MANAGEMENT CLASS DELIMITATION IN A SOYBEAN CROP USING ORBITAL IMAGES

Zanella, Marco A.; Queiroz, Daniel M. de; Valente, Domingos S. M.; Pinto, Francisco de A. de C.; Santos, Nerilson T.

doi:10.1590/1809-4430-Eng.Agric.v39n5p676-683/2019

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Scientific Paper, Precision Agricultural • Eng. agríc. (Online) 39 (5) • Sep-Oct 2019 • https://doi.org/10.1590/1809-4430-Eng.Agric.v39n5p676-683/2019 copy

MANAGEMENT CLASS DELIMITATION IN A SOYBEAN CROP USING ORBITAL IMAGES

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The delimitation of management classes is critical for successful precision agriculture. This process involves choosing the variables to use and analyzing the spatial variability of the variables. Thus, the objective of this study was to analyze the correlation between management class maps generated from orbital images and yield maps. A 95-hectare area of rainfed grain was evaluated. Yield maps were obtained for the 2015/2016 and 2016/2017 soybean crops. Orbital images were used from two dates for each crop to generate vegetation index maps. The spatial correlation between the vegetation indices and the yield maps was obtained using a bivariate Moran index. The delineated management classes were compared using the Kappa index. This study demonstrated that the Kappa values resulting from the comparison between the management class maps generated from the soybean yield and the vegetation index ranged from 5% to 67% depending on the number of delineated classes. The highest Kappa values were observed when the area was delineated into three classes.

KEYWORDS
precision agriculture; vegetation index; yield map

Crop season	Sowing date	Harvesting date	Platform	Imaging date	Number of days after sowing
2015/16	10/26/2015	03/02-04/2016	Landsat-8 Landsat-7	12/17/2015 01/26/2016	52 92
2016/17	10/29/2016	03/23-24/2017	Sentinel-2 Sentinel-2	01/08/2017 02/17/2017	77 111

Generic band name	Platform
Generic band name	Landsat-7	Landsat-8	Sentinel-2
Green	2 (520-600)	3 (530-590)	3 (543-578)
Red	3 (630-690)	4(640-670)	4 (650-680)
Near Infrared (NIR)	4 (770-900)	5(850-880)	8A (849-882)
Short Wave Infrared (SWIR)	5 (1550-1750)	6(1570-1650)	11 (1565-1655)

Vegetation Index	Equation	Author
Green Chlorophyll Vegetation Index (GCVI)	$\frac{ρ_{N I R}}{ρ_{g r e e n}} - 1$	(Gitelson et al., 2003Gitelson AA, Viña A, Arkebauer TJ, Rundquist D, Keydan G, Leavitt B (2003) Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters 30(5);4-7. DOI: https://doi.org/10.1029/2002GL016450 https://doi.org/10.1029/2002GL016450... )
Shortwave Infrared Water Stress Index (SIWSI)	$\frac{ρ_{S W I R} - ρ_{N I R}}{ρ_{S W I R} + ρ_{N I R}}$	(Fensholt & Sandholt, 2003Fensholt R, Sandholt I (2003) Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment. Remote Sensing of Environment 87(1): 111-121. DOI: https://doi.org/10.1016/j.rse.2003.07.002 https://doi.org/10.1016/j.rse.2003.07.00... )
Two band Enhanced Vegetation Index (EVI2)	$2.5 (\frac{ρ_{N I R} - ρ_{r e d}}{1 + ρ_{N I R} + 2.4 ρ_{r e d}})$	(Jiang et al., 2008Jiang Z, Huete AR, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10):3833-3845. DOI: https://doi.org/10.1016/j.rse.2008.06.006 https://doi.org/10.1016/j.rse.2008.06.00... )
Normalized Difference Vegetation Index (NDVI)	$\frac{ρ_{N I R} - ρ_{r e d}}{ρ_{N I R} + ρ_{r e d}}$	(Tucker, 1979Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8(2):127-150. DOI: https://doi.org/10.1016/0034-4257(79)90013-0 https://doi.org/10.1016/0034-4257(79)900... )
Soil-Adjusted Vegetation Index (SAVI)	$\frac{(ρ_{N I R} - ρ_{r e d})}{(ρ_{N I R} + ρ_{r e d} + L)} (1 - L)$	(Huete, 1988Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25(3):295-309. DOI: https://doi.org/10.1016/0034-4257(88)90106-X https://doi.org/10.1016/0034-4257(88)901... )
Modified Simple Ratio (MSR)	$\frac{(\frac{ρ_{N I R}}{ρ_{r e d}}) - 1}{\sqrt{(\frac{ρ_{N I R}}{ρ_{r e d}}) + 1}}$	(Chen, 1996Chen JM (1996) Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing 22(3):229-242. DOI: https://doi.org/10.1080/07038992.1996.10855178 https://doi.org/10.1080/07038992.1996.10... )

