基于HJ-1B卫星数据的疏勒河上游流域地表温度反演

地表温度是描述地表和大气之间物质交换和能量平衡的关键参数之一. 利用疏勒河上游流域2010年7月25日过境的HJ-1B卫星数据, 采用修正的Jimenez-Mufloz&Sobrino普适性单通道算法反演了疏勒河上游流域的地表温度. 与MODIS LST产品的对比验证表明, 该算法在研究区有较高的精度. 同时, 对不同土地利用类型的地表温度的分析表明, 土地利用类型不同, 其地表温度存在一定的差异, 其中, 稀疏草地的地表温度最高, 裸岩次之. 研究发现, 在河流和冰川区, 修正的单通道算法可能高估了表面温度. 同时, 对模型反演所需的关键地表参数的敏感性分析发现, 算法在一定范围(0~0.01)内对比辐射率不太敏感, 但超过该范围则变得非常敏感, 而对大气水汽含量比较敏感. 因此, 一定程度上, 地表比辐射率和大气水汽含量的精度对HJ-1B卫星数据反演地表温度的准确性起主要作用.     

MODIS地表温度产品在青藏高原连续多年冻土区的适用性分析

利用自动气象站观测的长波辐射计算得到的地表温度对MODIS地表温度(LST)产品在青藏高原中部连续多年冻土区的精度进行验证, 并利用具有较高空间分辨率的Landsat 5 TM和Landsat 7 ETM+反演的地表温度与MODIS LST产品进行了对比分析. 结果表明: 白天MODIS LST产品的平均绝对误差(MAE)和均方根误差(RMSE)分别约为3.42~4.41 ℃和4.41~5.29 ℃, 夜晚MODIS产品MAE和RMSE分别为2.15~2.90 ℃和3.05~3.78 ℃, 精度高于白天; MODIS LST与TM、ETM+反演的地表温度一致性较好, 相关系数分别达到0.85和0.95. 说明MODIS LST产品在连续多年冻土区的适用性较高, 是研究多年冻土地表热状况的一个非常好的数据源. 而且, 不同空间尺度的遥感数据之间一致性较好, 可考虑将多源遥感数据应用于多年冻土热状况监测研究.     

Comparison of univariate and multivariate geographically weighted regression for estimating air temperature over Iran

Station recording air temperature (T_a) has limited spatial coverage, especially in unpopulated areas. Since temperature can change greatly both spatially and temporally, stations data are often inadequate for meteorology and subsequently climatology studies. Time series of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (T_s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM), albedo from Era-Interim and meteorological data from 2006 to 2015, were used to estimate daily mean air temperature over Iran. Geographically weighted regression was applied to compare univariate and multivariate model accuracy. In the first model, which only interfered with land surface temperature (LST), the results indicate a weak performance with coefficient of determination up to 91% and RMSE of 1.08 to 2.9 °C. The mean accuracy of a four-variable model (which used LST, elevation, slope, NDVI) slightly increased (6.6% of the univariate model accuracy) when compared to univariate model. RMSE dropped by 19% of the first model. By addition albedo in the third model, the coefficient of determination increased significantly. This increase was 32% of the univariate model and 23.75% of the 4-variable model accuracy. The statistical comparison between the three models revealed that there is significant improvement in air estimation by applying the geographically weighted regression (GWR) method with interfering LST, NDVI, elevation, slope, and albedo with mean absolute RMSE of 0.62 °C and mean absolute R² of 0.99. In order to better illustrate the third model, t values were spatially mapped at 0.05 level.     

Comparison of split window algorithms to derive land surface temperature from satellite TIRS data

Remote sensing data can be used as the basis for meteorological data. Due to the limitations of meteorological stations on the Earth, derivation of land surface temperature is one of the most important aspects of the remote sensing application in climatology studies. In the present study, Landsat-8 thermal infrared sensor data of the scene located over Khuzestan province with row/path of 165/38 were used to derive land surface temperature (LST). Normalized difference vegetation index (NDVI), fraction of vegetation cover, satellite brightness temperature, and land surface emissivity were calculated as the vital criteria to derive LSTs using the split window algorithms. LST determination was performed by nine different split window algorithms. Eventually, LST products were evaluated using ground-based measurements at the meteorological stations of the study area. The results showed that algorithm of Coll and Casselles had a highest accuracy with RMSE 1.97 °C, and Vidal’s method presented the lowest accuracy to derive LST with RMSE 4.11 °C. According to the results, regions with high density of vegetation and water resources have lowest diurnal temperature and regions with bare soils and low density of vegetation have a highest diurnal temperature. Results of the study indicated that LST algorithm accuracy is an important factor in the environmental and climate change studies.     

