REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES

Andrade Junior, Aderson S. de; Silva, Silvestre P. da; Setúbal, Ingrid S.; Souza, Henrique A. de; Vieira, Paulo F. de M. J.

doi:10.1590/1809-4430-Eng.Agric.v42n2e20210177/2022

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Scientific Paper • Eng. agríc. (Online) 42 (2) • 2022 • https://doi.org/10.1590/1809-4430-Eng.Agric.v42n2e20210177/2022 copy

REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p<0.001) with gs, standing out as promising indexes for detection of soybean water status. In turn, simplified canopy chlorophyll content index (SCCCI), red-edge chlorophyll index (RECI), green ratio vegetation index (GRVI), and chlorophyll vegetation index (CVI) were correlated with NF (p<0.001), thus being considered promising for the detection of leaf N content in soybeans.

KEYWORDS
Glycine max L.; RPA; vegetation indexes; gas exchange

Depth Layer (m)	Sand	Fine Sand	Silt	Clay	Textural Classification
Depth Layer (m)	--------------------------- g kg^-1 ----------------------------
0.0-0.2	267.3	470.6	116.0	146.1	Loam-sandy
0.2-0.4	231.6	424.0	105.5	238.9	Loam-clay-sandy

Depth Layer (m)	P mg/dm³	K	Ca	Mg	H+Al	SB	CEC	OM dag/kg	S %	pH (CaCl₂)
Depth Layer (m)	P mg/dm³	---------------------- cmolc/dm³ --------------------						OM dag/kg	S %	pH (CaCl₂)
0.0-0.2	10.15	0.23	2.40	0.83	1.18	3.45	4.63	1.03	74.5	5.50
0.2-0.4	5.41	0.22	2.63	0.83	1.34	3.68	5.01	0.91	73.4	5.38

