Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

2005 ◽  
Vol 15 (2) ◽  
pp. 247-254 ◽  
Author(s):  
Jianjun Jiang ◽  
Suozhong Chen ◽  
Shunxian Cao ◽  
Hongan Wu ◽  
Li Zhang ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Eastern China ◽  
Canopy Reflectance ◽  
Area Index

scholarly journals Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

2016 ◽  
Vol XLI-B7 ◽  
pp. 121-128
Author(s):  
Lijuan Wang ◽  
Guimin Zhang ◽  
Hui Lin ◽  
Liang Liang ◽  
Zheng Niu
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Dark Spot ◽  
Canopy Reflectance ◽  
In Situ Experiment ◽  
Area Index

The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.

scholarly journals Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

2016 ◽  
Vol XLI-B7 ◽  
pp. 121-128 ◽  
Author(s):  
Lijuan Wang ◽  
Guimin Zhang ◽  
Hui Lin ◽  
Liang Liang ◽  
Zheng Niu
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Dark Spot ◽  
Canopy Reflectance ◽  
In Situ Experiment ◽  
Area Index

The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.

scholarly journals Comparison of different methods for measuring leaf area index in a mixed grassland

2007 ◽  
Vol 87 (4) ◽  
pp. 803-813 ◽  
Author(s):  
Yuhong He ◽  
Xulin Guo ◽  
John F Wilmshurst
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Canopy Reflectance ◽  
Area Index ◽  
Grassland Ecosystems ◽  
Grass Communities

Available LAI instruments have greatly increased our ability to estimate leaf area index (LAI) non-destructively. However, it is difficult to infer from existing studies which instrument has the advantages in measuring LAI over other instruments for grassland ecosystems. The objective of our study was to compare the LAI estimates by two instruments (AccuPAR, and LAI2000), and correlate the LAI measurements to remote sensing data for a mixed grassland. Leaf area index of four grass communities was measured by both the destructive method and instruments. Ground canopy reflectance was measured and further calculated to be LAI-related vegetation indices. Statistical analysis showed that destructively sampled LAI ranged from 0.61 to 5.7 in the study area. Both instruments underestimated LAI in comparison with the destructive method. However, the LAI2000 is better than AccuPAR for estimating LAI. Comparison of four grass communities indicated that the lower the grass LAI, the greater the underestimated percentage of LAI values collected by both instruments. The adjusted transformed soil-adjusted vegetation index (ATSAVI), was the best LAI estimator in the mixed grassland. Key words: Leaf area index, sward structure, nondestructive vegetation sampling, hyperspectral remote sensing, mixed grass prairie

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.

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 Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska

2015 ◽  
Author(s):  
Santonu Goswami ◽  
John Gamon ◽  
Sergio Vargas ◽  
Craig Tweedie
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Species ◽  
Vegetation Index ◽  
Plant Biomass ◽  
Plant Cover ◽  
Strongly Correlated ◽  
Area Index ◽  
Spectral Bands ◽  
Biomass Plant

Here we investigate relationships between NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species near Barrow, Alaska. We explore how key plant species differ in biomass, leaf area index (LAI) and how can vegetation spectral indices be used to estimate biomass and LAI for key plant species. A vegetation index (VI) or a spectral vegetation index (SVI) is a quantitative predictor of plant biomass or vegetative vigor, usually formed from combinations of several spectral bands, whose values are added, divided, or multiplied in order to yield a single value that indicates the amount or vigor of vegetation. For six key plant species, NDVI was strongly correlated with biomass (R2 = 0.83) and LAI (R2 = 0.70) but showed evidence of saturation above a biomass of 100 g/m2 and an LAI of 2 m2/m2. Extrapolation of a biomass-plant cover model to a multi-decadal time series of plant cover observations suggested that Carex aquatilis and Eriophorum angustifolium decreased in biomass while Arctophila fulva and Dupontia fisheri increased 1972-2008.

scholarly journals Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2460 ◽  
Author(s):  
Yangyang Zhang ◽  
Jian Yang ◽  
Xiuguo Liu ◽  
Lin Du ◽  
Shuo Shi ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Gaussian Process Regression ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Look Up Table ◽  
Band Combinations

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

scholarly journals An optimized non-linear vegetation index for estimating leaf area index in winter wheat

2019 ◽  
Vol 20 (6) ◽  
pp. 1157-1176 ◽  
Author(s):  
Wei Feng ◽  
Yapeng Wu ◽  
Li He ◽  
Xingxu Ren ◽  
Yangyang Wang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Area Index ◽  
Non Linear
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