DESIS - DLR Earth Sensing Imaging Spectrometer

Krutz, David; Venus, Holger; Eckardt, Andreas; Walter, Ingo; Sebastian, Ilse; Reulke, Ralf; Günther, Burghardt; Zender, Bernd; Arloth, Simone; Williges, Christian; Lieder, Matthias; Neidhardt, Michael; Grote, Ute; Schrandt, Friedrich; Wojtkowiak, Andreas

doi:10.1007/978-3-319-92753-4_28

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…In the same direction, Meng et al [63] selected Gaofen-5 satellite hyperspectral image to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of SOM content. These findings are important in the context of current and upcoming spaceborne imaging spectroscopy missions, such as the ESA's planned Copernicus Hyperspectral Imaging Mission (CHIME), NASA's planned Surface Biology and Geology mission (SBG), the upcoming German Environmental Mapping and Analysis Program (EnMAP) satellite to be launched in 2022, and the present hyperspectral sensors in orbit, such as the Italian PRISMA satellite [64] or the German DESIS satellite on the International Space Station [65], which could significantly improve estimations of soil variables [66].…”

Section: The Spectral Dimensionmentioning

confidence: 96%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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