Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data

In this study, the NIR-red spectral space of Landsat-8 images, which is manifested by a triangle shape, is deployed for developing two new Soil Moisture (SM) indices. First, ten parameters consisting of six distances and four angles were extracted using the position of a random pixel in this triangle. Then, some correlation assessments were made to derive those parameters that were useful for SM estimation, which were five parameters. To build a soil moisture index, all combinations of these five parameters, which were in total 31 different regression equations, were considered, and the best model was named the Triangle Soil Moisture Index (TSMI). The TSMI consists of three parameters. It showed a RMSE of 0.08 and correlation coefficient (R) of 0.67. Since the TSMI does not consider vegetation interface in SM estimation, the Modified TSMI (MTSMI), which takes into account the fraction of soil cover in each pixel, beside those parameters which were used in the TSMI, was developed (MTSMI: RMSE = 0.07, R = 0.74). The results of the TSMI and MTSMI were compared with each other, and with another soil moisture index (SMMRS introduced by Zhan et al. (2007)). It was concluded that the TSMI and MTSMI provide similar results for bare soil or sparsely vegetated surfaces. However, the MTSMI demonstrated a much better performance in densely vegetated surfaces. The accuracy of both the TSMI and MTSMI were significantly higher than the SMMRS. Moreover, the TSMI and MTSMI were validated by comparison with field measured SM data at five different depths. The results showed that satellite estimated SM by these two indices was more correlated with in situ data at 5 cm soil depth compared to other depths. Also, to show the high applicability of the proposed approach for SM estimation, we selected another set of field SM data collected in Australia. The results proved the effectiveness of the method in different study areas.     

Modelling the standing timber volume of Baden-Württemberg—A large-scale approach using a fusion of Landsat,airborne LiDAR and National Forest Inventory data

Remote sensing-based timber volume estimation is key for modelling the regional potential, accessibility and price of lignocellulosic raw material for an emerging bioeconomy. We used a unique wall-to-wall airborne LiDAR dataset and Landsat 7 satellite images in combination with terrestrial inventory data derived from the National Forest Inventory (NFI), and applied generalized additive models (GAM) to estimate spatially explicit timber distribution and volume in forested areas. Since the NFI data showed an underlying structure regarding size and ownership, we additionally constructed a socio-economic predictor to enhance the accuracy of the analysis. Furthermore, we balanced the training dataset with a bootstrap method to achieve unbiased regression weights for interpolating timber volume. Finally, we compared and discussed the model performance of the original approach (r² = 0.56, NRMSE = 9.65%), the approach with balanced training data (r² = 0.69, NRMSE = 12.43%) and the final approach with balanced training data and the additional socio-economic predictor (r² = 0.72, NRMSE = 12.17%). The results demonstrate the usefulness of remote sensing techniques for mapping timber volume for a future lignocellulose-based bioeconomy.     

High‐Precision Measurement of Molybdenum Isotopic Compositions of Selected Geochemical Reference Materials

Here we describe high‐precision molybdenum isotopic composition measurements of geological reference materials, performed using multi‐collector inductively coupled plasma‐mass spectrometry (MC‐ICP‐MS). Purification of Mo for isotopic measurements was achieved by ion exchange chromatography using Bio‐Rad AG^® 1‐X8 anion exchange resin. Instrumental mass bias was corrected using ¹⁰⁰Mo‐⁹⁷Mo double spiking techniques. The precision under intermediate measurement conditions (eighteen measurement sessions over 20 months) in terms of δ^98/95Mo was 0.10‰ (2s). The measurement output was approximately four times more efficient than previous techniques, with no compromise in precision. The Mo isotopic compositions of seven geochemical reference materials, seawater (IAPSO), manganese nodules (NOD‐P‐1 and NOD‐A‐1), copper‐molybdenum ore (HV‐2), basalt (BCR‐2) and shale (SGR‐1b and SCo‐1), were measured. δ^98/95Mo values were obtained for IAPSO (2.25 ± 0.09‰), NOD‐P‐1 (?0.66 ± 0.05‰), NOD‐A‐1 (?0.48 ± 0.05‰), HV‐2 (?0.23 ± 0.10‰), BCR‐2 (0.21 ± 0.07‰), SCo‐1 (?0.24 ± 0.06‰) and SGR‐1b (0.63 ± 0.02‰) by calculating δ^98/95Mo relative to NIST SRM 3134 (0.25‰, 2s). The molybdenum isotopic compositions of IAPSO, NOD‐A‐1 and NOD‐P‐1 obtained in this study are within error of the compositions reported previously. Molybdenum isotopic compositions for BCR‐2, SCo‐1 and SGR‐1b are reported for the first time.     

