Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs)

Abstract

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush) at the Blue Oak Ranch Reserve (BORR) ~20 km east of San Jose, California. We recorded multispectral imagery at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index), leaf water content and water potential as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit across treatments; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of leaf water content. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope).We found that PlanetScope data was able to capture the overall trend in treatments but unable to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.

The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture

“…For the Normalized Difference Red Edge index, the trend is reversed, with higher R 2 at C-band for the narrow leaf plants and slightly higher R 2 values at L-band for the broadleaf plants. Since NDRE is also positively correlated with VWC, further research examining their interdependencies for these plant types should be conducted to understand this behavior [84]. Negative correlations at C-band can be observed for the narrow-leaved plants for all soil and plant parameters studied, clearly demonstrating the attenuation effect on the backscattering signal within the elongated plants in both vertical and horizontal polarizations also after the heading stage, as observed in previous studies [69].…”

Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

“…Despite these observed biases, there was a strong correspondence between drone-derived VIs and the coarser-grained HyPlant and S2 datasets (NDVI R 2 = 0.91, CHL R 2 = 0.75-0.9, Figures 9 and 10), indicating that the drone VIs can reflect variations within maize canopy cover and can be compared across scales. This highlights the potential for integrating drone-based VI measurements within or as an alternative to coarser resolution workflows either as validation or additional measurements at desired time steps for monitoring purposes, such as identifying water limitation or phenology of vegetation canopies [22,49]. This remains feasible and cost-effective for study areas <5 ha [50].…”

“…Understanding the extent to which these uncertainties influence outputs is important in a precision agriculture context where VIs serve as indicators of local vegetation status. Ensuring that there is sufficient consistency between datasets is also crucial when integrating drone-based data with other remote sensing and in situ data sources, for improved temporal monitoring [20][21][22]. For scaling studies, surface reflectance values should further be traceable and comparable between sensors [23].…”

Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms

“…Many scholars monitored soil salinization using satellite-borne remote sensing (RS) data together with field measurement over last two decades (Yu et al, 2018;Allbed, Kumar & Sinha, 2014;Ivushkin et al, 2019b), yet satellite images can be easily affected by bad weather and unfavorable revisit times. The recent development of unmanned aerial vehicle (UAV) has ushered in a new era enabling monitoring of environment and agriculture at unprecedented temporal and spatial, especially in the monitoring of such soil ingredients as moisture, heavy metals and organic carbon (Gilliot, Vaudour & Joël, 2016;Bian et al, 2019;Yi et al, 2018;Easterday et al, 2019;Jay et al, 2018). There have been a few cases involving the application of UAV-borne RS in soil salinization monitoring.…”