Satellite-based precipitation monitoring at high spatial resolution is crucial for assessing the water and energy cycles at the global and regional scale. Based on the recently released 7th version of the Multi-satellite Precipitation Analysis (TMPA) product of the Tropical Rainfall Measuring Mission (TRMM), and the monthly precipitation data (3B43) are evaluated using observed monthly precipitation from 65 meteorological stations in Jiangsu Province, China, for the period 1998–2014. Additionally, the standardized precipitation index (SPI), which is derived by a nonparametric approach, is employed to investigate the suitability of the TRMM 3B43 precipitation data for drought monitoring in Jiangsu Province. The temporal correlations between observations and the TRMM 3B43 precipitation data show, in general, reasonable agreement for different time scales. However, in summer, only 50% of the stations present correlation coefficients that are statistically significant at the 95% confidence interval. The overall best agreement of TRMM 3B43 precipitation data at seasonal scale tends to occur in autumn (SON). The comparative analysis of the calculated SPI time series suggests that the accuracy of TRMM3B43 decreases with increasing time scale. Stations with significant correlation coefficients also become less spatially homogeneous with increasing time scale. In summary, the findings demonstrate that TRMM 3B43 precipitation data can be used for reliable short-term drought monitoring in Jiangsu province, while temporal-spatial limitations exist for longer time scales.

The long-term changes of the relationship between nighttime light and urbanization related built-up areas are explored using nighttime light data obtained from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS, data before 2013) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS, data after 2012) and information of the spatiotemporal heterogeneity of urban evolution. This study assimilates two datasets and diagnoses the spatial heterogeneity in administrative city scale based on built-up area tendencies, temporal heterogeneity in pixel scale based on nighttime light intensity tendencies, and GDP associated spatiotemporal variability over the Yangtze River Delta comparing the first two decades of this century (2001–2010 versus 2011–2019). The analysis reveals the following main results: (1) The built-up areas have generally increased in the second period with the center of fast expansion moving southward, including Suzhou-Wuxi-Changzhou, Hangzhou, Ningbo, Nanjing, and Hefei. (2) Urban development in the original city core has saturated and is spilling over to the suburbs and countryside, leading to nighttime light intensity tendency shift from a “rapid to moderate” and a “moderate to rapid” development (a “hot to cold” and a “cold to hot” spatial clustering distribution). (3) The tendency shifts of built-up area and nighttime light intensity occur most frequently in 2010, after which the urban development is transforming from light intensity growth to built-up area growth, particularly in the developed city cores. The urban agglomeration process with nighttime light intensity reaching saturation prior to the urban development spreading into the surrounding suburbs and countryside, appears to be a suitable model, which provides insights in addressing related environmental problems and contribute to regional sustainable urban planning and management.

Biodiversity in China is analyzed based on the components of the Dynamic Habitat Index (DHI). First, observed field survey based spatial patterns of species richness including threatened species are presented to test their linear relationship with remote sensing based DHI (2001–2010 MODIS). Areas with a high cumulative DHI component are associated with relatively high species richness, and threatened species richness increases in regions with frequently varying levels of the cumulative DHI component. The analysis of geographical and statistical distributions yields the following results on interdependence, polarization and change detection: (1) The decadal mean Cumulative Annual Productivity (DHI-\(\overline{cum}\) < 4) in Northwest China and (DHI-\(\overline{cum}\) > 4) in Southeast China are in a stable (positive) relation to the Minimum Annual Apparent Cover (DHI-\(\overline{min}\)) and is positively (negatively) related to the Seasonal Variation of Greenness (DHI-\(\overline{sea}\)); (2) The decadal tendencies show bimodal frequency distributions aligned near DHI-\(\overline{min}\)~0.05 and DHI-\(\overline{sea}\)~0.5 which separated by zero slopes; that is, regions with both small DHI-min and DHI-sea are becoming smaller and vice versa; (3) The decadal tendencies identify regions of land-cover change (as revealed in previous research). That is, the relation of strong and significant tendencies of the three DHI components with climatic or anthropogenic induced changes provides useful information for conservation planning. These results suggest that the spatial-temporal dynamics of China’s terrestrial species and threatened species richness needs to be monitored by first and second moments of remote sensing based information of the DHI.

