scholarly journals Review of Available Products of Leaf Area Index and Their Suitability over the Formerly Soviet Central Asia

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
M. W. Kappas ◽  
P. A. Propastin
Keyword(s):  
Leaf Area Index ◽  
Central Asia ◽  
Leaf Area ◽  
Primary Source ◽  
Data Sets ◽  
Environmental Modelling ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Research Activities ◽  
Soviet Central Asia

Leaf area index (LAI) is a key biophysical variable for environmental process modelling. Remotely sensed data have become the primary source for estimation of LAI at the scales from local to global. A summary of existing LAI data sets and a discussion of their appropriateness for the formerly Soviet Central Asia, especially Kazakhstan, which is known for its huge grassland area (about 2 million km2), are valuable for environmental modelling in this region. The paper gives a brief review of existing global LAI products, such as AVHRR LAI, MODIS LAI, and SPOT-VEGETATION LAI, and shows that validation of these products in Kazakhstan as well as in other countries of the formerly Soviet Central Asia has not been carried out yet. Apart from the global LAI products, there are just a few data sets retrieved by remote sensing methods at subregional and regional scales in Kazakhstan. More research activities are needed to focus on the validation of the available global LAI products over the formerly Soviet Central Asia and developing new LAI data sets suitable for application in environmental modelling at different scales in this region.

scholarly journals RANDOM FOREST REGRESSION FOR THE ESTIMATION OF LEAF AREA INDEX OF OKRA CROP USING GROUND BASED BISTATIC SCATTEROMETER

2018 ◽  
Vol XLII-5 ◽  
pp. 719-725
Author(s):  
S. A. Yadav ◽  
R. Prasad ◽  
A. K. Vishwakarma ◽  
V. P. Yadav
Keyword(s):  
Regression Analysis ◽  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Data Sets ◽  
Angle Of Incidence ◽  
Random Forest Regression ◽  
Area Index ◽  
Scattering Coefficients

<p><strong>Abstract.</strong> The specular bistatic scattering mechanism of Okra's crop was analyzed using dual polarized ground based bistatic scatterometer system at X, C, and L bands in the specular direction with the azimuthal angle(&amp;theta;<span class="thinspace"></span>=<span class="thinspace"></span>0&amp;deg;). An outdoor Okra crop bed of area 10<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>10<span class="thinspace"></span>m<sup>2</sup> was specially prepared for the estimation of leaf area index (LAI) at HH and VV polarizations over the angular range of incidence angle 20&amp;deg; to 60&amp;deg; at steps of 10&amp;deg;. The regression analysis was done between bistatic specular scattering coefficients and crop biophysical parameter at X, C, and L bands for HH and VV polarization at different angle of incidence to determine the optimum parameters of bistatic scatterometer system. The linear regression analysis showed the high correlation at 40&amp;deg; angle of incidence for all bands and polarizations for the Okra crop. The computed scattering coefficients and measured LAI of Okra crop for the seven growth stages at 40&amp;deg; angle of incidence were interpolated into 61 data sets. The data sets were divided into input, validation and testing for the training and testing of the developed random forest regression (RF) model for the estimation of LAI for Okra crop. The estimated values of LAI of Okra crop, by the developed RF regression model, were found more closer to the observed values at X band for VV polarization with coefficient of determination (R<sup>2</sup><span class="thinspace"></span>=<span class="thinspace"></span>0.928) and low root mean square error (RMSE<span class="thinspace"></span>=<span class="thinspace"></span>0.260<span class="thinspace"></span>m<sup>2</sup>/m<sup>2</sup>) in comparison to C and L bands.</p>

Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

2013 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Giorgos Papadavid ◽  
Dionysia Fasoula ◽  
Michael Hadjimitsis ◽  
P. Skevi Perdikou ◽  
Diofantos Hadjimitsis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Theoretical Background ◽  
Crop Canopy ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Crop Height ◽  
Statistical Relationships

AbstractIn this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index — NDVI — using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans’ canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters.

scholarly journals Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region

2000 ◽  
Vol 73 (1) ◽  
pp. 18-30 ◽  
Author(s):  
J Qi ◽  
Y.H Kerr ◽  
M.S Moran ◽  
M Weltz ◽  
A.R Huete ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Remotely Sensed ◽  
Semiarid Region ◽  
Remotely Sensed Data ◽  
Area Index

scholarly journals Applications of an Ecophysiological Model for Irrigated Rice (Oryza sativa)-EchinochloaCompetition

Weed Science ◽  
1996 ◽  
Vol 44 (1) ◽  
pp. 52-56 ◽  
Author(s):  
John L. Lindquist ◽  
Martin J. Kropff
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Expansion Rate ◽  
Weather Data ◽  
Data Sets ◽  
Area Index ◽  
Economic Thresholds ◽  
Weather Variation ◽  
Leaf Area Expansion ◽  
Area Expansion

A simulation model of rice-barnyardgrass competition for light was used for two management applications. First, simulations using 47 weather data sets from four locations in Asia were conducted to evaluate the influence of weather variation on single year economic threshold densities of barnyardgrass. Second, rapid leaf area expansion and leaf area index were evaluated as potential indicators of improved rice competitiveness and tolerance to barnyardgrass. Influence of weather variation on single year economic thresholds was small under the assumption that competition was for light only. Increasing early leaf area expansion rate reduced simulated barnyardgrass seed production and increased single year economic thresholds, suggesting that the use of competitive rice cultivars may reduce the need for chemical weed control. The model predicted that rice leaf area index 70 to 75 d after planting was a good indicator of early leaf area expansion rate.

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