ArticlePeerJ2019
Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index.Life (Basel, Switzerland) · 2025Article
- A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.Sensors (Basel, Switzerland) · 2024Review
- Mapping rapeseed (Frontiers in plant science · 2024Article
- Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey.Sensors (Basel, Switzerland) · 2023Review
- UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.Sensors (Basel, Switzerland) · 2023Article
- Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage.Frontiers in plant science · 2023Article
- Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height.Frontiers in plant science · 2022Article
- Applications of UAS in Crop Biomass Monitoring: A Review.Frontiers in plant science · 2021Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (
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