Assessing spatiotemporal consistency of vegetation productivity indicators (SIF, GPP, NPP) in northwest China: climate and vegetation perspectives

Accurate assessment of vegetation productivity dynamics is critical for understanding ecosystem responses to environmental change. However, the spatiotemporal consistency among key remote sensing indicators, such as solar-induced chlorophyll fluorescence (SIF), gross primary productivity (GPP), and net primary productivity (NPP), remains poorly quantified, especially in ecologically fragile drylands of Northwest China (NWC). In this study, we developed a dual-factor framework integrating climate and vegetation perspectives to evaluate the consistency and inconsistency among SIF, GPP, and NPP across diverse climatic gradients and vegetation types from 2000 to 2020. We found that all three indicators increased significantly across most of NWC (p < 0.05), with higher values in the southeast and lower values in the northwest. Only 61.41% of the pixels presented fully consistent trends, which were concentrated in humid eastern regions. In contrast, 38.59% of the pixels presented inconsistent trends among SIF, GPP, and NPP, which were predominantly located in arid and subarid transition zones and central grasslands. Notably, SIF and GPP demonstrated strong consistency in humid zones (R > 0.95) and grasslands (R = 0.91), making them suitable for tracking dynamic photosynthesis, whereas NPP was more reliable in arid zones because of its lower sensitivity to atmospheric interference. The proposed framework provides a reference approach for selecting appropriate vegetation productivity indicators based on local climatic and ecosystem contexts. Our findings lay a solid foundation for more reliably assessing the carbon cycle in global drylands.