Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model

Agricultural and Forest Meteorology

Volumn 247, Issue , 2017, Pages 12-20

  Liu, Shouyang ^a   Baret, Fred ^a   Abichou, Mariem ^b   Boudon, Fred ^c   Thomas, Samuel ^d   Zhao, Kaiguang ^e   Fournier, Christian ^f   Andrieu, Bruno ^b   Irfan, Kamran ^a   Hemmerlé, Matthieu ^g   Solan, Benoit de ^d  

^a UAPV   (France)

^b UMR 1402 ECOSYS   (France)

^c UMR AGAP   (France)

^d ARVALIS Institut du Vegetal   (France)

^e OHIO STATE UNIVERSITY   (United States)

^f UMR 759 LEPSE   (France)

^g Hi Phen   (France)

Author keywords

3D canopy structure model; ADEL Wheat; GAI; LiDAR; Neural networks; Phenotyping; Poisson model; Wheat

Indexed keywords

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CANOPY ARCHITECTURE; ESTIMATION METHOD; GROUND-BASED MEASUREMENT; INTERCEPTION; LIDAR; MEASUREMENT METHOD; PERFORMANCE ASSESSMENT; PHENOTYPE; POISSON RATIO; REMOTE SENSING; SURFACE REFLECTANCE; VERTICAL DISTRIBUTION; WHEAT;

TRITICUM AESTIVUM;

EID: 85025119424     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2017.07.007     Document Type: Article

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