multi-angle atmospherically-corrected surface
reflectance data and the LAI or FAPAR [1]. The LUTs are created using a radiativetransfer simulator, the Forest Light Environmental Simulator (FLiES) [2], and are fit to
the field-observed reference data collected from literatures. The FLiES simulates
radiative transfers in the forests and grasslands based on the Monte Carlo ray tracing
method. LAI and FAPAR are estimated by the multi-angle SGLI data to optimize the
LUTs. Figure 1 shows the processing flow.

Figure 1. Processing flow2

2.2 LUTs used for LAI/FAPAR retrieval
Eight kinds of LUTs are created using FLiES. Each LUT is created from a virtual forest
landscape scenarios and input parameters (Figure 2). The virtual forest landscapescenarios consist of the total number of trees, the geometric shapes of the trees, thepositions of the trees, and the heights of the trees. They are generated by an empiricalforest structure model [9]. The input parameters include the reflectance andtransmittance of canopy leaves, the reflectance and transmittance of understory
vegetation, the reflectance of stems, and the reflectance of soil (Table 1). They arecollected from literatures [3-8]. Virtual forest landscape scenarios in Figure 2 (A), (B),(C), (D), (E) and (F) correspond to dense needle-leaved forest, open needle-leaved
forest, dense broadleaved forest, open broadleaved forest, sparse forest, and non-forest
areas, respectively. Two types of input parameters are used for the virtual forest
landscape scenarios in Figure 2 (C), which corresponds to tropical forests and otherbroadleaf forests. Two types of input parameters are used for the virtual forest landscapescenarios in Figure 2 (F), which corresponds to paddy fields and grasslands/croplands.

Figure 2. Virtual forest landscape scenarios created for LAI/FAPAR estimation. Two types of input
parameters are used for (C) and (F).

Table 1 Parameters used for generating LUTs for LAI/FAPAR retrieval.
Virtual landscapescenarios in Fig.2

Reflectance of leaves(VN08 /VN11)

Transmittance of leaves(VN08 /VN11)

Reflectance of stem
(VN08 /VN11)

B

0.0496/0.4024

0.0256/0.4525

0.2220/0.4682

A

0.0494/0.4509

0.0295/0.4101

0.2220/0.4682

D

0.0607/0.4609

0.0368/0.4830

0.2220/0.4682

C

0.0464/0.4545

0.0324/0.5146

0.2220/0.4682

F

0.0881/0.4801

0.0615/0.4958

0.0/0.0F

0.1043/0.4636

0.0513/0.5024

0.0/0.0C

0.0571/0.5352

0.0195/0.3914

0.2220/0.4682

E

0.0607/0.4609

0.0368/0.4830

0.2220/0.4682

3

Multi-angle SGLI data need to match the bidirectional reflectance factor at the top of
canopy simulated by FLiES using input parameters and virtual forest landscapescenarios. The input parameters and forest landscape scenarios are selected so that thebidirectional reflectance factor at the top of the canopy can match the multi-angle SGLI
data. Consistency between them is checked at several sites where field-observed
reference LAI data are available. The field-observed reference LAI data are collectedfrom literatures. LUTs are created for each solar and sensor geometry. Figure 3 shows
the flow of LUT creation.

Figure 3. Flow of LUT creation

2.3 Base map
The “base map” has 16 land cover categories: broadleaf evergreen forest, sparse
broadleaf forest, very-open broadleaf forest, open broadleaf forest, closed broadleaf
forest, sparse or very-open needle-leaf forest, open needle-leaf forest, closed needle-leaf
forest, very-closed needle-leaf forest, open mixed forest, closed mixed forest, sparse
unknown forest, very-open unknown forest, unknown forest, non-forests and unknown
land covers. It was produced using SGLI surface reflectance data and the Global
PALSAR-2/PALSAR Forest/Non-Forest maps
(https://www.eorc.jaxa.jp/ALOS/palsar_fnf/fnf_jindex20140116.htm). We used the
Global PALSAR-2/PALSAR Forest/Non-Forest maps, because croplands and paddy
fields with no tree cover could be classified as sparse forest or open forest when only
SGLI data was used for classification. Nine different virtual landscape scenarios were4

used for mapping: 4 types (very dense, dense, open and very open) of needle-leaf forest,
4 types of broadleaf forest, and non-forest (2 types of leaves). Figure 4 shows the “basemap” used in this project.

