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Subject: High resolution × WGL Institute: PIK ×

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    Report on ICDP Deep Dust workshops: probing continental climate of the late Paleozoic icehouse–greenhouse transition and beyond
    (Sapporo : IODP, 2020) Soreghan, Gerilyn S.; Beccaletto, Laurent; Benison, Kathleen C.; Bourquin, Sylvie; Feulner, Georg; Hamamura, Natsuko; Hamilton, Michael; Heavens, Nicholas G.; Hinnov, Linda; Huttenlocker, Adam; Looy, Cindy; Pfeifer, Lily S.; Pochat, Stephane; Sardar Abadi, Mehrdad; Zambito, James
    Chamberlin and Salisbury's assessment of the Permian a century ago captured the essence of the period: it is an interval of extremes yet one sufficiently recent to have affected a biosphere with near-modern complexity. The events of the Permian - the orogenic episodes, massive biospheric turnovers, both icehouse and greenhouse antitheses, and Mars-analog lithofacies - boggle the imagination and present us with great opportunities to explore Earth system behavior. The ICDP-funded workshops dubbed "Deep Dust," held in Oklahoma (USA) in March 2019 (67 participants from nine countries) and Paris (France) in January 2020 (33 participants from eight countries), focused on clarifying the scientific drivers and key sites for coring continuous sections of Permian continental (loess, lacustrine, and associated) strata that preserve high-resolution records. Combined, the two workshops hosted a total of 91 participants representing 14 countries, with broad expertise. Discussions at Deep Dust 1.0 (USA) focused on the primary research questions of paleoclimate, paleoenvironments, and paleoecology of icehouse collapse and the run-up to the Great Dying and both the modern and Permian deep microbial biosphere. Auxiliary science topics included tectonics, induced seismicity, geothermal energy, and planetary science. Deep Dust 1.0 also addressed site selection as well as scientific approaches, logistical challenges, and broader impacts and included a mid-workshop field trip to view the Permian of Oklahoma. Deep Dust 2.0 focused specifically on honing the European target. The Anadarko Basin (Oklahoma) and Paris Basin (France) represent the most promising initial targets to capture complete or near-complete stratigraphic coverage through continental successions that serve as reference points for western and eastern equatorial Pangaea. © 2020 Copernicus GmbH. All rights reserved.
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    Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India
    (Basel : MDPI, 2021) Arumugam, Ponraj; Chemura, Abel; Schauberger, Bernhard; Gornott, Christoph
    Accurate and spatially explicit yield information is required to ensure farmers’ income and food security at local and national levels. Current approaches based on crop cutting experiments are expensive and usually too late for timely income stabilization measures like crop insurances. We, therefore, utilized a Gradient Boosted Regression (GBR), a machine learning technique, to estimate rice yields at ~500 m spatial resolution for rice-producing areas in India with potential application for near real-time estimates. We used resampled intermediate resolution (~5 km) images of the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and observed yields at the district level in India for calibrating GBR models. These GBRs were then used to downscale district yields to 500 m resolution. Downscaled yields were re-aggregated for validation against out-of-sample district yields not used for model training and an additional independent data set of block-level (below district-level) yields. Our downscaled and re-aggregated yields agree well with reported district-level observations from 2003 to 2015 (r = 0.85 & MAE = 0.15 t/ha). The model performance improved further when estimating separate models for different rice cropping densities (up to r = 0.93). An additional out-of-sample validation for the years 2016 and 2017, proved successful with r = 0.84 and r = 0.77, respectively. Simulated yield accuracy was higher in water-limited, rainfed agricultural systems. We conclude that this downscaling approach of rice yield estimation using GBR is feasible across India and may complement current approaches for timely rice yield estimation required by insurance companies and government agencies.