| Snow is a key element in the meteorological and hydrological studies. The amount of regional snow plays an important role in the climate change and also affects greatly the development of agriculture and farming industries in arid/semi-arid areas. Seasonal snow cover and glacier are important components of global water cycling. Thus, it is necessary to monitor the seasonal snow cover, the depositing and melting of the glacier. It will help us to acquire a more accurate understanding of global water cycling.In this study, we evaluate the capability of a multi-scattering microwave emission model that including the Dense Media Radiative Transfer Model (DMRT) and AIEM in simulation of dry snow emission with Matrix Doubling approach. We compared the predictions of this model with the ground experimental measurements. The comparison showed that our snow microwave emission model agreed well with the experimental measurements. In order to develop retrieval snow properties: snow depth or snow water equivalence (SWE) retrieval algorithm, we carried out the sensitivity test between the emission models with the different scattering-order: the zeroth-order, the first-order and the multi-scattering models. The results indicated that the multi-scattering effects have to be taken into account in the snow emission model, especially for large grain size. Due to the complexity of the multi-scattering model, we developed a parameterized inversion model using our multi-scattering emission model with a wide range of snow and under-ground properties for algorithm development purpose. Finally, we proposed a technique retrieval to estimate snow water equivalence.There are some new results obtained as follows in this study.1. New understanding of the snow microwave emission model and snow water equivalence (SWE) inversion algorithm. First, using AIEM model, we can take into account the properties of underground surface roughness and dielectric constant in the snow emission model, while in current emission...
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