Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

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.

Examination of appropriate observation time and correction of vegetation index for drone-based crop monitoring

“…In practice, vegetation index values do not remain fixed throughout the day, given that as solar altitude the angle of the sun above the horizon increases, vegetation index values are shown to decrease Cogliati et al, 2015;Rahman et al, 2015 . Furthermore, this effect is also influenced by the weather, with solar altitude affecting the vegetation index more on clear days when a greater proportion of the direct components of solar radiation reach lower altitudes and a smaller effect on cloudy days when the dispersion of sunlight components is greater Ishihara et al, 2015;Hashimoto et al, 2019 . In addition, vegetation density also has an effect, with areas of greater density being found to be affected to a lesser extent by solar altitude Ishihara et al, 2015 . The effect of sunlight conditions on the NDVI can be explained thus: the higher the solar altitude, the greater is the proportion of incoming light reaching the ground surface beneath the vegetation layer; under these circumstances, the ground has a greater influence on the reflected light being observed from above Ishihara et al, 2015 . The diffusion of incoming sunlight is greater on cloudy days, and the influence of sunlight conditions is reduced at higher vegetation densities. Moreover, these effects of sunlight conditions are apparent even when converting observational data to reflectance values.…”

Application of Vegetation Indexes to Assess the Condition of Crops

“…where NIR -near infrared reflection; Red -red reflection. As a result of simulations carried out by Japanese scientists, it is revealed that RVI (SR) is sensitive to solar radiation [17]. Its value is close to 1 if the object under study has the same reflection in both the Red and NIR channels, for example, soil.…”

Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

“…The digital processing of multispectral images acquired through UAV devices represents a useful tool to remotely observe the growth in riparian vegetation cover and, therefore, the hydrodynamic behavior of vegetated open channels colonized by riparian stands in natural phenological conditions. The first outcome of this research is represented by the direct correlation between NDVI derived by UAV-acquired multispectral images (NDVI UAV ) and LAI* measurements of the examined riparian vegetation cover, obtained by adopting the same methodology reported in previous precision agriculture studies on UAV-based crop production indicators [33][34][35]. Riparian vegetation NDVI UAV maps are extremely useful for flooding risk management in natural and urban vegetated areas.…”