Long-term, high-resolution GPP mapping in Qinghai using multi-source data and google earth engine

Abstract

ABSTRACTThe terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern. However, there is still a lack of a high-resolution GPP dataset for Qinghai Province. To address this issue, we processed all Landsat images of Qinghai from 1987 to 2021 using the GEE, and we combined multi-source auxiliary data to estimate GPP using the revised EC-LUE model. We compared our GPP dataset with flux observations to verify its accuracy. The results showed that our GPP dataset had a high correlation with the flux tower observations, with correlation coefficients of 0.984 at CF-AM site and 0.976 at CN-Ha2 site, respectively, and each site had an RMSE of [Formula: see text] and 12.986 [Formula: see text], respectively. There are different deviations between our GPP dataset and the mainstream GPP datasets in various vegetation types, with the average correlation coefficient ranging from 0.431 to 0.943. By comparing with the flux observations and the related analysis, we demonstrated that our GPP dataset features better accuracy, higher spatial resolution, and more temporal coverage than mainstream GPP datasets. This study offers the first long-term high-resolution GPP dataset for Qinghai Province, and we believe that this dataset has important implications for ecological management and climate research.

Keywords

• GPP
• landsat
• revised EC-LUE model
• GEE
• NDVI