A Novel Remote Sensing–Derived Indicator for Monitoring Watershed-Scale Ecological Pressure on Tributary Eutrophication in a Large Reservoir System

Monitoring landscape ecological pressure on river eutrophication requires spatially explicit, temporally comparable indicators derived from remote sensing data. This study develops the Net Ecological Pressure Index (NEPI _ws) to quantify watershed-scale ecological pressure on eutrophication based on the balance between agricultural and forest domination and ranged from −1 to 1,where positive values reveal domination of agriculture ecological pressure over forests ecological buffering, while negative values reveal the opposite domination pattern. Using 30 m land-use/land-cover data for 2010 and 2024, the spatio-temporal dynamics of NEPI _ws was analyzed for 25 1^st-order trib-utaries in the Three Gorges Reservoir Region, including 13 on the northern bank and 12 on the southern bank across reservoir sensitive, sub-sensitive, and non-sensitive zones to account for interaction with backwater effects. Results showed a clear spatial pat-tern, with NEPI ws increasing on both banks as backwater sensitivity decreased farther from the dam. Cumulative NEPI _bank of both banks revealed higher value on the north-ern bank in the non-sensitive and sub-sensitive zones, while showed higher value on the southern bank in the sensitive zone. From 2010 to 2024, the cumulative NEPI bank exhibited contrasting temporal trends. In the sensitive zone, cumulative NEPI _bank in-creased by 0.0896 on the southern bank, 2.05 times the increase on the northern bank 0.0436. In the sub-sensitive zone, NEPI _bank declined more on the northern bank (−0.105) than on the southern bank (−0.0401). In the non-sensitive zone, NEPI _bank de-creased on the northern bank (−0.0641) but increased on the southern bank (0.0242). Overall, NEPI effectively captured spatio‑temporal variations in watershed‑scale eco-logical pressure on tributary eutrophication using Earth observation data. Its stand-ardized and transferable formulation enables comparative assessment, eco‑functional zoning and spatial prioritization of eutrophication management in large reservoir sys-tems, demonstrating the value of remote sensing data for integrated water resources management in mixed agricultural–forest landscapes.