scholarly journals Regression-Based Models to Predict Rice Leaf Area Index Using Biennial Fixed Point Continuous Observations of Near Infrared Digital Images

2011 ◽  
Vol 14 (4) ◽  
pp. 365-376 ◽  
Author(s):  
Michio Shibayama ◽  
Toshihiro Sakamoto ◽  
Eiji Takada ◽  
Akihirov Inoue ◽  
Kazuhiro Morita ◽  
...  
Keyword(s):  
Fixed Point ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Near Infrared ◽  
Digital Images ◽  
Area Index ◽  
Rice Leaf

scholarly journals Estimating Paddy Rice Leaf Area Index with Fixed Point Continuous Observation of Near Infrared Reflectance Using a Calibrated Digital Camera

2011 ◽  
Vol 14 (1) ◽  
pp. 30-46 ◽  
Author(s):  
Michio Shibayama ◽  
Toshihiro Sakamoto ◽  
Eiji Takada ◽  
Akihiro Inoue ◽  
Kazuhiro Morita ◽  
...  
Keyword(s):  
Fixed Point ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Near Infrared ◽  
Digital Camera ◽  
Paddy Rice ◽  
Infrared Reflectance ◽  
Continuous Observation ◽  
Near Infrared Reflectance ◽  
Area Index

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.

Rice Leaf Area Index (LAI) estimates from hyperspectral data

2003 ◽  
Author(s):  
Xiuzhen Wang ◽  
Jingfeng Huang ◽  
Yunmei Li ◽  
Renchao Wang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Hyperspectral Data ◽  
Area Index ◽  
Rice Leaf

A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards

2008 ◽  
Vol 112 (10) ◽  
pp. 3762-3772 ◽  
Author(s):  
S DELALIEUX ◽  
B SOMERS ◽  
S HEREIJGERS ◽  
W VERSTRAETEN ◽  
W KEULEMANS ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Near Infrared ◽  
Area Index

scholarly journals Spectral composition of shortwave radiation transmitted by forest canopies

Trees ◽  
2020 ◽  
Vol 34 (6) ◽  
pp. 1499-1506
Author(s):  
Aarne Hovi ◽  
Miina Rautiainen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Near Infrared ◽  
Forest Type ◽  
Spectral Composition ◽  
Shortwave Radiation ◽  
Forest Canopies ◽  
Area Index ◽  
Infrared Transmittance ◽  
Shortwave Infrared

Abstract Key message Leaf area index and species composition influence red-to-near-infrared and red-to-shortwave-infrared transmittance ratios of boreal and temperate forest canopies. In this short communication paper, we present how the spectral composition of transmitted shortwave radiation (350–2200 nm) varies in boreal and temperate forests based on a detailed set of measurements conducted in Finland and Czechia. Our results show that within-stand variation in canopy transmittance is wavelength dependent, and is the largest for sparse forest stands. Increasing leaf area index (LAI) reduces the overall level of transmittance as well as red-to-near-infrared and red-to-shortwave-infrared transmittance ratios. Given the same LAI, these ratios are lower for broadleaved than for coniferous forests. These results demonstrate the importance of both LAI and forest type (broadleaved vs. coniferous) in determining light quality under forest canopies.

scholarly journals Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4911
Author(s):  
Xiaoning Zhang ◽  
Ziti Jiao ◽  
Changsen Zhao ◽  
Siyang Yin ◽  
Lei Cui ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Large Scale ◽  
Near Infrared ◽  
Canopy Structure ◽  
Leaf Angle ◽  
Infrared Band ◽  
Area Index ◽  
Input Parameters ◽  
Scattering Kernel

Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation.

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