The nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP) are useful for extracting large-scale built-up urban areas. However, most NSL-based studies are presently empirical threshold-based approaches. They always overestimate the areas of built-up land in urban regions because of the ‘blooming’ effect of NSL; and overlook small patches in developing towns where the NSL is much lower. In this study, a neighborhood statistics analysis (NSA) method is developed on the basis of the relative spatial variations between neighboring built-up and non-built-up pixels in DMSP-OLS images. It is applied to extract the built-up areas of eight cities in the Pearl River Delta in 1996, 2000, 2005, and 2009. The validations indicate that the total areas of the NSA-mapped results are highly correlated with those from Landsat TM/ETM+ data (R² = 0.94; p < 0.001). The comparison results, which are evaluated by landscape indices (the landscape shape index (LSI), the contiguity index (CONTIG), and the perimeter area ratio (PARA)), also show good correlations (R² > 0.46; p < 0.001). In addition, the total NSL of the built-up urban areas is significantly correlated with the statistical population data (R² = 0.62; p < 0.001), which indirectly confirms the effectiveness of our proposed method.

China’s urban agglomerations contribute 64 percent to China’s energy-related CO₂ emissions and thus play a vital role in determining the future of climate change. There is little information available about city-level energy consumption and CO₂ emissions; thus, we employ spatiotemporal modeling using Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) nighttime light imagery. Our findings show that such agglomerations have in fact experienced a remarkable decline in CO₂ emission intensity—from 0.43 t/thousand yuan to 0.20 t/thousand yuan between 1995 and 2013, which constitutes an average annual decline of 4.34 percent. Despite still very high CO₂ intensities in western China, a convergence of CO₂ intensities across the country has occurred over the last few decades. Using panel regression modeling, we analyze differences in the decline of CO₂ emission intensities due to regional differences in socioeconomic variables such as economic growth, population, economic structure, population density, and characteristics of urbanization. Factors that have hampered the decline of CO₂ intensities are the ongoing industrialization that demands the increase in the production of heavy industry, in infrastructure investment, and in housing stock. Key Words: CO₂ emission intensity, nighttime light imagery, spatiotemporal modeling, urban agglomerations. 中国的城市集聚, 生产了中国与能源相关的二氧化碳排放的百分之六十四, 因此在决定气候变迁的未来方面扮演了重要角色。但城市层级的能源消耗和二氧化碳排放的可及信息却相当稀少；因此我们运用时空模式化, 该模式化使用防卫气象卫星计画／线形扫描系统（DMSP/OLS）的夜间光影像。我们的研究发现显示, 这些集聚其实经历了二氧化碳排放密集度的明显减少——从1995年的0.43吨／千元到2013年的0.2吨／千元, 每年平均降低百分之四点三四。尽管中国西部的二氧化碳密度仍相当高, 过去数十年来仍发生了全国二氧化碳密度的聚合。我们运用面板迴归模型, 分析因诸如经济成长、人口、经济结构、人口密度与城市化特徵等社会经济变因的区域差异所导致的二氧化碳排放密度降低的差异。阻碍二氧化碳密度降低的因素是需要增加重工业生产、基础建设投资以及房地产需求的持续工业化。 关键词：二氧化碳排放密度, 夜间光影像, 时空模式化, 城市集聚。 Las aglomeraciones urbanas de China contribuyen el 64 por ciento de las emisiones chinas de CO₂ relacionadas con energía, para así desempeñar un papel vital en la determinación futura del cambio climático. Hay poca información disponible acerca del consumo de energía a nivel de ciudad y de las emisiones de CO₂; entonces, empleamos modelado espaciotemporal usando imágenes de luminosidad nocturna del Programa Satelital Meteorológico de la Defensa/Sistema Operacional de Escaneo en Línea (DMSP/OLS). Nuestros hallazgos muestran que de hecho tales aglomeraciones han experimentado una notable disminución en la intensidad de la emisión de CO₂ ––de 0.43t/miles de yuanes a 0.20t/miles yuanes entre 1995 y 2013, lo cual constituye una declinación promedio anual de 4.34 por ciento. Pese a las intensidades de CO₂ en China occidental todavía demasiado altas, una convergencia de intensidades del CO₂ a través del país ha ocurrido durante las pasadas pocas décadas. Usando modelado de regresión de panel, analizamos las diferencias de la declinación de las intensidades en la emisión de CO₂ debidas a diferencias regionales en variables socioeconómicas tales como crecimiento económico, población, estructura económica, densidad de población y características de la urbanización. Los factores que han dificultado la declinación en las intensidades del CO₂ son la industrialización en marcha que demanda incremento en la producción de la industria pesada, en inversiones para infraestructura y en el inventario de vivienda.

Based on the passive microwave radiative transfer equation, we developed a single-band model for retrieving surface soil moisture ( ) and land surface temperature ( ) using Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band brightness temperatures ( ) without any ancillary data. National-scale and of China in 2009 were jointly mapped. The average and retrieval errors are 0.037 g cm⁻³ and 2.00K when validated by in situ measurements and approximately 0.025 g cm⁻³ and 2.34K when compared with AMSR-E L3 and moderate-resolution imaging spectroradiometer daily products. In particular, most and estimation errors are under 0.37 g cm⁻³ and 2.00K, respectively, for regions with the microwave polarization difference index (MPDI) between 0.01 and 0.05, but increased clearly larger for those with MPDI smaller than 0.01 or larger than 0.05, which are mostly dense-vegetated regions in Southwestern China or plateaus and dry deserts such as Inner Mongolia plateau, Qinghai-Tibet Plateau, Taklimakan Desert and Turpan basin. In summary, the proposed retrieval algorithm might provide a new way to monitor and conditions for penetrable vegetation canopy regions, but further investigations are still needed, especially for specific plateau, desert and frozen regions.

Digitizing the land surface temperature (T_s) and surface soil moisture (m_v) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_s and m_v using the dual-polarized T_b of Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situ T_s and m_v in southern China showed the average root mean square errors (RMSE) of T_s and m_v retrievals reach 2.42 K (R² = 0.61, n = 351) and 0.025 g cm⁻³ (R² = 0.68, n = 663), respectively. The results were also validated using global in situ T_s (n = 2362) and m_v (n = 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R² = 0.86) and 0.039 g cm⁻³ (R² = 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_s (R² = 0.57; RMSE = 2.91 k) and ECV_SM m_v (R² = 0.51; RMSE = 0.045 g cm⁻³), respectively. Overall, this paper indicates an effective way to jointly modeling T_s and m_v using passive microwave remote sensing.