Snow-depth retrieval from passive microwave remote-sensing data has always been an active research area

though there are still several problems that need to be solved. Due to its concision and expansibility

the National Aeronautics and Space Administration （ NASA） algorithm has become the most widely used among the existing snow-depth retrieval algorithms. However

this algorithm still has its limitations: first

since it is based on linear fitting

the NASA algorithm needs to be re-fitted when we need to accurately measure snow depth greater than 1 m. Second

because the difference between the 19 GHz and 37 GHz brightness temperature measurements is completely saturated at different snow-depth ranges

the NASA algorithm will underestimate snow depth. In order to make improvements to these existing algorithms

the research in this article has attempted to develop a new algorithm of snow-depth retrieval based on the Ant Colony Optimization. Moreover

with respect to the underestimation of snow depth of the NASA algorithm

this article introduces 10. 7 GHz brightness temperature measurements taken by AMSR-E. Simulations from the Microwave Emission Model of Layered Snowpacks （ MEMLS） and the brightness temperature measurements of A MSR-E are applied to the snow-depth retrieval experiment. The retrieval accuracy of the algorithm is evaluated using the field-measured data and the AMSR-E Snow Water Equivalent （ SWE） product. Our results indicate that both of the algorithms produce accurate results

and the inversion results have improved to a certain extent compared to the AMSR-E product.