Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012

As is well known,clouds impact the radiative budget,climate change,hydrological processes,and the global carbon,nitrogen and sulfur cycles.To understand the wide-ranging effects of clouds,it is necessary to assess changes in cloud cover at high spatial and temporal resolution.In this study,we calculate global cloud cover during the day and at night using cloud products estimated from Moderate Resolution Imaging Spectroradiometer(MODIS)data.Results indicate that the global mean cloud cover from 2003 to 2012 was 66%.Moreover,global cloud cover increased over this recent decade.Specifically,cloud cover over land areas(especially North America,Antarctica,and Europe)decreased(slope=–0.001,R^2=0.5254),whereas cloud cover over ocean areas(especially the Indian and Pacific Oceans)increased(slope=0.0011,R^2=0.4955).Cloud cover is relatively high between the latitudes of 36°S and 68°S compared to other regions,and cloud cover is lowest over Oceania and Antarctica.The highest rates of increase occurred over Southeast Asia and Oceania,whereas the highest rates of decrease occurred over Antarctica and North America.The global distribution of cloud cover regulates global temperature change,and the trends of these two variables over the 10-year period examined in this study(2003–2012)oppose one another in some regions.These findings are very important for studies of global climate change.

Estimation of Basal Area in West Oak Forests of Iran Using Remote Sensing Imagery

The objective of this study is to evaluate the capability of satellite imagery for the estimation of basal area in Northern Zagros Forests. The data of the high resolution geometric (HRG) sensor of SPOT-5 satellite dated in July 2005 were used. Investigation of the quality of Satellite images shows that these images have no radiometric distortion. Overlaying of geocoded images with the digital topographic maps indicated that the images have high geometric precision. A number of 319 circular plots (0.1 ha) were established using systematic random method in the study area. All trees having diameter at breast height (DBH) (i.e. 1.3 m above ground) greater than 5 cm were callipered in each plot. Basal area in each plot was determined using field data. Main bands, artificial bands such as vegetation indices and principle component analysis (PCA) were studied. Digital numbers related to each plot were extracted from original and artificial bands. All plots were ordinated by major geographic aspects and the best fitted regression models were determined for both the study area without consideration of aspects and with consideration of major geographic aspects by multiple regression analysis (step wise regression). The results from regression analysis indicated that the square root of basal area without consideration of aspects has a high correlation with band B1 (r = –0.60). The consideration of aspects resulted in correlation of different indices with square root of basal area such that in northern forests, band B1 had higher correlation coefficient(r = –0.67) among other indices. In Eastern forests, the same band showed correlation of basal area with different correlation coefficient (r = –0.65). In southern and western forests, the square root of basal area had higher correlation (r = –0.68) with RVI. The use of the square root of basal area as a dependent variable in multivariate linear regression improved the results. The assessment of model validity indicated that the proposed models are properly valid.

Mapping lineaments using Landsat 8 OLI and SRTM data;a case study of the eastern part of the Ouarzazate Basin,Morocco

The integration of remote sensing and geographic information system(GIS)was employed in this study to delineate the structural lineaments within the eastern section of the Ouarzazate Basin,situated between the southern front of the Central High Atlas and the northern slopes of the Eastern Anti-Atlas(also known as the Saghro Massif).To achieve this objective,Landsat 8 Operational Land Imager(OLI)and Shuttle Radar Topography Mission(SRTM)data were used.Principal Component Analysis(PCA)was computed and a directional filter was applied to the first PCA and the panchromatic band(B8).Additionally,shading was applied to the SRTM data in four directions;N0°,N45°,N90°,N135°.After removing of the non-geological linear structures,the results obtained,using the automatic extraction method,allowed us to produce a synthetic map that included 1251 lineaments with an average length of 1331 m and was dominated by NE-SW,ENE-WSW and E-W directions,respectively.However,the high lineament density is clearly noted in the Anti-Atlas(Saghro Massif)and at the level of the northern part,extending from the Ait Ibrirne to Arg-Ali Oubourk villages.High lineament density can always be found around the major faults affecting this area.The data collected during the field investigations and from geological maps show that the major direction of the faults and structural accidents range mostly between N45°,N70°and N75°.The correlation of remote sensing results with those collected in the field shows a similarity and coincidence with each other.From these results,it is possible to consider the automatic extraction method as a supplementary kind that can serve classical geology by quickly enriching it with additional data.As shown in this work,this method provides more information when applied in arid areas where the fields are well outcropped.

