Monthly Dynamics and Drivers of Urban Net Primary Productivity in Chengdu, China: A Multi-Source Remote Sensing and Regression Analysis

Urban greening is central to climate-responsive planning, but month-to-month variation in urban net primary productivity (UNPP) remains insufficiently resolved. We estimated monthly UNPP from the MOD17A2H Gpp band, a gross primary productivity (GPP) product, across Chengdu’s five central districts during 2013–2022 and examined its associations with the normalized difference vegetation index (NDVI), land surface temperature (LST), precipitation, and night-time light intensity. Ordinary least squares regression used 119 overlapping citywide monthly means from January 2013 to November 2022. The model explained 86.1% of UNPP variance (adjusted R2 = 0.856). LST showed the largest standardized association (β = 0.596), followed by NDVI (β = 0.286) and precipitation (β = 0.135); night-time light intensity was not significant (β = 0.017, p = 0.632). HC3-robust inference retained the NDVI and LST associations, whereas the precipitation association weakened. District summaries showed the largest stage-wise UNPP increase in Qingyang and the largest NDVI increase in Chenghua. Greener spatial patterns after 2018 coincided temporally with the Park City Initiative but do not establish a causal policy effect. These findings support temporally aligned monitoring while identifying the need for field-based species, management, and impervious surface data.