鄂尔多斯盆地西南部延长组长7段浊积岩沉积特征

根据野外露头、岩心观察及粒度分析,对鄂尔多斯盆地西南部延长组长7段浊积岩的沉积特征进行了研究。结果表明,浊积岩主要为岩屑长石砂岩,以次棱角状为主,成分成熟度及结构成熟度较低;粒度分布上具有典型的浊流沉积的特征,发育粒序层理、包卷层理、沟模、槽模、重荷模、滑塌变形等沉积构造;常见的鲍马序列层序组合有AB型、ABC型、ADE型、AE型。研究区浊积扇可分为上扇、中扇、下扇亚相及主沟道、辫状沟道等相应的沉积微相,上扇、中扇分布范围较广,是浊积扇的主体。在古地理演化过程中,长73浊积岩体规模较小,长72—长71湖侵作用减弱,深湖线收缩,浊积岩逐渐发育,在华池—庆城一带连片展布;湖盆地形、物源供给及构造运动是影响浊积岩发育及分布的重要原因。     

高岭石和埃洛石-7Å对稀土元素吸附特征的实验研究

针对赣南风化淋积型稀土矿中稀土元素的分异现象,通过研究该稀土矿的主要矿物成分——高岭石和埃洛石-7的矿物特征及其在不同条件下的吸附特征,探讨了该类型稀土矿的成矿机制和稀土元素分异机理。高岭石和埃洛石-7吸附稀土元素的能力受体系p H值以及金属阳离子钾、钠的的影响,且埃洛石-7吸附稀土元素的能力高于高岭石。横向对比实验结果表明当体系中赋存有K+时,两种粘土矿物对稀土元素的吸附呈现出分化趋势,其中高岭石主要吸附中-重稀土元素,埃洛石-7反之,因此风化淋积型稀土矿风化过程中释放出的K+可能会使高岭石和埃洛石-7呈现出差异吸附特征,进而反过来影响稀土矿中稀土元素的配分。     

吴起薛岔地区长8油层组储层岩石学特征研究

吴起薛岔地区是近年来延长油田的潜力目标开发区,其长8油层组油藏分布面积和储量潜力大。因此,本文通过钻井岩心、铸体薄片、扫描电镜观察和粒度、X衍射全岩分析、黏土矿物定量分析,对研究区长8油层组储层岩石学特征进行了研究。结果显示,研究区长8油层组岩性主要由灰褐色细粒岩屑长石砂岩组成,长石砂岩次之,具有"低石,高长石"特点;碎屑颗粒多为次棱角状,分选性中等,胶结类型以孔隙及加大孔隙型为主;填隙物含量均大于15%,主要以绿泥石膜、水云母、方解石以及铁方解石等自生矿物为主,以及少量高岭石和网状粘土等。     

2012-2015年中缅油气管道重要节点土地利用/覆被时空变化分析

"中缅油气管道"是我国陆上第三大能源通道,该项目的建设将改变其沿线周边的土地利用/覆盖现状,同时对沿线地区的自然环境和社会经济发展产生重要影响。本文选取中缅油气管道沿线的国内11个重要节点和缅甸境内4个重要节点作为研究区,以2012年和2015年2期Landsat7 ETM+和Landsat8 OLI影像数据作为数据源,利用决策树分类算法提取2012年和2015年2期中缅油气管道沿线重要节点的土地利用/覆被信息,分析2012年和2015年2个时期的中缅油气管道沿线15个重要节点土地利用/覆被的时空变化。研究结果表明：①15个节点中,除德宏芒市、保山隆阳区、缅甸若开邦外,其他11个节点的土地覆被变化均在20%左右;②15个节点中,最主要的土地覆被变化为植被和裸土的相互转换,其次为其他土地覆被类型向建筑的转换;③由于中缅油气管道项目的辐射作用,带动当地经济发展,改变当地的经济作物结构,因此造成大量的植被和裸土的相互转换,并造成建筑用地需求增加,出现大量的其他地表覆被类型向建筑的转换。     

