1. College of Geomatics，Xi’an University of Science and Technology，Xi’an 710054，China
2. Aerospace Information Research Institute，Chinese Academy of Sciences，Beijing 100094，China
3. College of Arts and Sciences，Shanghai Maritime University，Shanghai 201306，China
Abstract：Solar-induced chlorophyll fluorescence (SIF) can sensitively reflect crop disease stress information, but the geometric structure of canopy and other factors seriously affected the ability of SIF to capture changes in photosynthetic function and stress status of vegetation. Therefore, in this paper, the normalized difference vegetation index (NDVI) and MERIS terrestrial chlorophyll index (MTCI), which can sensitively reflect crop biomass, were integrated with SIFP (SIFP-NDVI，SIFP-MTCI，SIFP-NDVI*MTCI), and the remote sensing monitoring accuracy of SIF on wheat stripe rust before and after the integration was compared and analyzed. The results show that: (1) at the O2-B, O2-A and H2O absorption at 719 nm bands, integrated reflectance spectral indices of SIFP-NDVI, SIFP-MTCI and SIFP-NDVI*MTCI showed different improvements in correlation with disease index (DI) than SIFP. The O2-B band increased the most significantly, by 23.48%, 33.61% and 36.49% respectively, while the O2-A band increased the least by 2.39%, 2.14% and 1.51%, respectively. (2) If SIFP-NDVI and SIFP-MTCI were regarded as independent variables respectively, the averaged R2 value of the prediction model based on random forest regression (RFR) algorithm were increased by 1.15% and 4.02%, and the averaged RMSE value were decreased by 2.7% and 14.41%, respectively, compared to those with SIFP as the independent variable. (3) The prediction model based on SIFP-NDVI*MTCI gave the best performance with an R2 value 5.74% higher than that of SIFP, and an RMSE value 22.52% lower than that of SIFP. The results of this paper are of great significance to improve the accuracy of remote sensing monitoring of wheat stripe rust and have a certain reference value for disease monitoring of other crops.
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