This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

Reliable Position, Navigation, and Timing (PNT) is becoming more and more important, considering the proliferation of highly autonomous safety- and liability-critical systems. Due to their vulnerability to various threats such as deliberate Radio Frequency Interference (RFI), including jamming, spoofing, and others, there is significant research into finding backup/fallback solutions that allow safe mission completion or termination. This work compares two such systems: one based on Angle of Arrival (AoA) measurement and one based on cellular (4G and 5G) signals. The results are generated using simulations, which are substantiated by real-world performance measurements. It is shown that both systems have the potential to serve as backup navigation solutions and that the cellular system outperforms the AoA-based solution, albeit at a much higher price and with higher computational requirements.

This paper presents a compact, cost-effective Unmanned Aerial Vehicle (UAV) payload, intended for coastal area remote sensing applications. The payload consists of a compact, in-house–built push-broom imaging spectrometer using Commercial Off-The-Shelf (COTS) components, complemented by navigation sensors for direct georeferencing. The payload furthermore incorporates an upward-facing spectrometer, enabling direct computation of remote sensing reflectance $\boldsymbol {\rho }_{\mathrm {RS}} \left ({{ \lambda }}\right)$ under varying illumination and atmospheric conditions. In contrast to existing micro imaging-spectrometer payloads, the presented Unmanned Aerial Vehicle (UAV) payload offers greater operational versatility and flexibility. While specifically tailored to coastal ocean color mapping, the system can easily be adapted to a wide range of remote sensing applications. Field experiments conducted with the presented payload demonstrate that remote sensing payloads, built with COTS components, allow the retrieval of $\boldsymbol {\rho }_{\mathrm {RS}} \left ({{ \lambda }}\right)$ scientifically usable datacubes. The contribution of this work lies in the proof-of-concept and demonstration that a compact UAV payload based on inexpensive COTS components and which permits onboard processing is able to generate good scientific data.

This paper presents a cost-effective Unmanned Aerial Vehicle (UAV) payload, intended for coastal area remote sensing applications. The payload consists of a compact pushbroom imaging spectrometer built in-house with commercial offthe-shelf (COTS) components, and it contains navigation sensors that allow for direct georeferencing. The payload furthermore incorporates an upward-facing spectrometer, enabling direct computation of remote sensing reflectance ρRS (λ) from imaging spectrometer data in different lighting and atmospheric conditions. In contrast to existing micro-imaging spectrometer payloads, the presented UAV payload offers enhanced versatility and flexibility. While specifically tailored to coastal ocean color mapping, the payload can easily be adapted to a wide range of remote sensing applications. The presented payload has been tested in the field. The results presented in this work, show that remote sensing payloads, built with COTS components, allow retrieving of remote sensing reflectance ρRS (λ) datacubes eligible for scientific use.