Estimation of Aboveground Vegetation Water Storage in Natural Forests in Jiuzhaigou National Nature Reserve of China Using Machine Learning and the Combination of Landsat 8 and Sentinel-2 Data

Abstract

Aboveground vegetation water storage (AVWS) is a fundamental ecological parameter of terrestrial ecosystems which participates in plant metabolism, nutrient and sugar transport, and maintains the integrity of the hydraulic system of the plant. The Jiuzhaigou National Nature Reserve (JNNR) is located in the Eastern Tibet Plateau and it is very sensitive to climate change. However, a regional estimate of the AVWS based on observations is still lacking in the JNNR and improving the model accuracy in such mountainous areas is challenging. Therefore, in this study, we combined the Landsat 8 and Sentinel-2 data to estimate AVWS using multivariate adaptive regression splines (MARS), random forest (RF) and extreme gradient boosting (XGBoost) with the linkage of 54 field observations in the JNNR. The results showed that AVWS varied among different forest types. The coniferous forests had the highest AVWS (212.29 ± 84.43 Mg ha−1), followed by mixed forests (166.29 ± 72.73 Mg ha−1) and broadleaf forests (142.60 ± 46.36 Mg ha−1). The average AVWS was 171.2 Mg ha−1. Regardless of the modelling approaches, both Sentinel-2 and Landsat 8 successfully estimated AVWS separately. Prediction accuracy of AVWS was improved by combining Landsat 8 and Sentinel-2 images. Among the three machine learning approaches, the XGBoost model performed best with a model efficiency of 0.57 and root mean square error of 48 Mg ha−1. Predicted AVWS using XGBoost showed a strong spatial pattern of across the study area. The total AVWS was 5.24 × 106 Mg with 67.2% coming from conifer forests. The results highlight the potential of improving the accuracy of AVWS estimation by integrating different optical images and using machine learning approaches in mountainous areas.