遥感生态指数（Remote Sensing Ecological Index，RSEI）是目前使用最多的生态环境质量评估模型。由于高分辨率遥感影像普遍缺少RSEI计算涉及到的短波红外和热红外波段，使得RSEI在高空间分辨率生态环境质量评价应用中受到限制。为了解决遥感影像波段与RSEI计算所需波段不匹配的问题，本文采用多分辨率波段融合技术，拟合生成高分辨率的短波红外波段和地表温度，基于RSEI原理构建了高分辨率遥感生态指数（High-resolution Remote Sensing Ecological Index，HRSEI），填补了RSEI在高分辨率尺度下研究的空白。本方法应用在河南省范县黄河滩区，结果表明：多分辨率波段融合技术可以有效弥补高分影像波段较少的劣势，突破了RSEI在精细尺度应用的限制性，拓展了高分辨率遥感数据的应用场景；4ｍ高分二号（GF-2）数据生成的HRSEI所呈现的信息丰度明显高于30ｍ Landsat8数据生成的RSEI；2016年和2023年HRSEI结果表明，范县生态环境质量总体向好，恶化区域多集中于黄河滩区农村居民地迁建区域，旧村拆除后未及时复垦是生态环境质量下降的重要因素。

Remote Sensing Ecological Index (RSEI) is the most widely used ecological environment quality assessment model. Generally, the four indicators (greenness, wetness, heat and dryness) of RSEI are calculated from Landsat images to construct an index that comprehensively reflects the ecological environment condition in pixel units. Since high-resolution remote sensing images generally lack the short-wave infrared and thermal infrared bands involved in the calculation of RSEI, the application of RSEI in high-resolution ecological environment quality assessment is limited. It is undoubtedly a great waste that the advantages of high-resolution remote sensing data cannot be fully utilized due to the limitation of spectral resolution. In order to solve the problem of mismatch between high-resolution remote sensing image bands and the bands required for RSEI calculation, this paper established a multi-resolution band fusion model with scale-invariant features. Based on Landsat8 and GF-2 remote sensing images, the short-wave infrared bands and surface temperature with high resolution (4m) were generated utilizing the statistical relationship between the bands. And the High-resolution Remote Sensing Ecological Index (HRSEI) was constructed based on the principle of RSEI, which fills the gap of RSEI research at fine scale. This method was applied in Fan County, Henan Province. The results showed that: (1) Utilizing the multi-resolution band fusion technique, high-resolution short-wave infrared band and surface temperature can be generated. The correlation coefficients between the fitted image and the original image were higher than 0.7, indicating that the machine learning model based on the random forest algorithm was effective. The obtained high-resolution band/product can be used in the subsequent ecological environment quality evaluation work. This method can effectively make up for the disadvantage of band absence of high-resolution images, breaking through the limitation of RSEI application in fine scale, and expanding the application scenario of high-resolution remote sensing data. (2) The calculation results of the first principal component of HRSEI showed that, the loadings of greenness and wetness were positive, and the loadings of heat and dryness were negative, which indicated that greenness and wetness promoted ecological environment quality, and heat and dryness impeded ecological environment quality. The above results are consistent with the objective actual pattern, and coincided with the trend of the results of RSEI. The Pearson correlation coefficient showed that HRSEI and RSEI were highly correlated (R=0.74). The contrast and information entropy of HRSEI for the three typical areas (built-up area, village and beach area) were greater than that of RSEI. It is sufficiently demonstrated that, with maintaining high relevance and consistency, the information abundance presented by HRSEI generated by 4m GF-2 data is significantly higher than that of 30m Landsat data. (3) The results of HRSEI in 2016 and 2023 showed that the ecological environment quality of Fan County had been generally improved. However, there were still some areas where the ecological environment quality deteriorated. There are two main factors that contribute to the deterioration. Firstly, urbanization has led to the expansion of built-up land, with previously cultivated or forested land being changed to impervious surfaces. Secondly, due to the policy of relocation and reclamation in the Yellow River beach area, the villages near the Yellow River have carried out the demolition of old villages and the construction of resettlement areas. Especially, the lack of timely reclamation after the demolition of old villages has seriously expanded the scope of the deterioration of the ecological environment quality.