## 1 Introduction  
Global network traffic has been proliferating in recent years [1]. To meet the increasing traffic demand in the network, ubiquitous networking and computing (UNC) have been widely used in production and life in recent years [2]. As an essential part of the ubiquitous network communication [3, 4], the low earth orbit (LEO) satellite constellations offer high coverage [5] and remain available in times of disaster [6]. It also plays an essential role in the military [7] and the field of autonomous driving [8]. With the development of satellite constellations in recent years, [9], AMC has been used for satellite-to-ground communications within complicated and variable channel environments [10, 11]. AMC adjusts the modulation and coding scheme according to the varying channel conditions [12, 13, 14]. The basis for the correct choice of AMC for satellite-to-ground communications is the accurate estimation of the satellite-to-ground channel state. [PERSON] et al. [15] introduced a noncoherent M-ary orthogonal AMC method for use in direct sequence code division multiple access scenarios. [PERSON] et al. [16] dynamically selected different low-density parity-check (LDPC) codes according to the bit error rate (BER). However, in these methods, how to estimate the complicated and variable satellite-to-ground channel is not taken into consideration. To obtain accurate estimations of the satellite-to-ground channel state, many research works have been carried out. [PERSON] et al. [17] predicted the channel quality using a smooth fading method to predict channel quality. [PERSON] et al. [18] and [PERSON] et al. [19] adopted support vector regression (SVR) [20], a variant of support vector machine (SVM) [21] to estimate the channel status. [PERSON] et al. [22] estimated the channel quality by subtracting the estimated feeder downlink signal to noise ratio (SNR) from the end-to-end SNR and selecting the coding scheme based on this. [PERSON] et al. [23] adopted machine learning and tested the prediction accuracy of multiple machine learning methods (i.e., linear regression and multilayer perception). Due to the strong regularity of satellite motion, historical channel information is highly relevant to the channel information to be predicted. With the development of machine learning in recent years, long short-term memory (LSTM) [24] has been widely used in the field of time series prediction. [PERSON] et al. [25] used LSTM in an audio-video coding scenario to predict the channel quality of the next moment. [PERSON] et al. [26] used LSTM that considers historical SNRs in predicting the channel quality. The above works have not considered the effect of real-time weather on the channel state and, therefore, cannot accurately predict the channel state. As mentioned, the weather condition strongly affects satellite-to-ground communications, so it should be involved in the estimation process of channel states. According to the second-generation standard for satellite broadband services, DVB-S2 [27], satellite-to-ground communications should allow for a fixed margin of 6 dB for rain loss. However, the average rain loss varies across the globe and is significantly lower at high latitudes compared with low ones [28]. Even at low latitudes, the real-time rain loss depends on whether it is raining locally and how much it rains. Therefore, the coding scheme can be selected based on real-time weather conditions. Thus, an encoding scheme that can transmit more data is selected when sunny, and a more secure encoding scheme when it is raining or snowing. [PERSON] et al. [29] considered weather and predicted the channel variation using an auto-regressive method. [PERSON] et al. [30] proposed a method to estimate satellite-to-ground channel quality according to the quality of service (QoS) under different weathers. These schemes only simulate the situation over a specific area. Nevertheless, LEO satellites move around the globe, and thus a model that can consider real-time multiple-region weather is necessary.