Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches
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Springer
2023-02-01
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| Subjects: | Grain quality, Machine learning, Nitrogen, Random forest, Short-wave infrared, Yield prediction, |
| Online Access: | http://hdl.handle.net/10261/310698 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/100012818 https://api.elsevier.com/content/abstract/scopus_id/85147176982 |
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Grain quality Machine learning Nitrogen Random forest Short-wave infrared Yield prediction Grain quality Machine learning Nitrogen Random forest Short-wave infrared Yield prediction |
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Grain quality Machine learning Nitrogen Random forest Short-wave infrared Yield prediction Grain quality Machine learning Nitrogen Random forest Short-wave infrared Yield prediction Pancorbo, J. L. Alonso-Ayuso, María Camino, Carlos Raya-Sereno, María D. Zarco-Tejada, Pablo J. Molina, I. Gabriel, José Luis Quemada, Miguel Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches |
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24 Pág. |
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Ministerio de Economía y Competitividad (España) |
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Ministerio de Economía y Competitividad (España) Pancorbo, J. L. Alonso-Ayuso, María Camino, Carlos Raya-Sereno, María D. Zarco-Tejada, Pablo J. Molina, I. Gabriel, José Luis Quemada, Miguel |
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artículo |
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Grain quality Machine learning Nitrogen Random forest Short-wave infrared Yield prediction |
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Pancorbo, J. L. Alonso-Ayuso, María Camino, Carlos Raya-Sereno, María D. Zarco-Tejada, Pablo J. Molina, I. Gabriel, José Luis Quemada, Miguel |
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Pancorbo, J. L. |
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Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches |
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Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches |
| title_full |
Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches |
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Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches |
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Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches |
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airborne hyperspectral and sentinel imagery to quantify winter wheat traits through ensemble modeling approaches |
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Springer |
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2023-02-01 |
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http://hdl.handle.net/10261/310698 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/100012818 https://api.elsevier.com/content/abstract/scopus_id/85147176982 |
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1802820001202176000 |
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dig-ias-es-10261-3106982024-05-18T21:01:32Z Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches Pancorbo, J. L. Alonso-Ayuso, María Camino, Carlos Raya-Sereno, María D. Zarco-Tejada, Pablo J. Molina, I. Gabriel, José Luis Quemada, Miguel Ministerio de Economía y Competitividad (España) Ministerio de Educación (España) Comunidad de Madrid European Commission Agencia Estatal de Investigación (España) Pancorbo, J. L. [0000-0003-1837-7589] Alonso-Ayuso, M. [0000-0002-4401-790X] Camino, C. [0000-0001-5188-4406] Raya-Sereno, M. D. [0000-0003-2249-4737] Zarco-Tejada, Pablo J. [0000-0003-1433-6165] Gabriel, José Luis [0000-0002-5508-4120] Quemada, Miguel [0000-0001-5793-2835] Grain quality Machine learning Nitrogen Random forest Short-wave infrared Yield prediction 24 Pág. Early prediction of crop production by remote sensing (RS) may help to plan the harvest and ensure food security. This study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery. Ground-truth wheat traits were measured at flowering and harvest in a field experiment combining four N and two water levels in central Spain over 2 years. Hyperspectral and thermal airborne images coincident with Sentinel-1 and Sentinel-2 were acquired at flowering. A parametric linear model using all hyperspectral normalized difference spectral indices (NDSI) and two non-parametric models (artificial neural network and random forest) were used to assess their estimation ability combining NDSIs and other RS indicators. The feasibility of using freely available multispectral satellite was tested by applying the same methodology but using Sentinel-1 and Sentinel-2 bands. Yield estimation obtained the highest R2 value, showing that the visible and short-wave infrared region (VSWIR) had similar accuracy to the hyperspectral and Sentinel-2 imagery (R2 ≈ 0.84). The SWIR bands were important in the GPC estimation with both sensors, whereas N output was better estimated using red-edge-based NDSIs, obtaining satisfactory results with the hyperspectral sensor (R2 = 0.74) and with the Sentinel-2 (R2 = 0.62). When including the Sentinel-2 SWIR index, the NDSI (B11, B3) improved the estimation of N output (R2 = 0.71). Ensemble models based on Sentinel were found to be as reliable as those based on hyperspectral imagery, and including SWIR information improved the quantification of N-related traits. This study was funded by the Ministerio de Economía y Competitividad (Spain; PID2021-124041OB-C21/C22; PRE2018-084215) and Ministerio de Educación (Spain; FPU17/01251), Comunidad de Madrid, Spain (AGRISOST-CM S2018/BAA-4330 project) and Structural Funds 2014-2020 (ERDF and ESF). The authors gratefully acknowledge the staff from La Chimenea research station (IMIDRA) for their technical support during the field campaigns, Javier López Llorens for his laboratory work, and QuantaLab-IAS-CSIC staff members A. Hornero, A. Vera, D. Notario, and R. Romero for airborne and laboratory assistance. Peer reviewed 2023-06-06T07:15:24Z 2023-06-06T07:15:24Z 2023-02-01 artículo http://purl.org/coar/resource_type/c_6501 Precision Agriculture: (2023) 1385-2256 http://hdl.handle.net/10261/310698 10.1007/s11119-023-09990-y 1573-1618 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/100012818 2-s2.0-85147176982 https://api.elsevier.com/content/abstract/scopus_id/85147176982 en #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI//PID2021-124041OB-C21 info:eu-repo/grantAgreement/AEI//PID2021-124041OB-C22 info:eu-repo/grantAgreement/MINECO//PRE2018-084215 info:eu-repo/grantAgreement/MEFP//FPU17/01251 S2018/BAA-4330 Departamento de Medio Ambiente y Agronomía Publisher's version Quemada, Miguel; Camino, Carlos; Pancorbo, J. L.; Raya-Sereno, María D.; Zarco-Tejada, Pablo J.; Alonso-Ayuso, María; Gabriel, José Luis; 2023; Data from airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches [Dataset]; Figshare; Version 1; https://doi.org/10.6084/m9.figshare.21865410.v1 https://doi.org/10.1007/s11119-023-09990-y Sí open application/pdf Springer |