Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs

Abstract

Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R2 ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

“…Random forest regression (RFR) models for barley whole crop dry biomass (DM) and leaf area index (LAI) built from UAV-borne images achieved good prediction accuracies. LAI could be predicted with high accuracy which is in line with previous findings [75][76][77] and was expected due to the strong correlation of reflectance in the visible spectrum and leaf pigments per area [78]. Different crop density near tree rows, indirectly reflected by LAI, thus was accurately depicted by the presented models.…”

Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

“…Light of different wavelengths has different effects on plant growth [42]. Image sensors mounted on UAVs are used to collect images of crops in different bands and extract different features [43,44]. The multi-spectral sensor we used (i.e., Airphen) is an imaging sensor.…”

Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

“…Leaf area index (LAI, defined as half of the total green leaf area per unit horizontal ground surface area [6]) is one of the most important crop variables that can be retrieved from remotely sensed data [7,8]. LAI characterises crop canopy structure and is, thus, linked to canopy-scale processes, including evapotranspiration, photosynthesis, respiration and the interception of precipitation and solar radiation [9,10].…”