Publications

Fan, JR; Lu, XP; Cai, GS; Lou, ZF; Wen, J (2025). Multi-Feature Driver Variable Fusion Downscaling TROPOMI Solar-Induced Chlorophyll Fluorescence Approach. AGRONOMY-BASEL, 15(1), 133.

Abstract
Solar-induced chlorophyll fluorescence (SIF), as a direct indicator of vegetation photosynthesis, offers a more accurate measure of plant photosynthetic dynamics than traditional vegetation indices. However, the current SIF satellite products have low spatial resolution, limiting their application in fine-scale agricultural research. To address this, we leveraged MODIS data at a 1 km resolution, including bands b1, b2, b3, and b4, alongside indices such as the NDVI, EVI, NIRv, OSAVI, SAVI, LAI, FPAR, and LST, covering October 2018 to May 2020 for Shandong Province, China. Using the Random Forest (RF) model, we downscaled SIF data from 0.05 degrees to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R-2 = 0.931, RMSE = 0.052 mW/m(2)/nm/sr, and MAE = 0.031 mW/m(2)/nm/sr for 2018-2019 and R-2 = 0.926, RMSE = 0.058 mW/m(2)/nm/sr, and MAE = 0.034 mW/m(2)/nm/sr for 2019-2020. The downscaled SIF products showed a strong correlation with TanSIF and GOSIF products (R-2 > 0.8), and consistent trends with GPP further confirmed the reliability of the 1 km SIF product. Additionally, a time series analysis of Shandong Province's wheat-growing areas revealed a strong correlation (R-2 > 0.8) between SIF and multiple vegetation indices, underscoring its utility for regional crop monitoring.

DOI:
10.3390/agronomy15010133

ISSN:
2073-4395