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.

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.

The quality of short-wave radiation within plant canopies

1969 ◽  
Vol 47 (12) ◽  
pp. 1989-1994 ◽  
Author(s):  
T. B. Daynard
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Communities ◽  
Short Wave ◽  
Wave Radiation ◽  
Leaf Angle ◽  
Visible Radiation ◽  
Area Index ◽  
Plant Canopies

In this paper, a mathematical attempt is made to predict the effects of leaf area index, leaf angle, and leaf spectral properties on changes in the relative composition of short-wave radiant fluxes as they penetrate plant canopies. Results of this theoretical analysis indicate that a sizeable change in the quality of visible radiation will only occur if the canopy is sufficiently dense to intercept at least 98% of the incident flux one or more times. By contrast, a significant increase in the proportion of infrared radiation is predicted within plant communities, even those of a low effective leaf area index. For natural plant communities, the results would indicate a minimal change in the composition of penetrating radiation at solar noon and a maximal change at sunrise or sunset.The implications of these phenomena to plant morphogenesis and to radiation-measuring techniques are discussed.

scholarly journals Examining the link between vegetation leaf area and land-atmosphere exchange of water, energy, and carbon fluxes using FLUXNET data

2020 ◽  
Author(s):  
Anne J. Hoek van Dijke ◽  
Kaniska Mallick ◽  
Martin Schlerf ◽  
Miriam Machwitz ◽  
Martin Herold ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Large Scale ◽  
Vegetation Type ◽  
Carbon Fluxes ◽  
Surface Fluxes ◽  
Energy Fluxes ◽  
Area Index ◽  
Vegetation Control

Abstract. Vegetation regulates the exchange of water, energy, and carbon fluxes between the land and the atmosphere. This regulation of surface fluxes differs with vegetation type and climate, but the effect of vegetation on surface fluxes is not well understood. A better knowledge of how and when vegetation influences surface fluxes could improve climate models and the extrapolation of ground-based water, energy, and carbon fluxes. We aim to study the large-scale link between vegetation and surface fluxes by combining MODIS leaf area index with flux tower measurements of water (latent heat), energy (sensible heat), and carbon (gross primary productivity and net ecosystem exchange). We show that the correlation between leaf area index and water and energy fluxes depends on vegetation and aridity. In water-limited conditions, the link between vegetation and water and energy fluxes is strong, which is in line with a strong stomatal or vegetation control found in earlier studies. In energy-limited forest we found no vegetation control on water and energy fluxes. In contrast to water and energy fluxes, we found a strong correlation between leaf area index and gross primary productivity that was independent of vegetation type and aridity index. This study provides insight in the large-scale link between vegetation and surface fluxes. The study indicates that for modelling or extrapolating large-scale surface fluxes, LAI can be useful in savanna and grassland, but only of limited use in deciduous broadleaf forest and evergreen needleleaf forest.

scholarly journals A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

2021 ◽  
Vol 14 (1) ◽  
pp. 98
Author(s):  
Quanjun Jiao ◽  
Qi Sun ◽  
Bing Zhang ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Leaf Angle ◽  
Transfer Model ◽  
Growth Monitoring ◽  
Random Forest Algorithm ◽  
Area Index ◽  
Important Indicator

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

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 Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography

2020 ◽  
Vol 126 (4) ◽  
pp. 765-773 ◽  
Author(s):  
Yingpu Che ◽  
Qing Wang ◽  
Ziwen Xie ◽  
Long Zhou ◽  
Shuangwei Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Large Scale ◽  
High Efficiency ◽  
Point Clouds ◽  
Phenotypic Traits ◽  
Area Index ◽  
Area Density ◽  
Leaf Area Density

Abstract Background and Aims High-throughput phenotyping is a limitation in plant genetics and breeding due to large-scale experiments in the field. Unmanned aerial vehicles (UAVs) can help to extract plant phenotypic traits rapidly and non-destructively with high efficiency. The general aim of this study is to estimate the dynamic plant height and leaf area index (LAI) by nadir and oblique photography with a UAV, and to compare the integrity of the established three-dimensional (3-D) canopy by these two methods. Methods Images were captured by a high-resolution digital RGB camera mounted on a UAV at five stages with nadir and oblique photography, and processed by Agisoft Metashape to generate point clouds, orthomosaic maps and digital surface models. Individual plots were segmented according to their positions in the experimental design layout. The plant height of each inbred line was calculated automatically by a reference ground method. The LAI was calculated by the 3-D voxel method. The reconstructed canopy was sliced into different layers to compare leaf area density obtained from oblique and nadir photography. Key Results Good agreements were found for plant height between nadir photography, oblique photography and manual measurement during the whole growing season. The estimated LAI by oblique photography correlated better with measured LAI (slope = 0.87, R2 = 0.67), compared with that of nadir photography (slope = 0.74, R2 = 0.56). The total number of point clouds obtained by oblique photography was about 2.7–3.1 times than those by nadir photography. Leaf area density calculated by nadir photography was much less than that obtained by oblique photography, especially near the plant base. Conclusions Plant height and LAI can be extracted automatically and efficiently by both photography methods. Oblique photography can provide intensive point clouds and relatively complete canopy information at low cost. The reconstructed 3-D profile of the plant canopy can be easily recognized by oblique photography.

Yield, leaf-area index and photosynthetic rate in some perennial ryegrass (Lolium perenne L.) selections

1972 ◽  
Vol 78 (3) ◽  
pp. 509-511 ◽  
Author(s):  
Ian Rhodes
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Lolium Perenne ◽  
Photosynthetic Rate ◽  
Perennial Ryegrass ◽  
Canopy Structure ◽  
Base Population ◽  
Area Index ◽  
Lolium Perenne L ◽  
Unit Leaf

SUMMARYYield, critical LAI and apparent photosynthetic rate per unit leaf area were measured in four families selected from L. perenne S. 321. Differences in yield were attributable to differences in canopy structure producing differing critical LAI. The most productive family, which was 33% more productive than the base population, produced the largest critical LAI but had the lowest photosynthetic rate.

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
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