## 4 Results  
### Model Accuracy and Importance Evaluation of Quantitative Remote Sensing Products  
Random forests can assess the importance of different variables in the classification process through the Gini index [72; 73]. From Figure 5 we can see the estimated importance of the variables of the data. Each histogram corresponds to a quantitative remote sensing parameter. The most important of these is GPP, ET ranks second, and NDVI ranks third. From this, we can find that quantitative remote sensing parameters are more important for classification than NDVI statistical indicators. It shows that quantitative remote sensing parameters play an important role in land cover classification. Figure 5: Feature importance of 6 quantitative remote sensing features such as albedo, ET, FAPAR, GPP, LAI and NDVI obtained through random forest. To prove the performance and accuracy of the Bi-LSTM, this study also carried out a comparison of five types of machine learning models, Random Forest, SVM, CNN, LSTM and Bi-LSTM, for land cover classification studies. The difference between the Bi-LSTM and the LSTM is that the Bi-LSTM adds a layer of a neural network with reverse input data to extract more time information [74]. The CNN is a common convolutional neural network that is good at dealing with images, especially large image-related machine learning problems, and is often used in the spatial and spectral fields [75]. The way CNN works is to extract 2D spatial features. We used six quantitative remote sensing parameters, and the time dimension of each quantitative remote sensing parameter was 23 (equivalent to one data per half month). We take the six quantitative remote sensing parameters of each sample as the X-axis and the Y-axis as the time dimension to form a 6 \(\times\) 23 2-dimensional input, then use the CNN model to convolve on this array, extract the features, and it carries out training and classification task. Then CNN usually extracts features layer-by-layer from multiple convolutional layers and pooling layers and finally completes classification through several fully connected layers. In this comparative experiment, for the three neural networks, a four-layer network structure is set up, and the number of neurons remains the same. When the accuracy of the training model is guaranteed to be stable, the five models all use the same test set to verify the accuracy of the model. The training process and final accuracy results of the model are shown in Table 3. In the case of using only the GPP as the input, the accuracies of the five neural network models reached a stable state, at 78.30%, 63.73%, 56.40%, 79.13%, and 81.86%, respectively, and the Bi-LSTM model had the highest accuracy. Therefore, the Bi-LSTM is chosen as the classification model for long time series land cover classification task. At the same time, based on this, this study gradually increases the data in quantitative remote sensing products. For each additional quantitative remote sensing product, the accuracy of the classification model has been improved to a certain extent, finally achieving a classification accuracy of 88.04%, and its mean FI-score [76] value also reached 0.878. At the same time, the importance of the NDVI and quantitative remote sensing products is also evaluated. When the NDVI is removed and only the quantitative remote sensing product is used as the model input, the accuracy of the model is only reduced by 0.65%, indicating that the quantitative remote sensing product