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

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.

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Multispectral remote sensing for the estimation of green leaf area index

Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences ◽

10.1098/rsta.1983.0039 ◽

1983 ◽

Vol 309 (1508) ◽

pp. 257-270 ◽

Cited By ~ 133

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Near Infrared ◽

Causal Relation ◽

Green Leaf ◽

Near Infrared Reflectance ◽

Area Index ◽

Remote Sensing Technique ◽

Green Leaf Area

The causal relation between multispectral reflectance and green leaf area index (l.a.i.) has enabled the estimation of green leaf area index by the judicious use of remotely sensed multispectral reflectance measurements. In this paper three topics are discussed. First, the reflectance properties of a vegetation canopy and the problems of determining the form of the relation between green l.a.i. and red and near-infrared reflectance: these problems include variability in substrate and leaf reflectance and the geometry of the scene and sensor. Second, the methodologies currently employed for estimating green l.a.i.: these methodologies are based on the production of simple, complex or modelled calibration curves. Third , current research at the University of Sheffield: this includes not only studies with multispectral reflectance collected from aircraft-mounted sensors to estimate the green l.a.i. of heathlands and grasslands but also multispectral reflectance collected from satellites to map estimated green l.a.i. It is concluded that the main applications for this remote-sensing technique are within the fields of agricultural intelligence, agricultural m anagement and ecological research.

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

Land ◽

10.3390/land10050505 ◽

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.

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An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

Functional Plant Biology ◽

10.1071/fp08045 ◽

2008 ◽

Vol 35 (10) ◽

pp. 1070 ◽

Cited By ~ 46

Author(s):

Sigfredo Fuentes ◽

Anthony R. Palmer ◽

Daniel Taylor ◽

Melanie Zeppel ◽

Rhys Whitley ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Gap Analysis ◽

Digital Camera ◽

Field Measurements ◽

Accurate Estimation ◽

Image Capture ◽

Area Index ◽

Modis Lai ◽

Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

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

Remote Sensing of Environment ◽

10.1016/j.rse.2008.05.003 ◽

2008 ◽

Vol 112 (10) ◽

pp. 3762-3772 ◽

Cited By ~ 57

Author(s):

S DELALIEUX ◽

B SOMERS ◽

S HEREIJGERS ◽

W VERSTRAETEN ◽

W KEULEMANS ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Near Infrared ◽

Area Index

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Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields

International Journal of Remote Sensing ◽

10.1080/01431160110115799 ◽

2002 ◽

Vol 23 (18) ◽

pp. 3595-3604 ◽

Cited By ~ 47

Author(s):

X. Xiao ◽

L. He ◽

W. Salas ◽

C. Li ◽

B. Moore ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Paddy Rice ◽

Rice Fields ◽

Area Index

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Early regeneration conifer identification and competition cover assessment using airborne digital camera imagery

The Forestry Chronicle ◽

10.5558/tfc76915-6 ◽

2000 ◽

Vol 76 (6) ◽

pp. 915-928 ◽

Cited By ~ 10

Author(s):

K. A. Haddow ◽

D. J. King ◽

D. A. Pouliot ◽

D. G. Pitt ◽

F. W. Bell

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Cover ◽

Early Stage ◽

Digital Camera ◽

Vegetation Management ◽

Forest Vegetation ◽

Crop Species ◽

Area Index ◽

Forest Vegetation Management

The potential of low cost, high-resolution airborne digital camera imagery for use in early stage forest regeneration assessment was investigated. Airborne imagery with 2.5-cm pixel size was acquired near Sault Ste. Marie, Ontario, over a forest vegetation management research site to: i) evaluate capabilities for identification and stem counting of two-year old conifer crop species under leaf-off and leaf-on conditions using classification of spectral and textural image information, and ii) develop models relating vegetation cover parameters to image spectral and texture information. Results indicate strong potential for identification and counting of conifer trees when competing vegetation cover is low or in leaf-off condition. However, systematic decreases in class separability and conifer count accuracy were observed with increasing competition. In image modelling of competition Leaf Area Index and Cover, statistically significant relations were found using primarily spectral measures. Stratification by competition species improved model fits and included texture measures in some models. Key words: airborne remote sensing, forest vegetation management, regeneration, digital cameras, leaf area index, cover, tree classification

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