Changqing Ke

Developing Daily Cloud-Free Snow Composite Products From MODIS Terra–Aqua and IMS for the Tibetan Plateau

Daily snow cover mapping is difficult when Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are cloud obscured. The daily cloud-free snow cover product provides an essential parameter for hydrological modeling, climate system studies, and snow-caused disaster monitoring on the Tibetan Plateau (TP). In this paper, we present an algorithm, Terra-Aqua-IMS (TAI), which combines MODIS Terra and Aqua (500 m) and the Interactive Multisensor Snow and Ice Mapping System (IMS; 4 km) to produce a daily cloud-free snow cover product (500 m). The overall accuracy of the new TAI over the TP is 94% as compared with ground stations in all-sky conditions; this value is significantly higher than the 64% of the blended MODIS Terra-Aqua product and the 55% and 50% of the original MODIS Terra and Aqua products, respectively. Without the IMS, the daily combination of MODIS Terra-Aqua can only remove limited cloud contamination: 37.3% of the annual mean cloud coverage compared with 46.6% (MODIS Terra) and 55.1% (MODIS Aqua). The resulting annual mean snow cover over the TP from the daily TAI data is 19.1%, which is much larger than the 4.7%-8.1% from the daily original MODIS Terra/Aqua and the blended Terra-Aqua snow product due to cloud blockage.

Assessing trend and variation of Arctic sea-ice extent during 1979-2012 from a latitude perspective of ice edge

Arctic sea-ice extent (in summer) has been shrinking since the 1970s. However, we have little knowledge of the detailed spatial variability of this shrinking. In this study, we examine the (latitudinal) ice extent along each degree of longitude, using the monthly Arctic ice index data sets (1979–2012) from the National Snow and Ice Data Center. Statistical analysis suggests that: (1) for summer months (July–October), there was a 34-year declining trend in sea-ice extent at most regions, except for the Canadian Arctic Archipelago, Greenland and Svalbard, with retreat rates of 0.0562–0.0898 latitude degree/year (or 6.26–10.00 km/year, at a significance level of 0.05); (2) for sea ice not geographically muted by the continental coastline in winter months (January–April), there was a declining trend of 0.0216–0.0559 latitude degree/year (2.40–6.22 km/year, at a significance level of 0.05). Regionally, the most evident sea-ice decline occurred in the Chukchi Sea from August to October, Baffin Bay and Greenland Sea from January to May, Barents Sea in most months, Kara Sea from July to August and Laptev Sea and eastern Siberian Sea in August and September. Trend analysis also indicates that: (1) the decline in summer ice extent became significant (at a 0.05 significance level) since 1999 and (2) winter ice extent showed a clear changing point (decline) around 2000, becoming statistically significant around 2005. The Pacific–Siberian sector of the Arctic accounted for most of the summer sea-ice decline, while the winter recovery of sea ice in the Atlantic sector tended to decrease.

Variability in the ice phenology of Nam Co Lake in central Tibet from scanning multichannel microwave radiometer and special sensor microwave/imager: 1978 to 2013

Abstract We used 35 years of brightness temperature data (1978 to 2013) from the scanning multichannel microwave radiometer (SMMR) and special sensor microwave/imager (SSM/I) to analyze the freezing, ablation, and duration time of ice on Nam Co Lake and validated the results using data from the advanced microwave scanning radiometer for Earth observation system and moderate resolution image spectroradiometer. The results indicate that the SMMR and SSM/I data can be applied to monitor lake ice phenology variability for a long time and the results are reliable. Since 1978, the duration of Nam Co lake ice has decreased by 19 to 20 days, with the freezing onset date delayed by 9 days and the ablation date advanced by 9 to 10 days. Between 1978 and 2010, there was a negative correlation between temperature and the duration of lake ice in Nam Co; after 2000, the temperature increased significantly in the Nam Co Basin. This caused a clear downward trend of lake ice duration. Therefore, the freezing onset date, ablation end date, and duration of lake ice are effective indicators of regional climate change.

