Abstract
Reflectance and vegetation indices obtained from aerial images are often used for monitoring crop fields. In recent years, unmanned aerial vehicles (UAVs) have become popular and aerial images have been collected under various solar radiation conditions. The value of observed reflectance and vegetation indices are considered to be affected by solar radiation conditions, which may lead to inaccurate estimations of crop growth. In this study, in order to evaluate the effect of solar radiation conditions on aerial images, canopy reflectance in paddy fields was simulated by a radiative transfer model, FLiES (Forest Light Environmental Simulator), for various solar radiation conditions and canopy structures. Several parameters including solar zenith angle, proportion of diffuse light for incident sunlight, plant height, coordinates of plants and leaf area density, were tested in FLiES. The simulation results showed that the solar zenith angle did not vary the canopy reflectance under the conditions of the proportion of diffuse light at 1.0, but the variation was greater at lower proportions of diffuse light. The difference in reflectance caused by solar radiation was 0.01 and 0.1 at the maximum for red and near-infrared bands, respectively. The simulation results also showed that the differences in reflectance affect vegetation indices (Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2)). The variation caused by solar radiation conditions was the least for NDVI and the greatest for SR. However, NDVI was saturated at the least leaf area index (LAI), whereas SR was only slightly saturated. EVI2 was intermediate between SR and NDVI, both in terms of variation and saturation. The simulated reflectance and vegetation indices were similar to those obtained from the aerial images collected in the farmers’ paddy fields. These results suggest that a large proportion of diffuse light (close to 1.0) or a middle range of solar zenith angle (45 to 65 degrees) may be desirable for UAV monitoring. However, to maintain flexibility of time and occasion for UAV monitoring, EVI2 should be used to evaluate crop growth, although calibration based on solar radiation conditions is recommended.
Highlights
An appropriate cultivation management strategy in crop fields should be employed to produce crop grain with high quality and quantity
The simulated reflectance values were plotted against leaf area index (LAI), which was applied as the target parameter for unmanned aerial vehicles (UAVs) monitoring in this study
To clarify how solar radiation conditions effect canopy reflectance in aerial multispectral images obtained using UAV, a canopy reflectance simulation using a radiative transfer model was conducted for various solar radiation conditions and canopy structure parameters
Summary
An appropriate cultivation management strategy in crop fields should be employed to produce crop grain with high quality and quantity. The estimation methods in these studies mostly used vegetation indices calculated using the canopy reflectance obtained from multispectral images taken in crop fields For this purpose, various types of vegetation indices have been developed, such as Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) [9,10,11,12,13,14]. Ali et al estimated wheat LAI based on NDVI and SAVI and improved the vegetation indices by adding a red-edge spectral band [6] These vegetation indices are often calculated from satellite remote sensing data, end users, such as farmers, have difficulties using the data because of the cost, especially with high spatial resolution, and the inflexibility of employing remote sensing techniques
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