Hydrochemical Characterisation and Assessment of the Level of Contamination of Groundwater Collected by Private Waterworks in the Town of Moundou in the South of Chad

Groundwater is the main source of drinking water for large cities in most African countries. In Moundou, for example, the conventional groundwater supply system is failing. To compensate for this state failure, the population is building boreholes and wells, most of which tap the surface water table, generally referred to as the “water table”. The aim of this study is to characterize these waters in order to assess their level of contamination and, by extension, the degree of pollution of the water table. Major elements such as: Chloride (Cl-), Sulfate (SO42-), Nitrate (NO3-), Calcium (Ca2+), magnesium (Mg2+), sodium (Na+) and potassium (K+) were analysed by Liquid Chromatography and the Bicarbonate ion (HCO3-) was determined by the titrimetric method. The methodology applied is based on a combination of hydrochemical techniques and statistical analysis (PCA and CHA). A sampling campaign was carried out during high-water periods. The results of the physico-chemical analyses show mineralization ranging from 7.29 to 3670 μS/cm, with an average of 487.44 μS/cm. The groundwater studied is generally acidic, with a pH ranging from 3.26 to 6.41. Based on their anions, they are classified into four main hydrochemical facies: chloride and sulphate facies, calcium and magnesium facies, sodium and potassium facies and bicarbonate facies. The various correlations between major ions and statistical analyses have enabled us to identify three hydrogeochemical processes involved in water mineralization. The dominant process is silicate hydrolysis, followed by cation exchange, then anthropogenic input, which influences mineralization by polluting the water.

The China Multimode Microwave Remote Sensor (CMMRS) Onboard the SZ-4 Spaceship

In this report, the specifications and some results of the China multimode microwave remote sensor (CMMRS) onboard China's SZ-4 spaceship are described. Technical details and initial processing results of the CMMRS measurement data are reported.

南极普里兹湾海域夏季表层水体颗粒有机碳及其同位素分布特征

基于中国第29次南极科学考察在普里兹湾海域采集的61个站位的表层悬浮体样品的颗粒有机碳(POC)及其稳定同位素(δ13CPOC)测试结果,结合卫星遥感解译的研究区海水表面温度(SST)、叶绿素a(Chl a)浓度和海冰覆盖率数据,研究了普里兹湾海域表层水体悬浮POC的分布特征,探讨了悬浮体中POC的来源及其形成过程。结果表明,2013年夏季普里兹湾表层悬浮POC浓度为0.28—0.84 mg·L-1,平均浓度为0.48 mg·L-1;δ13CPOC值的变化范围为-29.68‰—-26.30‰,平均值为-28.01‰。表层水体悬浮POC分布呈现近岸高于远岸,西部高于东部的特征。POC的高值区主要分布在冰架附近,与表层水体Chl a浓度和海冰覆盖率分布趋势基本一致,表明夏季普里兹湾表层水体悬浮POC主要由浮游植物现场生产,而浮游植物的生长受到了海冰的显著影响。在普里兹湾外部海域表层水体悬浮颗粒物δ13CPOC值从东向西逐渐偏负,而调查区东部从近岸向远海逐渐偏负,反映该海域δ13CPOC分布特征主要受到浮游植物吸收与固定CO2速率的影响。中山站附近海域δ13CPOC值显著偏负,可能是受到近岸海域陆源有机质输入和浮游生物种属改变的影响。

