Carrying abundant nutrition, terrigenous freshwater has a great impact on the spatial and temporal heterogeneity of phytoplankton in coastal waters. The present study analyzed the spatial-temporal variations of Chlorophyll-a (Chl-a) concentration under the influence of discharge from the Yangtze River, based on remotely sensed Chl-a concentrations. The study area was initially zoned to quantitatively investigate the spatial variation patterns of Chl-a. Then, the temporal variation of Chl-a in each zone was simulated by a sinusoidal curve model. The results showed that in the inshore waters, the terrigenous discharge was the predominant driving force determining the pattern of Chl-a, which brings the risk of red tide disasters; while in the open sea areas, Chl-a was mainly affected by meteorological factors. Furthermore, a diversity of spatial and temporal variations of Chl-a existed based on the degree of influences from discharge. The diluted water extended from inshore to the east of Jeju Island. This process affected the Chl-a concentration flowing through the area, and had a potential impact on the marine environment. The Chl-a from September to November showed an obvious response to the discharge from July to September with a lag of 1 to 2 months.

The effect of terrain shadow, including the self and cast shadows, is one of the main obstacles for accurate retrieval of vegetation parameters by remote sensing in rugged terrains. A shadow- eliminated vegetation index (SEVI) was developed, which was computed from only red and near-infrared top-of-atmosphere reflectance without other heterogeneous data and topographic correction. After introduction of the conceptual model and feature analysis of conventional wavebands, the SEVI was constructed by ratio vegetation index (RVI), shadow vegetation index (SVI) and adjustment factor (f (Δ)). Then three methods were used to validate the SEVI accuracy in elimination of terrain shadow effects, including relative error analysis, correlation analysis between the cosine of solar incidence angle (cosi) and vegetation indices, and comparison analysis between SEVI and conventional vegetation indices with topographic correction. The validation results based on 532 samples showed that the SEVI relative errors for self and cast shadows were 4.32% and 1.51% respectively. The coefficient of determination between cosi and SEVI was only 0.032 and the coefficient of variation (std/mean) for SEVI was 12.59%. The results indicate that the proposed SEVI effectively eliminated the effect of terrain shadows and achieved similar or better results than conventional vegetation indices with topographic correction.

The remote-sensing technique is a cost-effective tool for monitoring large-scale forest damage sustained by typhoon events. Taking Cangnan County as the study area, this study aimed to extract the spatial pattern of damaged forest and determine the influencing factors of Typhoon Saomai in 2006, using Landsat Enhanced Thematic Mapper Plus (ETM+) data before and after the typhoon event. The results showed that 183 km² of forest land were damaged by Typhoon Saomai. There was no obvious diverse influence on forest damage within 25 km of Saomai’s path, after that the area of damaged forest decreased rapidly. For the land uses of construction, crop, and grass, decrease in normalized difference vegetation index was considerable under 100 m elevation and the number of damaged forest pixels showed positive correlation with vegetation aggregation, because trees standing in isolation, alongside roads, or in small groupings were easily damaged. For forest land, the number of damaged forest pixels decreased with higher elevation and relative aspect; when the relative aspect was in the range 0–40°, the number of damaged forest pixels was highest. Considering the interactive effects of these factors on forest damage caused by the typhoon, vegetation aggregation had the strongest influence followed by elevation, land use, relative aspect, and distance from the typhoon’s path.

Research in vegetation phenology change has been one heated topic of current ecological and climate change study. The Tibetan Plateau, as the highest plateau of the earth, is more vulnerable and sensitive to climate change than many other regions. In this region, shifts in vegetation phenology have been intensively studied during recent decades, primarily based on satellite-retrieved data. In this study, we explored the spatiotemporal changes of vegetation phenology for different land-cover types in the Tibetan Plateau and characterized their relationship with temperature and precipitation by using long-term time-series datasets of normalized difference vegetation index (NDVI) from 1982 to 2014. Diverse phenological changes were observed for different land-cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature. For EOS, because it is less sensitive to climate change than SOS, the response of EOS for different land-cover types to precipitation and temperature were more complicated across the Tibetan Plateau.

The spatial and temporal characters of ground-level NO₂ concentration over eastern China were retrieved from the monthly averaged tropospheric NO₂ column densities from Global Ozone Monitoring Experiment (GOME, data used in this study are from April 1996 to December 2002) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY, data used in this study are from January 2003 to December 2011) measurements. Together with the NO₂ concentration and the dry deposition velocity maps of eastern China, the fluxes of NO₂ dry deposition were estimated for three specific regions. The results indicated that the surface NO₂ concentration in eastern China increased dramatically from 1996 to 2011, and it showed distinct regional and seasonal variational characteristics. The highest concentration occurred in winter while the lowest occurred in summer. There was also variation in the spatial distribution with the peak value of NO₂ concentration appearing in the plains of north China (R1), the Yangtze River delta (R2), and the Pearl River delta (R3). A sharp increase of NO₂ concentration appeared in R1 and R2, while it was invariant or showed an obvious decrease in R3 during the period of 1996–2011. Furthermore, we compared the NO₂ dry deposition fluxes estimated from the ground-level NO₂ concentration and the dry deposition velocity of NO₂ with the mass concentration of NO₂ dry deposition that, measured from the control experiments and by consulting the published literature, showed a significant correlation (P < 0.001) and had a high R value (R = 0.73). The results also indicated that the NO₂ dry deposition fluxes increased over eastern China, with a maximum value of 8.25 kg N ha⁻¹ yr⁻¹ from 1996 to 2011 in R3, while the value was characterized by fluxes of less than 2.27 kg N ha⁻¹ yr⁻¹ in R2. When comparing the NO₂ dry deposition over different land covers, the values distinctly peaked over artificial surfaces and evergreen forests, with maximum values of 10.07 and 9.49 kg N ha⁻¹ yr⁻¹ in R1, 5.05 and 4.94 kg N ha⁻¹ yr⁻¹ in R2, and 20.95 and 23.15 kg N ha⁻¹ yr⁻¹ in R3. However, the lowest value of NO₂ dry deposition flux appeared over needleleaf forests, with 0.53, 0.24, and 1.29 kg N ha⁻¹ yr⁻¹ for R1, R2, and R3, respectively.

Global nitrogen deposition has increased over the past 100 years. Monitoring and simulation studies of nitrogen deposition have evaluated nitrogen deposition at both the global and regional scale. With the development of remote-sensing instruments, tropospheric NO₂ column density retrieved from Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) sensors now provides us with a new opportunity to understand changes in reactive nitrogen in the atmosphere. The concentration of NO₂ in the atmosphere has a significant effect on atmospheric nitrogen deposition. According to the general nitrogen deposition calculation method, we use the principal component regression method to evaluate global nitrogen deposition based on global NO₂ column density and meteorological data. From the accuracy of the simulation, about 70% of the land area of the Earth passed a significance test of regression. In addition, NO₂ column density has a significant influence on regression results over 44% of global land. The simulated results show that global average nitrogen deposition was 0.34 g m⁻² yr⁻¹ from 1996 to 2009 and is increasing at about 1% per year. Our simulated results show that China, Europe, and the USA are the three hotspots of nitrogen deposition according to previous research findings. In this study, Southern Asia was found to be another hotspot of nitrogen deposition (about 1.58 g m⁻² yr⁻¹ and maintaining a high growth rate). As nitrogen deposition increases, the number of regions threatened by high nitrogen deposits is also increasing. With N emissions continuing to increase in the future, areas whose ecosystem is affected by high level nitrogen deposition will increase.