Leaf area index estimation from visible and near-infrared reflectance data

1995 ◽  
Vol 52 (1) ◽  
pp. 55-65 ◽  
Author(s):  
J Price
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Index Estimation ◽  
Reflectance Data

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 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.

Multispectral remote sensing for the estimation of green leaf area index

1983 ◽  
Vol 309 (1508) ◽  
pp. 257-270 ◽  
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.

scholarly journals Leaf area index estimation using top-of-canopy airborne RGB images

2021 ◽  
Vol 96 ◽  
pp. 102282
Author(s):  
Rahul Raj ◽  
Jeffrey P. Walker ◽  
Rohit Pingale ◽  
Rohit Nandan ◽  
Balaji Naik ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Rgb Images ◽  
Index Estimation

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.

Determination of Particle Size in Powders by Scatter Correction in Diffuse Near-Infrared Reflectance

1988 ◽  
Vol 42 (5) ◽  
pp. 722-728 ◽  
Author(s):  
J. L. Ilari ◽  
H. Martens ◽  
T. Isaksson
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Scatter Correction ◽  
Large Degree ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Analytical Technique ◽  
Scattering Effects ◽  
Reflectance Data

Diffuse near-infrared reflectance spectroscopy has traditionally been an analytical technique for determining chemical compositions in a sample. We will, in this paper, focus on light scattering effects and their ability to determine the mean particle sizes of powders. The reflectance data of NaCl, broken glass, and sorbitol powders are linearized and submitted to the Multiplicative Scatter Correction (MSC), and the ensuing parameters are used in subsequent multivariate calibrations. The results indicate that particle size can, to a large degree, be determined from NIR reflectance data for a given type of powder. Up to 99% of the partical size variance was explained by the regression.

Drone-Based Sensing for Leaf Area Index Estimation of Citrus Canopy

2020 ◽  
pp. 79-89 ◽  
Author(s):  
Rahul Raj ◽  
Saurabh Suradhaniwar ◽  
Rohit Nandan ◽  
Adinarayana Jagarlapudi ◽  
Jeffrey Walker
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Index Estimation

A comparison of sample preparation and calibration techniques for the estimation of nitrogen, oil and glucosinolate content of rapeseed by near infrared spectroscopy

1985 ◽  
Vol 104 (2) ◽  
pp. 317-323 ◽  
Author(s):  
Carol Starr ◽  
Janet Suttle ◽  
A. G. Morgan ◽  
D. B. Smith
Keyword(s):  
Stepwise Regression ◽  
Near Infrared ◽  
Glucosinolate Content ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Reflectance Analysis ◽  
Glucosinolate Concentration ◽  
Forward Stepwise ◽  
Calibration Techniques ◽  
Reflectance Data

SummaryPredictions of nitrogen, oil and glucosinolate concentration in rapeseed samples were made by near infrared reflectance analysis after various grinding treatments. Also examined were the effects of normalizing reflectance data and the possible advantage of using all combinations of two and three wavelengths in the calibration regression analysis over forward stepwise regression. The main conclusion was that drying the samples prior to a controlled grinding treatment gave the best results, although acceptable results for selection purposes could be obtained using whole seeds to predict nitrogen and oil. None of the treatments of the seed or reflectance data allowed acceptable prediction of glucosinolate content.

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