Regional eco-environmental quality assessment is not only the basis of national economic construction and sustainable development planning, but also one of the main directions of ecological research. For an assessment of eco-environmental quality in the Ningxia Plain along the Yellow River, based on land use/cover data and Landsat OLI data, this paper calculated Landscape Diversity Index (LDI) and Normalized Difference Vegetation Index (NDVI), Wet Index (WI), Normalized Difference Building-Soil Index (NDBSI), Remote Sensing Based Ecological Index (RSEI), and Modified RSEI (MRSEI) on ArcGIS and GEE platforms, respectively. The analysis under the best scale constraint of LDI showed that the LDI in the study area had significant scale dependence (P<0.001), and its threshold value occurred at 3000 m×3000 m of analysis window. Principal Component Analysis (PCA) explained that the MRSEI of the study area was mainly affected by NDVI and LDI, in which NDVI was the most important factor of PC1 (eigenvalue contribution of 68.98%) with eigenvector 0.8901 while LDI was the minor determinant with eigenvector -0.4146, which had a high value in the MRSEI calculation. LDI was the determinant of PC2 (eigenvalue contribution of 28.76%) with eigenvector 0.9100 while NDVI was the minor determinant with eigenvector 0.4056, and this component had a low score in MRSEI calculation. Regarding the application of MRSEI, its use of LDI instead of LST in the analysis effectively avoided the duplicate expression of ecological significance and high aggregation in the multi-factor vector projection between NDBSI and LST in the RSEI analysis. In terms of the MRSEI, the spatial heterogeneity was mainly distributed at the "poor" and "worse" levels in the ecotones of different land use/cover types and mainly around the study area. On the "poor" to "excelllent" gradient, the patch density tended to decrease from 8.3 to 5.9 patches/km², while the average patch area tended to increase from 0.120 to 0.169 km². The overall MRSEI value of 0.0117 was just above the lower limit of "good" level for eco-environmental quality in the study area.