Spaceborne imaging spectroscopy is undergoing a rapid expansion with a new generation of missions in recent years. Following the Hyperion (2000) and HICO (2009) missions, new spaceborne imaging spectroscopy missions have recently started operating: DESIS (2018), PRISMA (2019), HISUI (2019) and more recently EnMAP (2022) and EMIT (2022). These missions face the common challenge of providing accurate spectral and radiometric results over a wide spectral range. This requires accurate instrument calibration and the validation of the results obtained. In this contribution, we provide an overview of the calibration and validation (CalVal) activities in the EnMAP mission, and we present the CalVal results that were obtained as part of the commissioning phase (April - October 2022).

Since November 2022, the PACO [1] atmospheric correction program has operated routinely as the L2A processor in the ground segment for the hyperspectral missions DESIS [2] and EnMAP [3]. Both missions monitor the Earth’s environment similar to other operational hyperspectral missions like PRISMA [4] and EMIT [5]. The Ground Segment L2A processor for the DESIS and EnMAP missions corrects the at-sensor received terrestrial reflection of the incident solar radiation for the effects of atmospheric constituents generating Bottom-Of-Atmosphere (BOA) ground reflectance spectral image cube, along with pixel-classification masks, Aerosol Optical Thickness (AOT) at 550 nm and Water Vapor (WV) maps. In this contribution we summarize the lessons learned on the validation of the atmospheric correction and the related uncertainty of the hyperspectral L2A products, applying the same validation criteria, for both DESIS and EnMAP.

The German Hyperspectral Satellite Mission EnMAP (Environmental Mapping and Analysis Program; www.EnMAP.org) is an imaging spectroscopy remote sensing mission with the primary objective to measure, derive and analyze quantitative diagnostic parameters describing key processes on the Earth’s surface (see, e.g., [1]). In this article, an overview of the EnMAP processing chain is given with a focus on the geometric processing of EnMAP data. Furthermore, the geometric performance after one year of acquisitions is assessed and presented.

Soils contain the largest global carbon pool and thus, play an important role in atmospheric CO2 sequestration through increase in soil organic carbon (SOC) stock (Minasny et al., 2017). Therefore, large scale mapping and reporting of soil status, quality and health is starting to get a requirement amongst policy-makers and implemented in target setting of the Sustainable Development Goals and relevant EU policies. Soil quality and health monitoring is commonly described as a sum of physical, chemical and biological properties of soils. Since many years, multispectral and hyperspectral Earth Observation (EO) have been valuable data sources for analyzing the chemical and physical constitution of top soils (Chabrillat et al., 2019). Mainly two approaches are used, the Digital Soil Modelling (DSM) approach as well as the Spectral Soil Modelling (SSM) approach. Both approaches assimilate EO data products such as information regarding the vegetation dynamics and exposed soil reflectance data.

Soil organic matter is essential for preserving and maintaining a range of soil and ecosystem functions as well as supply and store carbon for climate change mitigation. Digital Soil Mapping techniques will be used to obtain a spatially continuous product, especially over permanently vegetated areas. Recently available satellite remote sensing data, with among other systems the Copernicus Sentinel, will be used as input for environmental covariates. Digital Soil Mapping, coupled together with Remote Sensing products, is a powerful tool to produce soil properties maps and monitoring the changes in soil conditions over time.

When working with hyperspectral data, it is very important, that the data is properly pre-processed in terms of systematic radiometric and spectral correction, geometric correction as well as atmospheric correction. Airborne and spaceborne sensors show some similarities regarding the processing, but also some differences. In this paper, these similarities and differences are discussed on the example of the HySpex processing chain in the generic processing environment Catena and the EnMAP processor that is currently developed at DLR. The paper presents the different sensors and their properties and gives an overview of the different workflows and the used algorithms.

The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. It is due to the fact that it measures distances which are mainly dependent on the position of the satellite and the terrain height. If the used DEM is of high accuracy, the resulting geocoded data are very precise. This precision can be used to improve the exterior orientation and thereby the geometric accuracy of optical satellite data. The technique used is the measurement of identical points in the images, either by manual measurements or through local image matching using mutual information and to estimate improvements for the attitude data through this information. By adjustment calculations falsely matched points can be eliminated and an optimal improvement can be found. The optical data are orthorectified using these improvements and the available DEM. The results are compared using conventional ground control information from GPS measurements.

The German Aerospace Center (DLR) is responsible for the development of prototype processors for PRISM and AVNIR-2 data under a contract of the European Space Agency. The PRISM processor comprises the radiometric correction, an optional deconvolution to improve image quality, the generation of a digital elevation model, and orthorectification. The AVNIR-2 processor comprises radiometric correction, orthorectification, and atmospheric correction over land. Here, we present the methodologies applied during these processing steps as well as the results achieved using the processors.