ArticleField crops research2022
Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield.
Article in Field crops research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Predictions of wheat phenotypic variability by integrating high-throughput phenotyping observations into a crop growth model.Plant phenomics (Washington, D.C.) · 2026Article
- A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China.Scientific data · 2026Article
- Improving maize LAI estimation by integrating multispectral imagery and digital surface models with ensemble learning.Frontiers in plant science · 2026Article
- Remote and Proximal Sensors Data Fusion: Digital Twins in Irrigation Management Zoning.Sensors (Basel, Switzerland) · 2024Article
- Estimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images.Frontiers in plant science · 2024Article
- A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm.Frontiers in plant science · 2024Article
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10 authors.
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Abstract
Mapping 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 R
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