确定天然气水合物饱和度的测井解释新方法

为了丰富和发展天然气水合物的定性识别方法和定量解释技术,研究试验了由测井资料计算孔隙水的氯离子质量浓度并确定水合物饱和度的方法。根据南海某海域水合物储层的测井特征和孔隙水氯离子质量浓度化验结果,证实了氯离子质量浓度与水合物饱和度有密切关系。利用孔隙度测井和电阻率测井资料,计算了孔隙水的氯离子质量浓度,由此确定了天然气水合物的饱和度。为了提高测井解释的可靠性,用回归方法建立了储层的孔隙度解释公式,确定了相应的阿尔奇参数m和a,用滤波和插值的方法得到了目的层的氯离子质量浓度的背景值。初步应用表明,由测井解释氯离子质量浓度计算的水合物饱和度与化验结果的平均值和分布范围都有较好的匹配。     

Total column ozone retrieval using INSAT-3D sounder in the tropics: A simulation study

The present study examines the potential of infrared sounder observations from Indian geostationary satellite INSAT-3D for the estimation of total column integrated ozone over the tropical Indian region. A dataset with diverse profiles was used to create training and testing datasets using forward simulations from a radiative transfer model for infrared sounder channels. A study was carried out for the standard tropical atmospheric profile to examine the sensitivity of ozone band radiance corresponding to the atmospheric temperature, water vapour, and ozone mixing ratios at different atmospheric pressure levels. Further, statistical retrieval technique has been used for the total column ozone estimation using two different approaches: (i) ozone channel observation along with the a-priori estimate of temperature and water vapour profile and (ii) only sounder channels observations. The accuracy of the retrieval algorithms was examined for different errors in the atmospheric profiles for the method (i) and different sensor noise specification for the method (ii). This study has shown that accurate temperature information is very important for ozone estimation and lower instrument noise results in better ozone estimates.     

PMP估算中大气可降水量计算方法的探讨

可降水量是估算可能最大降水(PMP)的一个重要中间量。为验证不同方法推求可降水量的准确性,利用1998~2010年东南沿海五省(自治区)及香港地区共13个探空站和地面露点资料,分别采用3种不同方法(探空法、经验公式法和假绝热法)计算各站的大气可降水量,并对三种估算方法的结果进行比较。结果表明:经验公式法求得可降水量最小,探空法次之,假绝热法求得结果最大,偏差随着纬度变化。纬度越低,假绝热法推求的可降水量相对误差越大,且在有降水日时的偏差更为明显;经验公式法和假绝热法得到的结果均与探空法的可降水量相关性较强;三种求算方法得到的大气可降水量年内变化基本一致。建议在PMP估算中,利用地面最大12 h持续露点温度通过假绝热法推求可降水量时,应考虑纬度校正。     

一种快速制备不同含水饱和度岩石试样的方法

岩石的含水饱和度对其力学参数有着较大的影响,针对目前关于制备不同含水饱和度岩样普遍存在着精度低、时间长、岩样含水分布不均匀等问题,根据化学-热力学原理,提出一种快速有效制备不同含水饱和度岩样的方法。该方法通过调节放置有岩样的恒湿负压环境的湿度,从而精确控制岩样最终稳定时组分水的逸度,以致控制岩样最终的含水饱和度。由于恒湿环境中水蒸气的逸度是恒定的,当环境中水蒸气的逸度与岩样中组分水的逸度不同时,外界恒湿环境中的水蒸气与岩样中组分水形成逸度差,当环境中水蒸气逸度小于岩样中组分水的逸度时,岩样中组分水渗出;反之,则水渗入岩样,最终岩样中组分水的逸度达到与外界恒湿环境相同的状态,制得相应含水饱和度的岩样。岩样是放置在负压环境中制备的,在负压环境中岩样孔隙中的空气减少,水蒸气在岩样中的渗透加快,从而缩短了岩样达到稳定的时间。根据气液平衡原理,岩样稳定时,岩样各个部分组分水的逸度相同,因此,制得的岩样含水分布均匀。     

