联合卷积神经网络与集成学习的遥感影像场景分类

针对人工设计的中、低层特征难以实现复杂场景影像的高精度分类以及卷积神经网络依赖大量训练数据等问题，结合迁移学习与集成学习，提出了一种联合卷积神经网络与集成学习的遥感影像场景分类算法。首先基于迁移学习的思想，利用在自然影像数据集上训练好的多个深层卷积神经网络模型作为特征提取器，提取图像多个高度抽象的语义特征；然后构建由Logistic回归和支持向量机组成的Stacking集成模型，对同一图像的多个特征分别训练Logistic模型，将预测概率结果融合构建概率特征；最后利用支持向量机对概率特征训练和预测，得到场景影像的分类结果。利用UCMerced_LandUse和NWPU-RESISC 45两种不同规模的遥感影像数据集进行试验，即使在只有10%的数据作为训练样本情况下，本文方法能够分别达到90.74%和87.21%的分类精度。     

AIEM模型在积雪散射模拟中的应用

 介绍一种基于一阶辐射传输的积雪散射模型。该模型考虑了积雪覆盖地表微波散射的3种回波分量： 雪层表面散射、下垫面散射以及雪层体散射。对于其中2个面散射分量，文章中应用一种新的面散射模型——AIEM取代原有的IEM模型进行处理。最后，使用Michigan大学的实测数据对改进后模型的模拟结果进行验证，并与改进前的模拟结果进行了对比。     

BIFoR FACE: Water–soil–vegetation–atmosphere data from a temperate deciduous forest catchment,including under elevated CO2

The ecosystem services provided by forests modulate runoff generation processes, nutrient cycling and water and energy exchange between soils, vegetation and atmosphere. Increasing atmospheric CO₂ affects many linked aspects of forest and catchment function in ways we do not adequately understand. Global levels of atmospheric CO₂ will be around 40% higher in 2050 than current levels, yet estimates of how water and solute fluxes in forested catchments will respond to increased CO₂ are highly uncertain. The Free Air CO₂ Enrichment (FACE) facility of the University of Birmingham's Institute of Forest Research (BIFoR) is the only FACE in mature deciduous forest. The site specializes in fundamental studies of the response of whole ecosystem patches of mature, deciduous, temperate woodland to elevated CO₂ (eCO₂). Here, we describe a dataset of hydrological parameters – seven weather parameters at each of three heights and four locations, shallow soil moisture and temperature, stream hydrology and CO₂ enrichment – retrieved at high frequency from the BIFoR FACE catchment.     

青藏高原大气水汽收支特征及影响

Based on 1961-2000 NCEP/NCAR monthly mean reanalysis datasets, vapor transfer and hydrological budget over the Tibetan Plateau are investigated. The Plateau is a vapor sink all the year round. In summer, vapor is convergent in lower levels (from surface to 500 hPa) and divergent in upper levels (from 400 to 300 hPa), with 450 hPa referred to as level of non-divergence. Two levels have different hydrologic budget signatures: the budget is negative at the upper levels from February to November, i.e., vapor transfers from the upper levels over the plateau; as to the lower, the negative (positive) budget occurs during the winter (summer) half year. Evidence also indicates that Tibetan Plateau is a "vapor transition belt", vapor from the south and the west is transferred from lower to upper levels there in summer, which will affect surrounding regions, including eastern China, especially, the middle and lower reaches of the Yangtze. Vapor transfer exerts significant influence on precipitation in summertime months. Vapor transferred from the upper layers helps humidify eastern China, with coefficient -0.3 of the upper budget to the precipitation over the middle and lower reaches of the Yangtze (MLRY); also, vapor transferred from east side (27.5°-32.5°N) of the upper level has remarkable relationship with precipitation, the coefficient being 0.41. The convergence of the lower level vapor has great effects on the local precipitation over the plateau, with coefficient reaching 0.44, and the vapor passage affects the advance and retreat of the rainbelt. In general, atmospheric hydrologic budget and vapor transfer over the plateau have noticeable effects on precipitation of the target region as well as the ambient areas.     

Further confirmations of mass transfer in Algol type binary U Sagittae

This paper describes how a new photometric V light curve solution of Algol type binary U Sge was obtained using Wilson–Devinney code. I also discuss how the physical and orbital parameters, along with absolute dimensions of the system, were determined. The Roche lobe configurations of the system indicate that the secondary component has filled its Roche lobe and therefore is losing mass at the rate of 6.15×10⁻⁷ M _sun yr⁻¹. The conservative mass flow is the most likely process in this system.     

Warming the early earth—CO2 reconsidered

Despite a fainter Sun, the surface of the early Earth was mostly ice-free. Proposed solutions to this so-called “faint young Sun problem” have usually involved higher amounts of greenhouse gases than present in the modern-day atmosphere. However, geological evidence seemed to indicate that the atmospheric CO₂ concentrations during the Archaean and Proterozoic were far too low to keep the surface from freezing. With a radiative-convective model including new, updated thermal absorption coefficients, we found that the amount of CO₂ necessary to obtain 273 K at the surface is reduced up to an order of magnitude compared to previous studies. For the late Archaean and early Proterozoic period of the Earth, we calculate that CO₂ partial pressures of only about 2.9 mb are required to keep its surface from freezing which is compatible with the amount inferred from sediment studies. This conclusion was not significantly changed when we varied model parameters such as relative humidity or surface albedo, obtaining CO₂ partial pressures for the late Archaean between 1.5 and 5.5 mb. Thus, the contradiction between sediment data and model results disappears for the late Archaean and early proterozoic.