Abstract
To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.
Highlights
Because the vegetation growth of a given month is heavily dependent on the growth state of the previous month, the normalized difference vegetation index (NDVI) of the previous month was included as an explanatory variable
This study developed a machine learning model using the XGBoost method to predict monthly NDVI, as an indicator of vegetation growth
Validation showed that the model could reproduce the spatial and seasonal variations of satellite-derived NDVI over the entire vegetated region of China
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Terrestrial vegetation growth plays an important role in regulating the global carbon cycle and atmospheric CO2 concentrations [1], mitigating climate change [2], and maintaining ecosystem structure and function [3,4]. A recent study revealed that seasonal changes in terrestrial vegetation growth drive the seasonality of atmospheric CO2 concentration [5]. Rising temperatures and increased drought have impacted terrestrial vegetation, resulting in global stagnation of vegetation growth [6,7,8]. Reliable, objective, and timely information regarding vegetation growth is vital [9]
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