Snow Cover Variations and Controlling Factors at Upper Heihe River Basin, Northwestern China

Snow is an important water resource and greatly influences water availability in the downstream areas. In this study, snow cover variations of the Upper Heihe River Basin (UHRB) during hydrological years (HY) 2003–2013 (September through August) is examined using the flexible multiday-combined MODIS snow cover products. Spatial distribution and pattern of snow cover from year to year for the basin is found to be relatively stable, with maximum snow cover area (SCA) and snow cover days occurring in HY2004, HY2008 and HY2012. A method, based on correlation coefficients between SCA and climate factors (mainly air temperature and precipitation), is presented to identify the threshold altitude that determines contributions of climate factors to SCA. A threshold altitude of 3650 ± 150 m is found for the UHRB, where below this altitude, both air temperature (Tair) and precipitation are negative factors on SCA, except in the winter season when both are positive factors. Above the threshold altitude, precipitation acts as a positive factor except in summer, while Tair is a negative factor except in autumn. Overall, Tair is the primary controlling factor on SCA below the threshold altitude, while precipitation is the primary controlling factor on SCA above the threshold altitude.

Satellite-derived estimations of spatial and seasonal variation in tropospheric carbon dioxide mass over China.

China has frequently been questioned about the data transparency and accuracy of its energy and emission statistics. Satellite-derived remote sensing data potentially provide a useful tool to study the variation in carbon dioxide (CO2) mass over areas of the earths surface. In this study, Greenhouse gases Observing SATellite (GOSAT) tropospheric CO2 concentration data and NCEP/NCAR reanalysis tropopause data were integrated to obtain estimates of tropospheric CO2 mass variations over the surface of China. These variations were mapped to show seasonal and spatial patterns with reference to Chinas provincial areas. The estimates of provincial tropospheric CO2 were related to statistical estimates of CO2 emissions for the provinces and considered with reference to provincial populations and gross regional products (GRP). Tropospheric CO2 masses for the Chinese provinces ranged from 53 ± 1 to 14,470 ± 63 million tonnes were greater for western than for eastern provinces and were primarily a function of provincial land area. Adjusted for land area troposphere CO2 mass was higher for eastern and southern provinces than for western and northern provinces. Tropospheric CO2 mass over China varied with season being highest in July and August and lowest in January and February. The average annual emission from provincial energy statistics of CO2 by China was estimated as 10.3% of the average mass of CO2 in the troposphere over China. The relationship between statistical emissions relative to tropospheric CO2 mass was higher than 20% for developed coastal provinces of China, with Shanghai, Tianjin, and Beijing having exceptionally high percentages. The percentages were generally lower than 10% for western inland provinces. Provincial estimates of emissions of CO2 were significantly positively related to provincial populations and gross regional products (GRP) when the values for the provincial municipalities Shanghai, Tianjin, and Beijing were excluded from the linear regressions. An increase in provincial GRP per person was related to a curvilinear increase in CO2 emissions, this being particularly marked for Beijing, Tianjin, and especially Shanghai. The absence of detection of specific elevation of CO2 mass in the troposphere above these municipalities may relate to the rapid mixing and dispersal of CO2 emissions or the proportion of the depth of the troposphere sensed by GOSAT.

Monitoring changes in the water volume of Hulun Lake by integrating satellite altimetry data and Landsat images between 1992 and 2010

Abstract. Lake level and volume are sensitive to climate change, and their changes can affect the sustainable utilization of regional water resources. Satellite radar/laser altimetry has effectively been used for monitoring water-level changes in recent years. In this study, satellite altimetry data and optical images were used to assess the changes in water level, area, and volume of Hulun Lake in north-eastern China. We derived a time series of lake levels for nearly two decades (1992 to 2010) from the altimetry data of two satellite sensors (Topex/Poseidon and Envisat RA-2); additionally, lake surface extent was extracted from Landsat TM/ETM+ images during the same period. The results indicate that the water level, area, and volume of Hulun Lake decreased over the past two decades. The water level shows a significant decrease (−0.36  m/year) of a total of −5.21  m from 1992 to 2010, specifically including a slight decrease (−0.4  m) during 1992 to 1999 and a sudden drop (−4.81  m) during 2000 to 2010. There has also been a consistent and significant reduction in lake area (−355.35  km2) and volume (−12.92  km3). An integrated examination on changes in temperature, evaporation, precipitation, and runoff during 1992 to 2010 shows that the main changes in the Hulun Lake area are correlated with increasing temperature (0.47°C/year) and evaporation (13.61  mm/year), as well as decreasing precipitation (−6.58  mm/year) and runoff (−1.04×108  m3/year). Thus, we infer that climate warming is likely the main cause of the changes in water level, area, and volume of Hulun Lake. In addition, anthropogenic factors accelerate the degradation of the Hulun Lake wetland to some extent.

