China’s largest freshwater lake, Poyang Lake, is characterized by rapid changes in its inundation area and hydrodynamics, so in this study, a hydrodynamic model of Poyang Lake was established to simulate these long-term changes. Inundation information was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and used to calibrate the wetting and drying parameter by assessing the accuracy of the simulated inundation area and its boundary. The bottom friction parameter was calibrated using current velocity measurements from Acoustic Doppler Current Profilers (ADCP). The results show the model is capable of predicting the inundation area dynamic through cross-validation with remotely sensed inundation data, and can reproduce the seasonal dynamics of the water level, and water discharge through a comparison with hydrological data. Based on the model results, the characteristics of the current velocities of the lake in the wet season and the dry season of the lake were explored, and the potential effect of the current dynamic on water quality patterns was discussed. The model is a promising basic tool for prediction and management of the water resource and water quality of Poyang Lake.

Although changes in water temperature influence the rates of many ecosystem processes in lakes, knowledge of the water temperature regime for large floodplain lake systems subjected to multiple stressors has received little attention. The coupled models can serve to derive more knowledge on the water temperature impact on lake ecosystems. For this purpose, we used a physically-based hydrodynamic model coupled with a transport model to examine the spatial and temporal behavior and primary causal factors of water temperature within the floodplain of Poyang Lake that is representative of shallow and large lakes in China. Model performance is assessed through comparison with field observations and remote sensing data. The daily water temperature variations within Poyang Lake were reproduced reasonably well by the hydrodynamic model, with the root mean square errors of 1.5–1.9 °C. The modeling results indicate that the water temperature exhibits distinct spatial and temporal variability. The mean seasonal water temperatures vary substantially from 29.1 °C in summer to 7.7 °C in winter, with the highest value in August and the lowest value in January. Although the degree of spatial variability differed considerably between seasons, the water temperature generally decreases from the shallow floodplains to the main flow channels of the lake. As expected, the lake water temperature is primarily affected by the air temperature, solar radiation, wind speed and the inflow temperature, whereas other factors such as cloud cover, relative humidity, precipitation, evaporation and lake topography may play a complementary role in influencing temperature. The current work presents a first attempt to use a coupled model approach, which is therefore a useful tool to investigate the water temperature behavior and its major causal factors for a large floodplain lake system. It would have implications for improving the understanding of Poyang Lake water temperature and supporting planning and management of the lake, its water quality and ecosystem functioning.

Extracting buildings from remote sensing images plays an important role in urban development planning, disaster assessment and mapping. Convolutional neural network (CNN) has been widely applied to building extraction because of its powerful deep semantic feature extraction ability. However, existing CNN-based building extraction methods are difficult to accurately extract multiscale buildings with accurate edges because of the limitation of feature receptive fields and the loss of spatial detail information. For the above problems, this paper proposes a multiscale receptive field network (MSRF-Net) to accurately extract multiscale buildings from remote sensing images. MSRF-Net includes multiscale receptive field feature encoder (MRFF-Encoder) and multipath decoder. In the MRFF-Encoder, a multiscale attentional down (MSAD) module and asymmetric residual inception (ARI) module are proposed to capture multiscale receptive field features. In the multipath decoder, convolutions with different kernel size and dilation are used in three parallel paths to learn localization-preserved multiscale features with multiscale receptive field. What’s more, the features of different branches and MRFF-Encoder are fused by the proposed feature combination module, which contribute to capture context information of multiscale receptive field while recovering the resolution of feature space. The experimental results show that compared with the latest MAP-Net, MSRF-Net has achieved F1 score growth of 1.14%, 0.42%, 1.11% and IoU score growth of 1.68%, 0.76% and 1.64% respectively on Massachusetts data set, WHU data set and the Typical Cities Building data set.

The SDGSAT-1 satellite, independently developed and launched by China, features a higher spatial resolution in its Glimmer Imager (GLI) data than the currently widely used nighttime light (NTL) remote sensing data, making it possible for high-precision extraction of urban built-up areas (UBAs). However, recent empirical studies have revealed that the higher resolution of the NTL data does not inherently guarantee enhanced accuracy of the UBA extraction, as urban morphology and city scale significantly influence the performance. Therefore, comprehensively investigating the UBA extraction performance of NTL data across cities with different morphologies and scales offers valuable guidance for optimizing urban remote sensing methodologies in diverse city contexts. Across 15 representative cities with various urban characteristics in China, this study conducted a comprehensive evaluation of the UBA extraction performance of SDGSAT-1 GLI data for the first time, with the NPP-VIIRS NTL data for comparison, and discussed how urban morphology and city scale affect the performance in UBA extraction. The results indicated that (1) In cities with different morphologies, the UBA extraction performance of SDGSAT-1 data follows from high to low: striped cities, clustered cities, and agglomerate cities. In cities with different scales, the UBA extraction performance of SDGSAT-1 data is megacities > super cities > medium cities > small cities > large cities. (2) Compared with NPP-VIIRS data, SDGSAT-1's performance in UBA extraction is not always superior and varies with the urban morphology and city scale as well as the extraction method, which necessitates context-sensitive selection for NTL data and method in UBA extraction.