Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models

2020 ◽  
pp. 1-12 ◽  
Author(s):  
Lamin R. Mansaray ◽  
Fumin Wang ◽  
Adam S. Kanu ◽  
Lingbo Yang
Author(s):  
Zdeněk Patočka ◽  
Kateřina Novosadová ◽  
Pavel Haninec ◽  
Radek Pokorný ◽  
Tomáš Mikita ◽  
...  

The leaf area index (LAI) is one of the most common leaf area and canopy structure quantifiers. Direct LAI measurement and determination of canopy characteristics in larger areas is unrealistic due to the large number of measurements required to create the distribution model. This study compares the regression models for the ALS-based calculation of LAI, where the effective leaf area index (eLAI) determined by optical methods and the LAI determined by the direct destructive method and developed by allometric equations were used as response variables. LiDAR metrics and the laser penetration index (LPI) were used as predictor variables. The regression models of LPI and eLAI dependency and the LiDAR metrics and eLAI dependency showed coefficients of determination (R2) of 0.75 and 0.92, respectively; the advantage of using LiDAR metrics for more accurate modelling is demonstrated. The model for true LAI estimation reached a R2 of 0.88.


Author(s):  
Rahul Raj ◽  
Jeffrey P. Walker ◽  
Rohit Pingale ◽  
Rohit Nandan ◽  
Balaji Naik ◽  
...  

Author(s):  
Rahul Raj ◽  
Saurabh Suradhaniwar ◽  
Rohit Nandan ◽  
Adinarayana Jagarlapudi ◽  
Jeffrey Walker

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