Remotely sensed data is generally influenced by temporal factors such as Sun-target-satellite geometry and atmospheric conditions

which are major concerns in quantitative monitoring of long-term changes of the Earth’s surface.The inconsistencies are usually eliminated with radiometric normalization or cross-sensor calibration.In most cases

these strategies use statistical relationships among multi-temporal images

which do not meet the rigorous requirements of quantitative remote sensing.While MODIS Level-1B（L1B） products have wide applications

a mathematical relationship was derived among the pixel values of pseudo-invariant features（PIFs） of the multi-temporal images.The quantitative relationship was validated using visible and reflective infrared bands of MODIS L1B products.The results showed that the quantitative relationship consists of additive and multiplicative parts relying on sun-target-satellite geometry

atmospheric conditions and sensor parameters.The derived relationship had a good agreement with the multi-temporal relationship of PIF pixels obtained from individual images.It offers a quantitative basis for statistically based radiometric normalization or cross-sensor calibration.