Using UAV-based thermal imagery to detect crop water status variability in cotton

Plant-based measurements such as leaf water potential (LWP) are widely used for irrigation scheduling because they are accurate at indicating when irrigation is needed. Despite being a good indicator, scheduling irrigation with LWP is time consuming and scale-limited. The work reported in this study...

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Autores principales:	Lorena N. Lacerda, John L. Snider, Yafit Cohen, Vasileios Liakos, Stefano Gobbo, George Vellidis
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Canopy temperature Thermal images Leaf water potential Crop water stress index Cotton UAV Agriculture (General) S1-972 Agricultural industries HD9000-9495
Acceso en línea:	https://doaj.org/article/961d0a6fc80b427fb5e1c11366076050
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spelling	oai:doaj.org-article:961d0a6fc80b427fb5e1c113660760502021-12-02T05:04:48ZUsing UAV-based thermal imagery to detect crop water status variability in cotton2772-375510.1016/j.atech.2021.100029https://doaj.org/article/961d0a6fc80b427fb5e1c113660760502022-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2772375521000290https://doaj.org/toc/2772-3755Plant-based measurements such as leaf water potential (LWP) are widely used for irrigation scheduling because they are accurate at indicating when irrigation is needed. Despite being a good indicator, scheduling irrigation with LWP is time consuming and scale-limited. The work reported in this study explored the potential of using thermal remote sensing to estimate cotton crop water status in the humid southeastern U.S.A. The study was conducted over two growing seasons (2018 and 2020) in southwestern Georgia, U.S.A using a complete randomized block design plot scheme with three irrigation treatments (0% ETc (crop evapotranspiration; rainfed), 100% ETc (well-irrigated), and 125% ETc (over-irrigated). To monitor the irrigation treatment effects on cotton physiological responses, predawn LWP (LWPPD), stomatal conductance (gs) and leaf area index (LAI) data were collected in both growing seasons. UAV-based images collected in the thermal infrared waveband were used to calculate crop water stress index (CWSI) based on three different methodologies and evaluated as predictors of LWPPD. Results in this study suggest that LWPPD values above -0.45 MPa indicate a non-stressed crop. No negative effects in leaf stomatal conductance and crop growth were observed in 2018. In 2020, the less and more irregular precipitation led to significant differences in LAI and gs, as well as in LWPPD. A moderate to strong relationship was observed for all dates in 2020, with the CWSI based on the Monteith approach (CWSIMonteith) showing the two highest R2 values among the 3 dates (0.65 and 0.58) with low RMSE values of 0.02 and 0.04 MPa, respectively. Overall, the results showed that there is potential of using an affordable UAV-based thermal system to produce predicted LWP maps that are representative of the current field water status.Lorena N. LacerdaJohn L. SniderYafit CohenVasileios LiakosStefano GobboGeorge VellidisElsevierarticleCanopy temperatureThermal imagesLeaf water potentialCrop water stress indexCottonUAVAgriculture (General)S1-972Agricultural industriesHD9000-9495ENSmart Agricultural Technology, Vol 2, Iss , Pp 100029- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Canopy temperature Thermal images Leaf water potential Crop water stress index Cotton UAV Agriculture (General) S1-972 Agricultural industries HD9000-9495
spellingShingle	Canopy temperature Thermal images Leaf water potential Crop water stress index Cotton UAV Agriculture (General) S1-972 Agricultural industries HD9000-9495 Lorena N. Lacerda John L. Snider Yafit Cohen Vasileios Liakos Stefano Gobbo George Vellidis Using UAV-based thermal imagery to detect crop water status variability in cotton
description	Plant-based measurements such as leaf water potential (LWP) are widely used for irrigation scheduling because they are accurate at indicating when irrigation is needed. Despite being a good indicator, scheduling irrigation with LWP is time consuming and scale-limited. The work reported in this study explored the potential of using thermal remote sensing to estimate cotton crop water status in the humid southeastern U.S.A. The study was conducted over two growing seasons (2018 and 2020) in southwestern Georgia, U.S.A using a complete randomized block design plot scheme with three irrigation treatments (0% ETc (crop evapotranspiration; rainfed), 100% ETc (well-irrigated), and 125% ETc (over-irrigated). To monitor the irrigation treatment effects on cotton physiological responses, predawn LWP (LWPPD), stomatal conductance (gs) and leaf area index (LAI) data were collected in both growing seasons. UAV-based images collected in the thermal infrared waveband were used to calculate crop water stress index (CWSI) based on three different methodologies and evaluated as predictors of LWPPD. Results in this study suggest that LWPPD values above -0.45 MPa indicate a non-stressed crop. No negative effects in leaf stomatal conductance and crop growth were observed in 2018. In 2020, the less and more irregular precipitation led to significant differences in LAI and gs, as well as in LWPPD. A moderate to strong relationship was observed for all dates in 2020, with the CWSI based on the Monteith approach (CWSIMonteith) showing the two highest R2 values among the 3 dates (0.65 and 0.58) with low RMSE values of 0.02 and 0.04 MPa, respectively. Overall, the results showed that there is potential of using an affordable UAV-based thermal system to produce predicted LWP maps that are representative of the current field water status.
format	article
author	Lorena N. Lacerda John L. Snider Yafit Cohen Vasileios Liakos Stefano Gobbo George Vellidis
author_facet	Lorena N. Lacerda John L. Snider Yafit Cohen Vasileios Liakos Stefano Gobbo George Vellidis
author_sort	Lorena N. Lacerda
title	Using UAV-based thermal imagery to detect crop water status variability in cotton
title_short	Using UAV-based thermal imagery to detect crop water status variability in cotton
title_full	Using UAV-based thermal imagery to detect crop water status variability in cotton
title_fullStr	Using UAV-based thermal imagery to detect crop water status variability in cotton
title_full_unstemmed	Using UAV-based thermal imagery to detect crop water status variability in cotton
title_sort	using uav-based thermal imagery to detect crop water status variability in cotton
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/961d0a6fc80b427fb5e1c11366076050
work_keys_str_mv	AT lorenanlacerda usinguavbasedthermalimagerytodetectcropwaterstatusvariabilityincotton AT johnlsnider usinguavbasedthermalimagerytodetectcropwaterstatusvariabilityincotton AT yafitcohen usinguavbasedthermalimagerytodetectcropwaterstatusvariabilityincotton AT vasileiosliakos usinguavbasedthermalimagerytodetectcropwaterstatusvariabilityincotton AT stefanogobbo usinguavbasedthermalimagerytodetectcropwaterstatusvariabilityincotton AT georgevellidis usinguavbasedthermalimagerytodetectcropwaterstatusvariabilityincotton
_version_	1718400607558041600

Using UAV-based thermal imagery to detect crop water status variability in cotton

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