Band/Index	Acronym	Equation	Reference
Green band	GREEN	/	/
Red band	RED	/	/
Red-Edge band	RED-EDGE	/	/
NIR band	NIR	/	/
Chlorophyll index red	CI-RED	$\frac{R_{n}}{R_{r}} - 1$	Gitelson et al. (2005)Gitelson AA, Vina A, Ciganda V, Rundquist DC, Arkebauer TJ (2005) Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32:1-4. DOI: https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688...
Chlorophyll vegetation index	CVI	$\frac{R_{n} R_{r}}{R_{g}^{2}}$	Vincini et al. (2008)Vincini M, Frazzi E, D’Alessio P (2008) A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture 9:303–319. DOI: https://doi.org/10.1007/s11119-008-9075-z https://doi.org/10.1007/s11119-008-9075-...
Enhanced vegetation index	EVI	$\frac{2.5 (R_{n} - R_{r})}{R_{n} + 6 R_{r} - 7.5 R_{b} + 1}$	Huete et al. (2002)Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83:195–213. DOI: https://doi.org/10.1016/S0034-4257(02)00096-2 https://doi.org/10.1016/S0034-4257(02)00...
Two-band enhanced vegetation index	EVI2	$\frac{2.5 (R_{n} - R_{r})}{R_{n} + 2.4 R_{r} + 1}$	Jiang et al. (2008)Jiang Z, Huete A, Miura T, Didan K (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112:3833-3845. DOI: https://doi.org/10.1016/j.rse.2008.06.006 https://doi.org/10.1016/j.rse.2008.06.00...
Excess green red	EXGR	$\frac{3 R_{g} - 2.4 R_{r} - R_{b}}{R_{r} + R_{g} + R_{b}}$	Meyer & Camargo Neto (2008)Meyer GE, Camargo Neto J (2008) Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture 63:282-293. DOI: https://doi.org/doi:10.1016/j.compag.2008.03.009 https://doi.org/doi:10.1016/j.compag.200...
Green chlorophyll index	GCI	$\frac{R_{n}}{R_{g}} - 1$	Gitelson et al. (2005)Gitelson AA, Vina A, Ciganda V, Rundquist DC, Arkebauer TJ (2005) Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32:1-4. DOI: https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688...
Green leaf index	GLI	$\frac{2 R_{g} - R_{r} - R_{b}}{2 R_{g} + R_{r} + R_{b}}$	Hunt et al. (2013)Hunt ER, Doraiswamy PC, McMurtrey JE, Daughtry CST, Perry EM, Akhmedov B (2013) A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observations 21:103–112. DOI: https://doi.org/10.1016/j.jag.2012.07.020 https://doi.org/10.1016/j.jag.2012.07.02...
Green normalized difference vegetation	GNDVI	$\frac{R_{n} - R_{g}}{R_{n} + R_{g}}$	Hunt e Daughtry (2018)Hunt ER, Daughtry CST (2018) What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing 39:5345–5376. DOI: https://doi.org/10.1080/01431161.2017.1410300 https://doi.org/10.1080/01431161.2017.14...
Green ratio vegetation index	GRVI	$\frac{R_{n}}{R_{g}}$	Sripada et al. (2006)Sripada RP, Heiniger RW, White J, Meijer AD (2006) Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal 98:968-977. DOI: https://doi.org/10.2134/agronj2005.0200 https://doi.org/10.2134/agronj2005.0200...
Modified excess green	MEXG	$1.262 R_{g} - 0.884 R_{r} - 0.311 R_{b}$	Burgos-Artizzu et al. (2011)Burgos-Artizzu XP, Ribeiro A, Guijarro M, Pajares G (2011) Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture 75:337–346. DOI: https://doi.org/10.1016/j.compag.2010.12.011 https://doi.org/10.1016/j.compag.2010.12...
Modified normalized green red difference	MNGRD	$\frac{R_{g}^{2} - R_{r}^{2}}{R_{g}^{2} + R_{r}^{2}}$	Bendig et al. (2015)Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp ML, Bareth G (2015) 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 Observations 39:79-87. DOI: https://doi.org/10.1016/j.jag.2015.02.012 https://doi.org/10.1016/j.jag.2015.02.01...
Normalized difference Red-Edge	NDRE	$\frac{R_{n} - R_{R E}}{R_{n} + R_{R E}}$	Wang et al. (2019)Wang S, Azzari G, Lobell DB (2019) Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote Sensing of Environment 222:303–317. DOI: https://doi.org/10.1016/j.rse.2018.12.026 https://doi.org/10.1016/j.rse.2018.12.02...
Normalized difference Red-Edge index	NDREI	$\frac{R_{R E} - R_{g}}{R_{R E} + R_{g}}$	Hassan et al. (2018)Hassan MA, Mengjiao Y, Awais R, Xiuliang J, Xianchun X, Yonggui X, Zhonghu H (2018) Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat. Remote Sensing 10:809-818. DOI: https://doi.org/10.3390/rs10060809 https://doi.org/10.3390/rs10060809...
Normalized difference vegetation index	NDVI	$\frac{R_{n} - R_{r}}{R_{n} + R_{r}}$	Gitelson et al. (2005)Gitelson AA, Vina A, Ciganda V, Rundquist DC, Arkebauer TJ (2005) Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32:1-4. DOI: https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688...
Normalized green	NG	$\frac{R_{g}}{R_{r} + R_{g} + R_{b}}$	Woebbecke et al. (1995)Woebbecke DM, Meyer GE, Bargen KV, Mortensen DA (1995) Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38(1):259-269. DOI: https://doi.org/10.13031/2013.27838 https://doi.org/10.13031/2013.27838...
