Preliminary evaluation of the long-term GLASS albedo product

Figures & data

Figure 1. The flowchart of the albedo production line in GLASS.

Table 1. List of GLASS albedo intermediate and final products.

Product name	Product type	Algorithm	Input data	Temporal step	Composition interval	Projection	Spatial resolution
GLASS02A21	Intermediate	AB1	MOD09GA	1 day	1 day	SIN	1 km
GLASS02A22	Intermediate	AB1	MYD09GA	1 day	1 day	SIN	1 km
GLASS02A23	Intermediate	AB2	MOD021km	1 day	1 day	SIN	1 km
GLASS02A24	Intermediate	AB2	MYD021km	1 day	1 day	SIN	1 km
GLASS02A06	Final	STF	GLASS02A21	1 day	17 days	SIN	1 km
			GLASS02A22
			GLASS02A23
			GLASS02A24
GLASS02B05	Final	AB1 + STF	LTDR AVHRR data-set	8 days	33 days	CMG	0.05°

Table 2. Quality control (QC) flags of the final GLASS albedo product.

Bit number	Parameter name	Value/state
0, 1	Overall quality	00: good	01: acceptable
		10: with uncertainty	11: prior value
2, 3	Land cover state	00: vegetation	01: bare ground
		10: snow	11: un-classified
4, 5	Length of composite window	00: 9-day	01: 17-day
		10: 25-day	11: 33-day
6–8	Number of actually used (clear-sky) intermediate products	000: 0010: 2–3100: 8–15110: 32–63	001: 1011: 4–7101: 16–31111: 64–127
9, 10	Ratio of actually used to total number of intermediate products	00: more than 50%10: 10–25%	01: 25–50%11: less than 10%
11–14	Uncertainty of albedo retrieval	0000: 0.00–0.01	0001: 0.01–0.02
		0010: 0.02–0.03	0011: 0.03–0.04
		0100: 0.04–0.05	0101: 0.05–0.06
		0110: 0.06–0.07	0111: 0.07–0.08
		1000: 0.08–0.09	1001: 0.09–0.10
		1010: 0.10–0.11	1011: 0.11–0.12
		1100: 0.12–0.13	1101: 0.13–0.14
		1110: 0.14–0.15	1111: >0.15
15	Albedo validity flag	0: valid value	1: invalid value

Figure 2. Monthly average percentage of GLASS albedo products of different overall quality flags in 2004. (a) AU-Tum; (b) BR-Cax; (c) DE-Hai; (d) RU-Che; (e) US-Fpe; (f) ZA-Kru.

Table 3. Information about six typical FLUXNET sites in different continents.

Site_name	Latitude	Longitude	Cover_type
AU-Tum	−35.6557	148.152	Evergreen broadleaf forest
BR-Cax	−1.71972	−51.459	Evergreen broadleaf forest
DE-Hai	51.0793	10.452	Deciduous broadleaf forest
RU-Che	68.6147	161.339	Mixed forest
US-Fpe	48.3077	−105.1019	Grassland
ZA-Kru	−25.0197	31.4969	Savanna

Figure 3. Spatial distribution of the average quality of the GLASS albedo product in 2003 and 2004. (a) Percent of the product with a ‘good’ quality flag. (b) Average product uncertainty.

Figure 4. Locations of the sample sites used in this paper.

Figure 5. Interannual variation of the quality flags of the GLASS albedo product, averaged on all BELMANIP sites and every three months.

Figure 7. Scatter plot of the FLUXNET ground measurements and the blue-sky albedo extracted from the GLASS product. (a) Clear-sky ground measurements and all GLASS data. (b) Clear-sky ground measurements and ‘good’ quality GLASS data.

Table 4. Statistics comparing the FLUXNET ground measurements with the GLASS albedo product.

Sample criterion	Number of observations	Bias	RMSD	R2
Clear-sky ground measurements and all GLASS data	28,881	−2.9516e−06	0.058668	0.80331
Clear-sky ground measurements and ‘good’ quality GLASS data	16,960	−0.00051115	0.045471	0.89306
Vegetation	14,833	−0.0017354	0.030269	0.61765
Bare ground	938	0.012068	0.053150	0.30924
Snow/ice	1189	0.0048384	0.12583	0.72306

Figure 8. Scatter plot of the black-sky albedo extracted from the GLASS02A06 product and the MCD43B3 product in BELMANIP sites. (a) All the GLASS data and valid MCD43 product. (b) The ‘good’ quality GLASS data and valid MCD43 product.

Table 5. Statistics of comparing the GLASS02A06 albedo product to the MCD43B3 product.

Sample criterion	Number of observations	Bias	RMSD	R2
All GLASS data and valid MCD43 data	29,719	0.0070278	0.031048	0.92225
‘Good’ quality GLASS data and valid MCD43 data	18,605	0.0073186	0.022632	0.96682
Vegetation	14,229	0.0068744	0.012155	0.92592
Bare ground	3319	−0.00041133	0.014702	0.97276
Snow/ice	1057	0.037569	0.079677	0.81386

Figure 9. Scatter plot of the black-sky albedo extracted from the MODIS-derived GLASS product and the AVHRR-derived GLASS product in the BELMANIP sites in 2003–2004. (a) All samples. (b) Samples with ‘good’ or ‘acceptable’ quality flags in the AVHRR-derived GLASS product.

Table 6. Statistics comparing the MODIS-derived GLASS product with the AVHRR-derived GLASS product.

Sample criterion	Number of observations	Bias	RMSD	R2
All samples	36,979	0.007993	0.036668	0.90732
‘Good’ or ‘acceptable’ quality AVHRR-derived product	31,478	0.006209	0.022548	0.95047
Vegetation	16,538	0.004738	0.012139	0.8790
Bare ground	2951	0.004935	0.022529	0.89184
Snow/ice	11,989	0.008552	0.031728	0.95422

Figure 10. Time series of the average black-sky albedo of different latitudinal zones, extracted from the GLASS product and in the BELMANIP sample sites. (a) S60–S30; (b) S30–N30; (c) N30–N60; (d) N60–N90.

Table 7. Results of the Mann–Kendall trend test of the yearly average albedo in different latitudinal zones.

	Latitude zone	S60–S30	S30–N30	N30–N60	N60–N90
Year range	Number of sites	15	118	238	31
1982–2010	Zs	4.7811	2.2918	3.4376	3.5167
	Standard deviation	0.0022679	0.00091598	0.0051761	0.0034161
	Trend/significance	I/Y	I/Y	I/Y	I/Y
	Trend anomaly year	1994	1997	2001	1998
1982–1999	Zs	3.5426	1.2358	−0.8239	0.4943
	Standard deviation	0.00066697	0.00018284	0.00038373	0.00043273
	Trend/significance	I/Y	I/N	D/N	I/N
	Trend anomaly year	1992	–	–	–
2000–2010	Zs	2.2576	0.0778	0.5449	−1.6348
	Standard deviation	0.0026565	0.0013428	0.0015823	0.0022592
	Trend/significance	I/Y	I/N	I/N	D/N
	Trend anomaly year	2005	–	–	2001

According to the principle of the Mann–Kendall test, the increasing trend is significant with above a 95% confidence level when Z_s is beyond 1.96, and the decreasing trend is significant above a 95% confidence level when Z_s is below −1.96. Here, ‘I’ means increasing, ‘D’ means decreasing and ‘Y’ means significant, ‘N’ means insignificant.