Figure 4. “Base map” used for producing SGLI LAI/FAPAR product. It was produced using SGLI
surface reflectance data.

2.4 LAI/FAPAR retrieval
In the GCOM-C product, LAI and FAPAR of the overstory and understory vegetation
are estimated from SGLI data using the LUTs (Figure 5). In forest areas, the LAI andFAPAR of the overstory vegetation and the Normalized Difference Vegetation Index(NDVI) of the understory vegetation are retrieved from the LUTs created from thevirtual forest landscape scenarios in Figure 2(A)-(E). In non-forest areas, the LAI andFAPAR of understory vegetation are retrieved from the LUT created from the virtual
forest landscape scenarios in Figure 2(F), as 0.0 for the LAI and FAPAR of the overstory
vegetation. NDVI of understory vegetation (understory NDVI) is later converted into
LAI and FAPAR. LAI and FAPAR for water and snow/ice areas are assigned as “nodata”.

Figure 5. Definition of overstory vegetation and understory vegetation in the GCOM-C LAI/FAPAR
product.

5A) Retrieved LAI and FAPAR for 1st version
The following value is retrieved as LAI and FAPAR.


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.



FAPAR: FAPAR of whole green vegetation (overstory and understory
vegetation are included).

B) Retrieved LAI and FAPAR for 2nd version and 3rd version.
The following values are retrieved as LAI.
 LAI: Total LAI (LAI for overstory and understory vegetation).
Overstory LAI: Overstory LAI (LAI for overstory vegetation).



FAPAR: FAPAR for whole green vegetation (FAPAR for overstory andunderstory vegetation).

※ “LAI for understory vegetation” = “LAI” – “Overstory LAI”.
※ “LAI for forest” in the 1st version = “Overstory LAI” in the 2nd or 3rd version.
※ “LAI for grassland” in the 1st version = “LAI” in the 2nd or 3rd version.
In non- forest areas, “Total LAI”, “Overstory LAI” (= 0.0) and “FAPAR” can be
retrieved directory using LUTs. In forest areas, “Overstory LAI” can be retrieved
directly using LUTs. “Total LAI” and “FAPAR” of forest areas are calculated from
“Overstory LAI” and “Understory NDVI” which is also retrieved directly using LUTs.
They are calculated as follows.
Total LAI (for forest areas)“Understory NDVI” is converted into apparent understory LAI by the following
equation,

LAIu = 6.7913*NDVIu4-4.2145*NDVIu3-0.1439*NDVIu2+2.2167*NDVIu-0.324 (1)(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 was obtained by fixing the soil type, the vegetation
type and sun-target-sensor geometry. Total LAI for forest areas is calculated by adding

LAIu to the LAI of overstory vegetation.
6

FAPAR (for forest areas)
FAPAR for forest areas are estimated by the equation,

FAPARt = FAPARo + (1 – FAPARo - REDrefl) * FAPAR0(2)

FAPAR0 = -0.0071* LAIu 4 + 0.0795* LAIu 3 - 0.3515* LAIu 2 + 0.8125* LAIu +0.0105(3)where FAPARt is the FAPAR for overstory and 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. FAPARt is the output FAPAR value. The equation is obtained by assumingthat the reflection by overstory vegetation is approximately equal to REDrefl by fixing thesoil type, the vegetation type and sun-target-sensor geometry.
The LUTs used for the LAI/FAPAR estimation are assigned by “base map”. The LUTs
used for each land cover category of the “base map” are shown in Table 2.
Table 2 Land cover categories and used LUTs
CodeLand cover classes in the base map

Used LUTs

LC1

Broadleaf evergreen forest

D, E

LC2

Closed broadleaf forest

A, B, C, D

LC3Open broadleaf forest

D

LC4

Very-open broadleaf forest

D

LC5Sparse broadleaf forest

D, G, H

LC6

Very-closed needle-leaf forest

A, B

LC7

Closed needle-leaf forest

A, B

LC8

Open needle-leaf forest

B

LC9

Very-open or sparse needle-leaf forest

B, G, H

LC10

Closed mixed forest

A, C

LC11

Open mixed forest

B, D

LC12

Unknown forest

B, D, F, G, H

LC13

Very-open unknown forest

B, D

LC14

Sparse unknown forest

B, D, G, H

LC15

Non-forest areas

G, H

LC16

Unknown land covers

A, B, C, D, G, H

A)
B)
C)
D)
E)
F)
G)
H)

