Trends in aerosol optical properties over Eastern Europe based on MODIS-Aqua

This study provides characteristics of aerosol columnar properties, measured over ten countries in Eastern Europe from 2002 to 2019. Aerosol optical depth (AOD) and Ångström exponent (AE) were obtained with the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 merged Dark Target and Deep Blue aerosol product. The product is validated using ground-based Aerosol Robotic Network (AERONET) situated at Minsk, Belsk, Moldova and Kyiv. The results showed that 76.15% of retrieved AOD data are within the expected error. It was established that 64.2% of AOD points are between 0 and 0.2 and 79.3% of all AE points are over 1. Mean AOD values in the region vary from 0.130 ​± ​0.04 (Moldova) to 0.193 ​± ​0.03 (Czech Republic) with mean value in the region 0.162 ​± ​0.05. Seasonal mean AOD (AE) values were at the maximum during the summer from 0.231 ​± ​0.05 (1.482 ​± ​0.09 in winter) to minimum 0.087 ​± ​0.04 during the winter (1.363 ​± ​0.17 in summer). Gradual AOD reduction is observed in all countries with annual trend from −0.0050 (Belarus) to −0.0029 (Russia). Finally, the relationship between AOD and AE was studied to classify various aerosol types and showed seasonal non-uniformity of their contribution depending on variation in sources. The entire region is under significant impact of various aerosol types, including clean continental (СС), mixed (MX) and anthropogenic/burning (AB) aerosols types that are at 59.77%, 24.72%, and 12.97% respectively. These results form an important basis for further regional studies of air quality and distribution of sources of pollution.     

黄河宁夏段凌汛期气温变化特征

利用宁夏1991~2010年20 a凌汛期水文和气温序列资料,研究了宁夏凌汛期特征、凌汛灾害发生主要时段,黄河封河、开河集中期与日平均气温之间的关系。结果表明:宁夏凌汛特征为封开河时序相反,小流量、高水位行洪,受强冷空气影响较大;凌汛灾害主要发生在封河和开河期间;日平均气温正负转化日期与封、开河期有对应关系,宁夏封河集中期在12月20日至1月19日;开河集中日期在2月13~20日。在凌汛关键期结合MODIS遥感数据对凌汛灾害监测防范有一定指导意义。     

Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015 via Remote Sensing

Mongolia is an important part of the Belt and Road Initiative “China-Mongolia-Russia Economic Corridor” and a region that has been severely affected by global climate change. Changes in grassland production have had a profound impact on the sustainable development of the region. Our study explored an optimal model for estimating grassland production in Mongolia and discovered its temporal and spatial distributions. Three estimation models were established using a statistical analysis method based on EVI, MSAVI, NDVI, and PsnNet from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and measured data. A model evaluation and accuracy comparison showed that an exponential model based on MSAVI was the best simulation (model accuracy 78%). This was selected to estimate the grassland production in central and eastern Mongolia from 2006 to 2015. The results show that the grassland production in the study area had a significantly fluctuating trend for the decade study; a slight overall increasing trend was observed. For the first five years, the grassland production decreased slowly, whereas in the latter five years, significant fluctuations were observed. The grassland production (per unit yield) gradually increased from the southwest to northeast. In most provinces of the study area, the production was above 1000 kg ha ^-1, with the largest production in Hentiy, at 3944.35 kg ha ^-1. The grassland production (total yield) varied greatly among the provinces, with Kent showing the highest production, 2341.76×10 ⁴ t. Results also indicate that the trend in grassland production along the China-Mongolia railway was generally consistent with that of the six provinces studied.     

南极海冰密集度多源数据的交叉检验

通过研究交叉检验船测样点上的7种不同尺度的海冰密集度数据,发现相同时间和相同空间尺度的海冰密集度值吻合度最高,不同时间不同尺度的海冰密集度值的相关性较弱。由数据获取时间不同引起的密集度差异在高分辨率数据上体现明显。真实船测点与伪船测点之间的吻合度不高,受观测者主观因素、天气条件、影像处理质量和伪船测点提取方法的影响。虽然伪船测点方法在海冰边界研究中具有快速、大面积提取边界点的优势,但需要控制提取算法中的误差传播。     

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.     

