Radiative transfer model inversion using high-resolution hyperspectral airborne imagery – Retrieving maize LAI to access biomass and grain yield

dc.creatorAhmed Kayad
dc.creatorRodrigues, F.
dc.creatorNaranjo, S.
dc.creatorSozzi, M.
dc.creatorPirotti, F.
dc.creatorMarinello, F.
dc.creatorSchulthess, U.
dc.creatorDefourny, P.
dc.creatorGerard, B.
dc.creatorWeiss, M.
dc.date2022-03-15T01:25:16Z
dc.date2022-03-15T01:25:16Z
dc.date2022
dc.date.accessioned2023-07-17T20:09:04Z
dc.date.available2023-07-17T20:09:04Z
dc.identifierhttps://hdl.handle.net/10883/22017
dc.identifier10.1016/j.fcr.2022.108449
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7513783
dc.descriptionMapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R2 value of 0.5 against ground LAI with RMSE of 0.8 m2/m2. Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R2 value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.
dc.languageEnglish
dc.publisherElsevier
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source282
dc.source0378-4290
dc.sourceField Crops Research
dc.source108449
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectPROSAIL
dc.subjectVegetation Indices
dc.subjectField Variability
dc.subjectDigital Farming
dc.subjectPRECISION AGRICULTURE
dc.subjectMAIZE
dc.subjectGRAIN YIELD
dc.subjectBIOMASS
dc.subjectVEGETATION
dc.subjectVEGETATION INDEX
dc.titleRadiative transfer model inversion using high-resolution hyperspectral airborne imagery – Retrieving maize LAI to access biomass and grain yield
dc.typeArticle
dc.typePublished Version
dc.coverageAmsterdam (Netherlands)


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