Detecção do estado hídrico em híbridos de milho com imagens aéreas obtidas por aeronave remotamente pilotada

Lopes, Alzeneide da S.; Andrade Júnior, Aderson S. de; Bastos, Edson A.; Sousa, Carlos A. F. de; Casari, Raphael A. das C. N.; Moura, Magna S. B. de

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Scientific Article • Rev. Caatinga 37 • 2024 • https://doi.org/10.1590/1983-21252024v3711701rc copiar

Detecção do estado hídrico em híbridos de milho com imagens aéreas obtidas por aeronave remotamente pilotada

Autoria SCIMAGO INSTITUTIONS RANKINGS

RESUMO

Objetivou-se avaliar a capacidade de índices de vegetação (IV), obtidos de imagens aéreas por aeronave remotamente pilotada (ARP), em detectar o estado hídrico de híbridos de milho submetidos a diferentes regimes hídricos, nas condições de solo e clima de Teresina, Piauí, Brasil. Avaliou-se a aplicação de cinco regimes hídricos (RHs) com base na evapotranspiração da cultura (ETc) (40%, 60%, 80%, 100% e 120% da ETc) em três híbridos de milho: BRS 3046 (híbrido triplo convencional); BRS 2022 (híbrido duplo convencional); e Status VIP3(híbrido simples transgênico). O delineamento experimental foi o de blocos ao acaso, parcelas subdivididas, sendo as parcelas os RHs e as subparcelas os híbridos, com quatro repetições. Utilizou-se uma ARP para a aquisição das imagens multiespectrais. Avaliaram-se 18 índices de vegetação, os quais foram correlacionados com medidas de condutância estomática (gs), conteúdo relativo de água na folha (CRA) e produtividade de grãos (PG). Os IVs TCARI-RE e NDVI apresentaram correlação com gs e os IVs MNGRD e GCI foram correlacionados com o CRA e, portanto, são considerados promissores na detecção do estado hídrico do milho. Os IVs NDVI e WDRVI apresentaram correlações com a PG. Os mapas de NDVI, MNGRV e WDRVI mostraram correlação espacial com as medidas de gs, CRA e PG, respectivamente em resposta aos RHs, indicando aplicação potencial na detecção do estado hídrico do milho por meio de imagens aéreas obtidas por ARPs.

Palavras-chave:
Drone; Cultivar; Estresse hídrico; Índice de vegetação; Sensoriamento remoto

Layer (m)	OM	pH	P	K	Mg	Ca	Na	CEC	BS
Layer (m)	g kg^-1	H₂O	mg dm^-3	cmolc dm^-3					%
0.0-0.2	12.9	5.78	31.12	0.09	0.35	0.78	0.02	2.94	42.32
0.2-0.4	11.2	5.95	23.49	0.09	0.42	0.73	0.02	2.89	44.11

Layer (m)	Density (g cm^-3)	Sand	Silt	Clay	Өcc	Өpmp
Layer (m)	Density (g cm^-3)	------------------------- g kg^-1 -------------------------			(%, volume)
0.0-0.2	1.70	876.5	37.5	86.0	21.7	5.3
0.2-0.4	1.65	811.5	52.5	136.0	20.8	6.0

Period	DAS	40% ETc	60% ETc	80% ETc	100% ETc	120% ETc
Aug 05 to Sep 09, 2019	0–36	184.0	184.6	186.4	182.1	179.3
Sep 10 to Sep 24, 2019*	37–50	37, 2	57.0	74.2	91.6	108.8
Sep 25 to Nov 12 2019**	51– 99	114.8	165.2	210.7	272.3	330.5
Total	0–99	336.0	406.8	471.3	546.0	618.6