Spring–summer albedo variations of Antarctic sea ice from 1982 to 2009

This study examined the spring–summer (November, December, January and February) albedo averages and trends using a dataset consisting of 28 years of homogenized satellite data for the entire Antarctic sea ice region and for five longitudinal sectors around Antarctica: the Weddell Sea (WS), the Indian Ocean sector (IO), the Pacific Ocean sector (PO), the Ross Sea (RS) and the Bellingshausen–Amundsen Sea (BS). Time series data of the sea ice concentrations and sea surface temperatures were used to analyse their relations to the albedo. The results indicated that the sea ice albedo increased slightly during the study period, at a rate of 0.314% per decade, over the Antarctic sea ice region. The sea ice albedos in the PO, the IO and the WS increased at rates of 2.599% per decade (confidence level 99.86%), 0.824% per decade and 0.413% per decade, respectively, and the steepest increase occurred in the PO. However, the sea ice albedo in the BS decreased at a rate of −1.617% per decade (confidence level 95.05%) and was near zero in the RS. The spring–summer average albedo over the Antarctic sea ice region was 50.24%. The highest albedo values were mainly found on the continental coast and in the WS; in contrast, the lowest albedo values were found on the outer edge of the sea ice, the RS and the Amery Ice Shelf. The average albedo in the western Antarctic sea ice region was distinctly higher than that in the east. The albedo was significantly positively correlated with sea ice concentration (SIC) and was significantly negatively correlated with sea surface temperature (SST); these scenarios held true for all five longitudinal sectors. Spatially, the higher surface albedos follow the higher SICs and lower SST patterns. The increasing albedo means that Antarctic sea ice region reflects more solar radiation and absorbs less, leading to a decrease in temperature and much snowfall on sea ice, and further resulted in an increase in albedo. Conversely, the decreasing albedo leads to more solar radiation absorbing and sea ice melting, thus resulting in a decrease in albedo.

Detecting Urban Vegetation Using a Object-oriented Method with QuickBird Imagery

Monitoring urban vegetation is one of the major environmental applications in remote sensing. This paper presents multi-scale segmentation and object-oriented image analysis approaches to extract urban vegetation information from high spatial resolution images. Multi-scale segmentation is used to segment an image into highly homogeneous image objects in any chosen resolution and generate a hierarchical image object network. Advantages of object-oriented analysis are meaningful statistic and texture calculation, an increased uncorrelated feature space using shape and topological features (neighbor, super-object, etc.), and the close relation between real-world objects and image objects. This paper applies these new techniques to extract urban vegetation information in Lianyungang City with QUICKBIRD images. Four object layers which represent different area of vegetation are built to extract different scales of vegetation. To different applications, the segmentation scale differs in thousands ways, and here four segmentation scales 800, 300, 90 and 20 are selected corresponding to four object layers. Based on multi-scale segmentation, Classification is conducted by fuzzy logic, and membership functions are used to produce class description, which consists of a set of fuzzy expressions from appropriate sample objects. Image objects are classified to proper classes by evaluation of membership function classifiers. Not only spectral information but also spatial, texture, and contextual characteristics of image are considered for classification. The result of vegetation information extraction is promising and the accuracy of classification is higher than other conventional approaches. It is obvious that these new image analysis approaches offer satisfying solutions to extract information quickly and efficiently for high resolution images and can be applied to many other application fields as well.

Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier–Feature Assembly

Sea ice type is one of the most sensitive variables in Arctic ice monitoring and detailed information about it is essential for ice situation evaluation, vessel navigation, and climate prediction. Many machine-learning methods including deep learning can be employed for ice-type detection, and most classifiers tend to prefer different feature combinations. In order to find the optimal classifier–feature assembly (OCF) for sea ice classification, it is necessary to assess their performance differences. The objective of this letter is to make a recommendation for the OCF for sea ice classification using Cryosat-2 (CS-2) data. Six classifiers including convolutional neural network (CNN), Bayesian,

Surface velocity estimations of ice shelves in the northern Antarctic Peninsula derived from MODIS data

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