Estimating the Spatial-Temporal Distribution of Leaf Area Index Using Sentinel-2 Satellite Images (Case Study: Silage Maize Farms of South of Tehran)

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AKBARI E.; DARVISHI BOLOORANI A.; Neysani Samany N.; HAMZEH S.; SOUFIZADEH S.; Pignatti S.

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Journal: Iranian Journal of Irrigation and Drainage Year:2020 | Volume:13 | Issue:3 Page(s): 967-980

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Title

Estimating the Spatial-Temporal Distribution of Leaf Area Index Using Sentinel-2 Satellite Images (Case Study: Silage Maize Farms of South of Tehran)

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Keywords

Kernel-based algorithmQ1

Leaf Area IndexQ2

Hemispherical photographyQ1

Regional studyQ1

Pixel-based mapQ1

Abstract

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 country 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.

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APA: Copy

AKBARI, E., DARVISHI BOLOORANI, A., Neysani Samany, N., HAMZEH, S., SOUFIZADEH, S., & Pignatti, S.. (2020). Estimating the Spatial-Temporal Distribution of Leaf Area Index Using Sentinel-2 Satellite Images (Case Study: Silage Maize Farms of South of Tehran). IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, 13(3 ), 967-980. SID. https://sid.ir/paper/399694/en

Vancouver: Copy

AKBARI E., DARVISHI BOLOORANI A., Neysani Samany N., HAMZEH S., SOUFIZADEH S., Pignatti S.. Estimating the Spatial-Temporal Distribution of Leaf Area Index Using Sentinel-2 Satellite Images (Case Study: Silage Maize Farms of South of Tehran). IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE[Internet]. 2020;13(3 ):967-980. Available from: https://sid.ir/paper/399694/en

IEEE: Copy

E. AKBARI, A. DARVISHI BOLOORANI, N. Neysani Samany, S. HAMZEH, S. SOUFIZADEH, and S. Pignatti, “Estimating the Spatial-Temporal Distribution of Leaf Area Index Using Sentinel-2 Satellite Images (Case Study: Silage Maize Farms of South of Tehran),” IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, vol. 13, no. 3 , pp. 967–980, 2020, [Online]. Available: https://sid.ir/paper/399694/en

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