The Hanjiang River Basin is the source area of the Middle Route Project of the South-to-North Water Diversion Project, and the vegetation coverage in this basin directly affects the quality of the ecological environment. This study is based on long time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data synthesized over 16 days from 2000 to 2016 in the Hanjiang River Basin. Major climatic data (temperature and rainfall) and topographic data (elevation, slope, and aspect) are employed to analyze the driving forces of NDVI changes. The results demonstrate the following: for the 2000–2016 period, the average annual NDVI is 0.823, with a change trend of 0.025 year⁻¹. The overall NDVI upstream is higher than that downstream. The average annual value of NDVI upstream is 0.844, with a change trend of 0.036 year⁻¹, and that of downstream is 0.799, with a change trend of 0.022 year⁻¹. The spatial distribution of NDVI was significantly increased in the area around the upstream section of the river and near the Danjiangkou Reservoir, and the distribution of NDVI around the central city was significantly reduced. The NDVI was positively correlated with temperature and rainfall, and the impacts differed among different regions. At elevations below 2000 m, the NDVI shows an increasing trend with increasing elevation, and at elevations exceeding 2000 m, the NDVI is negatively correlated with elevation. Slope is positively correlated with the NDVI. The influence of aspect on the NDVI was small.

The presence of bare patches within otherwise vegetated coastal marshes is sometimes considered to be a symptom of marsh dieback and the subsequent loss of important ecosystem services. Here we studied the topographical conditions determining the presence and revegetation of bare patches in three marsh sites with contrasting tidal range, sediment supply, and plant species: the Scheldt estuary (the Netherlands), Venice lagoon (Italy), and Blackwater marshes (Maryland, USA). Based on GIS (geographic information system) analyses of aerial photos and lidar imagery of high resolution ($\le \mathrm{2}\times \mathrm{2}$ m pixels), we analyzed the topographic conditions under which bare patches occur, including their surface elevation, size, distance from channels, and whether they are connected or not to channels. Our results demonstrate that, for the different marsh sites, bare patches can be connected or unconnected to the channel network and that there is a positive relationship between the width of the connecting channels and the size of the bare patches, in each of the three marsh sites. Further, pixels located in bare patches connected to channels occur most frequently at the lowest elevations and farthest distance from the channels. Pixels in bare patches disconnected from channels occur most frequently at intermediate elevations and distances from channels, and vegetated marshes dominate at highest elevations and shortest distances from channels. In line with previous studies, revegetation in bare patches is observed in only one site with the highest tidal range and highest sediment availability, and it preferentially occurs from the edges of small unconnected bare patches at intermediate elevations and intermediate distances from channels. Although our study is only for three different marsh sites with large variations in local conditions, such as tidal range, sediment availability, and plant species, it suggests that similar topographic conditions determine the occurrence of bare patches. Such insights may inform decision makers on coastal marsh management on where to focus monitoring of early signatures of marsh degradation.

Urban Black and Odorous Water (BOW) has become an environmental problem in many cities in China. The use of satellite remote sensing technology to identify BOW is still in its infancy, and there are many problems that need further solutions. In order to monitor BOW by satellite, between 2016 and 2017, the reflectance of remote sensing and some other parameters of 173 samples were collected from multiple field water experiments first. The samples were located at the major BOW in the urban areas of four Chinese cities, and the differences in remote sensing reflectance of severe BOW (SBOW), moderate BOW (MBOW), and general water (GW) were analyzed. Based on field measurements of spectral data, six remote sensing classification or identification models of BOW were compared in terms of their correct identification rate and reliability. The results show that compared with the GW in the study area, the urban BOW has the lowest reflectance. The peaks and valleys were not obvious in the visible band, especially the remote sensing reflectance of heavy BOW, which fluctuated very little in the visible band. Compared with the other five models, the H Index model had the best identification correctness and reliability.

The Normalized Difference Vegetation Index (NDVI) is an important parameter frequently used to characterize the land surface using remotely sensed satellite imagery. Because of the heterogeneity of the land surface, pixel-wise NDVI derived from coarse resolution sensors may induce errors or uncertainty. Several studies have investigated the spatial scale dependencies of NDVI in different approaches without consensus. This paper aims to quantify the crossscale relationship of NDVI using some indictors describing spatial heterogeneity. Experiment results showed that Geary's c and scale ratio are the best two indictors, with the assistant of which an empirical model was then built describing the cross-scale relationship of NDVI.

The need for an accurate model that can derive the absorption coefficient of optical activity constituents from both marine water and coastal water remains necessary. This study aimed to develop an algorithm for determining the absorption coefficients for both phytoplankton and non-phytoplankton pigments [a_ph(λ) and a_dg(λ), respectively]. This algorithm included two portions: (1) the total absorption coefficients at the blue and green bands were computed using a neural network technology-based, quasi-analytical algorithm; and (2) the relationship between the a_dg(λ) coefficient and the coefficient of total absorption was analyzed. This algorithm was evaluated with both in-situ observations and remote-sensed satellite data. The results showed that the algorithm could produce acceptable results in the retrievals of a_dg(λ) and a_ph(λ) in both turbid and clear waters. The results also indicated that the proposed algorithm was effective for distinguishing between a_dg(λ) and a_ph(λ) from the total coefficients of absorption, even though more independent assessments using in-situ and remote-sensed data are required to further improve the approach.