The accurate monitoring of short-term bathymetric changes in shallow waters is essential for effective coastal management and planning. Machine Learning (ML) applied to Unmanned Aerial Vehicle (UAV)-based multispectral imagery offers a rapid and cost-effective solution for bathymetric surveys. However, models based solely on multispectral imagery are inherently limited by confounding factors such as shadow effects, poor water quality, and complex seafloor textures, which obscure the spectral–depth relationship, particularly in heterogeneous coastal environments. To address these issues, we developed a hybrid bathymetric inversion model that integrates digital surface model (DSM) data—providing high-resolution topographic information—with ML applied to UAV-based multispectral imagery. The model training was supported by multibeam sonar measurements collected from an Unmanned Surface Vehicle (USV), ensuring high accuracy and adaptability to diverse underwater terrains. The study area, located around Lazarus Island, Singapore, encompasses a sandy beach slope transitioning into seagrass meadows, coral reef communities, and a fine-sediment seabed. Incorporating DSM-derived topographic information substantially improved prediction accuracy and correlation, particularly in complex environments. Compared with linear and bio-optical models, the proposed approach achieved accuracy improvements exceeding 20% in shallow-water regions, with performance reaching an R² > 0.93. The results highlighted the effectiveness of DSM integration in disentangling spectral ambiguities caused by environmental variability and improving bathymetric prediction accuracy. By combining UAV-based remote sensing with the ML model, this study presents a scalable and high-precision approach for bathymetric mapping in complex shallow-water environments, thereby enhancing the reliability of UAV-based surveys and supporting the broader application of ML in coastal monitoring and management.

Mesoscale meteorological modelling was conducted to evaluate air temperature at 2‐m above surface (T2), wind speed/direction and relative humidity (RH) in Singapore, a tropical city, for a dry period. A sensitivity study was conducted to determine the best combination of schemes for the physical modules. The model was used to study the Urban Heat Island (UHI) effect and urban cooling effect by applying cool coating on various urban surfaces. Maximum UHI intensity of 3.2°C is found at nighttime (2100 hr) at a hot spot in the Commercial/Industrial area. At nighttime, when the UHI effect is generally more intense than daytime, applying cool coating on all urban surfaces can reduce the UHI effect by about 30% in residential areas, and about 6% in commercial/industrial areas. Maximum T2 reduction of 3.1^oC and Surface Skin Temperature (TSK) reduction of 9.8^oC due to cool coating is found at 1300 hr at certain locations. The cool urban surfaces reduce radiative heat absorption during daytime, reducing heat storage in urban structures. This leads to subsequent reduction of stored heat release from urban structures, mitigating UHI effect during nighttime. Applying cool coating on horizontal surfaces (roofs and roads) provides more cooling effect than vertical surfaces (walls). Cool roofs provide more cooling effect than cool roads since roofs cover more urban horizontal surfaces than roads do in the current setting. Part of the radiation reflected by cool roads could be absorbed by other urban structures, reducing its cooling effect as compared to cool roofs.

The atmospheric infrared sounder (AIRS) exhibits great potential for providing atmospheric observation data for long-term regional and global carbon-cycle studies, which are essential for understanding the uncertainty of climate change. The sensitivity of global atmospheric $CO2$ retrieval from the AIRS observations by quantifying errors related to $CO2$ measurements in the infrared spectrum is investigated. A line-by-line radiative transfer model is used to evaluate the effects of atmospheric temperature profile, water vapor profile, and ozone ($O3$) data on the accuracy of $CO2$ measurements under five standard atmospheric models. The analytical results indicate that temperature, water vapor, and $O3$ are important factors, which have great influences on the sensitivity of atmospheric $CO2$ retrieval from the AIRS observations. The water vapor is the most important factor in the tropics, whereas the temperature represents major interference for multitude and subarctic regions. The results imply that precise measurements of temperature, water vapor, and $O3$ can improve the quality of atmospheric $CO2$ data retrieved from the AIRS observations.

Hyperspectral imagery has been widely used in terrain classification for its high resolution. Urban vegetation, known as an essential part of the urban ecosystem, can be difficult to discern due to high similarity of spectral signatures among some land-cover classes. In this paper, we investigate a hybrid approach of the genetic-algorithm tuned fuzzy support vector machine (GA-FSVM) technique and apply it to urban vegetation classification from aerial hyperspectral urban imagery. The approach adopts the genetic algorithm to optimize parameters of support vector machine, and employs the K-nearest neighbor algorithm to calculate the membership function for each fuzzy parameter, aiming to reduce the effects of the isolated and noisy samples. Test data come from push-broom hyperspectral imager (PHI) hyperspectral remote sensing image which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. Experimental results show the GA-FSVM model generates overall accuracy of 71.2%, outperforming the maximum likelihood classifier with 49.4% accuracy and the artificial neural network method with 60.8% accuracy. It indicates GA-FSVM is a promising model for vegetation classification from hyperspectral urban data, and has good advantage in the application of classification involving abundant mixed pixels and small samples problem.