Juanle Wang

Assessing Consistency of Five Global Land Cover Data Sets in China

Global land cover mapping with high accuracy is essential to downstream researches. Five global land cover data sets derived from moderate-resolution satellites, i.e., Global Land Cover Characterization (GLCC), University of Maryland land cover product (UMd), Global Land Cover 2000 project data (GLC2000), MODIS Land Cover product (MODIS LC), and GLOBCOVER land cover product (GlobCover), have been widely used in many researches. However, these data sets were produced using different data sources and class definitions, which led to high uncertainty and inconsistency when using them. This study looked into the consistencies and discrepancies among the five data sets in China. All of the compared data sets were aggregated to consistent spatial resolution and extent, along with a 12-class thematic classification schema; intercomparisons among five datasets and each with reference data GLCD-2005 were performed. Results show reasonable agreement across the five data sets over China in terms of the dominating land cover types like Grassland and Cropland; while discrepancies of Forest classes, particularly Shrubland and Wetland among them are great. Additionally, GLC2000 has the highest agreement with GLCD-2005; MODIS LC gets the highest map-specific consistency compared with others; whereas UMd has the lowest agreement with GLCD-2005, but also has the lowest map-specific consistency.

Spatial and temporal variations of chlorophyll- a concentration from 2009 to 2012 in Poyang Lake, China

Water eutrophication in Poyang Lake, the largest freshwater lake in China, has been considered to be an obstacle to aquatic environment protection and regional sustainable development. Chlorophyll-a concentration is one of the most important indices of water eutrophication. This paper builds seasonal chlorophyll-a concentration retrieval models using a semi-analytical model. Quarterly distributions of chlorophyll-a concentration from 2009 to 2012 are explored using multi-spectra data from a moderate-resolution imaging spectroradiometer (MODIS). The correlation coefficient of the retrieval models primarily ranged from 0.6 to 0.9. The results show that the chlorophyll-a concentration in Poyang Lake has significant seasonality characteristics that present low values in the winter and spring, and present relatively high values in the summer and autumn; this report also presents an obvious, increasing trend of inter-annual variability from 2009 to 2012. The spatial distribution of the chlorophyll-a concentration has regional differences that give relatively high values adjacent to the shore in the north area of Poyang Lake, in the flow in river entries, and in the main channel area in the central and south areas of Poyang Lake. The natural hydrology features have a close relationship with the variation in the chlorophyll-a concentration. Intensive human activities are the main driving forces for the increasing chlorophyll-a concentration.

Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003-2011)

This paper presents a comparison of CO2 products derived from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS), with reference to calibration data obtained using the high-resolution ground-based Fourier Transform Spectrometers (g-b FTS) in the Total Carbon Column Observing Network (TCCON). Based on the monthly averages, we calculate the global offsets and regional relative precisions between satellite products and g-b FTS measurements. The results are as follows: the monthly means of SCIAMACHY data are systemically slightly lower than g-b FTS, but limited in coverage; the GOSAT data are superior in stability, but inferior in systematic error; the mean difference between AIRS data and that of g-b FTS is small; and the monthly global coverage is above 95%. Therefore, the AIRS data are better than the other two satellite products in both coverage and accuracy. We also estimate linear trends based on monthly mean data and find that the differences between the satellite products and the g-b FTS data range from 0.25 ppm (SCIAMACHY) to 1.26 ppm (AIRS). The latitudinal distributions of the zonal means of the three satellite products show similar spatial features. The seasonal cycle of satellite products also illustrates the same trend with g-b FTS observations.

An improvement of the Ts-NDVI space drought monitoring method and its applications in the Mongolian plateau with MODIS, 2000–2012

Surface soil moisture is a key variable to describe water and energy exchanges at the surface/atm interface and measure drought and aridification. The Ts-NDVI space is an effective method to monitor regional surface soil moisture status. Due to the disturbance of multiple factors, the established dry or wet boundary with monotemporal remote sensing data is unstable. This paper developed a Ts-NDVI triangle space with MODIS NDVI dataset to monitor soil moisture in the Mongolian Plateau in 2000–2012. Based on the temperature vegetation dryness index (TVDI), the spatiotemporal variations of drought were studied. The results indicated that (1) the general Ts-NDVI space method is an effective way to monitor regional soil moisture. However, if the single time space shows perfect structure, there would be no differences between the inverted results of the single time space and the general space. (2) The TVDI calculated in the paper is expected to show the water deficit for the region from low (bare soil) to high (full vegetation cover) NDVI values, and it is found to be in close negative agreement with precipitation and soil moisture; changes in the TVDI are dependent on the water status in the study area. (3) In the Mongolian Plateau, TVDI presented a zonal distribution with changes in Land Use/Land Cover types, vegetation cover, and latitude. Drought was serious in bare land, construction land, and grassland. Drought was widely spread throughout the Mongolian Plateau, and there was aridification in the study period. Vegetation degradation, overgrazing, and climate warming could be considered as the main reasons.

