Black and odorous water bodies represent a topic of significant interest in the field of water pollution prevention and control. Remote sensing technology is increasingly exploited for the monitoring of black and odorous water bodies because of its high efficiency and large-scale monitoring potential. In the present study, the Sentinel-2A imagery data were combined with data obtained by measuring spectral properties of black and odorous water bodies to produce a classification and regression tree (CART) model-based improved remote sensing recognition method for such water bodies. This method transforms the traditional single-feature empirical threshold segmentation algorithm to a multi-feature fuzzy decision-tree classification algorithm. The results reveal overall accuracy values of 84.78%, 92.85%, and 72.23% for the CART decision-tree algorithm, the confidence zone classification, and the fuzzy zone node classification, respectively. The method proposed in the present study enables the highly precise extraction of features representing black and odorous water bodies from satellite imagery. The characterization of confidence and fuzzy zones minimizes the need for field inspections, and it enhances the efficiency of diverse applications including engineering.

Gross primary production (GPP) is a key indicator to evaluating ecosystem carbon sinks. Southwest China is characterised by diverse ecosystems and abundant forest resources and represents one of the most important carbon reservoirs in China. Therefore, a quantitative assessment of the uncertainty of existing GPP products and their influencing factors is important. This study investigates GPP uncertainties and its influencing factors based on the three-cornered hat (TCH) and XGBoost and SHAP methods. Thirteen products were examined, including six products from the light use efficiency (LUE) model, two products from the process-based (Process) model, three products from the machine learning (ML) model and two products from satellite-based direct proxies (Proxies). The results reveal the following: (1) All products show similar spatial patterns, with Process products fluctuating notably in 2010, 2011, and 2014, while others remain stable. (2) Relative uncertainty is lowest annually, increasing monthly and daily; ML products exhibit greater stability. Among them, CEDAR has the least uncertainty and strongest agreement with flux observations (r = 0.82), whereas EC-LUE shows the highest uncertainty. (3) Vegetation index, elevation and radiation are more influential than other factors. These findings aid GPP product selection and uncertainty assessment in complex terrains with sparse ground data.