3.2.5. Test Site GPP without LSWI

In this experiment, all LSWI data were deleted from the training data, which were then trained for each site flux. The final prediction results are shown in Figure 13. Compared to the prediction results without any feature ablation of the training data, the prediction accuracy of the AR site improves significantly and is only lower than that after removing NDVI. The DXST, DXSW, NMC, and ZF sites have similar prediction accuracy as in the previous ablation experiments, with a large decrease in accuracy, and the prediction accuracy of the ZF site is only higher than that after removing EVI. The GL site shows a slight decrease in accuracy, while the HBKO, HBSH, and HBSW sites show no obvious changes in prediction accuracy.

#### 3.2.6. Summary of Factor Ablation Experiments

To observe the influence of different features on the prediction accuracy of each site more intuitively, we summarized all the feature ablation experiment results, as shown in Table 5.

**Figure 13.** Predicted GPP vs. labeled GPP at a single site (no LSWI).



Numbers in red indicate that the prediction results for the corresponding site have increased in accuracy compared to the previous results without setting a distance range; numbers in blue indicate that the results have decreased in accuracy; and numbers in black indicate that the results have no significant changes.

Table 5 indicates that removing any feature from the AR site would result in a significant improvement in accuracy, with the largest improvement obtained after removing NDVI. The removal of any feature for the DXST, DXSW, and ZF sites would lead to a degradation of accuracy, with the DXST site showing a large accuracy decline and the

lowest accuracy after removing LSWI. The DXSW site also shows a decline in accuracy, although it is smaller than that of the DXST site. The ZF site has a noticeable decline in accuracy after removing EVI. The NMC site has an abnormal increase in accuracy after removing NDVI and a decline after removing other features except NDVI. The GL site is insensitive to the removal of Ta or NDVI and shows slight decreases in accuracy after removing other features in addition to Ta and NDVI. The HBKO, HBSH, and HBSW sites are insensitive to the removal of any feature and have no obvious changes in accuracy.

#### **4. Conclusions**

In this work, we used satellite remote sensing data and flux site observation data to introduce the GeoMAN model based on an encoder–decoder framework with an attention mechanism for site features, and we obtained good results. According to the experiments on training data selection based on distance and vegetation type, we found that both distance and vegetation type had an impact on GPP prediction results, with vegetation type having a larger impact. Through the feature ablation experiments, we found that different sites showed sensitivity to different factors, with the site located in the alpine swamp meadow being insensitive to changes in environmental factors, while the site located in the alpine meadow steppe showed a different trend since the GPP prediction accuracy decreased sharply with the changes in environmental factors. The GPP prediction accuracy of the site located in the alpine Kobresia meadow also varied with environmental factor changes but was more stable than the other sites. The results of this work show that deep learning models have high accuracy when simulating site-scale GPP and, to some extent, reflect the correlation between a target site's GPP and other sites' distances, vegetation types, and meteorological factors. Our work could be used in the prediction of other factors, for example, AGB (Above Ground Biomass) and RE (Ecosystem Respiration). However, this work has some limitations. Firstly, we do not consider some factors that have an influence on productivity in Tibetan grasslands, such as soil development and drought regimes. Secondly, the data we used in this work only cover a partial area of the Tibetan Plateau region, and this introduces constraints to regional GPP assessment. In our future work, we will add more factors to the training data, for example, soil pH, soil fertility, and soil organic matter (SOM), since high soil pH and a lack of soil fertility limit plant productivity [36], and SOM is able to enhance alpine grassland productivity by improving the soil structure, aggregates, and cation-exchange capacity (CEC) under high aridity conditions [37]. Moreover, ecological factors, such as growing and non-growing seasons, will be considered, and larger regional-scale data will be used in future training and learning processes. These improvements will help us perform more accurate and larger-scale GPP simulations.

**Author Contributions:** Conceptualization, Q.Y. and Y.W.; methodology, Q.Y. and N.N.; software, Q.Y.; validation, Q.Y., Z.W. and M.W.; formal analysis, R.C.; investigation, Q.Y.; resources, X.R.; data curation, Q.Y. and X.W.; writing—original draft preparation, Q.Y.; writing—review and editing, N.N. and X.W.; visualization, Q.Y. and W.L.; supervision, Y.W.; project administration, Q.Y.; funding acquisition, N.N. and Y.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research and Development Program of China (Grant No. 2021YFF0703902).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