基于MODIS LST产品估算青藏高原地区的日平均地表温度

地表温度是衡量地-气界面热状况的重要指标,在冻土制图、城市热岛效应等研究领域有重要应用.MODIS数据只提供了每日4次过境的瞬时温度值,而实际应用中往往需要日平均地表温度.对实测0cm温度数据进行分析后,发现其日变化曲线呈分段函数特性,白天按正弦曲线变化,夜间则呈线性下降.据此提出对白天和晚上的数据进行分段拟合的方案,并与已有的两种拟合方法作对比验证.地面验证工作包括两个方面:一是模拟的验证,按卫星过境时间从逐小时的实测0cm数据中挑出4个瞬时值,将上述拟合方法应用于这4个瞬时值,得到拟合的日平均温度,再同24h的算术平均值比较.结果表明:Sin-Linear法精度最高,平均误差在1K以内;其二是地面对MODIS的验证,将3种方法应用于MODIS数据,再与地面平均值比较,3种方法中Sin-Linear法误差最小,且具有最好的线性相关,R2超过0.9.     

被动微波遥感反演地表温度研究进展

热红外遥感技术在地表温度反演中已经获得了丰厚的果实,反演精度可达到1 K,然而,大气中云雾和尘埃对热红外遥感探测地表温度影响很大,限制了热红外遥感反演地表温度的应用.相反,被动微波遥感受大气干扰小,可穿透云层获取地表辐射信息,并具有全天候、多极化等特点,在地表温度反演中具有独特的优越性.但是微波信号也受多种因素的影响,其反演地表温度的算法目前尚不成熟,有待进一步研究.根据不同微波辐射计特征,系统讨论并评估了被动微波反演地表温度的经验模型、物理模型以及半经验模型及其发展过程,指出目前研究的难点和缺点,为今后相关研究提供参考.     

基于MODIS数据的东北地区积雪覆盖率估算

东北地区是我国三大积雪区之一,森林覆盖面积占总面积的40%左右。受森林冠层的影响,当前的MODIS雪盖产品（V6）提供的积雪覆盖率标准模型对东北地区积雪覆盖率估算结果存在明显的低估现象。基于此,采用分区建模的方式：在森林地区,计算归一化差值林地积雪指数（NDFSI）,建立像元积雪覆盖率（FSC）与NDFSI及NDVI之间的线性关系;在非森林地区,采用MOD10A1 V6提供的归一化差值积雪指数（NDSI）,建立像元积雪覆盖率（FSC）与NDSI及NDVI之间的线性关系。采用Landsat 8 OLI数据提取的积雪覆盖率（FSC）对分区建模的估算结果与标准模型的估算结果进行对比,发现进行估算的过程中均方根误差和平均绝对误差这两项指标的数值相对于标准模型有了大幅下降,这一结果在林区有更显著的表现。计算得到的决定系数R²,在本文模型也有提高。以T1林区影像为例,本文模型的均方根误差和平均绝对误差分别为0.246、0.055,而标准模型的两项指标则分别为0.420、0.348。本文模型和标准模型的决定系数分别为0.675、0.641。     

Accuracy analysis of the 2014–2015 Global Shuttle Radar Topography Mission (SRTM) 1 arc-sec C-Band height model using International Global Navigation Satellite System Service (IGS) Network

Global Shuttle Radar Topography Mission (SRTM) data products have been widely used in Earth Sciences without an estimation of their accuracy and reliability even though large outliers exist in them. The global 1 arc-sec, 30 m resolution, SRTM C-Band (C-30) data collected in February 2000 has been recently released (2014–2015) outside North America. We present the first global assessment of the vertical accuracy of C-30 data using Ground Control Points (GCPs) from the International GNSS Service (IGS) Network of high-precision static fiducial stations that define the International Terrestrial Reference Frame (ITRF). Large outliers (height error ranging from –1285 to 2306 m) were present in the C-30 dataset and 14% of the data were removed to reduce the root mean square error (RMSE) of the dataset from ～187 to 10.3 m which is close to the SRTM goal of an absolute vertical accuracy of RMSE ～10 m. Globally, for outlier-filtered data from 287 GCPs, the error or difference between IGS and SRTM heights exhibited a non-normal distribution with a mean and standard error of 6.5 ± 0.5 m. Continent-wise, only Australia, North and South America complied with the SRTM goal. At stations where all the X- and C-Band SRTM data were present, the RMSE of the outlier-filtered C-30 data was 11.7 m. However, the RMSE of outlier-included dataset where C- and X-Band data were present was ～233 m. The results suggest that the SRTM data must only be used after regional accuracy analysis and removal of outliers. If used raw, they may produce results that are statistically insignificant with RMSE in 100s of meters.     

