Abstract
Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R2) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.
Highlights
Determination of Leaf Area Index (LAI) is essential for modeling the interaction between the atmosphere and the biosphere (Zhu et al, 2020)
This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles
The capability of discrete return LiDAR data was investigated for predicting LAIeff
Summary
Determination of Leaf Area Index (LAI) is essential for modeling the interaction between the atmosphere and the biosphere (Zhu et al, 2020). Ground-based LiDAR data can acquire data at a very high spatial resolution over shorter crops compared to airborne platforms, and depending on the plant structure, can potentially penetrate deeper into the canopy. These platforms are not subject to localized changes in position, elevation, and look angle that are common with airborne platforms, but are restricted to operation in field conditions during which they can drive and collect data. Contributions of the study include investigation of multiple LiDAR-based features for multitemporal prediction of LAI via regression models and evaluation of the capability of LiDAR sensors and platforms for acquiring data to predict sorghum and maize LAI at multiple times during the growing season
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