Y-梯度广义最小二乘加权校正的土壤速效氮野外原位光谱预测

土壤速效氮是影响作物生长发育的重要养分指标。野外原位可见近红外光谱(VIS-NIR)分析技术具有快速无损等特点,对速效氮的定量预测具有较好的应用前景。野外条件下进行原位光谱采集更节省人力物力,且为土壤养分实时传感器的开发提供了数据基础。但由于野外原位光谱中通常存在大量的无关环境因子干扰信息,易导致回归模型预测精度降低,达不到实用要求。针对位于以色列中部和北部的两个试验点共76个样本开展研究,提出利用Y-梯度广义最小二乘加权算法(Y-GLSW)对样本的野外原位VIS-NIR反射率光谱(350~2 500nm)进行滤波校正,以提高回归模型的预测能力。首先使用SG平滑、一阶导数变换、标准正态变换等常规方法对原始光谱进行预处理和变换;在此基础上再使用Y-GLSW构建滤波模型对变换后的光谱进行滤波校正;最后使用偏最小二乘回归算法(PLS-R)分别结合原始光谱RW、预处理变换后的光谱PPT和滤波校正后的光谱Y-GLSW建立回归分析模型对速效氮进行定量预测。结果表明:利用RW光谱建立的回归预测模型是不可靠的;利用PPT光谱建立的回归模型在测试集的相对分析误差(RPD)为1.41,解释总方差占实际总方差之比(SSR/SST)为0.57,模型具有一定的可靠性;Y-GLSW光谱建立的回归模型在测试集的RPD和SSR/SST分别为2.07和0.69,相对于PPT模型分别提高了46.81%和21.05%。因此,利用Y-GLSW对野外原位VIS-NIR光谱进行滤波校正,能够有效去除光谱中的无关信息数据,提高模型的预测精度和解释能力。

沙尘天气激光雷达监测技术研究

使用LB10-D200激光雷达，对2006年4月北京地区天气连续监测，通过监测数据的分析，研究沙尘天气的大气垂直结构及其运动发展情况，同时与通过使用E-BAM贝塔沙尘监测仪器、AERONET、MODIS和DREAM预测模型等手段的分析结果进行综合对比分析。结果表明：4月份共有10d AOT浓度大于1，最高峰达到4；激光雷达监测结果和其他手段得到的结果具有良好的一致性，能精确地表达典型沙尘天气垂直大气的时空分布；沙尘层AOT对整个大气AOT的贡献率约为60％～80％。

Climate change and glacier area variations in China during the past half century

Glacier variations in the Tibetan Plateau and surrounding mountain ranges in China affect the livelihood of over one billion people who depend on water from the Yellow, Yangtze, Brahmaputra, Ganges and Indus rivers originating in these areas. Based on the results of the present study and published literature, we found that the glaciers shrank ：5.7% in area from 1963 to 20：0 with an annual area change of -0.33%. The shrinkage generally decreased from peripheral mountain ranges to the interior of Tibet. The linear trends of annual air temperature and precipitation at 147 stations were 0.36℃（10a）^-1 and 8.96 mm （10a）^-1 respectively from 1961 to 2010. The shrinkage of glaciers was well correlated with the rising temperature and the spatial patterns of the shrinkage were influenced by other factors superimposed on the rising temperature such as glacier size, type, elevation, debris cover and precipitation.

Extended Range(10–30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model

Extended range （10-30 d） heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error （NCPE） algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1-2 d, 3-9 d and 10-30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10-30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.

Sharpness of the paired 660-km discontinuity beneath the Izu-Bonin area

The 660-km discontinuity that separates the Earth's upper and lower mantle has primarily been attributed to phase changes in olivine and other minerals.Resolving the sharpness is essential for predicting the composition of the mantle and for understanding its dynamic effects.In this study,we used S-to-P conversions from the 660-km interface,termed S660P,arriving in the P-wave coda from one earthquake in the Izu–Bonin subduction zone recorded by stations in Alaska.The S660P signals were of high quality,providing us an unprecedented opportunity to resolve the sharpness of the discontinuity.Our study demonstrated,based on the impedance contrast given by the IASP91 model,that the discontinuity has a transitional thickness of^5 km.In addition,we observed a prominent arrival right after the S660P,which was best explained by S-to-P conversions from a deeper discontinuity at a depth of^720 km with a transitional thickness of^20 km,termed S720P.The 720-km discontinuity is most likely the result of a phase transition from majoritic garnet to perovskite in the segregated oceanic crust(mainly the mid-oceanic ridge basalt composition)at the uppermost lower mantle beneath this area.The inferred phase changes are also consistent with predictions from mineral physics experiments.