基于GLDAS的中国区地表能量平衡数值试验

利用全球陆面数据同化系统(GLDAS)的软件平台—陆面信息系统(LIS)模拟了中国区域2003年能量平衡方程的各个分量,在此基础上进行了残差分析和地表温度的对比验证。残差的分布具有一定的时空分布特征,残差的时间分布特征表明LIS对春秋两季的模拟效果要好于其它季节,残差的空间分布特征表明LIS对纬度较高和海拔较高地区的模拟效果要逊于其他地区。对比了模拟的GLDAS地表温度与MODIS地表温度产品,结果显示两者之间的差值介于-5～5K之间,两者的散点图和标准离差显示模拟的夜间地表温度要比模拟的白天地表温度精确2～3K。     

基于遗传算法优化BP神经网络的玉米遥感估产方法

以内蒙古自治区开鲁县玉米作物为研究对象,将生育期内玉米遥感影像所提取的多种植被指数和实地采样点的测产数据作为训练值,利用BP(back propagation)神经网络和遗传算法优化BP(GA-BP)神经网络估产模型,得出网络预测的玉米产量数值。通过决定系数R 2和均方根误差RMSE,比较实测产量与预测产量之间的精度,BP神经网络模型R^2为0.8452,RMSE(%)为28.37;遗传算法优化BP神经网络模型R^2为0.9850,RMSE(%)为6.70,表明遗传算法优化BP神经网络估产模型具有一定可行性和可信度。     

Using CRD method for quantification of groundwater recharge in the Gaza Strip, Palestine

Rainfall is the main source of groundwater recharge in the Gaza Strip area in Palestine. The area is located in the semi-arid zone and there is no source of recharge other than rainfall. Estimation of groundwater recharge from rainfall is not an easy task since it depends on many uncertain parameters. The cumulative rainfall departure (CRD) method, which depends on the water balance principle, was used in this study to estimate the net groundwater recharge from rainfall. This method does not require much data as is the case with other classical recharge estimation methods. The CRD method was carried out using optimisation approach to minimise the root mean square error (RMSE) between the measured and the simulated groundwater head. The results of this method were compared with the results of other recharge estimation methods from literature. It was found that the results of the CRD method are very close to the results of the other methods, but with less data requirements and greater ease of application. Based on the CRD method, the annual amount of groundwater recharge from rainfall in the Gaza Strip is about 43 million m³. An erratum to this article can be found at     

Horizontal moisture distribution from TIROS-N high resolution infrared sounder (HIRS)

A direct linear relationship between water vapour content of lower, middle and upper-middle troposphere respectively with the radiances for 8.3, 7.3 and 6.7 μm spectral observations has been attempted. From the atmospheric simulations such a relationship is found to exist with a good degree of correlation and is seen to be quite insensitive to temperature changes. Such an approach is used to interpret the water vapour imageries obtained from TIROS-N sounder through construction of detailed water vapour distribution maps.     

我国主要积雪区AMSR-E被动微波雪深算法对比验证研究

采用高级微波扫描辐射计(AMSR-E)亮温数据, 选取Chang算法、GSFC 96算法、AMSR-E SWE 算法、青藏高原改进算法和Savoie算法等5种雪深反演算法, 利用2010年2月10-12日3 d的气象站台雪深观测数据, 对比分析了5种雪深反演算法在新疆地区、青藏高原、内蒙古地区、东北地区、西北地区和华北平原的精度和适用性. 结果表明:总体验证中, 青藏高原改进算法3 d的结果均优于其他算法, 其均方根误差(RMSE)为9.16 cm、9.96 cm和9.63 cm, 平均相对误差(MRE)分别为59.77%、52.79%和48.47%. 分区验证中, 结果最佳的算法分别为:在新疆地区, GSFC 96算法RMSE为6.85~7.48 cm;内蒙古地区, 青藏高原改进算法的RMSE分别为5.9 cm、6.11 cm和5.46 cm;东北地区, 青藏高原改进算法RMSE为6.21~7.83 cm;西北地区和华北平原5种算法的适用性不佳;青藏高原由于缺乏实测数据, 无法得到该区验证统计结果.     

