Oil and gas potential assessment for coal measure source rocks on absolute concentration of n-alkanes and aromatic hydrocarbons

From the viewpoint of environmental remote-sensing applications, this article explains the overall technical characteristics of the Chinese HJ-1A and HJ-1B satellites. It also investigates the spectral characteristics and potential applications of charge-coupled devices, and the infrared and hyperspectral data obtained by the satellites. Examples of applications, such as the remote-sensing monitoring of algal bloom in Taihu Lake, straw burning in southern China, and aerosol optical depth in the area around Bohai sea are presented. These examples illustrate the application characteristics of the HJ-1A and HJ-1B satellite data.     

Photosynthetically active radiation retrieval based on HJ-1A/B satellite data

Photosynthetically active radiation (PAR) is essential for plant photosynthesis and carbon cycle, and is also important for meteorological and environmental monitoring. To advance China’s disaster and environmental monitoring capabilities, the HJ-1A/B satellites have been placed in Earth orbit. One of their environmental monitoring objectives is the study of PAR. We simulated direct solar, scattered and environment radiation between 400 and 700 nm under different atmospheric parameters (solar zenith angle, atmospheric water vapor, atmospheric ozone, aerosol optical thickness, surface elevation and surface albedo), and then established a look-up table between these input parameters and PAR. Based on the look-up table, we used HJ-1A/B aerosol and surface albedo outputs to derive the corresponding PAR. Validation of inversed instantaneous and observed PAR values using HJ-1 Heihe experimental data had a root mean square error of 25.2 W m⁻², with a relative error of 5.9%. The root mean square error for accumulated daily PAR and observed values was 0.49 MJ m⁻², with a relative error of 3.5%. Our approach improved significantly the computational efficiency, compared with using directly radiation transfer equations. We also studied the sensitivity of various input parameters to photosynthetically active radiation, and found that solar zenith angle and atmospheric aerosols were sensitive PAR parameters. Surface albedo had some effect on PAR, but water vapor and ozone had minimal impact on PAR.

     

Detection of the urban heat island in Beijing using HJ-1B satellite imagery

Satellite images are used extensively in studying the urban heat island (UHI) phenomenon. We evaluated the suitability of thermal infrared (TIR) data from the HJ-1B satellite for detecting UHI using a case study in Beijing. Two modified algorithms for retrieving the land surface temperature (LST) from HJ-1B data were tested. The results were compared with LST images derived from a Landsat TM thermal band and the MODIS LST output. The spatial pattern of UHI generated using HJ-1B data matched well with that produced using TM and MODIS data. Of the two algorithms, the mono-window algorithm performed better but further tests are necessary. With more frequent coverage than TM and higher spatial resolution than MODIS, the HJ-1B TIR data present a unique opportunity to study thermal environments in cities in China and neighboring countries.

     

Photosynthetically active radiation retrieval based on HJ-1A/B satellite data

Photosynthetically active radiation (PAR) is essential for plant photosynthesis and carbon cycle, and is also important for meteorological and environmental monitoring. To advance China’s disaster and environmental monitoring capabilities, the HJ-1A/B satellites have been placed in Earth orbit. One of their environmental monitoring objectives is the study of PAR. We simulated direct solar, scattered and environment radiation between 400 and 700 nm under different atmospheric parameters (solar zenith angle, atmospheric water vapor, atmospheric ozone, aerosol optical thickness, surface elevation and surface albedo), and then established a look-up table between these input parameters and PAR. Based on the look-up table, we used HJ-1A/B aerosol and surface albedo outputs to derive the corresponding PAR. Validation of inversed instantaneous and observed PAR values using HJ-1 Heihe experimental data had a root mean square error of 25.2 W m^?2, with a relative error of 5.9%. The root mean square error for accumulated daily PAR and observed values was 0.49 MJ m^?2, with a relative error of 3.5%. Our approach improved significantly the computational efficiency, compared with using directly radiation transfer equations. We also studied the sensitivity of various input parameters to photosynthetically active radiation, and found that solar zenith angle and atmospheric aerosols were sensitive PAR parameters. Surface albedo had some effect on PAR, but water vapor and ozone had minimal impact on PAR.     

