Robust ocean color from drones: Viewing geometry, sky reflection removal, uncertainty analysis, and a survey of the Gulf Stream front

Abstract

Accurate and robust retrieval of ocean color from remote sensing enables critical observations of aquatic natural systems, from open ocean biological oceanography, coastal biodiversity, and water quality for human health. In the last decade, studies have increasingly highlighted the important role of small‐scale processes in coastal and marine ecology and biogeochemistry, but observation and modeling at these scales remains technologically limited. Unoccupied aircraft systems (UAS, aka drones) can rapidly sample large areas with high spatial and temporal resolution; but the challenge of accurately retrieving ocean color, particularly with common wide field‐of‐view multispectral imagers, has limited the adoption of this technology. As UAS endurance, autonomy, and sensor capabilities continue to increase, so does this technology's potential to observe the ocean at fine scales, but only if proper protocols are followed. The present study provides a guide for achieving (1) ideal viewing geometry of UAS‐borne ocean color sensors, (2) techniques for the removal of sun glint and reflected skylight to derive water‐leaving radiances, (3) characterization of uncertainty in these measurements, and (4) converting water‐leaving radiances to remote‐sensing reflectance for analytic end products such as chlorophyll a estimates. Documented open‐source code facilitates replication of this emerging technique. Using this methodology, we briefly describing fine‐scale variability of the Gulf Stream front off North Carolina alongside synoptic satellite data and in situ measurements for comparison. These results demonstrate how UAS‐based ocean color measurements complement and enhance conventional ocean observations and modeling to resolve fine‐scale variability and close the lacuna between satellite and in situ methods.

Enhancing Georeferencing and Mosaicking Techniques over Water Surfaces with High-Resolution Unmanned Aerial Vehicle (UAV) Imagery

“…For this reason, most UAVbased marine studies are restricted to the coastline [12,19,20] or to small water bodies [21], where the influence of terrestrial areas provides common features to overcome the limitations of this technology in the immediately adjacent water portion. Another challenge is the reflective behavior of the water surface; UAV imagery can be significantly impacted by sun glint and surface-reflected light [8,22]. Consequently, highly reflective bright patches can appear in single captures and in the final orthomosaic even with photogrammetric processing techniques [13].…”

“…Several techniques have been developed to georeference and mosaic aerial imagery collected over water surfaces [8,9,[22][23][24]. De Keukelaere et al (2023) achieved high-quality georeferenced orthomosaics through the application of the commercial VITO MapEO water software, which allowed the generation of final water quality orthomosaics (chlorophyll-a and turbidity concentration) with a spatial resolution of 2 m/px [8].…”

Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

“…This is inadequate for coastal areas or small islands such as Malta. Although in the last few years several advancements in ocean colour remote-sensing protocols have been registered, issues related to the spectral and temporal gaps that do not allow for sub-mesoscale information to be acquired still persist [26].…”

“…Clouds do not affect data collection. Moreover, UAVs are much cheaper to procure and operate than other remote-sensing tools [3,13,26,27]. Various lightweight sensors can be mounted to measure optical RGB, thermal, multispectral, and LiDAR bands to estimate in-turn values for a range of water-quality parameters similar to those obtained by satellite, such as sea surface temperature (SST), TSS, Chl-a, and total nitrogen or total phosphorus [11].…”

“…Although a number of structure from motion (SfM) photogrammetry techniques are available, these use a combination of numerical methods to generate georeferenced reflectance maps by identifying and stitching common points in different photos. Since the surface of the sea is constantly changing, such methods fail when applied to images captured when UAVs fly over water [26]. That is why most water-quality studies that use these tools are limited to the coastline (e.g., [11,25,28]), or to small water bodies such as lakes and reservoirs (e.g., [14,29,30]).…”