###### Abstract  
An ecological index that is constructed based on remote sensing images can enable a rapid evaluation of the quality of the urban ecological environment and can provide a scientific basis for the construction of urban ecological civilization. Taking Meizhou City, in Guangdong in China, as a study area, based on the Landsat TM/OLI and MODIS remote sensing data, this paper extracts the total primary productivity (GPP), land surface temperature (LST), humidity component (Wetness), and bare soil index (SI), which represent the remote sensing ecological index (RSEI) evaluation indicators. The greenness, heat, humidity, and dryness in the indicators are used to characterize the quality of the regional ecological environment and its change characteristics. The results show that: (1) the high-quality areas of ecological environment in the study area are increasing, and the proportion of high-grade RSEI areas has increased from 61.7% to 66.2%. (2) About 26.3% of the area in the study area has an optimized ecological environment quality. (3) The correlation between POI and each index is significant, among which GPP and LST have a high correlation with RSEI, while POI and RSEI have a moderately negative correlation. (4) MODIS data are suitable for regional ecological environmental quality assessments. In the future, research on RSEI data sources and processing efficiency and the spatiotemporal changes of ecological quality and environmental factors can be strengthened, and the sustainable development of ecological protection and urban construction planning can be explored. e 16 October 202  
of reference. [PERSON] [7] proposed a new remote sensing-based ecological index (RSEI) in 2013; since then, many studies refer to this indicator system and modify some indicators according to the characteristics of the natural environment in the study area [13; 14] or study the ecological impacts of potential population and impervious surface increases, based on improved data accuracy [15]. In other studies, it was used for the monitoring and evaluation of dynamic changes in the urban ecological environment [16], watershed [17], and so on. This method has been widely used since it was first proposed [18; 19], and its practicality, as well as accuracy, has been verified [20; 21]. The above studies were mostly based on Landsat TM/OLI data, using it to calculate RSEI indicators. Due to the high spatial resolution of Landsat TM/OLI remote sensing images, they are widely used in the calculation of remote sensing ecological indexes. However, this has more stringent requirements for image quality, such as needing less cloud cover and similar timeframes for stitched-together images. In addition, not all regions corresponding to the images meet the research requirements. Recently, some scholars have used MODIS, obtained through the GEE platform, as the data source [22; 23]. In the case of the MODIS data, although it is of lower resolution, some product datasets can directly represent long-term natural environment conditions, such as MOD17A2, MYD11A2, and other product datasets [24]. Points of interest (POI) represent a new spatial data source in the form of a series of point-like data representing real geographic entities, including spatial information, such as latitude and longitude and addresses, and attribute information, such as names and categories [25]. These data have been widely used in the quantitative identification of urban functional areas [26], the classification of tourist attractions [27], and space coupling between land-use level and ecological security [28]. Different satellite remote sensing data have their own advantages and disadvantages, and the data processing technology of different platforms can be difficult or easy. We assumed that using Landsat and MODIS data to calculate the remote sensing ecological index could obtain the RSEI value faster and more conveniently; once combined with POI data, the impact of human activities on the ecological environment could then be studied.