Algorithm Theoretical Basis Document

GCOM-C/SGLI Level-2 Statistics (G4A)

31 March 2017
Masahiro Hori
Japan Aerospace Exploration Agency

1

Contents
1. Introduction
1.1 Objectives
1.2 Development strategy
1.3 Processing targets
2. Theoretical Description
2.1 Processing flow
2.2 Statistics
2.3 QC process
2.4. Sample images
2.4.1 NDVI
3. Current status and remaining issues

21. Introduction
1.1 Objectives
The objective of the G4A algorithm is to take temporal statistics of SGLI Level-2 (L2)
Tile geophysical variable (GV) products for land and cryosphere. The temporal intervalof the statistics is 8-day or 1-month. The spatial resolutions of the tile products are kept
the same.
1.2 Development strategy
The G4A algorithm is designed to use a same program code for taking statistics of all
GVs in order to make the maintenance of the code simple. Basically the output of theG4A processing contains the same statistics variables except for the case of snow and ice
cover extent (SICE) product (see next sub-section). The SICE is a flag product and theoutput of SICE statistics is customized so that the number of snow/ice cover observation
during the temporal interval can be calculated. Finally, to make the addition or deletionof GVs quite easy, the GV names of the statistical targets are defined in an external text
file so that the change could be made without compiling the code.
1.3 Processing targets and outputs
Processing targets of the L2 Tile GV statistics are the land and cryosphere products.
Input and output variables are summarized in Table 1.
Table. 1 List of the target products, GVs, and output types of the G4A processing
Product Long Name

Geophysical Variables Output

ID(GVs)Variables*1

NDVI, EVI, and SDI

AVE, RMS, Nused,

VGI_Vegetation index

Ninput, MIN, MAX,Date, QA_flag
LAI_Leaf area index

LAI and FAPAR

Same as above

AGB_Above-ground biomass

AGB and VRISame as above

LST_Land surface temperature

LST

Same as above

SIPR

Snow and ice physical properties SGSL and SIST

Same as above

SICE

Snow and ice cover extent

Nsnow1, Nsnow2,

SICE

Nsnow3, Nused,
Ninput, Date,
QA_flag*1 AVE: Average of valid GV data, RMS: Root Mean Square of valid GV data, Nused: Number of valid
GV data actually used in the statistics, Ninput: Number of input GV data, Min: Minimum of valid GVdata, Max: Maximum of valid GV data, Date: Dates of the SGLI observations during the 8-day or
1-month interval period, QA_flag: Flag for quality assurance information, Nsnow1: Number of snow orice cover, Nsnow2: Number of snow with vegetation or bare ice, Nsnow3: Number of melting snow

3Basically (except for the case of snow and ice cover extent (SICE) products) thestatistics taken and stored in the output files are the eight values or flag shown inTable1. When processing the SICE product which stores surface classification flags
including snow and ice covers, the statistics of Ave, RMS, Min, and Max are not taken.
Instead, only three snow/ice-related counts are stored in the output file.
2. Theoretical Description
2.1 Processing flow
Figure 1 indicate the flow of the G4A L2 Tile GV statistics processing. Input is the L2
tile daily GV products with spatial resolution of 250m or 1km. Output is the 8-day or
1-month statistics product of individual GVs. In case that an input file contains
multiple GVs, the G4A process generates output files for every GVs separately. For
example, the “VGI_“ product contains three GVs of NDVI, EVI and SDI as shown inTable 1. The outputs of the G4A processing for VGI_ are three files with the following
product IDs, i.e., NDVI, EVI_, and SDI_.

1/480deg (250m) Tile [EQA](Terrestrial L2：VGI,LAI,LST)

1/120deg (1km) Tile [EQA](Terrestrial L2：AGB, Cryosphere L2)

Input: Daily GP × 8-day or1-month

G4A: L2 Tile Statistics(AVE, RMS, MAX, MIN, Ninput, Nused, Date, QA_flag)

1/480deg (250m) Tile [EQA] (L2 Statistics)(Terrestrial L2 Temporal binning: 8D,1M)

1/120deg (250m) Tile [EQA] (L2 Statistics)(Terrestrial L2：AGB, Cryosphere L2 Temporalbinning:8D,1M)

Output: 8-day or 1-monthstatistics

Fig. 1 Flow of the G4A L2 Tile GV statistics processing
2.2 Definition of the statistics
Equations for calculating “AVE” and “RMS” are the followings;
AVE =1𝑛𝑛∑𝑖𝑖 𝑥𝑥𝑥𝑥
1

RMS = � (∑𝑖𝑖 𝑥𝑥𝑥𝑥 2 )𝑛𝑛Where n is the total number of observation days with valid GVs, xi is the daily valueof a GV to be processed.
“Nused” is the number of valid GV data actually used in the statistics, whereas “Ninput”
4

is the number of all the input GV data.
“MIN” and “MAX” are the minimum and maximum of valid GVs data during thetemporal interval.
“Date” is a 8-bit value storing the dates of SGLI observations employed in thestatistics calculation. For example, when the Date value is 40 (i.e., 101000), then SGLI
observations of 4th and 6th days are used in the statistics.
2.3 QA process“QA_flag” currently stores flags for discriminating land and water. In future update,
QA information for GVs will be included.
2.4. Sample images
2.4.1 NDVI
Figure 2 indicate sample images for the input and output of G4A processing (GV isNDVI and the temporal interval is 8-day).

Input: Daily NDVI

Output: 8-day averaged NDVI

Fig. 2 Sample images of the G4A processing. (NDVI, Tile No.: 0528, 8-day)

3. Current status and remaining issues of the G4A code implementation
The G4A process works well without system errors. Processing speed and memory
size are also within the expectations. Remaining tasks are the implementation of theQA_flag for the output of quality assurance information.
5