LUT of dense needle-leaved forest (used virtual forest landscape scenarios: A in Figure 2)
LUT of open needle-leaved forest (used virtual forest landscape scenarios: B in Figure 2)
LUT of dense broadleaved forest (used virtual forest landscape scenarios: C in Figure 2)
LUT of open broadleaved forest (used virtual forest landscape scenarios: D in Figure 2)
LUT of tropical forest (used virtual forest landscape scenarios: C in Figure 2)
LUT of sparse forest (used virtual forest landscape scenarios: E in Figure 2)
LUT of paddy fields (used virtual forest landscape scenarios: F in Figure 2)
LUT of grasslands/croplands (used virtual forest landscape scenarios: F in Figure 2)

7

3. Inputs and outputs
3.1 Input data
The SGLI atmospherically collected land surface reflectance (RSRF) data andgeometry data (solar zenith angle, solar azimuth angle, satellite zenith angle and satellite
azimuth angle) are used as inputs. The reflectance for nadir direction (VN08 and VN11)
and the reflectance for slant direction (PI01 and PI02) are used for the LAI/FAPAR
retrieval in forests, while only NDVI is used in non-forests and sparse forests because of
the strong influence of soils. SGLI data for the past 7 days are not used in ver. 3, because
cloud conditions have a greater influence on LAI/FAPAR retrieval than observation
angles. Only NDVI is used for retrieval as a backup algorithm. LAI and FAPAR cannot
be retrieved if “no data” or “cloud” is set in the QA flag of RSRF data, or if the land
water flag is set to 0 (water).
3.2 Output data
The global SGLI LAI/FAPAR product is supplied as 36 × 18 Sinusoidal tiles, though
219 tiles are not supplied because they have no valid input data. LAI/FAPAR isestimated for land pixels of each tile (Figure 6). Four layers are provided for daily
LAI/FAPAR product (GC1SG1_yyyymmddD01D_Thhvv_L2SG_LAI_Q_3000.h5).
A) For the 1st version: LAI, Understory_NDVI, FAPAR, QA_flagB) For the 2nd and 3rd version: LAI, Overstory_LAI, FAPAR, QA_flag
Each tile includes approximately 250 m pixels of 4800 × 4800. Datatype of each layer
are 16-bit unsigned integer. Sample images of the daily global LAI and FAPAR on
August 1, 2020 and their composite images from August 1 to August 10, 2020, areshown in Figure 7. A sample image for one-tile can be viewed on the site of “Standard
Products and Algorithm”.
( https://suzaku.eorc.jaxa.jp/GCOM_C/data/product_std_j.html )8

Figure 6. Pixels where LAI/FAPAR is estimated. This figure was created using the 2 months of SGLI
data for July and August, 2021. LAI and FAPAR are estimated at yellow-colored pixels. Blue and red
pixels are water pixels and pixels with no valid data, respectively. White-colored tiles are tiles with no
input data.

Figure 7. Global maps of LAI (a), Overstory LAI (b) and FAPAR (c). Daily maps are generated using
RSRF data for August 1, 2020 as input. Composite maps are generated using daily maps from August 1
to Augusts 10, 2020.

9

3.3 Information about the QA flag
The explanation of QA_flag is shown in Table 3. It is suggested not to use data with the“cloud shadow”, “quality level: poor or unreliable” or “backup algorithm” bit standing.
Data with “bad air condition” bit standing is also of low quality, although there are cases
where the bit may be standing at high mountain and certain land covers.
Table 3 Bit specification of QA flag
Bit

Level-3
mask

Description

Meaning0

no data

Any of input SGLI data has error value

1

land/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

bad air condition

1: The air condition is not good5

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°or
sensor zenith angle for slant direction < 40°

mask

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

quality level

00: good quality
10: acceptable
01: unreliable
11: poor

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

15

backup algorithm

1: Backup algorithm was applied for the retrieval

8-10

11-12

mask

3.3.1 Land cover types assigned by the “base map” [bit 08-10]
Bits 08 to 10 correspond to the landcover type assigned in the “base map”. The
LUT used for LAI/FAPAR retrieval can be known for each pixel from theinformation described in 2.4 and these bits.
For the 1st versionA) [bit 08-10: 000]✓
B)✓

