scholarly journals Multiscale leaf area index assimilation for Moso bamboo forest based on Sentinel-2 and MODIS data

2021 ◽  
Vol 104 ◽  
pp. 102519
Author(s):  
Jiayi Ji ◽  
Xuejian Li ◽  
Huaqiang Du ◽  
Fangjie Mao ◽  
Weiliang Fan ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Moso Bamboo ◽  
Bamboo Forest ◽  
Area Index ◽  
Modis Data ◽  
Moso Bamboo Forest ◽  
Sentinel 2

scholarly journals Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 505
Author(s):  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Water Vapor ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Linear Regression Models ◽  
Area Index ◽  
Estimation Performance ◽  
Red Edge ◽  
Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

scholarly journals A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

2018 ◽  
Vol 10 (5) ◽  
pp. 763 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco García-Haro ◽  
Lorenzo Busetto ◽  
Luigi Ranghetti ◽  
Beatriz Martínez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Polar System ◽  
Critical Comparison ◽  
Landsat 7 ◽  
Sentinel 2

Crop Yield Modelling Applying Leaf Area Index Estimated from Sentinel-2 and Proba-V Data at JECAM site in Poland

2018 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Maciej Bartold ◽  
Radoslaw Gurdak ◽  
Martyna Gatkowska ◽  
Wojciech Kiryla ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Area Index ◽  
Sentinel 2

Estimation of Leaf Area Index of Moso Bamboo Canopies

2021 ◽  
pp. 1-16
Author(s):  
Feilong Huang ◽  
Weiliang Fan ◽  
Huaqiang Du ◽  
Xiaojun Xu ◽  
Jun Wu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Moso Bamboo ◽  
Area Index

scholarly journals Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

2019 ◽  
Vol 154 ◽  
pp. 189-201 ◽  
Author(s):  
Jie Wang ◽  
Xiangming Xiao ◽  
Rajen Bajgain ◽  
Patrick Starks ◽  
Jean Steiner ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Aboveground Biomass ◽  
Landsat Images ◽  
Area Index ◽  
Sentinel 2
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