## 1 Introduction  
Over the past decade, the number of applications of unmanned aerial vehicles (UAVs, also referred to as drones, unmanned aerial/aircraft systems (UAS), or remotely piloted aircraft systems (RPAS) has exploded. Already in 2008, unmanned robots were envisioned to bring about a new era in agriculture [1]. Recent studies have shown that UAV remote sensing techniques are revolutionizing forest studies [2], spatial ecology [3], ecohydrology [4; 5] and other environmental monitoring applications [6]. The main driver for this revolution is the fast pace of technological advances and the miniaturization of sensors, airframes, and software [7]. A wide range of UAV platforms and sensors have been developed in the last decade. They have given individual scientists, small teams, and the commercial sector the opportunity to repeatedly obtain low-cost imagery at ultra-high spatial resolutions (1 cm to 1 m) that have been tailored to specific areas, products, and delivery times [3; 8; 9; 10; 11]. Moreover, computing power and easy to use consumer-grade software packages, which include modern computer vision and photogrammetry algorithms such as structure from motion (SfM) [12], are becoming cheaper and available to many users. Before the era of UAVs, the majority of spectral datasets was produced by external data suppliers (companies or institutions) in a standardized way using a few types of sensors on-board satellites and manned aircraft. Today, research teams own or even build their own sensing systems and process their data themselves without the need for external data suppliers. Technology is developing rapidly and offering new types of sensors. This diversification makes data quality assurance considerations even more critical--in particular for quantitative and spectral remote sensing approaches, given the complexity of the geometric and radiometric corrections required for accurate spectroscopy-focused environmental remote sensing. Spectral remote sensing gathers information by measuring the radiance emitted (e.g., in the case of chlorophyll fluorescence), reflected, and transmitted from particles, objects, or surfaces. However, this information is influenced by environmental conditions (mainly the illumination conditions) and modified by the sensor, measurement protocol, and the data-processing procedure. Thus, it is critical to understand the full sensing process, since undesired effects during data acquisition and processing may have a significant impact on the confidence of decisions made using the data [13]. Moreover, it is also a prerequisite to later use pixels to understand the biological processes of the Earth system (c.f. [14]). Recently, several papers have reviewed the literature for UAV technology and its application in Earth observation [7; 8; 9; 10; 15; 16; 17]. With the issues potentially arising from an increasing diversity of small spectral sensors for UAV remote sensing, there is also a growing need to spread knowledge on sensor technology, data acquisition, protocols, and data processing. Thus, the objective of this review is to describe and discuss recent spectral UAV sensing technology, its integration on UAV platforms, and geometric and radiometric data-processing procedures for spectral data captured by UAV sensing systems based on the literature, but also on more than a decade of our own experiences. Our aim is to follow the signal through the sensing process (Figure 1) and discuss important steps to acquire reliable data with UAV spectral sensing. Additionally, we reflect on the current revolution in remote sensing to identify trends and potentials. This review is structured as follows. Different technical solutions for spectral UAV sensor technology are described in Section 2. The geometric and radiometric processing steps are elaborated in  
Figure 1: The path of information from a particle (e.g., pigments within the leaf), object, or surface to the data product.