基于被动微波的寒旱区地表温度反演

以各频段水平极化和垂直极化发射率问的相关关系为条件,利用被动微波数据反演地表温度.算法既解决了地表温度反演过程中发射率难以确定的问题,又克服了热红外遥感受大气影响较大的缺点,其物理意义清晰,计算简便.算法以MODIS温度产品为评价标准,对36.5GHz和89 GHz反演结果进行分析.结果表明:89 GHz亮温数据反演精度高于36.5 GHz;与耕地和草场反演精度相比,裸土和山地反演精度较高.其原因在于高频数据穿透能力较低,能更好地表达地表温度.同时,低频数据相对高频更容易受到地表土壤水分变化的影响,发射率相对不够稳定,对反演结果有一定影响.     

Comparison of Measured and PTF Predictions of SWCCs for Loess Soils in China

There are significant advantages in using indirect pedo-transfer functions, (PTFs) for the estimation of unsaturated soil properties. The pedo-transfer functions can be used for the estimation of the soil–water characteristic curve (SWCC) which in turn is used for the estimation of other unsaturated soil properties. The accuracy of the indirect pedo-transfer function method for the estimation of the SWCC depends on the PTF and the equation used to best-fit the particle-size distribution (PSD) data. The objectives of this study are to: (1) evaluate the performance of the Fredlund et al. (Can Geotech J 37:817–827, 2000) equation for best-fitting the particle-size distribution, (PSD) data, and, (2) compare the predictions made by two of the commonly used PTFs; namely, Arya and Paris (Soil Sci Soc Am J 45:1023–1030, 1981) and Fredlund et al. (Can Geotech J 39:1103–1117, 2002), for estimating the SWCC from the PSD. The authors used 258 measured PSDs and SWCC datasets from the Loess Plateau, China, for this study. The dataset consisted of 187 silt–loam soils, 41 loam soils, 11 silt–clay–loam soils, 10 sand–loam soils, 6 silt–clay soils, and 3 loam–sand soils. The SWCC and PSD datasets were measured using a Pressure Plate apparatus and the pipette method, respectively. The comparison between the estimated and measured particle-size distribution curves showed that the Fredlund et al. (Can Geotech J 37:817–827, 2000) equation closely prepresented the PSD for all soils in the Loess Plateau, with a lower root mean square error (RMSE) of 0.869%. The comparison between the estimated and measured water contents at the same suction showed that the Fredlund et al. (Can Geotech J 39:1103–1117, 2002) PTF performed somewhat better than the Arya and Paris (Soil Sci Soc Am J 45:1023–1030, 1981) function. The Fredlund et al. method had RMSE value of 0.039 cm³ cm⁻³ as opposed to 0.046 cm³ cm⁻³ for the Arya and Paris (Soil Sci Soc Am J 45:1023–1030, 1981) method. The Fredlund et al. (Can Geotech J 39:1103–1117, 2002) PTF produced the closest predictions for sand–loam, loam–sand, and loam soils, with a lower RMSE for gravimetric water content ranging from 0.006 to 0.036 cm³ cm⁻³. There were consistent over-estimations observed for silt–loam, silt–clay–loam, and slit–clay soils with RMSE values for gravimetric water content ranging from 0.037 to 0.043 cm³ cm⁻³. The measured and estimated air-entry values were closest when using the Fredlund et al. (Can Geotech J 39:1103–1117, 2002) PTF. The measured and estimated maximum slopes on the SWCC were closest when using the Arya and Paris (Soil Sci Soc Am J 45:1023–1030, 1981) PTF.     

Hidden Markov Model for quantitative prediction of snowfall and analysis of hazardous snowfall events over Indian Himalaya

A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six observations and six states of the model. The most probable observation and state sequence has been computed using Forward and Viterbi algorithms, respectively. Baum–Welch algorithm has been used for optimizing the model parameters. The model has been validated for two winters (2012–2013 and 2013–2014) by computing root mean square error (RMSE), accuracy measures such as percent correct (PC), critical success index (CSI) and Heidke skill score (HSS). The RMSE of the model has also been calculated using leave-one-out cross-validation method. Snowfall predicted by the model during hazardous snowfall events in different parts of the Himalaya matches well with the observed one. The HSS of the model for all the stations implies that the optimized model has better forecasting skill than random forecast for both the days. The RMSE of the optimized model has also been found smaller than the persistence forecast and standard deviation for both the days.