High-Resolution Mapping of Gross Primary Production in Northeast China Using Landsat-8/9 and Sentinel-2 A/B

Abstract

Accurately estimating gross primary production (GPP) in terrestrial ecosystems is crucial for gaining a deeper understanding of the carbon cycle within the ecosystem and for predicting climate change. Although many GPP datasets are available, they often have low resolution, typically around 500 m or lower, which restricts their effectiveness in monitoring fragmented croplands and areas with high heterogeneity. In this study, we utilized optical satellite data from Landsat-8/9 and Sentinel-2A/B, along with meteorological data from ERA5, to generate a GPP dataset with a spatial resolution of 30 m for three provinces in Northeast China. This dataset was developed based on the Vegetation Photosynthesis Model and exhibited robust validation results when compared with SIF data and other existing GPP datasets. It provides a high-resolution GPP product that significantly enhances the precision of carbon cycle research in Northeast China. This research underscores the feasibility of producing high spatial resolution GPP products using Landsat-8/9 and Sentinel-2A/B optical satellite data. The resulting dataset offers a more refined GPP estimate for studies related to the terrestrial carbon cycle.

Keywords

• Gross primary Production (GPP)
• image integration
• mapping
• vegetation photosynthesis model (VPM)