Urbanization drives human social development and natural environmental changes and shows complex implications for sustainability and challenges of future development, particularly in emerging countries. While extensive studies focus on extracting urban areas more precisely, less attention has been devoted to understand megaregion evolution and its related socioeconomic processes, not by socioeconomic statistics, but by comparing remote sensing based spatiotemporal evolution and the related spillover effect. Three main megaregions (with large area, high population and total gross domestic product) in China are selected for the analysis of development changes in an urbanization (magnitude, development)-diagram, of growth pattern changes based on Gravity Center and weighted Standard Deviation Ellipses and of the megaregions’ spillover effect. Employing the spatiotemporally continuous lighted areas (DN ≥ 12) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime signal (1992–2013) to the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) leads to the following results: (i) Developments in the (magnitude, development)-diagram indicate 25.97%, 45.95%, and 39.10% of the first (high urbanization, fast development) class of the BTH, YRD, and PRD megaregions are rapidly developing into highly urbanized regions. The first class may slow down in the future like the second (high urbanization, slow development) class acting from 1992 to 2013, and the third (moderate urbanization, fast development) class shows potential to become the first class in the future. (ii) The original core function zones of YRD and PRD have highly developed till 1992 and expanding out with fast development from 1992 to 2013. Contrarily, BTH indicates more fast development toward the original core function zones while spatial expansion. (iii) The gravity distance evolution of the three megaregions shows a tendency towards the geometric distance 2013. However, YRD and PRD (BTH) indicate a light intensity expansion (concentration). This may relate to a positive spillover effect of YRD and PRD upon their neighbor cities, with the strongest signal in the early 21st Century and thereafter adjusting and followed by another positive spillover.

The Qaidam Basin is a sensitive climate transition zone revealing a wide spectrum of local climates and their variability. In order to obtain an objective and quantitative expression of local climate regions as well as avoid the challenge to pre-define the number of heterogeneous local climates, the ISODATA cluster method is employed to achieve the fine-grained climate divisions of the Qaidam Basin, which can heuristically alter the number of clusters based on the input of monthly temperature and precipitation data. The fine-grained climate classification extends the traditional Köppen climate classification and represents the complex climate transformation processes in terms of fine-grained climate clusters. The following results are observed: (i) The Qaidam Basin is divided into an arid desert basin area and the surrounding alpine mountainous areas. The climate distribution is affected by both the altitude and the dryness ratio, which, employing the Budyko framework, largely characterizes the local energy–water fluxes at the surface and the related vegetation regimes (biomes). The fine-grained climate classification successfully captures their causal relationships and represents them well by the local climates: the climatic spatial differentiation in the mountainous areas is highly consistent with the topography and reveals an elevation-dependent circular distribution from the edges to the center of the basin; the climate heterogeneity within the basin presents a west-to-east meridional distribution due to the combined effect of the mid-latitude westerlies and the Indian monsoon. (ii) The climate gradients are spatially different over the Qaidam Basin. The surrounding mountainous areas have a large climate gradient compared to the inner basin; the southern mountain edge is governed by a more severe climate change than the north-eastern one; and the climate gradient is larger in the eastern than in the western basin. (iii) The lake regions within the basin show an obvious lake effect and reveal a local lake climate. Spatially, a common structure emerges with a dryer-climate zone or watershed embedding a wetter lake-affected area, which appears to migrate eastward becoming stepwise wetter from the very dry center to the wet eastern boundary of the Qaidam basin. This provides a topographically induced insight of the wet climate expansion of initially arid climates and is crucial to improve the Qaidam Basin’s ecological environment. Finally, although this work mainly focuses on the local-scale climates and their variability in the Qaidam Basin, the data-driven cluster methodology for climate refinement is transferable to regional- even global-scale climate studies, which offers broad application prospects.

Essential for directing conservation resources is to identify threatened vertebrate regions and diagnose the underlying causalities. Through relating vertebrates and threatened vertebrates to the rainfall-runoff chain, to the food chain, and to the human impact of urbanization, the following relationships are noticed: (i) The Earth’s vertebrates generally show increasing abundance and decreasing threatened species indicator (threatened species number/species abundance) for a higher Normalized Difference Vegetation Index (NDVI) or larger city-size. (ii) Regional vertebrates reveal a notable ‘U-shape profile’ (‘step-like jump’) of threatened species indicator occurs in the moderate (high) NDVI regions in China (America). (iii) Positive/green city states emerge in China and are characterized by the lowest threatened species indicators in areas of low to moderate greenness, where the greenness trend of change during the last 30 years is about three times higher in the urbanized areas than over land. (iv) Negative/brown city states emerge in America revealing high threatened species indicators for greenness exceeding NDVI > 0.2, where similar greenness trends are of both urbanized and land areas. The occurrence of green and brown city states suggests a biodiversity change pattern characterized by the threatened species indicator declining from city regimes with high to those with low indicator values for increasing ratio of the city-over-land NDVI trends.