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A global monthly evapotranspiration (ET) product without spatial-temporal gaps for 2000–2017 is delivered by using an energy balance (EB) algorithm and MODIS satellite data. It provides us with a moderate resolution estimate of ET without spatial-temporal gaps on a global scale. The model is driven by monthly remote sensing land surface temperature and ERA-Interim meteorological data. A global turbulent exchange parameterization scheme was developed for global momentum and heat roughness length calculation with remote sensing information. The global roughness length was used in the energy balance model, which uses monthly land-air temperature gradient to estimate the turbulent sensible heat, and take the latent heat flux as a residual of the available energy. This study produced an ET product for global landmass, at a monthly time step and 0.05-degree spatial resolution. The performance of ET data has been evaluated in comparison to hundreds flux sites measurements representing a broad range of land covers and climates. The ET product has a mean bias of 3.3 mm/month, RMSE value of 36.9 mm/month. The monthly ET product can be used to study the global energy and hydrological cycles at either seasonal or inter-annual temporal resolution.

The population of Milky Way (MW) satellites contains the faintest known galaxies and thus provides essential insight into galaxy formation and dark matter microphysics. Here we combine a model of the galaxy-halo connection with newly derived observational selection functions based on searches for satellites in photometric surveys over nearly the entire high Galactic latitude sky. In particular, we use cosmological zoom-in simulations of MW-like halos that include realistic Large Magellanic Cloud (LMC) analogs to fit the position-dependent MW satellite luminosity function. We report decisive evidence for the statistical impact of the LMC on the MW satellite population due to an estimated 6 ± 2 observed LMC-associated satellites, consistent with the number of LMC satellites inferred from Gaia proper-motion measurements, confirming the predictions of cold dark matter models for the existence of satellites within satellite halos. Moreover, we infer that the LMC fell into the MW within the last 2 Gyr at high confidence. Based on our detailed full-sky modeling, we find that the faintest observed satellites inhabit halos with peak virial masses below 3.2× 10 M· at 95% confidence, and we place the first robust constraints on the fraction of halos that host galaxies in this regime. We predict that the faintest detectable satellites occupy halos with peak virial masses above 10 M·, highlighting the potential for powerful galaxy formation and dark matter constraints from future dwarf galaxy searches., This research received support from the National Science Foundation (NSF) under grant No. NSF AST-1517422, grant No. NSF PHY17-48958 through the Kavli Institute for Theoretical Physics program “The Small-Scale Structure of Cold(?) Dark Matter,” and grant No. NSF DGE-1656518 through the NSF Graduate Research Fellowship received by E.O.N. Support for Y.-Y.M. was provided by the Pittsburgh Particle Physics, Astrophysics and Cosmology Center through the Samuel P. Langley PITT PACC Postdoctoral Fellowship and NASA through NASA Hubble Fellowship grant No. HST-HF2-51441.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. Part of this work was performed at the Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1607611. This research made use of computational resources at SLAC National Accelerator Laboratory, a U.S. Department of Energy Office; the authors are thankful for the support of the SLAC computational team. This research made use of the Sherlock cluster at the Stanford Research Computing Center (SRCC); the authors are thankful for the support of the SRCC team. This research made use of https://arXiv.org and NASA’s Astrophysics Data System for bibliographic information. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at UrbanaChampaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the NSFʼs National Optical-Infrared Astronomy Laboratory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. This work is based in part on observations at Cerro Tololo Inter-American Observatory, NSFʼs National Optical-Infrared Astronomy Laboratory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under grant Nos. AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. The IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/ 2007-2013), including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2). This manuscript has been authored by the Fermi Research Alliance, LLC, under contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

Airborne photogrammetry and airborne laser scanning are two commonly used technologies used for topographical data acquisition at the city level. Change detection between airborne laser scanning data and photogrammetric data is challenging since the two point clouds show different characteristics. After comparing the two types of point clouds, this paper proposes a feed-forward Convolutional Neural Network (CNN) to detect building changes between them. The motivation from an application point of view is that the multimodal point clouds might be available for different epochs. Our method contains three steps: First, the point clouds and orthoimages are converted to raster images. Second, square patches are cropped from raster images and then fed into CNN for change detection. Finally, the original change map is post-processed with a simple connected component analysis. Experimental results show that the patch-based recall rate reaches 0.8146 and the precision rate reaches 0.7632. Object-based evaluation shows that 74 out of 86 building changes are correctly detected.