用被动微波AMSR数据反演地表温度及发射率的方法研究

 针对对地观测卫星多传感器的特点，提出了借助MODIS地表温度产品从被动微波数据中反演地表温度的方法。即利用MODIS地表温度产品和AMSR不同通道之间的亮度温度，建立地表温度的反演方程。该方法克服了以往需要测量同步数据的困难，为不同传感器之间的参数反演相互校正和综合利用多传感器的数据提供实际应用和理论依据。文中以MODIS地表温度产品作为评价标准，对方法进行检验，其平均误差为2~3℃。另外，微波的发射率是土壤水分反演的关键参数，在对微波地表温度反演的基础上，进一步对发射率进行了研究。     

基于查找表的MODIS逐像元大气校正方法研究

以6S大气辐射传输模型为基础,计算了气溶胶光学厚度、太阳天顶角、传感器天顶角以及地表海拔变化对于校正得到的地表反射率的影响,讨论了6S模型对于这些参数的敏感性,提出了一种基于查找表的大气校正方法,利用6S模型离线计算建立了不同气溶胶的光学厚度、太阳天顶角、传感器天顶角以及地表海拔条件下大气校正系数的查找表,基于该查找表对MODIS影像进行逐像元大气校正。通过对本文方法、6S在线校正方法和利用统一输入参数校正方法的比较表明,本文方法的计算结果与6S在线校正方法很接近,说明本文方法可以有效地改善由于大气条件、传感器位置等空间分布差异对MODIS图像大气校正的影响。     

2000年以来青藏高原湖泊面积变化与气候要素的响应关系

青藏高原星罗密布的湖泊对气候变化十分敏感,在自然界水循环和水平衡中发挥着重要作用.以MODIS MOD09A1和SRTM DEM为数据源,提取了2000-2016年青藏高原丰水期面积大于50 km²的湖泊边界,从内外流分区、湖泊主要补给来源和湖水矿化度三个方面对2000年以来湖泊面积变化进行分析,并结合青藏高原近36年气象数据,根据气象要素变化趋势分区,初步探讨青藏高原湖泊面积变化与气候要素的关系.结果表明：青藏高原面积大于50 km²的138个湖泊整体扩张趋势显著,总面积增加2340.67 km²,增长率为235.52 km²/a.其中,扩张型湖泊占67.39%,萎缩型湖泊占12.32%,稳定型湖泊占20.29%.内流湖扩张趋势显著,外流湖扩张趋势较明显;以冰雪融水为主要补给来源的湖泊整体扩张趋势明显,以地表径流和河流补给为主要补给源的湖泊也呈扩张趋势;盐湖和咸水湖以扩张为主,淡水湖的扩张、萎缩和稳定三种类型较均衡.在青藏高原气候暖湿化方向发展背景下,湖泊面积变化与气候要素具有显著的区域相关性.气温和降水变化趋势分区结果表明,气温增加、降水增加强趋势的高原Ⅰ区湖泊扩张程度（78.18%）依次大于气温降低、降水量呈增加趋势的Ⅴ区（66.67%）,气温、降水量呈增加趋势的Ⅱ区（60.78%）,气温呈降低、降水量呈增加强趋势的Ⅳ区（58.83%）和气温呈增加、降水量呈减少趋势的Ⅲ区（50.00%）.湖泊面积变化对气候变化响应研究表明,升温引起的冰雪融水补给对Ⅰ区、Ⅱ区和Ⅲ区湖泊面积扩张的影响显著,加之降水量的增加,湖泊扩张速率明显;Ⅳ区和Ⅴ区湖泊面积扩张主要受降水量增加影响显著.整体而言,气温主要影响以冰雪融水为主要补给来源的湖泊,降水量主要影响以降水和地表径流为主要补给来源的湖泊.     

Plume and bloom: effect of the Mississippi River diversion on the water quality of Lake Pontchartrain

The opening of the Bonnet Carré spillway to prevent flood threat to New Orleans in April 2008 created a sediment plume in the Lake Pontchartrain. The nutrient rich plume triggered a massive algal bloom in the lake. In this article, we have quantified the spatio-temporal distribution of the plume (suspended solids) and the bloom (chlorophyll-a (chl-a)) in the lake using remotely-sensed data. We processed the Moderate-resolution Imaging Spectroradiometer satellite data for mapping the total suspended solids (TSS) and chl-a concentrations. An existing algorithm was used for estimating TSS whereas a novel slope model was developed to predict the per-pixel chl-a concentration. Both algorithms were successful in capturing the spatio-temporal trend of TSS and chl-a concentrations, respectively. Algal growth was found to be inversely related to TSS concentrations and a time lag of ～45 days existed between the spillway opening and the appearance of the first algal bloom at an observation location.     

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。