Peter Gege’s research while affiliated with German Aerospace Center (DLR) and other places

This study presents what we believe is the first extensive assessment of the water reflectance products from the German hyperspectral Environmental Mapping and Analysis Program (EnMAP). We evaluate EnMAP’s standard normalized water leaving reflectance [ρ W ] N over 17 water sites in the first two years of the mission. The EnMAP [ρ W ] N standard product is generated by a dedicated water atmospheric correction (AC) called the Modular Inversion Program (MIP). The quality of the [ρ W ] N retrievals was assessed using in situ hyperspectral measurements and Aerosol Robotic Network - Ocean Colour (AERONET-OC) multispectral measurements. The results showed very good agreement between in situ hyperspectral match-ups and EnMAP [ρ W ] N , with an underestimation of EnMAP of −17.37% (bias, β) and an error ( $\epsilon$ ϵ ) of 23.75% at 418 – 797 nm. Two other AC processors were also investigated: the polynomial based algorithm applied to MERIS (Polymer) and the atmospheric correction for OLI lite (Acolite). The intercomparison exercise between the three AC methods applied to EnMAP data using the hyperspectral match-up dataset showed better statistical metrics for MIP ( $\epsilon = 23{\% }, \beta = -17.37{\% }$ ϵ = 23 % , β = − 17.37 % ) compared to Polymer ( $\epsilon = 42.20{\% }, \beta = -2.43{\% }$ ϵ = 42.20 % , β = − 2.43 % ) and Acolite ( $\epsilon = 97{\% }, \beta = 97{\% }$ ϵ = 97 % , β = 97 % ). The superior performance of MIP was further confirmed by the validation results obtained with the multispectral match-up dataset; MIP retrievals show good agreement with in situ measurements at the majority of study sites. Conversely, Polymer and Acolite retrievals tended to overestimate, especially in clearer waters as the Lampedusa study site.

Spatial in-situ measurements of spectral remote sensing reflectance for the validation of optical satellite data over inland water bodies are usually carried out from boats or ships, where hull, superstructures and crew have an unknown influence on the results. Drift, caused by wind and currents, makes it impossible to stay precisely at the same position or to keep a constant geometry when gathering series of measurements. We developed a low-profile, remotely or autonomously operated vehicle which is capable of holding or changing both position and orientation independently. For the validation of optical satellite data and its derivates (e.g. satellite derived bathymetry), it can be equipped with a multiplexed spectrometer system, a winch with a profiler, an echosounder, or with an underwater camera system for the two- or three-dimensional mapping of shallow water areas. Its capability of keeping a constant position during the change of orientation also allows for automated BRDF studies.

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Peru is amongst the most affected countries by climate change in the world, with severe consequences on the availability of water across the country. The GIZ funded project "Multisectoral management of water resources in the Mantaro River basin" (ProGIRH) aims to improve the integrated and climate-sensitive water resource management in the Mantaro River basin. Within this framework, an IMF Team supports the Peruvian national water authority (ANA) in establishing remote sensing methodologies as a complement to traditional sampling-based water analysis. With a permanent focus on capacity building of the regional partners, the Team combines multi- and hyperspectral satellite imagery with in-situ spectral data, in order to define the possibilities and technical requirements necessary to establish a self-dependent and locally managed long-term observation of water quality and availability.

A spectral weighting function is presented which optimizes phytoplankton classification from hyperspectral data taking the signal-to-noise ratio of the current image into account. The improvements are illustrated using a DESIS image from Lake Constance.

The many bands and the high spectral resolution of hyperspectral sensors such as PRISMA, DESIS or EnMAP appear very promising for phytoplankton classification, but their increased sensor noise compared to multispectral sensors imposes limitations on threshold concentrations and the number of phytoplankton groups that can be distinguished. An analytic equation for a spectral weighting function (w) of the sensor bands is presented which optimizes the retrieval of phytoplankton groups from hyperspectral data. The function w depends on the reflectance differences (dR) induced by variable phytoplankton type and concentration, and on the signal-to-noise ratio (SNR) of the measurement. Extensive simulations covering wide concentration ranges of water constituents and major phytoplankton groups have been made to derive typical spectra of dR. Examples of w are presented based on these simulated dR spectra and on measured SNR spectra from hyperspectral satellite sensors. The improvement for phytoplankton classification is demonstrated for simulated measurements and for some hyperspectral images from PRISMA and DESIS.

Spectral measurements in aquatic remote sensing are usually carried out from ships, boats or stationary platforms. While the latter only covers a single location, mobile platforms can introduce significant errors due to unexpected movement (drift and rotation), reflection and shadowing effects from the ship’s hull, superstructures and the personnel conducting the measurements. To overcome these caveats, we developed the low-profile robotic platform LimnoVIS that can be operated autonomously or remotely controlled and is capable of keeping its position and orientation accurately through its omnidirectional maneuverability. The onboard measurement system comprises a VIS/NIR spectrometer (350-880 nm, 1 nm resolution) which is connected to four different optics via a fiber optical switch. This allows for rapid subsequent measurement of upwelling radiance above and under water, sky radiance and downwelling irradiance using reflectance standards or a cosine corrector, all by the same spectrometer. LimnoVIS carries also a profiler, which can be lowered by up to 30 m. It is equipped with a spectrometer and a tiltable diffusor for measuring benthic reflectance, LED and halogen lamps, a laser range finder, a camera, and sensors for temperature and pressure. Multiple onboard cameras with recording and live viewing capabilities are used for navigation, visual supervision and documentation of the measurements and for compiling shallow-water orthomosaics. Furthermore, LimnoVIS is equipped with a sonar for deriving bathymetry in the range of 0.5 to 30 m.