Normalized green red difference	NGRD	$\frac{R_{g} - R_{r}}{R_{g} + R_{r}}$	Hamuda et al. (2016)Hamuda E, Glavin M, Jones E (2016) A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture 125:184-199. DOI: https://doi.org/10.1016/j.compag.2016.04.024 https://doi.org/10.1016/j.compag.2016.04...
Optimized Soil Adjusted Vegetation Index	OSAVI	$\frac{R_{n} - R_{r}}{R_{n} + R_{r} + 0.16}$	Rondeaux et al. (1996)Rondeaux G, Steven M, Baret F (1996) Optimization of soil adjusted vegetation indices. Remote Sensing of Environment 55:95-107. DOI: https://doi.org/10.1016/0034-4257(95)00186-7 https://doi.org/10.1016/0034-4257(95)001...
Pigment-specific normalized difference index	PSND	$\frac{R_{n} - R_{b}}{R_{n} + R_{b}}$	Blackburn (1998)Blackburn, GA (1998) Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment 66:273-285. DOI: https://doi.org/10.1016/S0034-4257(98)00059-5 https://doi.org/10.1016/S0034-4257(98)00...
Renormalized Difference Vegetation Index	RDVI	$\frac{R_{n} - R_{r}}{{(R_{n} - R_{r})}^{0.5}}$	Roujean & Breon (1995)Roujean JL, Breon FM (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment 51:375–384. DOI: https://doi.org/10.1016/0034-4257(94)00114-3 https://doi.org/10.1016/0034-4257(94)001...
Red-Edge chlorophyll index	RECI	$\frac{R_{n}}{R_{R E}} - 1$	Gitelson et al. (2005)Gitelson AA, Vina A, Ciganda V, Rundquist DC, Arkebauer TJ (2005) Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32:1-4. DOI: https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688...
Red-green difference	GDPR	$R_{r} - R_{g}$	Sanjerehei (2014)Sanjerehei, MM (2014) Assessment of spectral vegetation indices for estimating vegetation cover in arid and semiarid shrublands. Range Management Agroforest 35(1):91–100. DOI: https://www.cabdirect.org/cabdirect/abstract/20143381736 https://www.cabdirect.org/cabdirect/abst...
Ratio vegetation index	RVI	$\frac{R_{n}}{R_{r}}$	Tucker (1979)Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8:127-150. 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	$\frac{1.5 (R_{n} - R_{r})}{(R_{n} + R_{r} + 0.5)}$	Zhong et al. (2019)Zhong L, Hu L, Zhou H (2019) Deep learning-based multi-temporal crop classification. Remote Sensing of Environment 221:430–443. DOI: https://doi.org/10.1016/j.rse.2018.11.032 https://doi.org/10.1016/j.rse.2018.11.03...
Simplified canopy chlorophyll content index	SCCCI	$\frac{N D R E}{N D V I}$	Raper & Varco (2015)Raper TB, Varco JJ (2015) Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status. Precision Agriculture 16:62-76. DOI: https://doi.org/10.1007/s11119-014-9383-4 https://doi.org/10.1007/s11119-014-9383-...
Transformed chlorophyll absorption in Nir index	TCARI	$3 [(R_{n} - R_{r}) - 0.2 (R_{n} - R_{g}) (\frac{R_{n}}{R_{r}})]$	Haboudane et al. (2002)Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81:416-426. DOI: https://doi.org/10.1016/S0034-4257(02)00018-4 https://doi.org/10.1016/S0034-4257(02)00...
TCARI/OSAVI index	TCARI-OSAVI	$\frac{T C A R I}{O S A V I}$	Haboudane et al. (2002)Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81:416-426. DOI: https://doi.org/10.1016/S0034-4257(02)00018-4 https://doi.org/10.1016/S0034-4257(02)00...
Transformed chlorophyll absorption in the Red-edge index	TCARI-RE	$3 [(R_{R E} - R_{r}) - 0.2 (R_{R E} - R_{g}) (\frac{R_{R E}}{R_{r}})]$	Daughtry et al. (2000)Daughtry CST, Walthall CL, Kim MS, Colstoun EB, McMurtrey JE (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74:229-239. DOI: https://doi.org/10.1016/S0034-4257(00)00113-9 https://doi.org/10.1016/S0034-4257(00)00...
TCARI/OSAVI-RE index	TCARI-OSAVI-RE	$\frac{T C A R I_{R E}}{O S A V I}$	Daughtry et al. (2000)Daughtry CST, Walthall CL, Kim MS, Colstoun EB, McMurtrey JE (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74:229-239. DOI: https://doi.org/10.1016/S0034-4257(00)00113-9 https://doi.org/10.1016/S0034-4257(00)00...
Triangular greenness index	GIT	$R_{g} - (0.39 R_{r}) - (0.61 R_{b})$	Hunt et al. (2011)Hunt ERJ, Daughtry, CST, Eitel JUH, Long DS (2011) Remote sensing leaf chlorophyll content using a visible band index. Agronomy Journal 103:1090-1099. DOI: https://doi.org/10.2134/agronj2010.0395 https://doi.org/10.2134/agronj2010.0395...
Wide dynamic range vegetation index	WDRVI	$\frac{(0.12 R_{n}) - R_{r}}{(0.12 R_{n}) + R_{r}}$	Gitelson (2004)Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology 161:165-173. DOI: https://doi.org/10.1078/0176-1617-01176 https://doi.org/10.1078/0176-1617-01176...
Weighted difference vegetation index	WDVI	$R_{n} - R_{r} (\frac{R_{n}^{'}}{R_{r}^{'}})$	Clevers (1989)Clevers JGPW (1989) The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sensing of Environment 29:25-37. DOI: https://doi.org/10.1016/0034-4257(89)90076-X https://doi.org/10.1016/0034-4257(89)900...

