The validation of a model estimating the Leaf Area Index of grasslands in southern China

In order to estimate the leaf area index (LAI) over large areas in southern China, this paper analysed the relationships between normalised difference vegetation index (NDVI) and the vegetation light transmittance and the extinction coefficient based on the use of moderate resolution imaging spectroradiometer data. By using the improved Beer–Lambert Law, a model was constructed to estimate the LAI in the grassy mountains and slopes of southern China with NDVI as the independent variable. The model was validated with field measurement data from different locations and different years in the grassland mountains and slopes of southern China. The results showed that there was a good correlation between the simulated and observed LAI values, and the values of R2 achieved were high. The relative root mean squared error was between 0.109 and 0.12. This indicated that the model was reliable. The above results provided the theoretical basis for the effective management of the grassland resources in southern China and the effective estimation of grassland carbon sink.

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

Australian Journal of Botany ◽

10.1071/bt96021 ◽

1997 ◽

Vol 45 (5) ◽

pp. 757 ◽

Cited By ~ 28

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.

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Leaf area index as a function of precipitation within a hydrological model

Hydrology Research ◽

10.2166/nh.2013.143 ◽

2013 ◽

Vol 45 (4-5) ◽

pp. 660-672 ◽

Cited By ~ 3

Author(s):

Tobias Törnros ◽

Lucas Menzel

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Hydrological Model ◽

Area Index ◽

Annual Variations ◽

Long Run ◽

Normalised Difference Vegetation Index ◽

Dry Conditions ◽

Good Agreement

The Leaf Area Index (LAI) was derived from the Normalised Difference Vegetation Index (NDVI) obtained from Advanced Very High Resolution Radiometer (AVHRR) data for the years 1982–2004. The NDVI-derived LAI showed a very good agreement (correlation coefficient r up to 0.96) with MODIS LAI. To address the relation between precipitation and LAI, linear correlation analysis between gridded precipitation and the NDVI-derived LAI was conducted for several land uses and each month of the year. Based on the regression coefficients, LAI could be simulated as a function of precipitation. During validation, the simulated LAI showed a very good agreement (r ≥ 0.75) with the NDVI-derived LAI. The simulated dynamic LAI was thereafter implemented in a hydrological model. For comparison, a model run with a static LAI without any inter-annual variations was also conducted. During abnormally dry conditions, the dynamic LAI was lower than the static LAI and less transpiration was therefore simulated. It is shown that a dynamic LAI contributes to a more realistic simulation approach during individual weather events but also that in the long run the simulated transpiration is much more strongly influenced by inter-annual variations in weather than by the additional vegetation dynamics in a semi-arid region.

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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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A Low-Cost System to Estimate Leaf Area Index Combining Stereo Images and Normalized Difference Vegetation Index

Progress in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-030-30241-2_21 ◽

2019 ◽

pp. 236-247

Author(s):

Jorge Miguel Mendes ◽

Vítor Manuel Filipe ◽

Filipe Neves dos Santos ◽

Raul Morais dos Santos

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Low Cost ◽

Stereo Images ◽

Area Index ◽

Cost System

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

10.7287/peerj.preprints.913v1 ◽

2015 ◽

Cited By ~ 6

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.

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

Sensors ◽

10.3390/s20092460 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2460 ◽

Cited By ~ 1

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.

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

Precision Agriculture ◽

10.1007/s11119-019-09648-8 ◽

2019 ◽

Vol 20 (6) ◽

pp. 1157-1176 ◽

Cited By ~ 6

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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Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images

Sensors ◽

10.3390/s20236732 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6732

Author(s):

Haixia Qi ◽

Bingyu Zhu ◽

Zeyu Wu ◽

Yu Liang ◽

Jianwen Li ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Field Experiments ◽

Predictive Accuracy ◽

Vegetation Indices ◽

Planting Density ◽

Coefficient Of Determination ◽

Multispectral Images ◽

Area Index

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

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Development and Evaluation of a Leaf Disease Damage Extension in Cropsim-CERES Wheat

Agronomy ◽

10.3390/agronomy9030120 ◽

2019 ◽

Vol 9 (3) ◽

pp. 120 ◽

Cited By ~ 3

Author(s):

Georg Röll ◽

William Batchelor ◽

Ana Castro ◽

María Simón ◽

Simone Graeff-Hönninger

Keyword(s):

Sensitivity Analysis ◽

Leaf Area Index ◽

Leaf Area ◽

Mean Squared Error ◽

Septoria Tritici Blotch ◽

Septoria Tritici ◽

Extended Model ◽

Simple Method ◽

Area Index ◽

The Impact

Developing disease models to simulate and analyse yield losses for various pathogens is a challenge for the crop modelling community. In this study, we developed and tested a simple method to simulate septoria tritici blotch (STB) in the Cropsim-CERES Wheat model studying the impacts of damage on wheat (Triticum aestivum L.) yield. A model extension was developed by adding a pest damage module to the existing wheat model. The module simulates the impact of daily damage on photosynthesis and leaf area index. The approach was tested on a two-year dataset from Argentina with different wheat cultivars. The accuracy of the simulated yield and leaf area index (LAI) was improved to a great extent. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). In addition, a sensitivity analysis of different disease progress curves on leaf area index and yield was performed using a dataset from Germany. The sensitivity analysis demonstrated the ability of the model to reduce yield accurately in an exponential relationship with increasing infection levels (0–70%). The extended model is suitable for site specific simulations, coupled with for example, available remote sensing data on STB infection.

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