基于MODIS雪盖数据的北疆雪深多元非线性回归克里金插值

为了提高北疆地区雪深时空分布监测的准确性,以该区域48个气象站点2006年12月—2007年1月的月平均雪深观测数据为基础,通过分析月均雪深空间自相关性及其与经纬度、高程的相关性,结合MODIS雪盖数据构建了多元非线性回归克里金插值方法,插值获得了北疆地区较高精度的雪深空间分布数据。将插值雪深数据与普通克里金插值法、考虑高程为辅助变量的协同克里金插值法的预测结果进行比较,结果表明:1相对普通克里金和协同克里金方法,多元非线性回归克里金法的12月份雪深预测精度分别提高了15.14%和9.54%,1月份的提高了4.8%和6.7%;2由于充分利用了经纬度和地形信息,多元非线性回归克里金法的雪深预测结果可提供更多细节信息;3预测结果客观地表达了雪深随经纬度和地形变化的趋势,反映了积雪深度的空间变异性;4基于不显著相关的协变量高程的协同克里金插值法预测的雪深数据精度劣于普通克里金插值法的预测结果。     

地表利用城市化在一次极端高温过程数值模拟中的影响

地表种类的城市化对城市区域的热力结构和局地环流都会产生巨大的影响。利用地面、高空观测资料和MODIS地表覆盖资料,使用耦合城市冠层模式(UCM)的区域中尺度数值模式(WRF),对2013年8月8日福州地区的一次极端高温天气过程进行数值模拟,研究地表利用变化对福州城市热岛效应及其对福州城市区域局地环流的影响。结果表明:地表利用的城市化使得午后城市热岛现象更加明显而夜间热岛效应呈现出减小的趋势;地表利用城市化后,中心城区的近地面风速减小,但城区与山区以及城区与海洋之间的局地热力环流明显加强,促进了山谷风和海陆风环流的发展;同时地表加热效应增强,促进了垂直运动的发展。      

太湖地区气溶胶光学厚度的分布及其在大气校正中的应用

采用太湖地区水面光谱数据以及MODIS遥感影像数据,利用辐射传输模式6S,选择自定义气溶胶类型,反演得到太湖地区气溶胶光学厚度(aerosol optical depth,AOD)分布,将其与太阳光度计CE318实测气溶胶光学厚度分别应用于太湖区域的大气校正中,得到不同的水面反射率,并参考实测水面反射率进行对比分析。结果表明:反演的太湖地区气溶胶光学厚度分布较为合理,造成此分布的原因可能是太湖北岸工业较发达,污染较严重。太湖颗粒物的吸收特性和卫星接收到的表观反射率导致反演数据的差异,是反演气溶胶光学厚度分布不均匀的主要原因。使用MODIS数据反演得到的太湖地区AOD进行大气校正,更加精确。该研究方法和结果可为气溶胶光学厚度反演、精确卫星数据大气校正提供参考。     

基于CALIPSO卫星资料的京津冀地区MODIS卷云云顶高度订正

卷云在大气辐射中扮演着重要角色,对天气系统和气候变化产生重要影响。相比传统地基观测手段,卫星遥感更容易探测到高层卷云的信息,本文利用CALIOP主动遥感仪器可获取较为准确的薄卷云特性的特点,针对MODIS被动遥感探测器反演的薄卷云云顶高度的偏差开展订正研究。研究选取2013～2017年京津冀地区MODIS云产品,结合CALIPSO卫星的卷云云顶高度数据,基于交叉验证的方法得到线性拟合方案,对MODIS卷云云顶高度进行订正。订正后的MODIS与CALIPSO卷云云顶高度差值的分布区间由?3～2 km变为?2.0～2.5 km,峰值由?0.8 km左右变为0.2 km左右。订正效果随云顶高度和云光学厚度的不同有所变化,其中较低层卷云和光学薄卷云的订正效果更明显。     

2000-2018年东亚地区云顶高度的时空变化特征

为了了解区域云顶高度对过去气候变化的响应,基于卫星搭载的MODIS传感器提供的2000年3月至2018年2月MOD03_08_v6.0数据,分析了东亚地区云顶高度2000—2018年的时空变化特征,并探讨其长期变化的原因。研究发现,东亚地区云顶高度呈西南高东北低的特征。云顶高度在东亚地区以0.020 km/a的变率增长,其中大陆东部云顶高度的年际变率为0.035 km/a,东部海域年际变率为0.034 km/a。在东部海域地区云顶高度的变化同海表温度的变化相关性较高,相关系数为0.68,这表明云顶高度的变化受下垫面的影响。在东亚地区30°~40°N区域内,年平均云顶高度的增加较为明显。此外,夏季云顶高度在长江中下游盆地、塔里木盆地、吐鲁番盆地以及四川盆地东北部呈-0.03 km/a的减少趋势,这是由于更多低云的形成降低了云顶高度;冬季云顶高度在东亚地区40°N以北呈下降趋势,而在40°N以南呈增加趋势。     

The climate-population nexus in the East African Horn: Emerging degradation trends in rangeland and pastoral livelihood zones

