Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.     

Predictive geochemical mapping using environmental correlation

The distribution of chemical elements at and near the Earth's surface, the so-called critical zone, is complex and reflects the geochemistry and mineralogy of the original substrate modified by environmental factors that include physical, chemical and biological processes over time.Geochemical data typically is illustrated in the form of plan view maps or vertical cross-sections, where the composition of regolith, soil, bedrock or any other material is represented. These are primarily point observations that frequently are interpolated to produce rasters of element distributions. Here we propose the application of environmental or covariate regression modelling to predict and better understand the controls on major and trace element geochemistry within the regolith. Available environmental covariate datasets (raster or vector) representing factors influencing regolith or soil composition are intersected with the geochemical point data in a spatial statistical correlation model to develop a system of multiple linear correlations. The spatial resolution of the environmental covariates, which typically is much finer (e.g. ∼90 m pixel) than that of geochemical surveys (e.g. 1 sample per 10-10,000 km²), carries over to the predictions. Therefore the derived predictive models of element concentrations take the form of continuous geochemical landscape representations that are potentially much more informative than geostatistical interpolations.Environmental correlation is applied to the Sir Samuel 1:250,000 scale map sheet in Western Australia to produce distribution models of individual elements describing the geochemical composition of the regolith and exposed bedrock. As an example we model the distribution of two elements – chromium and sodium. We show that the environmental correlation approach generates high resolution predictive maps that are statistically more accurate and effective than ordinary kriging and inverse distance weighting interpolation methods. Furthermore, insights can be gained into the landscape processes controlling element concentration, distribution and mobility from analysis of the covariates used in the model. This modelling approach can be extended to groups of elements (indices), element ratios, isotopes or mineralogy over a range of scales and in a variety of environments.     

A note on “Origin of components in Chilean thermal waters” by

Risacher et al. (2011) have presented voluminous data on thermal waters of Chile that is quite appreciable; however, their work still falls short on several counts. The most notable shortcoming of the work is the presentation and treatment of data. The interpretations are based on questionable premises (viz., extent of seawater intrusion) and considerations (viz., using average chemical composition of rock types for geochemical modeling, considering volcanic rocks as the only rock type in contact with the geothermal fluids) and assumptions not always substantiated by facts (with proper references) could have been corroborated. Use of Cl/Br ratio for discrimination purpose is unconvincing, considering the uncertainty in the measurement of low (<1 mg/L) Br concentration.     

月球东海盆地综合解析与撞击初始条件的研究

东海是月球上最年轻的多环撞击盆地,关于其形成机制的研究很多,但成果大都基于正撞击的机制提出的,虽然有部分学者提出东海是斜撞击的,但缺乏具体撞击参数。本文通过多源数据融合,综合分析LRO影像数据、LOLA地形数据、M~3高光谱数据和IIM高光谱数据,对东海地区的地貌特征、物质成分进行了较为系统的解译,发现在东海中央熔融区存在一条与东海撞击方向垂直的中央隆起区域(中央隆起线),其也是中央熔融区粗糙部分与光滑部分的分界线,结合撞击坑成坑理论,认为其可能是撞击过程冲击波作用引起的堆叠作用形成的。同时利用GRAIL数据及对该地区的重力异常的成因进行了分析,认为异常是由于压强、温度及岩石粘度的改变引起局部莫霍面抬升和中央熔融物的形成而出现的,进而估算出熔融物占盆地内物质的25%,约为1.1×10~6km~3。同时,对GRAIL数据的剖面分析结果也支持了本文的斜撞击理论。最后,综合多方面的信息和撞击理论获取东海盆地构造分布图,并根据中央隆起线、溅射物及线性构造的分布特征等,提出东海盆地理论上是由一直径在50~100km的撞击体以10~30km/s的速度自东偏北约20°~30°方向以20°~30°的角度斜撞击月表而形成的。这可为研究更早期的月球撞击坑提供理论参考。     

黑潮延伸体上游中尺度涡场的年代际振荡及其相关机制

黑潮延伸体上游区域的中尺度涡场的涡动能和涡特征尺度存在显著地年代际振荡,和黑潮延伸体路径的年代际变化有很好的相关性。当黑潮延伸体路径比较稳定时,其上游区域涡动能比较高,涡特征尺度比较大,反之相反。通过对黑潮延伸体上游区域的中尺度涡场进行集合分析发现:当黑潮延伸体处于稳定状态时,上游涡场几乎是各向均匀地,有轻微的径向伸长;而当黑潮延伸体处于不稳定状态时,上游的中尺度涡场有显著地纬向伸长。对与中尺度涡场的产生相关的线性斜压不稳定和正压不稳定进行了计算分析,结果显示,线性斜压不稳定不是控制中尺度涡场年代际变化的机制,而正压不稳定对中尺度涡场的年代际变化有积极的贡献。不稳定产生的中尺度涡之间存在非线性涡-涡相互作用。     

地震勘探技术在煤炭勘查中的应用与展望

地震勘探作技术为一种重要的勘探方法,可以经济、有效的发现资源和解决资源开采过程中遇到的构造、地层、岩性等问题。煤炭地震勘探发展过程中地震野外数据采集、地震数据处理和地震解释都取得了重大成就,目前煤炭地震勘探正进一步向高信噪比、高分辨率、高保真度、高清晰度、高精度、定量分析的方向发展,未来也必将在煤炭资源发现和矿井隐蔽致灾因素的探测中发挥重要作用。     

粉煤灰基地质聚合物水热原位转化NaP型沸石分子筛的研究

以固体废弃物粉煤灰基地质聚合物为基本骨架,通过水热合成法将其原位转化为自支撑Na P型沸石分子筛型体,并研究了水热过程中碱液浓度、碱液体积、水热温度和水热时间对NaP型沸石分子筛结晶程度、形貌和机械强度的影响,得到粉煤灰基地质聚合物原位转化Na P型沸石分子筛型体的适宜条件为水热温度100℃、NaOH浓度2.0mol/L、NaOH体积50 m L、水热时间24 h。所得Na P型沸石分子筛型体的抗压强度为23.21 MPa,BET比表面积为50.46 m~2/g,已达到工业生产P型沸石的水平。该法工艺简单,环境友好,可控成型。在此方法的基础上通过改变原料配比和反应条件,可以合成其他类型的沸石分子筛型体。     

Revisiting the death of geography in the era of Big Data: the friction of distance in cyberspace and real space

Many scholars have argued that the importance of geographic proximity in human interactions has been diminished by the use of the Internet, while others disagree with this argument. Studies have noted the distance decay effect in both cyberspace and real space, showing that interactions occur with an inverse relationship between the number of interactions and the distance between the locations of the interactors. However, these studies rarely provide strong evidence to show the influence of distance on interactions in cyberspace, nor do they quantify the differences in the amount of friction of distance between cyberspace and real space. To fill this gap, this study used massive amounts of social media data (Twitter) to compare the influence of distance decay on human interactions between cyberspace and real space in a quantitative manner. To estimate the distance decay effect in both cyberspace and real space, the distance decay function of interactions in each space was modeled. Estimating the distance decay in cyberspace in this study can help predict the degree of information flow across space through social media. Measuring how far ideas can be diffused through social media is useful for users of location-based services, policy advocates, public health officials, and political campaigners.