Aerosol optical depth retrieval by HJ-1/CCD supported by MODIS surface reflectance data

The high spatial resolution and temporal observation frequency of HJ-1/CCD make it suitable for aerosol monitoring. However, because of the lack of a shortwave infrared band, it is difficult to use HJ-1/CCD imagery to retrieve aerosol optical depth (AOD). We developed a new algorithm for HJ-1/CCD AOD retrieval by introducing MODIS surface reflectance outputs (MOD09) as support. In this algorithm HJ-1/CCD blue band surface reflectance was retrieved through MOD09 blue band surface reflectance by band matching of the two sensors. AOD at 550 nm was then generated through a pre-calculated look-up table for HJ-1/CCD. Eighteen HJ-1/CCD images covering the Jing-Jin-Tang (Beijing-Tianjin-Tangshan) region were used to retrieve AOD using the new algorithm, and the AODs were then validated using AERONET ground measurements in Beijing and Xianghe. The validation shows that compared with AERONET ground measurements, 27/29 AODs have error less than 0.1 in absolute value.

     

Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data

The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance. Forest aboveground biomass (AGB) is an important component in the land surface carbon cycle. In this study, using forest inventory data and forest distribution data, the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device (CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast (HJ-1) and Geoscience Laser Altimeter System (GLAS) waveform data from the Ice, Cloud and land Elevation Satellite (ICESat). The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing (CQ) Forest Center, which was calculated from forest inventory data. The resulted statistical regression models have a R ²=0.68 for conifer and R ²=0.71 for broadleaf forests. These models were used to estimate biomass for all GLAS footprints of forest located in the study area. All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models (R ²=0.55 and R ²=0.52 for needle and broadleaf respectively). Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data. Biomass data obtained from forest inventory data of the Zhuanglin (ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data (R ²=0.71). About 80% of biomass samples had an error less than 20 t ha⁻¹, and the mean error of all validation samples is 5.74 t ha⁻¹. The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area. It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision, and the biomass data can be used as input data for future carbon budget analysis.

     

Detection of the urban heat island in Beijing using HJ-1B satellite imagery

Satellite images are used extensively in studying the urban heat island (UHI) phenomenon. We evaluated the suitability of thermal infrared (TIR) data from the HJ-1B satellite for detecting UHI using a case study in Beijing. Two modified algorithms for retrieving the land surface temperature (LST) from HJ-1B data were tested. The results were compared with LST images derived from a Landsat TM thermal band and the MODIS LST output. The spatial pattern of UHI generated using HJ-1B data matched well with that produced using TM and MODIS data. Of the two algorithms, the mono-window algorithm performed better but further tests are necessary. With more frequent coverage than TM and higher spatial resolution than MODIS, the HJ-1B TIR data present a unique opportunity to study thermal environments in cities in China and neighboring countries.     

Development of environmental monitoring satellite systems in China

With the increase in global environmental problems, the necessity and urgency of remote sensing technology being applied to environmental monitoring has been widely recognized around the world. China has launched the environment and disaster monitoring and forecasting small satellite constellation HJ-1A/B and the FY3 atmosphere and environmental satellite, but they still cannot fully satisfy requirements for environmental monitoring. This paper summarizes the current status of satellite environmental monitoring in China and the existing problems of inadequate load design and low data utilization efficiency, and discusses the demand for environmental monitoring satellites. Based on the development of foreign satellite systems for environmental monitoring, the future development and key tasks of the environmental monitoring satellite system in China is discussed, as are some related initiatives.     

Land cover mapping using time series HJ-1/CCD data

It is very difficult to have remote sensing data with both high spatial resolution and high temporal frequency; thus, two categories of land-use mapping methodology have been developed separately for coarser resolution and finer resolution data. The first category uses time series of data to retrieve the variation of land surface for classification, which are usually used for coarser resolution data with high temporal frequency. The second category uses fine spatial resolution data to classify different land surface. With the launch of Chinese satellite constellation HJ-1in 2008, four 30 m spatial resolution CCDs with about 360 km coverage for each one onboard two satellites made a revisit period of two days, which brought a new type of data with both high spatial resolution and high temporal frequency. Therefore, by taking the spatiotemporal advantage of HJ-1/CCD data we propose a new method for finer resolution land cover mapping using the time series HJ-1/CCD data, which can greatly improve the land cover mapping accuracy. In our two study areas, the very high resolution remote sensing data within Google Earth are used to validate the land cover mapping results, which shows a very high mapping accuracy of 95.76% and 83.78% and a high Kappa coefficient of 0.9423 and 0.8165 in the Dahuofang area of Liaoning Province and the Heiquan area of Gansu Province respectively.     

Hydrological appraisal of rainfall estimates from radar,satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin,China

ABSTRACT

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km². All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.     