LC1, LC4[bit 08-10: 100]
LC3
10

C)

[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

For the 3rd version
I)
✓
J)
✓

[bit 08-10: 000]
LC8[bit 08-10: 100]
LC6, LC711K) [bit 08-10: 010]✓

LC3, LC11

L) [bit 08-10: 110]✓

LC2, LC10

M) [bit 08-10: 001]✓

LC1

N) [bit 08-10: 101]✓

LC4, LC5, LC9, LC12, LC13, LC14O) [bit 08-10: 011]✓

LC15P) [bit 08-10: 111]✓

LC16

3.3.2 Assigned quality level [bit 11-12]
Bits 11 to 12 show the quality of retrieved LAI/FAPAR.
A) [bit 11-12: 00]
LAI/FAPAR were reasonably retrieved.
B) [bit 11-12: 10]
LAI/FAPAR were retrieved, though they were retrieved from the insufficientnumber of values or the variance was high.
C) [bit 11-12: 01]
LAI/FAPAR were retrieved from the input SGLI data that is of low quality.
D) [bit 11-12: 11]
LAI/FAPAR were retrieved from much lower quality SGLI data.
4. Validation plan
The accuracy of the retrieved LAI/FAPAR is assessed using the in-situ observation
data which are collected at 13 sites around the global. The validation is held mainly on
grasslands and forests. Data collected from available databases are also used forvalidation. Tentative comparison results between ground measured LAI and FAPAR and
SGLI retrievals at a pixel level are shown in Figure 8. The ground measured data weremainly contributed by GCOM-C PIs, AKITSU Tomoko, HONDA Yoshiaki, KAJIWARA
Koji, KOBAYASHI Hideki, MORIYAMA Masao, NAGAI Shin, NASAHARA Kenlo
Nishida, and SUSAKI Junichi [13-16].

12

(a)

(b)

(c)

(d)

Figure 8. Comparisons between measured LAI and FAPAR, and SGLI retrievals: (a) total LAI, (b)overstory LAI and (c) FAPAR. The reference FAPAR for forest sites in (c) is the total FAPAR, though
the retrieved FAPAR is the green FAPAR, which does not include PAR absorption by non-green
elements. The distribution of the sites where the measured data were collected is shown in (d): FHK,
Fuji Hokuroku Flux Observation site, Japan; Lambir, Lambir Hills site, Malaysia; Lambir Oil Palm, Lambir Oil Palm
Plantation site, Malaysia; PFRR, Poker Flat Research Range site, USA; SSP, Spasskaya Pad site, Russia; TMM,
Tokachi Mitsumata JAXA Super Site 500, Japan; TOS, Hokkaido University Tomakomai Experimental Forest site,
Japan; URY, Hokkaido University Uryu Experimental Forest site, Japan; YNF, Yona Field, Japan; Yamashiro,
Yamashiro site, Japan; DGT, Delgertsogt JAXA Super Site 500, Mongolia; KYM, Khar Yamaat JAXA Super Site 500,
Mongolia; MBN, Baganuul JAXA Super Site 500, Mongolia; and WTR, Watarase JAXA Super Site 500, Japan.

13

5. Known issues⚫

The product has the tendency of overestimating LAI at the open needle-leaved forests.

⚫

Sometimes “Overstory LAI” does not become “0.0” at croplands or grasslands with
dense vegetation.

⚫

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

⚫

“LAI” may be incorrectly estimated in places where the land cover is incorrectly
assigned on the “base map”.

