Integration of canopy water removal and spectral triangle index for improved estimations of leaf nitrogen and grain protein concentrations in winter wheat

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
• Main Authors: Yan, Yan, Li, Dong, Kuang, Qianliang, Yao, Xia, Zhu, Yan, Cao, Weixing, Cheng, Tao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Absorption; Canopies; Canopy; canopy level; Crops; Estimation; GPC; Indexes; Leaves; LNC; Nitrogen; Plant cover; PROSAIL; Proteins; Radiative transfer; Reflectance; Reflectivity; Removal; Root-mean-square errors; Short wave radiation; Spectra; Spectral sensitivity; SWIR; Water; Water absorption; water removal technique; Wheat; Winter wheat
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3277456

Previous studies on estimating leaf nitrogen concentration (LNC) and grain protein concentration (GPC) from canopy reflectance did not pay particular attention to the nitrogen (N) and protein absorption features, which are mostly located in the shortwave infrared (SWIR) region and challenging to use due to the adverse effect of water absorption. This study aimed to develop a new approach to mitigate the effect of water signals from canopy reflectance spectra of winter wheat and enhance the absorption features of N and protein in the SWIR region. The water effect at the canopy level was removed by using the simulated dry-canopy reflectance (SDR) spectra based on the radiative transfer models and a three-band spectral triangle index (STI) was constructed to capture the chemical-specific absorption variations of N and protein. The results demonstrated that these absorption features became sufficiently apparent by canopy water removal. With the SDR spectra, the STIs with three bands related to N and protein absorption features in SWIR sub-regions exhibited remarkably stronger correlations with LNC and GPC than those with the measured reflectance (MR) spectra. Specifically, STI 2060-2180-2240 from the SDR spectra achieved the top sensitivity to LNC and GPC. The combination of plant pigment ratio (PPR) and STI 2060-2180-2240 yielded significantly lower root mean square error (RMSE) values (LNC: RMSE = 0.19%; GPC: RMSE = 0.67%) than either one alone. The integration of water removal and STI could help us better understand the underlying mechanism on the relationships of LNC and GPC with N and protein absorption features.