Abstract: High-accuracy and near-real-time simulation of yield of winter wheat is of great significance for ensuring national food security and addressing climate change. In recent years, solar-induced chlorophyll fluorescence (SIF) parameters have been widely applied in crop growth monitoring. Based on Sentinel-5P/TROPOMI SIF data and the mechanistic light-response model (MLR), we integrated a winter wheat gross primary productivity (GPP) estimation model for the winter wheat-producing areas of the North China Plain, which could characterize the energy supply of light reactions and the carbon assimilation process of dark reactions without requiring complex physiological parameter inputs. We used this model to assess wheat yield. The results showed that, during 2019-2023, the annual coefficients of determination between the model-simulated GPP and site-observed GPP ranged from 0.74 to 0.95; at the regional scale, compared with the GPP products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Orbiting Carbon Observatory-2 SIF (GOSIF), this model showed clear advantages in reducing systematic bias. The yield estimation results showed that annual coefficients of determination between the model-estimated yield and the county-level measured yield ranged from 0.552 to 0.824, while the annual root mean square error values ranged from 508.56 to 563.42 kg·hm^-2. Overall, the SIF-based MLR model demonstrated good applicability and reliability in GPP and county-level yield assessment for the winter wheat-producing areas of the North China Plain, providing an important technical reference for the operational application of agricultural remote sensing monitoring in China.

Key words: solar-induced chlorophyll fluorescence (SIF), gross primary productivity (GPP), mechanistic light response model, winter wheat, yield, North China Plain