References[1] Ono, Y., Murakami, H., Kobayashi, H., Nasahara, K., Kajiwara, K. and Honda, Y., 2015.
Development of a leaf area index estimation algorithm for GCOM-C/SGLI multi-spectral
and multi-angle observation data, the 30th International Symposium on Space Technology
and Science, Kobe, Japan, 2015-n-59p.
[2] Kobayashi, H. and Iwabuchi, H., 2008. A coupled 1-D atmosphere and 3-D canopy
radiative transfer model for canopy reflectance, light environment, and photosynthesis
simulation in a heterogeneous landscape, Remote. Sens. Environ, 112, pp. 173-185.
[3] Noda, H. M., Motohka, T., Murakami, K., Muraoka, H., Nasahara, K.H, 2014.
Reflectance and transmittance spectra of leaves and shoots of 22 vascular plant species andreflectance spectra of trunks and branches of 12 tree species in Japan, Ecological Research,
29, pp. 111.
[4] Hosgood, B., Jacquemoud, S., Andreoli, G, Verdebout, J., et al., 1995. Leaf Optical
Properties EXperiment 93 (LOPEX 93). European Commission: Ispra, Italy.
[5] Hosgood, B., Jacquemoud, S., Andreoli, G, Verdebout, J., et al., 1993. Leaf Optical
Properties EXperiment Database (LOPEX 93). Dataset. Available on-line from the
Ecological Spectral Information System (EcoSIS): http://ecosis.org[6] Wu, J., Kobayashi, H., Stark, S. C., Meng, R., Guan, K., et al., 2018. Biological processes
dominate seasonality of remotely sensed canopy greenness in Amazon evergreen forest,

New Phytologist, 217, pp. 1507-1520.
[7] Meerdink, S. K., Hook, S. J., Roberts, D. A., Abbott, E. A., 2019. The ECOSTRESS
spectral library version 1.0. Remote Sensing of Environment, 230, pp. 1–8.
[8] Baldridge, A. M., Hook, S. J., Grove, C. I., Rivera, G., 2009. The ASTER Spectral
Library Version 2.0. Remote Sensing of Environment, 113, pp. 711-715.
[9] Yang, W., Kobayashi, H., Chen, X., Nasahara, K. N., Suzuki, R., et al., 2018. Modelingthree-dimensional forest structures to drive canopy radiative transfer simulations of14

bidirectional reflectance factor, International Journal of Digital Earth, 11, pp. 981-1000.
[10] Arino, O., Bicheron, P., Achard, F., Latham, J., et al., 2008. GLOBCOVER: The Most
Detailed Portrait of Earth, European Space Agency Bulletin, 136, pp. 24–31.
https://earth.esa.int/web/guest/-/globcover-the-most-detailed-portrait-of-earth-5910.
[11] ESA (European Space Agency) and UCL (Université Catholique de Louvain), 2011.
GLOBCOVER 2009 (Global Land Cover Map). Dataset. Available on-line fromhttp://due.esrin.esa.int/page_globcover.php[12] Tateishi, R., Hoan, N.T., Kobayashi, T., Alsaaideh, B., Tana, et al., 2014. Production of
global land cover data – GLCNMO2008, Journal of Geography and Geology, 6, pp. 99-122.
[13] Akitsu, T. K., Nakaji, T., Yoshida, T., Sakai, R., Mamiya, W., Terigele, Takagi, K.,
Honda, Y., Kajiwara K., Nasahara, K. N., 2020. Field data for satellite validation and forest
structure modeling in a pure and sparse forest of Picea glehnii in northern Hokkaido,

Ecological Research, 2020, pp.1–15. doi:10.1111/1440-1703.12114.
[14] Akitsu, T. K., Nakaji, T., Kobayashi, H., Okano, T., Honda, Y., Bayarsaikhan, U.,
Terigele, Hayashi, M., Hiura, T., Ide, R., Igarashi, S., Kajiwara, K., Kumikawa, S., Matsuoka,
Y., Nakano, Ta., Nakano, To., Okuda, A., Sato, T., Tachiiri, K., Takahashi, Y., Uchida, J.,
Nasahara, K. N., (in press). Large-scale ecological field data for satellite validation in
deciduous forests and grasslands, Ecological Research, 2020, doi:10.1111/1440-1703.12155.
[15] Kobayashi, H., Nagai, S., Kim, Y., Yang, W., Ikeda, K., Ikawa, H., Nagano, H. andSuzuki, R., 2018. In situ observations reveal how spectral reflectance responds to growing
season phenology of an open evergreen forest in Alaska, Remote Sensing, 10(7), p.1071.
(Data are available from Arctic Data archive System (ADS), Japan,
https://ads.nipr.ac.jp/dataset/A20181212002)[16] Nagai, S., Kotani, A., Sato, T., Sugimoto, A., Maximov, T. C., Nogovitcyn, A.,
Miyamoto, Y., Kobayashi, H., and Tei, S., 2020. Direct measurement of leaf area index in a
deciduous needle-leaf forest, eastern Siberia, Polar Science, 25(7), doi:
10.1016/j.polar.2020.100550

15