Evaluation of compositing algorithms from the regular and non-regular intervals using MODIS daily images in the Amazon region with a high percentage of cloud cover

Salgado, Cristiane Batista; Carvalho Junior, Osmar Abílio de; Santana, Nickolas Castro; Gomes, Roberto Arnaldo Trancoso; Guimarães, Renato Fontes; Silva, Cristiano Rosa

doi:10.14393/SN-v34-2022-65356

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Papers • Soc. nat. 34 • 2022 • https://doi.org/10.14393/SN-v34-2022-65356 copy

Evaluation of compositing algorithms from the regular and non-regular intervals using MODIS daily images in the Amazon region with a high percentage of cloud cover

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

One challenge in the study of optical remotely sensed time series in the Amazon is the constant cloud cover. The present study evaluates different compositing techniques using regular and non-regular intervals to obtain cloud-free images over large areas. The study area was the municipality of Capixaba, State of Acre, belonging to the Amazon region. The tests considered four compositing algorithms (maximum, minimum, mean, and median) for daily MODIS sensor data (b1 and b2, 250m). The compositing technique from regular intervals adopted the following periods: 8, 16, 24, 32, 40, and 48 days. The irregular interval composite images adopted different composition intervals for dry seasons (April to September) and rainy (October to March). The cloud mask and viewing angle constraint allowed to obtain information without atmospheric interference and closest to the nadir view. The composite images using regular intervals did not allow for overcoming the high frequency of cloud cover in the region. The composite images from non-regular intervals presented a higher percentage of cloud-free pixels. The mean and median methods provided a better visual appearance of the images, corroborating with the homogeneity test. Therefore, composite images from non-regular intervals may be an appropriate alternative in places with constant cloud coverage.

Keywords:
Time series; Remote sensing; Amazon Forest; Composite image

Model 1 (RI) (Cloud free pixels)	Model 2 (RI-Z) (Cloud-free pixels + restricted zenith sensor angle)	Regular intervals
RI-8	RI-Z-8	8 days
RI-16	RI-Z-16	16days
RI-24	RI-Z-24	24 days
RI-32	RI-Z-32	32days
RI-40	RI-Z-40	40 days
RI-48	RI-Z-48	48 days

	AUG 2014	SEP 2014	OCT 2014	FEB 2015	APR 2015	MAY 2015	JUN 2015	JUL 2015	Total composite images
II-1	10,10,10	10,10,10	15,15,15,15,15,15,15,15,15,15,15,15	10,10,10	10,10,10		10,10,10	10,10,10	30
II-2	10,10,10	10,10,10	30, 30, 30, 30, 30, 30	10,10,10	10,10,10	10,10,10	10,10,10	24
II-3	10,10,10	10,10,10	45,45,45,45	10,10,10	10,10,10	10,10,10	10,10,10	22
II-4	10,10,10	10,10,10	50,50,50,50	10	10,10,10	10,10,10	10,10,10	21
II-5	10,10,10	10,10,10	60,60,60,60			10,10,10	10,10,10	16
II-6	30	30	90, 90	30	30	30	30	8