Vegetation index	Equation Mathematical	Reference
Green chlorophyll index	$G C I = \frac{R_{n}}{R_{g}} - 1$	Gitelson et al. (2005)GITELSON, A. A. et al. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: 1-4, 2005.
Modified excess green	MEXG = 1.262R_g - 0.884R_r - 0.311R_b	Burgos-Artizzu et al. (2011)BURGOS-ARTIZZU, X. P. et al. Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75: 337-346, 2011.
Modified normalized green red difference	$M N G R D = \frac{R_{g}^{2} - R_{r}^{2}}{R_{g}^{2} + R_{r}^{2}}$	Bendig et al. (2015)BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39: 79-87, 2015.
Normalized difference Red-Edge –	$N D R E = \frac{R_{n} - R_{R E}}{R_{n} + R_{R E}}$	Wang, Azzari and Lobell (2019)WANG, S.; AZZARI, G.; LOBELL, D. B. Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote Sensing of Environment, 222: 303-317, 2019.
Normalized difference Red-Edge index	$N D R E I = \frac{R_{R E} - R_{g}}{R_{R E} + R_{g}}$	Hassan et al. (2018)HASSAN, M. A. et al. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sensing, 10: 809-828, 2018.
Normalized Difference Vegetation Index	$N D V I = \frac{R_{n} - R_{r}}{R_{n} + R_{r}}$	Gitelson et al. (2005)GITELSON, A. A. et al. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: 1-4, 2005.
Normalized green red difference –	$N G R D = \frac{R_{g} - R_{r}}{R_{g} + R_{r}}$	Hamuda, Glavin and Jones (2016)HAMUDA, E.; GLAVIN, M.; JONES, E. A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125: 184-199, 2016.
Pigment-specific normalized difference index	$P S N D = \frac{R n - R b}{R n + R b}$	Blackburn (1998)BLACKBURN, G. A. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66: 273-285, 1998.
Red-Edge chlorophyll index	$R E C I = \frac{R_{n}}{R_{R E}} - 1$	Gitelson et al. (2005)GITELSON, A. A. et al. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: 1-4, 2005.
Red green difference –	RGD =R_r - R_g	Sanjerehei (2014)SANJEREHEI, M. M. Assessment of spectral vegetation indices for estimating vegetation cover in arid and semiarid shrublands. Range Management & Agroforest, 35: 91-100, 2014.
Ratio vegetation index RVI	$R V I = \frac{R_{n}}{R_{r}}$	Tucker (1979)TUCKER, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127-150, 1979.
Soil Adjusted Vegetation Index	$S A V I = \frac{1.5 (R_{n} - R_{r})}{(R_{n} + R_{r} + 0.5)}$	Zhong, Hu and Zhou (2019)ZHONG, L.; HU, L.; ZHOU, H. Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221: 430-443, 2019.
Transformed chlorophyll absorption in Nir index	TCARI = 3[(R_n - R_r) - 0.2(R_n - R_g)(R_n/R_r)]	Haboudane et al. (2002)HABOUDANE, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81: 416-426, 2002.
TCARI/OSAVI index	$T C A R I - O S A V I = \frac{T C A R I}{O S A V I}$	Haboudane et al. (2002)HABOUDANE, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81: 416-426, 2002.
Transformed chlorophyll absorption in Red-edge index	$T C A R I - R E = 3 [(R_{R E} - R_{r}) - 0.2 (R_{R E} - R_{g}) (\frac{R_{R E}}{R_{r}})]$	Daughtry et al. (2000)DAUGHTRY, C. S. T. et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74: 229-239, 2000.
Wide dynamic range vegetation index	$W D R V I = \frac{(0.12 R_{n}) - R_{r}}{(0.12 R_{n}) + R_{r}}$	Gitelson (2004)GITELSON, A. A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161: 165-173, 2004.

Source of Variation	DF	gs	RWC	GY
Water regime (WR)	4	14995*	490.940***	93362560***
Block	3	5371	80.110	3813255
Error a	12	3309	40.720	495152
Hybrid (H)	2	2211^ns	107.850^ns	7644094***
RH×H	8	7003^ns	28.410^ns	6402201***
Error b	30	4873	41.640	787322
Total	59
CV WR (%)		22.97	8.50	15.23
CV H (%)		27.87	8.60	19.21

VI	WR	H	WR×H	VI	WR	H	WR×H
GCI	*	**	ns	RECI	***	ns	ns
MEXG	*	***	*	RVI	***	ns	ns
MNGRD	*	**	ns	SAVI	***	ns	ns
NDRE	***	***	ns	TCARI	***	***	ns
NRDEI	*	ns	ns	TCARI-OSAVI	***	***	ns
NDVI	**	ns	ns	RGD	ns	ns	ns
NGRD	*	***	ns	TCARI-RE	**	***	ns
PSND	***	ns	ns	WDRVI	***	**	ns

VI	gs	RWC	GY	VI	gs	RWC	GY
GCI	-0.860^**	-0.920**	-0.62ns	RDVI	0.722^ns	0.563^ns	0.911^**
MEXG	-0.479^ns	-0.214^ns	-0.737^ns	RVI	0.882^**	0.740^ns	0.949^**
MNGRD	0.872^**	0.925^**	0.635^ns	SAVI	0.720^ns	0.557^ns	0.909^**
NDRE	0.796^ns	0.603^ns	0.923^**	TCARI	-0.798^ns	-0.627^ns	-0.927^**
NDREI	0.169^ns	0.394^ns	0.487^ns	TCARI-OSAVI	-0.816^**	-0.653^ns	-0.934^**
NDVI	0.946**	0.875**	0.928^**	TCARI-RE	-0.928^**	-0.861^**	-0.939^**
NGRD	0.877^ns	0.916**	0.640^ns	RGD	-0.026^ns	-0.254^ns	0.288^ns
PSND	0.908^**	0.803 ^ns	0.920^**	WDRVI	0.919**	0.813**	0.948**

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[1] *Corresponding author: <agro.neide@hotmail.com>