Deep learning-based monitoring of overshooting cloud tops from geostationary satellite data

Overshooting tops can cause a variety of severe weather conditions, such as cloud-to-ground lightning, strong winds, and heavy rainfall, which can affect flight and ground operations. Many previous studies have developed overshooting top (OT) detection models. However, rather than identifying individual pixels in satellite images as OTs or non-OTs, we typically find OTs through visual inspection of the contextual information of pixels (i.e., dome-like shape). Such an approach is more intuitive, accurate, and generalizable regardless of the OT characteristics that are used in the existing OT detection algorithms. In this paper, a new approach is proposed for OT detection using deep learning, more specifically a convolutional neural network (CNN), which can mimic the human process by convolution operation. Himawari-8 satellite images were used as input data, which were chopped into patches (i.e., grids) with a 31 × 31 window size and binary detection (OT or non-OT) for each patch was the output of the model. The validation results show that CNN can be successfully applied for the detection of OTs over the tropical regions, showing a mean probability of detection (POD) of 79.68% and a mean false alarm ratio (FAR) of 9.78%.     

鄂尔多斯盆地合水地区长81亚油层组沉积相及对储层物性的影响

为研究鄂尔多斯盆地上三叠统延长组长81亚油层组沉积微相与储层物性的关系,通过岩心相、测井相及物性等资料,结合前人研究成果,对盆内合水地区长8₁亚油层组进行分析。认为:(1)长8₁亚油层组浅水三角洲前缘沉积发育水下分流河道、河口坝、前缘席状砂、分流间湾4种沉积微相。多期叠置的水下分流河道是控制砂体发育的主要微相类型,根据砂地比又可分为河道核部、河道中部及河道侧翼;(2)长8₁²小层水下分流河道砂体发育频率较高、沉积期次较多、连通性较强,其次为长88₁²小层与长8₁³小层;(3)由沉积微相决定的岩石结构成熟度控制了储层的初始孔隙度,进而在一定程度上影响了储层现今孔隙度。水下分流河道核部储层物性最好,是有利微相类型。研究区长8₁²小层有利微相发育规模最大,为后期勘探开发首要目的层位。     

Use of fallout tracers 7Be, 210Pb and 137Cs to distinguish the form of sub‐surface soil erosion delivering sediment to rivers in large catchments

Fallout radionuclides (FRNs) ¹³⁷Cs and ²¹⁰Pb are well established as tracers of surface and sub‐surface soil erosion contributing sediment to river systems. However, without additional information, it has not been possible to distinguish sub‐surface soil erosion sources. Here, we use the FRN ⁷Be (half‐life 53 days) in combination with ¹³⁷Cs and excess ²¹⁰Pb to trace the form of erosion contributing sediment in three large river catchments in eastern Australia; the Logan River (area 3700 km²), Bowen River (9400 km²) and Mitchell River (4700 km²). We show that the combination of ¹³⁷Cs, excess ²¹⁰Pb and ⁷Be can discriminate horizontally aligned sub‐surface erosion sources (rilled and scalded hillslopes and the floors of incised drainage lines and gully ‘badland’ areas) from vertical erosion sources (channel banks and gully walls). Specifically, sub‐surface sources of sediment eroded during high rainfall and high river flow events have been distinguished by the ability of rainfall‐derived ⁷Be to label horizontal soil surfaces, but not vertical. Our results indicate that in the two northern catchments, erosion of horizontal sub‐surface soil sources contributed almost as much fine river sediment as vertical channel banks, and several times the contribution of hillslope topsoils. This result improves on source discrimination provided previously and indicates that in some areas erosion of hillslope soils may contribute significantly to sediment yield, but not as topsoil loss. We find that in north‐eastern Australia, scalded areas on hillslopes and incising drainage lines may be sediment sources of comparable importance to vertical channel banks. Previous studies have used the combination of ¹³⁷Cs, excess ²¹⁰Pb and ⁷Be to estimate soils losses at the hillslope scale. Here, we show that with timely and judicious sampling of soil and sediment during and immediately after high flow events ⁷Be measurements can augment fallout ¹³⁷Cs and ²¹⁰Pb to provide important erosion source information over large catchments. Copyright © 2013 John Wiley & Sons, Ltd.