Models	Total composite images	Distribution of the number of composite images (and their respective percentage) by the percentage of pixels with valid data
Models	Total composite images	0 - 20%	21 - 40%	41 - 60%	61 - 80%	81 - 99%	100%
RI-8	45	20 (0.44%)	6 (0.13%)	4 (0.09%)	0 (0.00%)	4 (0.09%)	11 (24.4%)
RI-Z-8	45	23 (0.51%)	4 (0.09%)	4 (0.09%)	1 (0.02%)	6 (0.13%)	7 (15.6%)
RI-16	22	7 (0.16%)	3 (0.07%)	3 (0.07%)	1 (0.02%)	2 (0.04%)	6 (27.3%)
RI-Z-16	22	10 (0.22%)	1 (0.02%)	3 (0.07%)	1 (0.02%)	2 (0.04%)	5 (22.7%)
RI-24	15	4 (0.09%)	0 (0.00%)	3 (0.07%)	1 (0.02%)	2 (0.04%)	5 (33.3%)
RI-Z-24	15	4 (0.09%)	2 (0.04%)	2 (0.04%)	1 (0.02%)	2(0.04%)	4 (26.7%)
RI-32	11	1 (0.02%)	3 (0.07%)	1 (0.02%)	0 (0.00%)	2 (0.04%)	4 (36.4%)
RI-Z-32	11	3 (0.07%)	1 (0.02%)	2 (0.04%)	1 (0.02%)	1 (0.02%)	3(27.3%)
RI-40	9	0 (0.00%)	1 (0.02%)	3 (0.07%)	0 (0.00%)	2 (0.04%)	3(33.3%)
RI-Z-40	9	1 (0.02%)	1 (0.02%)	2 (0.04%)	0 (0.00%)	3 0.07	2(22.2%)
RI-48	7	0 (0.00%)	1 (0.02%)	2 (0.04%)	0 (0.00%)	1 (0.02%)	3(42.9%)
RI-Z-48	7	1 (0.02%)	1 (0.02%)	1 (0.02%)	1 (0.02%)	1 (0.02%)	2(28.6%)

TEST	MAX	MIN	MC	MDN
RI-8	10.76	1.64	3.33	1.50
RI-16	11.05	3.82	2.28	1.33
RI-24	11.07	4.28	2.29	1.35
RI-32	11.62	6.79	2.03	1.49
RI-40	11.82	9.09	9.09	1.64
RI-48	11.15	6.33	2.12	1.41
Average RI	11.24 (4)	5.32(3)	3.52(2)	1.45 (1)
RZ-8	0.85	1.19	0.94	0.85
RZ-16	2.45	2.98	1.20	0.91
RZ-24	2.91	3.44	1.26	0.92
RZ-32	2.69	5.26	1.55	1.23
RZ-40	2.73	7.46	2.18	1.70
RZ-48	3.65	5.11	1.42	1.01
Average RIZ	2.55 (3)	4.24(4)	1.43(2)	1.10(1)

TEST	Normal				reference subtraction
TEST	MAX	MIN	MC	MDN	MAX	MIN	MC	MDN
RI-8	3.52	5.97	3.99	4.08	1.97	0.82	1.41	1.18
RI-16	3.31	6.90	3.88	4.00	2.06	2.21	1.19	1.00
RI-24	3.19	7.40	3.80	3.88	1.85	4.00	1.25	1.22
RI-32	3.17	8.31	3.88	3.95	2.19	4.27	1.21	1.11
RI-40	2.99	9.31	3.80	3.92	2.17	4.84	1.13	1.08
RI-48	2.95	9.54	3.79	3.91	2.09	5.22	1.18	0.96
Average RI	3.19	7.91	3.86	3.96	2.05	3.56	1.23	1.09
RI-Z-8	4.79	5.78	5.05	5.08	0.62	0.56	0.51	0.49
RI-Z-16	4.75	6.71	5.16	5.14	0.88	1.41	0.54	0.44
RI-Z-24	4.49	7.28	5.02	5.04	1.08	3.55	1.46	1.43
RI-Z-32	4.21	8.05	4.98	4.98	0.95	2.93	0.63	0.52
RI-Z-40	3.96	8.01	4.77	4.79	0.98	3.86	0.77	0.65
RI-Z-48	3.93	8.65	4.63	4.57	1.17	4.22	0.74	0.50
Average RIZ	4.36	7.41	4.94	4.93	0.94	2.75	0.77	0.67

Composite	Number of images per percentage of information						Total	Percentage of observations 100% valid in the test
Composite	0 - 20%	21 - 40%	41 - 60%	61 - 80%	81 - 99%	100%
II-1	9	3	5	0	4	9	30	30.0
II-2	6	1	4	1	3	9	24	37.5
II-3	5	1	3	1	2	9	21	42.9
II-4	3	1	4	0	3	9	20	45.0
II-5	0	1	2	1	3	9	16	56.3
II-6	0	1	0	1	1	5	8	62.5