Problems of snowmelt runoff modelling for a variety of physiographic and climatic conditions

Abstract

Snowmelt runoff is a significant component of the hydrological cycle in many regions. Major problems of snowmelt runoff modelling associated with the physiographic and climatic conditions of these regions, and problem solutions being investigated, are reviewed. Problems common to all regions include: (a) definition of the spatial and temporal distribution of model input; (b) measurement or estimation of snow accumulation, snowmelt, and runoff process parameters for a range of applications and scales; and (c) development of accurate short term and long term snowmelt runoff forecasts. Procedures being investigated to solve these problems include: (a) integrating conventional and remote-sensing data to improve estimates of input data; (b) developing snowmelt process algorithms which have parameters that are closely related to measurable basin and climatic characteristics; and (c) updating model parameters and components using measured data or knowledge of past uncertainty. Research needs include development of improved model capabilities and establishment of standardized techniques and measures to evaluate model performance and results.     

Spatial disaggregation and intensity correction of TRMM-based rainfall time series for hydrological applications in dryland catchments

Abstract

A novel approach is presented for combining spatial and temporal detail from newly available TRMM-based data sets to derive hourly rainfall intensities at 1-km spatial resolution for hydrological modelling applications. Time series of rainfall intensities derived from 3-hourly 0.25° TRMM 3B42 data are merged with a 1-km gridded rainfall climatology based on TRMM 2B31 data to account for the sub-grid spatial distribution of rainfall intensities within coarse-scale 0.25° grid cells. The method is implemented for two dryland catchments in Tunisia and Senegal, and validated against gauge data. The outcomes of the validation show that the spatially disaggregated and intensity corrected TRMM time series more closely approximate ground-based measurements than non-corrected data. The method introduced here enables the generation of rainfall intensity time series with realistic temporal and spatial detail for dynamic modelling of runoff and infiltration processes that are especially important to water resource management in arid regions.

Editor D. Koutsoyiannis

Citation Tarnavsky, E., Mulligan, M. and Husak, G., 2012. Spatial disaggregation and intensity correction of TRMM-based rainfall time series for hydrological applications in dryland catchments. Hydrological Sciences Journal, 57 (2), 248–264.     

Remote sensing of snow and ice / La télédétection de neige et de glace

Abstract

Monitoring of snow and ice on the Earth's surface will require increasing use of satellite remote sensing techniques. These techniques are evolving rapidly. Active and passive sensors operating in the visible, near infrared, thermal infrared, and microwave wavelengths are described in regard to general applications and in regard to specific USA or USSR satellites. Meteorological satellites (frequent images of relatively crude resolution) and Earth resources satellites such as Landsat (less frequent images of higher resolution) have been used to monitor the areal extent of seasonal snow, but problems exist with cloud cover or dense forest canopies. Snow mass (water equivalent) can be measured from a low-flying aircraft using natural radioactivity, but cannot yet be measured from satellite altitudes. A combination of active and passive microwave sensors may permit this kind of measurement, but not until more is known about radiation scattering in snow. Satellite observations are very useful in glacier inventories, correcting maps of glacier extent, estimating certain mass balance parameters, and monitoring calving or surging glaciers. Ground ice is virtually impossible to monitor from satellites; ice on rivers and lakes can be monitored only with very high-resolution sensors. Microwave sensors, due to their all-weather capability (the ability to see through clouds) provide exciting data on sea ice distribution. Analysis of digital tapes of satellite data requires the archiving and scanning of huge amounts of data. Simple methods for extracting quantitative data from satellite images are described.     

Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

The spatial distribution of sub-pixel components has an impact on retrieval accuracy, and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index (LAI). To investigate this effect, we constructed three realistic scenarios with the same LAI values and other properties, except that the simulated plants had different distributions. We implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor (BRF) datasets based upon these simulated scenes. The inversion was conducted using these data, which showed that spatial distribution affects retrieval accuracy. The inversion was also conducted for LAI based on charge-coupled device (CCD) data from the Environment and Disaster Monitor Satellite (HJ-1), which depicted both forest and drought-resistant crop land cover. This showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI inversion. The spatial distribution of global fractal dimension index, which can be used to describe the area of sub-pixel components and their spatial distribution modes, shows good consistency with the coarse resolution LAI inversion error.

