Summer maize LAI retrieval based on multi-source remote sensing data

Leaf Area of Index (LAI) refers to half the total leaf area all crops per unit area. It is an important index represent photosynthetic capacity and biomass crops. To obtain LAI conditions summer maize in different growth stages quickly accurately, further guiding field fertilization irrigation. The Unmanned aerial vehicles (UAV) multispectral data, growing degree days, canopy height model 2020-2021 were used carry out inversion. vegetation was constructed by ground hyperspectral data same range bands. correlation analysis conducted verify accuracy data. include many bands as possible, four indices which included R, G, B, NIR selected this study test spectral accuracy. There nine calculated with UAV based on red band near-infrared band. Through index, a higher construct inversion model. In addition, Canopy Height Model (CHM) Growing days (GDD) also build carried multi-growth using general linear regression (GLR), Multivariate nonlinear (MNR), partial least squares (PLSR) models. R² RMSE assess results show that between greater than 0.64, significant. Wide Dynamic Range Vegetation (WDRVI), Normalized Difference (NDVI), Ratio (RVI), Plant Biochemical (PBI), Optimized Soil-Adjusted (OSAVI), CHM GDD have LAI. By comparing models three methods, it found PLSR has best effect. OSAVI, GDD, RVI, PBI, CHM, NDVI, WDRVI, training model’s being 0.8663, testing 0.7102, 1.1755. This showed improves effectively. That means crop population structure change influenced certainly, method can provide decision support for monitoring fertilization. Keywords: maize, multispectral, day, model, DOI: 10.25165/j.ijabe.20231602.7285 Citation: Pan F L, Guo J K, Miao C, Xu H Y, Tian B Q, Gong D et al. Summer retrieval multi-source remote sensing Int Agric & Biol Eng, 2023; 16(2): 179-186.