The validation of surface information in remote sensing image determines the performance of information extraction and quantitative applications. However

image quality evaluation based on pixel gray and human visual system cannot evaluate the expressive capability of surface information. Therefore

this study evaluates multi-spectral image quality based on the precision of reflectance and normalized difference vegetation index(NDVI) from the perspective of land surface validation. A method of evaluating multi-spectral images based on the validation of land surface parameters was designed

and synchronous observation was proposed to improve the accuracy of image quality evaluation. At the same time as the remote sensing satellite imagery

the ground measurement of atmospheric optical properties and land surface spectrum were implemented. The synchronous imaging on the experimental area of GF-1 and SPOT-7 was successfully completed

and the atmospheric optical properties and land surface spectrum were synchronously measured on the ground. The radiometric error of multi-spectral images was processed with the atmospheric optical properties to eliminate the influence of atmosphere. The equivalent reflectance and NDVI of multiple surface features were calculated using the measured spectrum. Meanwhile

the reflectance and NDVI of image were calculated through multi-spectral images using window analysis. The analysis of validation of land surface parameters was based on the statistics of absolute and relative errors in reflectance and NDVI between ground measurement and image. The multi-spectral image quality was evaluated using the error of each spectral band combined with the application in information expression. In the artificial targets

the reflectance error in four bands of GF-1 is within 5% and is more accurate than that in those of SPOT-7. In the vegetation features

the reflectance error in the blue

green

and red bands of SPOT-7 is within 4%. In the near-infrared band

the reflectance error is within 15%

the error of NDVI is within 16%

and the reflectance and NDVI of SPOT-7 are more accurate than those of GF-1. In the ground features

the reflectance error in four bands of GF-1 is within 6% and is more accurate than that in those of SPOT-7. Therefore

SPOT-7 multi-spectral image has high spectrum validation in vegetation features

and GF-1 multi-spectral image has high spectrum validation in ground features. Comparing the reflectance between SPOT-7 and GF-1 multi-spectral images shows that the recognition of vegetation features is stronger and the growth monitoring of vegetation features is weaker in SPOT-7 than in GF-1. The detection in type

water content

and surface roughness of hard features is more sensitive in GF-1 than in SPOT-7. Comparing the NDVI between SPOT-7 and GF-1 multi-spectral images shows that the performance in growth state

coverage density

and distribution of vegetation is more accurate in SPOT-7 than in GF-1. The proposed evaluation method for multi-spectral image considers the validation of land surface parameters

and can evaluate image quality effectively based on the precision in spectral information expression.