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.

A Neural Network Method for Monitoring Snowstorm: A Case Study in Southern China

It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.

Evaluation of Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) for Water Quality Monitoring:A Case Study for the Estimation of Salinity

Landsat-5 Thematic Mapper(TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum(6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square(OLS) regression and Geographically Weighted Regression(GWR) based on in situ data of October 2009. Results show that the coefficient of determination(R^2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher(R^2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay(north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant(32 practical salinity units) towards the open sea.

A New Factorized Backprojection Algorithm for Stripmap Synthetic Aperture Radar

Factorized backprojection is a processing algorithm for reconstructing images from data collected by synthetic aperture radar (SAR) systems. Factorized backprojection requires less computation than conventional time-domain backprojection with minimal loss in accuracy for straight-line motion. However, its implementation is not as straightforward as direct backprojection. This paper provides a new, easily parallelizable formulation of factorized backprojection designed for stripmap SAR data that includes a method of implementing an azimuth window as part of the factorized backprojection algorithm. We compare the performance of windowed factorized backprojection to direct backprojection for simulated and actual SAR data.

Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Crop phenology is critical for agricultural management,crop yield estimation,and agroecosystem assessment.Traditionally,crop growth stages are observed from the ground,which is time-consuming and lacks spatial variability.Remote sensing Vegetation Index(VI)time series has been used to map land surface phenology(LSP)and relate to crop growth stages mostly after the growing season.In recent years,high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season.This paper summarizes two classes of near-real-time mapping methods,i.e.,curve-based and trend-based approaches.The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages.The curve-based approaches are capable of a shortterm prediction.The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds.The trend-based approaches only use current observations.Both curve-based and trend-based approaches are promising in mapping crop growth stages timely.Nevertheless,mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages.The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season.Recent satellite datasets such as the harmonized Landsat and Sentinel-2(HLS)are promising for mapping crop phenology within the season over large areas.Operational applications in the near future are feasible.

Modelling Soil Erosion and Sedimentation in the Oued Haricha Sub-Basin (Tahaddart Watershed, Western Rif, Morocco): Risk Assessment

This study evaluates the annual loss of soil in the sub-basin of Oued Haricha (Tahaddart basin, Western Rif, NW Morocco). The integration of revised (RUSLE) and modified (MUSLE) soil loss empirical equations of Wischmeier and Smith in combination with GIS permits the modelling of soil erosion at the scale of parcels. The characteristics of precipitation and runoff, the soil properties, the culture system and the current working practices of soil in the sub-basin of the Oued Haricha are collected from local data. The digital terrain model is used to generate topographic factors. The combination of different RUSLE factors shows that the annual soil is 62.72 t/ha/year and corresponds to an average level of risk. The total losses calculated by MUSLE method are valued at 221,468 t/year. The rates of loss due to linear erosion are 82,652 t/year. These soil losses represent 20.33% of the total losses, and confirm that the losses on the slopes outweigh the losses due to the river system. Sedimentation module shows that the areas of high erosion (greater than 200 t/ha/year) are concentrated in the reliefs with average and high slope and occupy 38% of the total area. The deposition areas occupy the centre of sub-basin and constitute 9.12% of the total area. These deposits were concentrated on the edges of major rivers and the outlet of the sub-basin and contributed to siltation of the April 9, 1947 dam.