Crop season	Date of image acquisition	Indices	2 classes	3 classes	4 classes
2015/2016	52 DAS^* * DAS: Days after sowing.	Indices	GCVI - MSR NDVI	60.6% 29.9%	60.8% 61.2%	9.5% 13.7%
	92 DAS	SIWSI NDVI	21.3% 19.4%	47.5% 47.1%	8.2% 6.4%
	Using both dates together	GCVI - MSR - SIWSI NDVI	67.3% 29.7%	35.5% 42.8%	11.9% 7.4%
2016/2017	77 DAS	– NDVI	– 14.6%	– 46.8%	– 3.3%
	111 DAS	SIWSI NDVI	18.6% 16.6%	57.9% 56.2%	15.3% 16.5%
	Using both dates together	SIWSI NDVI	18.6% 14.6%	57.9% 47.1%	15.3% 8.1%
Using both crop seasons	– –	Vegetation that presented a positive correlation with the crop yield NDVI obtained on the four dates of acquisition	21.7% 18.5%	46.3% 42.6%	10.5% 20.3%

Number of classes	Yield Map		Vegetation Indices		NDVI
Number of classes	FPI	MPE	FPI	MPE	FPI	MPE
2	0.5543	0.6186	0.6468^* * Minimum value of FPI or MPE index, representing the optimum number of management classes.	0.7122^* * Minimum value of FPI or MPE index, representing the optimum number of management classes.	0.7663^* * Minimum value of FPI or MPE index, representing the optimum number of management classes.	0.8158
3	0.4942^* * Minimum value of FPI or MPE index, representing the optimum number of management classes.	0.5403^* * Minimum value of FPI or MPE index, representing the optimum number of management classes.	0.6926	0.7221	0.7874	0.8121^* * Minimum value of FPI or MPE index, representing the optimum number of management classes.
4	0.5679	0.5764	0.7236	0.7280	0.8271	0.8267

Analyzed Class	Class Type	Minimum	Maximum	Mean Value	SD	CV
Class 1 using vegetation indices	Low	37.6	45.8	41.8a	2.41	0.06
Class 2 using vegetation indices	High	36.2	50.8	44.0b	3.80	0.09
Class 1 using NDVI with 2 classes	Low	37.6	45.1	41.3a	1.98	0.05
Class 2 using NDVI with 2 classes	High	36.2	50.8	44.5b	3.75	0.08
Class 1 using crop yield	Low	37.6	43.4	43.1a	1.94	0.05
Class 2 using crop yield	Average	36.2	49.7	44.0b	4.83	0.11
Class 3 using crop yield	High	37.6	50.8	43.2a	3.41	0.08
Class 1 using NDVI with 3 classes	Low	36.2	43.4	40.7a	2.27	0.06
Class 2 using NDVI with 3 classes	High	37.8	49.7	44.7b	3.30	0.07
Class 3 using NDVI with 3 classes	Average	40.7	50.8	45.1b	3.34	0.07

Associação Brasileira de Engenharia Agrícola SBEA - Associação Brasileira de Engenharia Agrícola, Departamento de Engenharia e Ciências Exatas FCAV/UNESP, Prof. Paulo Donato Castellane, km 5, 14884.900 | Jaboticabal - SP, Tel./Fax: +55 16 3209 7619 - Jaboticabal - SP - Brazil
E-mail: revistasbea@sbea.org.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] *Corresponding author. Universidade Federal de Viçosa/ Viçosa - MG, Brasil. E-mail: marco.a.zanena@gmail.com