Validation of Land Cover Maps in China Using a Sampling-Based Labeling Approach

This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover product) and GlobCover (GLOBCOVER land cover product), and a national land cover map GLCD-2005 (Geodata Land Cover Dataset for year 2005) against an independent reference data set over China. The land cover reference data sets in three epochs (1990, 2000, and 2005) were collected on a web-based prototype system using a sampling-based labeling approach. Results show that, in China, the highest overall accuracy is observed in GLCD-2005 (72.3%), followed by MODIS LC (68.9%), GLC2000 (65.2%), GlobCover (57.7%) and GLCC (57.2%), while UMd has the lowest accuracy (48.6%); all of the products performed best in representing “Trees” and “Others”, well with “Grassland” and “Cropland”, but problematic with “Water” and “Urban” across China in general. Moreover, in respect of GLCD-2005, there are significant accuracy differences across seven geographical locations of China, ranging from 46.3% in the Southwest, 77.5% in the South, 79.2% in the Northwest, 80.8% in the North, 81.8% in the Northeast, 82.6% in the Central, to 89.0% in the East. This study indicates that a regionally focused land cover map would in fact be more accurate than extracting the same region from a globally produced map.

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.

A Study on the Organizational Architecture and Standard System of the Data Sharing Network of Earth System Science in China

The aim of this paper is to discuss the organizational architecture and standard system for sharing research data at the national level. The Data Sharing Network of Earth System Science (DSNESS) is one of the nine pilot projects of the Scientific Data Sharing Project in China that has become a long-term operational research data-sharing platform in the National Science and Technology Infrastructure (NSTI) of China. First, a data sharing union mechanism was designed with the core principle being, “data come from research and will be reused in research”. Second, a data sharing organizational architecture was constructed that consists of three sections: data resource architecture, data management architecture, and data services architecture. A physical data sharing network was constructed that includes one general center and 15 distributed sub-centers based on the architecture. Third, a series of data sharing standards and specifications were designed and implemented in the DSNESS. The reference model of the DSNESS standard system includes three levels of standards: directive standards, general standards, and application standards. In total, 21 high level standards and specifications were developed and implemented in the DSNESS. Several core standards and specifications, such as the extensible metadata standard, data quality control specifications, and so on, were analyzed in detail. Finally, the data service effect was summarized in three aspects: dataset services, standard and specification services, and international cooperation services. This research shows that the organizational architecture and standard system is a very important soft environment for research data sharing. The practices of DSNESS will provide useful experiences for multi-disciplinary data sharing in Earth science and will help to eliminate the data gap between the rich and poor at the national level.

Spatial and Temporal Variations of Lower Tropospheric Methane During 2010–2011 in China

Estimates of methane (CH4) concentrations in the lower troposphere over the land surfaces of the world, and in more detail for China, were derived from data from the Atmospheric Infrared Sounder (AIRS). Derivation of CH4 estimates is described, and these were validated with reference to CH4 concentrations analyzed for air samples in three regions of China and the observations from three global baseline sites. The values of sample points and three global baseline sites were extracted from remotely sensed images. The correlation coefficients between remotely sensed geographical point estimates of CH4 and observations were equal to 0.779 and 0.763. Seasonal and monthly variation of CH4 concentrations for the 2010-2011 year and the spatial variation of these concentrations for the troposphere over China derived from remotely sensed data were mapped and interpreted with reference to possible sources of CH4 emission. Methane concentrations were about 15 ppb higher in winter than summer. The changes in concentrations were about 50 ppb in Inner Mongolian Plateau between September and October 2010, northwest China between February and March 2011. Variations of CH4 concentration are considered with reference to mixing of atmospheric gases by oceanic influences and to sources of CH4 emissions including vegetation cover particularly wetland, crop, and pastoral land use.

Study of remote sensing based parameter uncertainty in production Efficiency Models

The remote sensing based Production Efficiency Models (PEMs), springs from the concept of “Light Use Efficiency” and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (ε). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since its applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy.

Updating land cover automatically based on change detection using satellite images: case study of national forests in Southern California

Observing dynamic change patterns and higher-order complexities from remotely sensed images is warranted, but the main challenges include image inconsistency, plant phenological differences, weather variations, and difficulties of incorporating natural conditions into automatic image processing. In this study, we proposed a new algorithm and demonstrated it by producing 2002–2008 and 2010 land-cover maps in heterogeneous Southern California based on an existing 2009 land-cover map. The new algorithm improves the baseline land-cover map quality by discarding potential bad land-cover pixels and dividing each land-cover type into several subclasses. Time series Landsat images were used to detect changed and unchanged areas between baseline year and target year t. Subsequently, for each individual year t, each pixel that was identified as unchanged inherited the baseline classification. Otherwise, each pixel in the changed areas was classified by a similar surrogate majority classifier. The demonstration results in Southern California showed that the land-cover temporal pattern captured the observed successional stages of the ecosystem very well. The accuracy assessment had an overall classification accuracies ranging from 81% to 86% and overall kappa coefficients ranging from 0.79 to 0.83.