فهرست مطالب stefano pignatti

Leaf area index (LAI) plays an important role in hydrological, agricultural, and land irrigation management studies. In order to adopt an appropriate, accurate, and robust algorithm to estimate the spatial-temporal distribution of LAI using Sentinel-2 images, the Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Relevance Vector Machines (RVM), and Gaussian Process Regression (GPR) were calibrated and investigated. The research data were collected from silage maize farms in Ghaleh-Now county in Tehran province during the whole growing season in summer 2018 through destructive measurement and hemispherical photography. Our results were compared with the conventional algorithms in this field, i.e. random forest (RF) and artificial neural network (ANN). The results revealed that the GPR algorithm not only has higher accuracy (in 20-m band group, R2=0.913 and RMSE=0.641), speed, and robustness to estimate the LAI, but also it has the unique ability to generate uncertainty pixel-based map (uncertainty and relative uncertainty were less than 0.7 and 30% by 96% and 74% of the total area, respectively). Based on R2 and RMSE, SVR is the second accurate technique for LAI estimation followed by RVM, KRR, RF and ANN, respectively. Comparison of the estimated and field LAI at sampling times with RMSE=0.276 and bias=0.099 and other superiorities indicated the efficiency of GPR algorithm to estimate the spatial-temporal distribution of LAI.

The present study aimed to evaluate the efficiency of digital hemispherical photography (DHP) in deriving LAI in silage maize farms in the south of Tehran. For this purpose, the DHP as well as destructive measurements for comparison were used to estimate LAI in silage maize farms in Qaleh-Now County in the south of Tehran in 2018 considering the nature of spatio-temporal variability in agricultural fields during a growing season. The results showed LAI obtained through DHP at different periods of plant growth has a strong linear correlation with the values measured by the destructive method (R2 = 0.92, RMSE = 0.45 and Bias = 0.31). However, the intermediate LAI range (LAI: 2 -