WR (% ETc)	S – IT	IT – TT	TT – TI	Season
BONUS
50 % ETc	125.3	132.9	87.6	345.8
100 % ETc	127.1	252.8	85.3	465,2
BRS-8980
50 % ETc	125.3	174.6	56.1	356.0
100 % ETc	127.1	326.5	57.7	511.3

VI	BRS-8980			BONUS			VI	BRS-8980			BONUS
VI	WR	NS	WR*NS	WR	NS	WR*NS	VI	WR	NS	WR*NS	WR	NS	WR*NS
gs	^** * p <0.01;	ns	ns				OSAVI	^* ***** p <0.001;	ns	ns	^** * p <0.01;	ns	ns
NF				^* * p <0.05;	^* * p <0.05;	ns	PSND	^** * p <0.01;	ns	ns	^** * p <0.01;	^* * p <0.05;	ns
CI-RED	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns	RDVI	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns
CVI	^* ***** p <0.001;	^* ***** p <0.001;	ns	^* ***** p <0.001;	ns	ns	RECI	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns
EVI	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns	RED	ns	ns	ns	^** * p <0.01;	^* * p <0.05;	ns
EVI2	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns	RED-EDGE	^* * p <0.05;	ns	ns	^** * p <0.01;	^* * p <0.05;	ns
EXGR	^* * p <0.05;	^* ***** p <0.001;	ns	^* * p <0.05;	^** * p <0.01;	ns	GDPR	^* * p <0.05;	ns	ns	ns	ns	ns
CI	^* ***** p <0.001;	^** * p <0.01;	ns	^* ***** p <0.001;	ns	ns	RVI	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns
GLI	^** * p <0.01;	^* ***** p <0.001;	ns	ns	^** * p <0.01;	ns	SAVI	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns
GNDVI	^* ***** p <0.001;	^* * p <0.05;	^* * p <0.05;	^** * p <0.01;	ns	ns	SCCCI	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns
GREEN	^* * p <0.05;	^** * p <0.01;	ns	^** * p <0.01;	ns	^* * p <0.05;	ICART	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns
GRVI	^* ***** p <0.001;	^** * p <0.01;	ns	^* ***** p <0.001;	ns	ns	TCARI-OSAVI	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns
MEXG	^** * p <0.01;	^* ***** p <0.001;	ns	^** * p <0.01;	ns	ns	TCARI-OSAVI-RE	ns	^* * p <0.05;	ns	^** * p <0.01;	^* * p <0.05;	^* * p <0.05;
MNGRD	ns	^* ***** p <0.001;	ns	^** * p <0.01;	^** * p <0.01;	ns	TCARI-RE	ns	^* * p <0.05;	ns	^** * p <0.01;	^* * p <0.05;	^* * p <0.05;
NDRE	^* ***** p <0.001;	^* * p <0.05;	ns	^* ***** p <0.001;	ns	ns	GIT	^** * p <0.01;	^* ***** p <0.001;	ns	^** * p <0.01;	ns	ns
NRDEI	^** * p <0.01;	^* * p <0.05;	ns	^** * p <0.01;	ns	ns	WDRVI	^* ***** p <0.001;	ns	ns	^** * p <0.01;	ns	^* * p <0.05;
NDVI	^* ***** p <0.001;	ns	ns	^** * p <0.01;	ns	ns	WDRVI-1	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	^* * p <0.05;
NG	^** * p <0.01;	^* ***** p <0.001;	ns	ns	^** * p <0.01;	ns	WDRVI-2	^* ***** p <0.001;	ns	ns	^** * p <0.01;	ns	ns
NGRD	ns	^* ***** p <0.001;	ns	^** * p <0.01;	^** * p <0.01;	ns	WDVI	ns	ns	ns	ns	ns	^* * p <0.05;
NIR	^* ***** p <0.001;	ns	ns	^* ***** p <0.001;	ns	ns

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. Embrapa Meio-Norte/ Teresina - PI, Brazil. E-mail: aderson.andrade@embrapa.br