Microseism Variations in Response to Antarctic Seasonal Changes in Sea Ice Extent

The temporal and spatial distributions of Antarctic sea ice play important roles in both the generation mechanisms and the signal characteristics of microseisms. This link paves the way for seismological investigations of Antarctic sea ice. Here we present an overview of the current state of seismological research about microseisms on Antarctic sea ice. We first briefly review satellite remote-sensing observations of Antarctic sea ice over the past 50 years. We then systematically expound upon the generation mechanisms and source distribution of microseisms in relation to seismic noise investigations of sea ice, and the characteristics of Antarctic microseisms and relationship with sea ice variations are further analyzed. We also analyze the continuous data recorded at seismic station BEAR in West Antarctica from 2011 to 2018 and compare the microseism observations with the corresponding satellite remotesensing observations of Antarctic sea ice. Our results show that:(1) the microseisms from the coastal regions of West Antarctica exhibit clear seasonal variations,SFM with maximum intensities every April-May and minimum intensities around every October-November;while DFM intensities peak every February-March,and reach the minimum around every October. Comparatively,the strong seasonal periodicity of Antarctic sea ice in better agreement with the observed DFM;and(2) microseism decay is not synchronous with sea ice expansion since the microseism intensity is also linked to the source location,source intensity(e. g.,ocean storms,ocean wave field),and other factors. Finally, we discuss the effect of Southern Annular Mode on Antarctic sea ice and microseisms,as well as the current limitations and potential of employing seismological investigations to elucidate Antarctic sea ice variations and climate change.

A warmer but drier Marine Isotope Stage 11 during the past 650 ka as revealed by the thickest loess on the western Chinese Loess Plateau

Marine Isotope Stage 11（MIS 11; ca. 423-362 ka） is generally considered to be the best analogue for the present interglacial（Holocene）, and investigation of it will improve our understanding of current climate variability and assist in predictions of future climate change. However, many recent studies primarily focus on the structure and duration of MIS 11. Little research has focused on climate warmth and stability recorded in the Chinese loess-paleosol sequences（LPS） during the S4 paleosol formation（equivalent to MIS 11）. On the basis of previous work, this study presents a high-resolution record（ca. 75 a/cm） that spans from MIS 1 to MIS 15, as preserved in the thickest known Jingyuan loess section on the western Chinese Loess Plateau（CLP）. This LPS is almost 165 m thick and was sampled from the upper part of L6 to the modern soil at 2-cm depth intervals. Measurements of magnetic susceptibility, mean grain size and ＆gt;63 μm particle content, carbonate content, total organic carbon, and soil color of samples were made to reconstruct the paleoclimate variation, and a grain-size age model was used to constrain the chronological framework. The primary results show that a generally warm-humid climate dominated the S4 paleosol development, but the climate condition was extremely unstable during the whole of MIS 11. Two obviously different climate regimes controlled the MIS 11 climate variation： the early part of MIS 11 was extremely warm and stable, but the latter part was relatively cool（non-glacial） and unstable. This climate pattern was consistent with records on the central CLP and wavelet analysis suggested that it was forced by the 65°N insolation variability modulated by a quasi-100-ka cycle. In addition, a multi-proxy comparative study on the climate conditions during S0 to S5 paleosol development indicates that the period of S4 development might be the warmest interglacial of the past 650 ka. However, the climate condition during S4 development was not the most humid episode as recorded in Xifeng and Luochuan loess sections on the central CLP. On the contrary, it was drier than both the MIS 15 and the present interglacial on the western CLP, which is somewhat similar to the present climate pattern on the central CLP.

Principle Question of Harmonic Quantitative Analysis of Spectrum Expansion

The numerical analysis was made in terms of questions that quantity is stable or unstable. The results show that the stability due to “infinitesimal difference of they are well-matched in large quantity" or “Chaos" leads to numerical calculation meaningless. The quantity instability due to magnitude explosive increasing does not follow the suitability. Both unstable magnitude be restricted by the suitability and the numerical calculating by “ill-posed equation" of “infinitesimal difference of they are well-matched in large quantity" might fall into producing false information.