Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes

Abstract

As a promising method, unmanned aerial vehicle (UAV) multispectral remote sensing (RS) has been extensively studied in precision agriculture. However, there are numerous problems to be solved in the data acquisition and processing, which limit its application. In this study, the Micro-MCA12 camera was used to obtain images at different altitudes. The piecewise empirical line (PEL) method suitable for predicting the reflectance of different ground objects was proposed to accurately acquire the reflectance of multi-altitude images by comparing the performance of the conventional methods. Several commonly utilized vegetation indices (VIs) were computed to estimate the rice growth parameters and yield. Then the rice growth monitoring and yield prediction were implemented to verify and evaluate the effects of radiometric calibration methods (RCMs) and UAV flying altitudes (UAV-FAs). The results show that the variation trends of reflectance and VIs are significantly different due to the change in component proportion observed at different altitudes. Except for the milking stage, the reflectance and VIs in other periods fluctuated greatly in the first 100 m and remained stable thereafter. This phenomenon was determined by the field of view of the sensor and the characteristic of the ground object. The selection of an appropriate calibration method was essential as a result of the marked differences in the rice phenotypes estimation accuracy based on different RCMs. There were pronounced differences in the accuracy of rice growth monitoring and yield estimation based on the 50 and 100 m-based variables, and the altitudes above 100 m had no notable effect on the results. This study can provide a reference for the application of UAV RS technology in precision agriculture and the accurate acquisition of crop phenotypes.

Assessing Paddy Field Health Using High-Resolution Multi-Spectral Camera in Subang Indonesia to Implement Precision Agriculture 4.0

“…Altitude will affect the spatial resolution of imagery and different observation angles on the same target. In Luo research [12], there are differences in reflectance value of paddy using 100 m -250 m altitude, which affects the value of the vegetation index. It is found that 100 m altitude is the most stable altitude to produce vegetation index value.…”

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

“…Ground remote sensing systems are close to the surface, facilitating more detailed information on rice, but are limited in their coverage due to fixed station locations ( Gnyp et al., 2012 ). In comparison, the application of near-ground UAVs provides significant assistance in improving the temporal and spatial resolution of rice monitoring, with the advantages of cost-effectiveness, operational simplicity, and high efficiency in large-scale monitoring ( Wan et al., 2020 ; Bascon et al., 2022 ; Luo et al., 2022 ; Xu et al., 2022a ). In particular, consumer-grade UAVs equipped with visible and multispectral cameras can conveniently obtain canopy structural parameters and key spectral, color and texture features, which have been widely used to estimate AGB ( Xu et al., 2022b ; Zheng et al., 2023 ).…”

The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery

“…Radiometric compensation for images acquired by UAV platforms is inherently challenging due to variations in imaging conditions for each capture. Therefore, an established and systematic procedure is imperative to conduct comprehensive radiometric compensation and generate multispectral images with reflectance [28][29][30].…”