000			03702nab a22004097a 4500
001			65070
003			MX-TxCIM
005			20220920144143.0
008			220314s2022 ne ||||| |||| 00| 0 eng d
022			_a0378-4290
040			_aMX-TxCIM
041			_aeng
100	0		_926816 _aAhmed Kayad
245			_aRadiative transfer model inversion using high-resolution hyperspectral airborne imagery – Retrieving maize LAI to access biomass and grain yield
260			_aAmsterdam (Netherlands) : _bElsevier, _c2022.
500			_aOpen Access
520			_aMapping 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.
546			_aText in English
591			_aNaranjo, S. : Not in IRS staff list but CIMMYT Affiliation
650		7	_aPrecision agriculture _2AGROVOC _94619
650		7	_aMaize _2AGROVOC _91173
650		7	_aGrain yield _2AGROVOC _91339
650		7	_aBiomass _2AGROVOC _91897
650		7	_aVegetation _2AGROVOC _97637
700	1		_8I1705451 _9782 _aRodrigues, F. _gFormerly Sustainable Intensification Program
700	1		_924821 _aNaranjo, S.
700	1		_926817 _aSozzi, M.
700	1		_926818 _aPirotti, F.
700	1		_926819 _aMarinello, F.
700	1		_aSchulthess, U. _gSustainable Intensification Program _gSustainable Agrifood Systems _8CSCU01 _92005
700	1		_96338 _aDefourny, P.
700	1		_8INT3372 _9946 _aGerard, B. _gFormerly Sustainable Intensification Program
700			_910250 _aWeiss, M.
773			_dAmsterdam (Netherlands) : Elsevier, 2022. _gv. 282, art. 108449 _tField Crops Research _wG444314 _x0378-4290
856			_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22017
942			_2ddc _cJA _n0
999			_c65070 _d65062