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 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 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 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 Estimating the Seasonal Dynamics of the Leaf Area Index Using Piecewise LAI-VI Relationships Based on Phenophases

2019 ◽  
Vol 11 (6) ◽  
pp. 689 ◽  
Author(s):  
Kun Qiao ◽  
Wenquan Zhu ◽  
Zhiying Xie ◽  
Peixian Li
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Empirical Relationship ◽  
The Other ◽  
Active Period ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
The Mean

The leaf area index (LAI) is not only an important parameter used to describe the geometry of vegetation canopy but also a key input variable for ecological models. One of the most commonly used methods for LAI estimation is to establish an empirical relationship between the LAI and the vegetation index (VI). However, the LAI-VI relationships had high seasonal variability, and they differed among phenophases and VIs. In this study, the LAI-VI relationships in different phenophases and for different VIs (i.e., the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv)) were investigated based on 82 site-years of LAI observed data and the Moderate Resolution Imaging Spectroradiometer (MODIS) VI products. Significant LAI-VI relationships were observed during the vegetation growing and declining periods. There were weak LAI-VI relationships (p > 0.05) during the flourishing period. The accuracies for the LAIs estimated with the piecewise LAI-VI relationships based on different phenophases were significantly higher than those estimated based on a single LAI-VI relationship for the entire vegetation active period. The average root mean square error (RMSE) ± standard deviation (SD) value for the LAIs estimated with the piecewise LAI-VI relationships was 0.38 ± 0.13 (based on the NDVI), 0.41 ± 0.13 (based on the EVI) and 0.41 ± 0.14 (based on the NIRv), respectively. In comparison, it was 0.46 ± 0.13 (based on the NDVI), 0.55 ± 0.15 (based on the EVI) and 0.55 ± 0.15 (based on the NIRv) for those estimated with a single LAI-VI relationship. The performance of the three VIs in estimating the LAI also varied among phenophases. During the growing period, the mean RMSE ± SD value for the estimated LAIs was 0.30 ± 0.11 (LAI-NDVI relationships), 0.37 ± 0.11 (LAI-EVI relationships) and 0.36 ± 0.13 (LAI-NIRv relationships), respectively, indicating the NDVI produced significantly better LAI estimations than those from the other two VIs. In contrast, the EVI produced slightly better LAI estimations than those from the other two VIs during the declining period (p > 0.05), and the mean RMSE ± SD value for the estimated LAIs was 0.45 ± 0.16 (LAI-NDVI relationships), 0.43 ± 0.23 (LAI-EVI relationships) and 0.45 ± 0.25 (LAI-NIRv relationships), respectively. Hence, the piecewise LAI-VI relationships based on different phenophases were recommended for the estimations of the LAI instead of a single LAI-VI relationship for the entire vegetation active period. Furthermore, the optimal VI in each phenophase should be selected for the estimations of the LAI according to the characteristics of vegetation growth.

Using high-resolution airborne spectral data to estimate forest leaf area and stand structure

1991 ◽  
Vol 21 (7) ◽  
pp. 1127-1132 ◽  
Author(s):  
N. J. Smith ◽  
G. A. Borstad ◽  
D. A. Hill ◽  
R. C. Kerr
Keyword(s):  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spectral Data ◽  
Near Infrared ◽  
Stand Structure ◽  
Douglas Fir ◽  
Area Index ◽  
Spectrum Data

Techniques are developed to estimate stand leaf area index using high-resolution airborne spectral imagery. Leaf area index on 8 m radius plots was found to be strongly related to the normalized ratio of wavelengths in the red (674–687 nm) and near infrared (751–755 nm) part of the spectrum. Data from 17 stands were used. Leaf area index estimates included both the overstory (Douglas-fir, Pseudotsugamenziesii Mirb. (Franco)) and the understory (salal, Gaultheriashallon Pursh) over a range of stem densities.

A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass

2018 ◽  
Vol 144 ◽  
pp. 314-323 ◽  
Author(s):  
Xinyan Fan ◽  
Kensuke Kawamura ◽  
Wei Guo ◽  
Tran Dang Xuan ◽  
Jihyun Lim ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stage ◽  
Near Infrared ◽  
Italian Ryegrass ◽  
Camera System ◽  
Area Index

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.

Estimation of Eucalypt Forest Leaf Area Index on the South Coast of New South Wales using Landsat MSS Data

1997 ◽  
Vol 45 (5) ◽  
pp. 757 ◽  
Author(s):  
Nicholas Coops ◽  
Antoine Delahaye ◽  
Eddy Pook
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
New South ◽  
New South Wales ◽  
Monitoring Site ◽  
Area Index ◽  
South Wales ◽  
Normalised Difference Vegetation Index

Research over the last decade has shown that regional estimation of Leaf Area Index (LAI) is possible using the ratio of red and near infrared radiation derived from satellite or airborne sensors. At landscape levels, however, this relationship has been more difficult to establish due to (i) logistic difficulties in measuring seasonal variation in LAI across the landscape over an extended period of time and (ii) difficulties in establishing the effect of understorey, canopy closure, and soil on the spectral radiation at fine spatial resolutions (< 100 m). This paper examines the first issue by utilising a temporal sequence of LAI data of a Eucalyptus mixed hardwood forest (E. maculata Hook., E. paniculata Sm., E. globoidea Blakely, E. pilularis Sm., E. sieberi L.Johnson) in south-eastern New South Wales and comparing it to historical Landsat Multi-Spectral Scanner (MSS) data covering a 9 year period. Field LAI was compared to the Normalised Difference Vegetation Index (NDVI) and the Simple Ratio (SR) derived from the MSS data. Linear relationships were shown to be appropriate to relate both transformations to the LAI data with r2 -values of 0.71 and 0.53 respectively. Using the NDVI relationship, LAI values were estimated along a transect originating from the monitoring site and these were compared to percentage canopy cover values derived from aerial photography.

scholarly journals Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

2019 ◽  
Vol 11 (15) ◽  
pp. 1809 ◽  
Author(s):  
He ◽  
Zhang ◽  
Su ◽  
Lu ◽  
Yao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Visible Region ◽  
Canopy Reflectance ◽  
Area Index ◽  
Red Edge ◽  
Rice Panicles

The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice “panicle line”—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the “panicle line” and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720–740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (θ) developed the PRDVI, which was obtained while using the “panicle line”, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.

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