Spatiotemporal Patterns of kNDVI and GPP and Their Multiple Driving Forces Across Complex Mountain Landscapes of the Qinling Mountains, China

The Qinling Mountains form a major geographical boundary and climatic transition zone in China. This study derived the kernel Normalized Difference Vegetation Index (kNDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data and evaluated it against conventional NDVI and gross primary productivity (GPP) in the Shaanxi section of the Qinling Mountains for 2000–2025, using trend, partial-correlation, variance-partitioning and geographical-detector analyses. (1) Vegetation improved overall: annual kNDVI rose from 0.593 to 0.668 at 0.0027 yr−1 (R2 = 0.90), and 84.55% of the area increased significantly. (2) kNDVI was only slightly better correlated with GPP than NDVI (pixel-wise r = 0.566 versus 0.563; difference 0.0026, 95% CI 0.0025–0.0027), and no pixel differed significantly after false-discovery-rate correction, so local superiority cannot be claimed. (3) Temperature and precipitation promoted growth whereas sunshine duration inhibited it; the three factors uniquely explained 44.3%, 39.2% and 15.9% of the climatically explained variance of kNDVI, and temperature alone 74.2% for GPP. (4) After significance masking, 60.08% of the kNDVI grid and 33.94% of the GPP grid had no significant dominant driver; temperature dominated 64.96% of the area for GPP, whereas precipitation dominated kNDVI in the west and temperature in the east. Annual climate explains only about one-fifth to one-quarter of inter-annual variability, leaving a large, spatially coherent residual trend.