Estimating yield of sorghum using root zone water balance model and spectral characteristics of crop in a dryland Alfisol

Mandal, Uttam Kumar; Victor, U. S.; Srivastava, N. K.; Sharma, K. L.; Ramesh, V.; Vanaja, M.; Korwar, G. R.; Ramakrishna, Y. S.

doi:10.1016/j.agwat.2006.08.002

Cited by 18 publications

(7 citation statements)

References 28 publications

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“…This effect has also been recorded in studies of Baret and Guyot, 1991;Huete, 1988;Rondeaux et al, 1996;and Qi et al, 1994. The greatest values of the determination coefficients obtained using SAVI and OSAVI have also been confirmed by Borge and Leblanc, 2001;Glenn et al, 2008;Mandal et al, 2007;Yueting et al, 2008.…”

Section: Discussionsupporting

confidence: 66%

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

Uździcka¹,

Stróżecki²,

Urbaniak³

et al. 2017

International Agrophysics

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'Assessment of a seasonal and spatial variation of plant biophysical and spectral indices in the context of CO 2 , CH 4 , H 2 O gas exchange between wetland, forest and arable ecosystems and the atmosphere', 2010-2013.A b s t r a c t. The aim of this paper is to demonstrate that spectral vegetation indices are good indicators of parameters describing the intensity of CO 2 exchange between crops and the atmosphere. Measurements were conducted over 2011-2013 on plots of an experimental arable station on winter wheat, winter rye, spring barley, and potatoes. CO 2 fluxes were measured using the dynamic closed chamber system, while spectral vegetation indices were determined using SKYE multispectral sensors. Based on spectral data collected in 2011 and 2013, various models to estimate net ecosystem productivity and gross ecosystem productivity were developed. These models were then verified based on data collected in 2012. The R 2 for the best model based on spectral data ranged from 0.71 to 0.83 and from 0.78 to 0.92, for net ecosystem productivity and gross ecosystem productivity, respectively. Such high R 2 values indicate the utility of spectral vegetation indices in estimating CO 2 fluxes of crops. The effects of the soil background turned out to be an important factor decreasing the accuracy of the tested models.

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Section: Discussionsupporting

confidence: 66%

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

Uździcka¹,

Stróżecki²,

Urbaniak³

et al. 2017

International Agrophysics

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“…MODIS data have been successfully used for yield estimation and prediction (Bala and Islam, 2009;Doraiswamy et al, 2004;Huang et al, 2012;Mkhabela et al, 2011;Ren et al, 2008;Shunlin et al, 2004;Son et al, 2013c). In particular, studies have used remotely sensed vegetation indices, such as the normalized difference vegetation index (NDVI) (Kastens et al, 2005;Mkhabela et al, 2011;Mkhabela et al, 2005;Moriondo et al, 2007;Quarmby et al, 1993;Ren et al, 2008), the enhanced vegetation index (EVI) (Bolton and Friedl, 2013;Kyoungdo et al, 2013;Shunlin et al, 2004), and the soil-adjusted vegetation index (SAVI) (Gontia and Tiwari, 2011;Mandal et al, 2007;Noureldin et al, 2013;Panda et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation

Son

Chen

et al. 2014

Agricultural and Forest Meteorology

192

102

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“…They found that the predictive powers of SAVI and PVI were better than the NDVI for LAI prediction and estimation of green cover percentage of vegetation. The SAVI and PVI provided R 2 value of 0.87 and 0.86, respectively, for green cover estimation using narrow bands while the NDVI provided R [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

Panda¹,

2010

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Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, OPEN ACCESSRemote Sensing 2010, 2 674 coefficient of determination (r 2 ), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89-93%. Similarly, the PVI pool model provided coefficient of determination (r 2 ) value of 0.45 as compared to 0.31-0.37 for other index models. Log 10 data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log 10 transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r 2 value of 0.72 and standard error of prediction of 0.05 t/ha.

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Estimating yield of sorghum using root zone water balance model and spectral characteristics of crop in a dryland Alfisol

Cited by 18 publications

References 28 publications

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation

Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

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