Composite images	Number of cloud-free images used in the 8-day composite image
	1	2	3	4	5	6	7	8	9	10	>10	Sum
Img 1/Aug	0	0	0	5	53.3	38.4	3.3	0	0	0	0	100
Img 2/Aug	0	0	0	0	1.1	40.4	56.4	2.1	0	0	0	100
Img 3/Aug	0	1.1	7	19.3	22.1	28.7	18.8	3.1	0	0	0	100
Img 4/Sep	0	0.4	1.2	7.2	16.2	40.8	28.8	5.4	0	0	0	100
Img 5/Sep	17.2	25.2	28.3	19	6.6	0.1	0	0	0	0	0	96.4
Img 6/Sep	22.8	25.7	35.6	7	1.9	0	0	0	0	0	0	92.9
Img 7/Oct	32.6	30.2	26.9	7.6	0.3	0	0	0	0	0	0	97.6
Img 8 /Oct	0.3	51.6	48	0.1	0	0	0	0	0	0	0	100
Img 9/ Nov	38	4.6	0	0	0	0	0	0	0	0	0	42.7
Img 10/Nov	42.2	8.5	0	0	0	0	0	0	0	0	0	50.7
Img 11/Dec	41	3.7	0	0	0	0	0	0	0	0	0	44.6
Img 12/Dec	1.2	0	0	0	0	0	0	0	0	0	0	1.2
Img 13/Jan	9.8	0.6	0	0	0	0	0	0	0	0	0	10.4
Img 14/Jan	4	0	0	0	0	0	0	0	0	0	0	4
Img 15/Feb	51.4	1.4	0	0	0	0	0	0	0	0	0	52.9
Img 16/Feb	1.4	0	0	0	0	0	0	0	0	0	0	1.4
Img 17/Mar	19.9	2.2	0	0	0	0	0	0	0	0	0	22.1
Img 18/Mar	21.7	0	0	0	0	0	0	0	0	0	0	21.7
Img 19/Apr	1.1	0.1	0	0	0	0	0	0	0	0	0	1.2
Img 20/Apr	14	0.1	0	0	0	0	0	0	0	0	0	14.2
Img 21/Apr	5.3	0	0	0	0	0	0	0	0	0	0	5.3
Img 22/May	13.7	0	0	0	0	0	0	0	0	0	0	13.7
Img 23/May	1.7	0	0	0	0	0	0	0	0	0	0	1.7
Img 24/May	51.8	32.6	3.3	0	0	0	0	0	0	0	0	87.7
Img 25/Jun	37.7	4.9	0.2	0	0	0	0	0	0	0	0	42.8
Img 26/Jun	19.9	5.8	0.3	0	0	0	0	0	0	0	0	26.1
Img 27/Jun	0	0	0	0.8	10.2	26.3	60.1	2.5	0	0	0	100
Img 28/Jul	0	1	15.3	60.8	19.9	3	0	0	0	0	0	100
Img 29/Jul	4.8	40.8	43.1	10.2	1.1	0	0	0	0	0	0	100
Img 30/Jul	0	0	0	0.5	26.4	63.1	9.9	0	0	0	0	100

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[1] AUTHORS’ CONTRIBUTION

Cristiane Batista Salgado and Osmar Abílio de Carvalho Junior conceptualized, curated the data, made the formal analysis, investigated, carried out the methodology, visualized, wrote - original draft, reviewed and edited. Furthermore, Osmar Abílio de Carvalho Junior worked with the software. Nickolas Castro Santana curated the data, made the formal analysis and investigated. Roberto Arnaldo Trancoso Gomes conceptualized curated the data, made the formal analysis, investigated, carried out the methodology. Renato Fontes Guimarães curated the data, made the formal analysis, investigated and carried out the methodology. Cristiano Rosa Silva carried out the methodology and the Software.