Google Earth Engine (GEE) is a cloud-based platform that provides powerful capabilities for remote sensing image compositing, processing, and analysis. Among GEE’s diverse applications, temporal aggregation of multiple images is one of its widely used techniques. Nevertheless, a systematic exploration of the advantages and disadvantages of its five primary image compositing methods – minimum (Min), maximum (Max), Median, Mean, and Mode – has not been conducted. It remains unclear whether the commonly used Median method is genuinely universal. Additionally, it is usually unknown which input images and their seasonal origins predominantly contribute to the final composite image. Therefore, this study systematically explores the performance of the five compositing methods across four regions in China and Uganda, with different geographic locations and climatic conditions, by examining their strengths, weaknesses, and applicable scenarios. A novel quantitative metric, contribution percentage (CP), is developed to identify which input images and bands in a time series primarily contribute to the final composite image. The results show that the commonly used Median metric is not always the optimal choice. The Min and Mode methods perform better than the Median method in haze and cloudy regions, particularly in haze removal and vegetation monitoring. The Max method also exhibits superior performance in compositing thermal infrared and near-infrared images compared to the Median. Based on this study, the applicable scenarios for the five compositing methods have been clarified. Furthermore, the proposed CP metric effectively reveals the input images and bands that contribute principally to the final composite. Understanding these insights is crucial for users to choose appropriate GEE compositing methods and study plant phenology and surface thermal environments, thereby providing a foundation for the scientific application of GEE compositing methods.

The ecological quality of a region is significantly influenced by its geographical conditions, which can yield different effects on ecosystems. Nevertheless, the lack of adequate technology has impeded quantitative investigations into these differences. Consequently, there is an increasing demand for effective techniques to quantitatively measure differences in ecological quality resulting from variations in geographical conditions. This study applied the novel Remote Sensing-based Ecological Index (RSEI) concurrently to two distinct provincial-level regions in China, Fujian and Ningxia, to quantitatively detect their ecological differences. These two regions possess contrasting geographical conditions, with Fujian having high forest coverage and abundant rainfall, while Ningxia features low forest coverage and extensive loess plateau and desert terrain. By linking geographical factors with their corresponding ecological responses, we conducted a comprehensive analysis to determine whether the contrasting geographical conditions between the two regions had caused significant disparities in their ecological status. The results indicate that the contrasting geographical conditions have indeed led to marked ecological differences, with Fujian exhibiting excellent ecological status, while Ningxia lags behind due to unfavorable geographical conditions. In terms of RSEI scores, Fujian consistently achieved higher RSEI values (>0.8) in the study years, reaching an excellent ecological level, whereas Ningxia recorded scores lower than 0.45 during the comparable years, corresponding to a poor to moderate ecological level. Regarding the impact of geographical factors on ecological conditions, the positive contributions of greenness and wetness indicators to the ecology in Fujian were significantly greater than those in Ningxia (58% vs. 39%), whereas the contributions of negative indicators, dryness and hotness, were notably higher in Ningxia compared to Fujian (|–61|% vs. |–42|%). The successful concurrent application of RSEI to these two geographically distant regions also demonstrates the robustness of the RSEI technique.