Laboratory for
Climatology and Remote Sensing

Abstract:

The EnMAP (Environmental Mapping and Analysis Program) satellite, launched on 1 April 2022, is designed to provide high-resolution imaging spectroscopy data for environmental monitoring, resource management and land use mapping. The integrity and quality of the official data products are assessed by the mission's external product validation activities, which are independent of the calibration/validation activities from the ground segment.The external validation is intended to assess, monitor, and report the quality of the L1B (top-of-atmosphere radiance in sensor coordinates), L1C (top-of-atmosphere radiance in map coordinates), and L2A (surface reflectance in map coordinates) user products. This work presents an overview of the EnMAP product validation activities during the commissioning phase and the first two years of the operational phase. Radiometric, spectral, geometric, and general uniformity aspects are evaluated with scene-based analysis methods and through comparison with ground-based reference methods. We find that EnMAP's radiometric calibration is <5%, both smile and keystone are <20% of a pixel, and the Bottom-Of-Atmosphere (BOA) reflectance and Normalized Water-Leaving reflectance are well inside the mission requirements. These results confirm that the EnMAP products fulfill the mission requirements and pave the way for the scientific exploitation and use of the EnMAP products for land, water, and atmospheric applications.

Abstract:

We propose simple analytical equations for the modeling of clean and dusty flat glacier ice surfaces, which can be used to inversely derive the parameters of microstructure of flat bare glacier ice and snow using both ground – based and spaceborne observations of the hyperspectral solar reflectance. The retrievals are based on the asymptotic radiative transfer equations valid for the case of weak light absorption in the semi-infinite turbid medium. The light reflection at the air - ice and ice - air interfaces is fully accounted for. To demonstrate the validity of the approach, the derived equations are exemplarily applied to both ground – based and EnMAP satellite measurements over the Hardangerjøkulen glacier (Norway). A number of important parameters controlling spectral signatures of the snow and glacier ice surfaces have been derived. The ground-based measurements confirm that the theoretical formulation presented in this work can be used to represent the solar light spectral reflectivity of glaciers. The application to satellite hyperspectral imagery shows that this novel technique allows for the determination of the glacier ice albedo (spectral, broadband) based on spaceborne glacier ice reflectance measurement. Additionally, the results demonstrate that not spectrally neutral soot but rather deposited atmospheric dust which enhances the absorption towards UV is responsible for the light absorption by snow for the case studied. Spatial distribution maps of ice grain diameter and dust concentration are derived over the glacier. These findings show that the analytical theory presented in this work can support further research on the characterization and monitoring of glaciers based on current and upcoming hyperspectral remote sensing satellite missions.

Abstract:

The Tibetan Plateau suffers from progressive degradation caused by over-grazing due to improper live- stock management, global climate change and herbivory from small mammals. Therefore, a robust indicator system for rangeland degradation has to be developed and tested. This paper investigates local patterns of degradation at two sites (Lake Namco and Mt. Kailash) in Xizang province (China) that are cov- ered by vegetation types typical of a large portion of the plateau. The suitability of a two-indicator system is analysed using hyperspectral field measurements, and its transferability to spaceborne data is tested. The indicators are (1) land-cover fractions derived from linear spectral unmixing and (2) chlorophyll content as a proxy for nutrient and water availability calculated using hyperspectral vegetation indices and partial least squares regression. Because cattle remain near settlements overnight in the local semi- nomadic pastoral system, it can be expected that grazing intensity is highest near the settlement and declines with increasing distance. Therefore, we tested the effect of distance on both indicators using a Spearman correlation analysis. The predicted chlorophyll content and land cover fractions of the indica- tor system were in good agreement with field observations (correlation coefficients between 0.70 and 0.98). High correlations between distance from settlements and land-cover fractions at both study sites demonstrated that the land-cover fraction is a reliable indicator for degradation. A positive correlation between distance from settlements and photosynthetically active vegetation (PV) revealed over-grazing patterns at the first site. Furthermore, the chlorophyll indicator was proven suitable because chlorophyll concentration declined with increasing distance from settlements. This underlines the over-grazing pat- tern because cattle excrement was the only external source of nutrients in the ecosystem and it was positively correlated with grazing intensity. However, at the second site, we found a significant positive effect of distance on the amount of photosynthetically non-active vegetation; no effect of distance on PV and chlorophyll content was found. Therefore, no evidence of pasture degradation was detected at the second site. Regarding the potential use of satellite data for degradation monitoring, we found that (1) the land-cover indicator derived from multispectral data was more robust than using noise-filtered hyper- spectral information and (2) the chlorophyll amount indicator was estimated from simulated EnMAP data with low error rates. Because the proposed two-indicator system can be derived from multi- and hyperspectral satellite data and combines site conditions and local plant cover, it provides a time-saving and robust method to measure pasture degradation across large areas, assuming that respective satellite data are available.