Abstract: Solar-induced chlorophyll fluorescence (SIF) is an important indicator of plant photosynthetic status and a key metric for estimating vegetation gross primary productivity (GPP). Meteorological conditions influence SIF variations by affecting plant physiological status and photosynthetic processes. To achieve SIF inversion under different meteorological conditions, the maize planting areas in Liaoning Province were selected as the study area. Using data from 21 meteorological stations in Liaoning Province during 2014—2023, meteorological parameters simulated by the Mountain Microclimate Simulation Model, the Global SIF product (GOSIF) derived from OCO-2 data, and meteorological gridded datasets, SIF values during May-September in drought years and precipitation-suitable years were inverted using partial least squares regression (PLSR), principal component analysis (PCA), fully connected neural network (FCNN), and ridge regression (RR) methods. The results showed that the PLSR model achieved the highest validation set accuracy in site-scale model comparisons, while the city-level regional RR model obtained good accuracy in most regions, with the coefficient of determination (R²) of the training set mostly reaching above 0.65 (P<0.05). Except for Benxi and Dandong cities, the normalized root mean square error (NRMSE) was below 20% in other regions. Spatial correlation analysis between the inverted SIF results and GOSIF products showed that the spatial correlation coefficients in most regions exceeded 0.7, and the spatial variation trends of both were generally consistent. By selecting monthly mean actual evapotranspiration, monthly mean air temperature, monthly mean water vapor pressure, and monthly precipitation as input variables, the SIF inversion method for regional maize farmland could be effectively established. This provided a technical reference for regional-scale SIF estimation using meteorological data and smart agriculture remote sensing applications.

Key words: Solar-induced chlorophyll fluorescence, drought, maize, GOSIF, Liaoning province