Increasing climate variability and extreme weather conditions along with declining trends in both rainfall and temperature represent major risk factors affecting agricultural production and food security in many regions of the world. The rangelands of Ethiopia, Kenya, and Somalia in the East African Horn remain one of the world's most food insecure regions, yet have substantially increasing human populations predominantly dependent on pastoralist and agro-pastoralist livelihoods. We identify regions where substantial rainfall decrease between two periods interrupted by the 1998 El Nino event (1981–2012) in the East African Horn is coupled with human population density increases. Vegetation in this region is characterized by a variable mosaic of land covers, generally dominated by grasslands necessary for agro-pastoralism, interspersed by woody vegetation. Recent assessments indicate that vegetation degradation is occurring, adversely impacting fragile ecosystems and human livelihoods. Using AVHRR and MODIS vegetation products from 1981 to 2012, we observe changes in vegetation patterns and productivity over the last decade across the East African Horn. We observe vegetation browning trends in areas experiencing reduced main-growing season precipitation; these areas are also concurrently experiencing increasing population pressures. We also found that the drying precipitation patterns only partially statistically explain the vegetation browning trends, indicating that other factors such as population pressures and land use changes might be responsible for the observed declining vegetation condition. Furthermore, we show that the general vegetation browning trends persist even during years with normal rainfall conditions such as 2012, pointing to potential long-term degradation of rangelands on which approximately 10 million people depend. These findings may have implications for current and future regional food security monitoring and forecasting as well as for mitigation and adaptation strategies in a region where population is expected to continue increasing against a backdrop of drying climate trends and increased climatic variability.     

三套NDVI长时间序列植被指数的对比——以玛曲为例

NDVI／MODIS、NDVI／GIMMS和NDVI／NSMC是时间长度不同、空间分辨率相差甚远的3套ND—VI数据集,如何集成应用这些不同时间长度、不同分辨率的数据进行相关研究,数据集间的比较是最基础的工作。本文以甘肃省甘南州玛曲县为例,用直方图、相关分析、趋势分析等方法研究了这3套NDVI产品数据集的相互关系。结果表明：1）NDVI／NSMC与NDVI/MODIS的直方图具有类似的图像分布特征,但是NDVI／MODIS数据分布范围更大;2）3套NDVI在数值上表现为NDVI／MODIS〉NDVI／GIMMS〉NDVI／NSMC;3）3套数据集空间图像特征一致,两两间均具有十分显著的空间相关性,其中1月份相对最弱,5、10月份最强,三者相比NDVIfNSMC与NDVI／MODIS的空间相关性更强;4）1—3月、5—8月及年均的NDVI／GIMMS与NDVI／NSMC值存在显著的时间相关性,但两者逐年变化趋势存在较大差别,两者气候倾向率相差最大的高达5倍之多。NDVIfNSMC数据集在处理过程中可能未进行大气订正及交叉定标,这是造成共同源的NDVIfGIMMS与NDVI／NSMC差异较大的重要原因。     

Assessing Disagreement and Tolerance of Misclassification of Satellite-derived Land Cover Products Used in WRF Model Applications

As more satellite-derived land cover products used in the study of global change,especially climate modeling,assessing their quality has become vitally important.In this study,we developed a distance metric based on the parameters used in weather research and forecasting(WRF) to characterize the degree of disagreement among land cover products and to identify the tolerance for misclassification within the International Geosphere Biosphere Programme(IGBP) classification scheme.We determined the spatial degree of disagreement and then created maps of misclassification of Moderate Resolution Imaging Spectoradiometer(MODIS) products,and we calculated overall and class-specific accuracy and fuzzy agreement in a WRF model.Our results show a high level of agreement and high tolerance of misclassification in the WRF model between large-scale homogeneous landscapes,while a low level of agreement and tolerance of misclassification appeared in heterogeneous landscapes.The degree of disagreement varied significantly among seven regions of China.The class-specific accuracy and fuzzy agreement in MODIS Collection 4 and 5 products varied significantly.High accuracy and fuzzy agreement occurred in the following classes:water,grassland,cropland,and barren or sparsely vegetated.Misclassification mainly occurred among specific classes with similar plant functional types and low discriminative spectro-temporal signals.Some classes need to be improved further;the quality of MODIS land cover products across China still does not meet the common requirements of climate modeling.Our findings may have important implications for improving land surface parameterization for simulating climate and for better understanding the influence of the land cover change on climate.     

Study on Quantitative Identification Sand and Dust Storm Using MODIS Data

In order to detect the scope and the intensity quanti cationally, the spectrum characteristic of sand and dust storm was analyzed in detail by using several MODIS data; bands that can distinguish sand and dust from cloud and surface were detected; two indices for determining the scope and intensity of sand and dust storm were found out, and were tested in several storms. Our study result shows: 1) The spectral characteristic of sand and dust in solar wavelengths is that the reflectance increases with the increasing of the wavelength. This is similar to the characteristic of the spectrum of soil. Also, the reflectance of large size dusts increases faster than small size dusts. 2) Small size dusts show typical characteristic of aerosol, being sensitive to blue band of 0.46 μm and insensitive to short wave infrared bands of 1.6 and 2.1 μm. 3) Large size dusts do not have aerosol characteristic, not sensitive to blue band but sensitive to short wave infrared bands. 4) Bands of 3.7 and 8.5 μm are sensitive to dusts. The difference of two bands can be used to identify sands and dusts and reflect the intensity to a certain extent. And 5) the two indices are very effective to monitor sands and dusts by testing a lot of sand and dust storm cases during the period of 2002-2005. Also, the method is simple and easy for operational use.     

QUANTITATIVE MODELING OF SUSPENDED SEDIMENT IN MIDDLE CHANGJIANG RIVER FROM MODIS

1INTRODUCTIONThe quality of water is traditionally determined by col-lectingwatersamplesandanalyzingthesamplesin a lab-oratory. However, sampling is very much hard sledding,time-consuming, slow and expensive, and easily in thecontrol of weather conditions, and does not include allwater areas. Satellite estimates of water quality havefound widespread application. Remote sensing instru-ments have been applied in water quality monitoringwith varying success (DANA and RICHARD, 1999;RUDD…