     

A methodology for using optimal MSIS parameters retrieved from SSULI data to compute satellite drag on LEO objects

A key application to be derived from Space Weather research will be to forecast atmospheric drag on low Earth orbit (LEO) satellites with significantly better accuracy than is attainable today. The recently launched STP P91-1 ARGOS mission will serve as a testbed for the use of future operational ultraviolet remote-sensing data to achieve such an improvement. This paper describes the associated methodology, which uses discrete inverse theory in conjunction with the data to derive correction factors in near real time for the MSISE-90 empirical thermospheric model. To simulate the application of this technique to orbit prediction, we use the Jacchia-71 operational model to generate an evolving “ground truth” upper atmospheric state over a 48 h time period. This permits a state-of-the-art Satellite Tool Kit orbit propagator to synthesize a corresponding “ground truth” orbit on a standard LEO test object at 350 km altitude. Our tests show that, for orbit prediction, the “data-enhanced” MSIS density specification can provide significant improvement over the uncorrected MSIS specification. However, for orbit prediction, the results are sensitive to the strategy selected for applying the correction factors. We contrast our results for orbit prediction with those of Marcos et al. (1998. Astrodynamics. Vol. 97(1). AAS, San Diego, pp. 501–513) for precision orbit determination. An important result in the context of Space Weather is that the Jacchia and MSIS models can show significant point-to-point disagreement, which has major implications for operational specification of thermospheric drag.     

Publications received by the Editor

Abstract

Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m.

Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.     

基于HJ-CCD和MODIS的吉林省中西部湖泊透明度反演对比

水体透明度能够反映光在水体中的穿透程度,影响水生植被及以光为依赖条件的水生生物的分布,获取透明度的传统方法是采用透明度盘进行测量,但也可以通过遥感方法获得.环境减灾卫星是专门用于环境与灾害监测预报的小卫星星座,影像覆盖范围广,空间、时相分辨率较高,可以为水环境遥感提供较好的数据源.MODIS数据在近岸水体和内陆大型湖泊水环境监测中也有广泛应用,它的时相分辨率也很高,但空间分辨率低.利用HJ-1A卫星CCD数据和MODIS日反射率产品(MOD09GA),以2012年9月吉林省石头口门水库、二龙湖、查干湖、月亮泡等地的实测透明度为基础(实测点数74个,最小值为0.134 m,最大值为1.410 m,平均值为0.488 m),根据灰色关联度选取构建模型的波段组合,建立水体透明度反演模型.HJ1A-CCD数据与MOD09GA数据建立的模型R2分别为0.639和0.894,均方根误差(RMSE)分别为0.248和0.135,模型验证的平均相对误差(MRE)分别为17.1%和9.5%,RMSE分别为0.207和0.089.MODIS数据以其较高的辐射分辨率使模型精度较高,但是HJ数据在应用于透明度小于1 m的水体时精度也较高(MRE=13.5%,RMSE=0.066).HJ-CCD数据在空间分辨率上的优势使其能够获得透明度空间分布的细节信息.比较两者反演得到的湖泊平均透明度,结果较为一致.     

几何校正对暗像元算法及离水辐亮度反演精度的影响:以太湖为例

几何校正将引起几何校正前后像元的像素值发生变化,进而导致暗像元的辐亮度在几何校正前后存在偏差.本文以太湖为研究水区,以2003年10月28日的Landsat/TM影像、2008年7月24日的CBERS影像和2009年4月25 日的HJ-1B影像作为数据基础,研究与探讨了几何校正对暗像元大气校正算法以及离水辐亮度反演精度...     

Estimation of the elastic earth parameters using slr data for the low satellites STARLETTE and STELLA

In this paper we present estimated values for the global elastic parameters (k ₂, k ₃) and (h ₂, l ₂) derived from the analysis of Satellite Laser Ranging (SLR) data. We analyse SLR data for two low satellites, STARLETTE and STELLA, collected over a period of two years, from 1 January 2005 to 1 January 2007, from 18 globally distributed ground stations. We carry out a sequential analysis for the two satellites jointly, and study the stability of the estimates as a function of the length of the data set used. The adjusted final values of (k ₂, k ₃) and (h ₂, l ₂) for STARLETTE and STELLA are compared to, and are largely found to support, the estimates we previously published based on data for two high satellites LAGEOS 1 and LAGEOS 2. A major discrepancy between the two solutions was only found for the Shida number l ₂.     

Book review

Abstract

This paper, which is partly drawn from the thesis work of one of the authors, presents experimental results of remote-sensing of plumes by optical radar.

Plume-rise theories from single stack emissions, due to Briggs (1969) and Moore (1974), were tested at different sites. Furthermore, a model for the rise of plumes will be described based on multiple sources of different heights and buoyancy fluxes. Finally, the fine structure of the plume cross-sections, deduced from the analysis of LIDAR echoes in terms of opacity (turbidity) ratios, will be shown