Against the background of global climate change and urbanization, the biodiversity of birds is facing unprecedented threats. In this paper, taking the Nanjing megapolis as an example, based on the distribution sites of 79 bird species and 17 environmental variables, the MaxEnt model was used to simulate the distribution of bird diversity, and the most important environmental variables were analyzed. The results show that (1) the MaxEnt model is suitable for simulation of the potential distribution of bird diversity in the Nanjing megapolis; (2) the areas with the highest bird diversity in the Nanjing megapolis were mainly distributed in the mountains and hills where the forests are located; (3) the five most important environmental variables affecting bird distribution were ranked as maximum temperature of the warmest month (MTWM) > Digital Elevation Model (DEM) > precipitation of the wettest month (PWM) > distance to the nearest forest (DF) > Fractional Vegetation Cover index (FVC), with MTWM, PWM, and DF being negatively correlated with bird diversity, whereas FVC and DEM exhibited positive correlations with bird diversity; and (4) the contribution rates of the three types of environmental variables were ranked as follows: habitat environmental variables > meteorological environmental variables > disturbance environmental variables. Sufficient bird habitats should be maintained in cities, and disturbances from human activities should be reduced to achieve the harmonious coexistence of humans and animals in the context of climate change and urbanization, thereby promoting sustainable development.

Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with Soil Moisture Active Passive (SMAP) SSM products as the true value. The errors in CYGNSS SSM are primarily attributed to med–high elevation and large relief. Compared with the Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products, CYGNSS exhibits superior performance in terms of AD and RMSE (median AD = −0.10 m³/m³, RMSE = 0.14 m³/m³). The ubRMSE of CYGNSS (median ubRMSE = 0.094 m³/m³) outperforms SMOS, but is slightly worse than AMSR2, with the differences mainly observed in med–high elevation and large-relief regions. The three satellites complement each other in detecting complex terrain. CYGNSS errors (AD, RMSE) are higher in the rainy season than in the dry season, with greater discrepancies observed in large-relief, high-elevation regions compared to flatter, lower-elevation areas. This study provides the first comprehensive analysis of CYGNSS in such a complex region, offering valuable insights for improving the application of GNSS-R inversion technology.