Using vegetation health indices and partial least squares method for estimation of corn yield

Salazar, Luis Alonso; Kogan, Felix; Roytman, Leonid

doi:10.1080/01431160701271974

Cited by 43 publications

(23 citation statements)

References 50 publications

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“…Usually, remote sensing derived indicators are connected to crop yield using empirical regression-based models. Traditionally, vegetation indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Vegetation Health Index (VHI) are used as input parameters into empirical models (Becker-Reshef et al, 2010;Franch et al, 2015;Kogan et al, 2013a;Kowalik et al, 2014;Salazar et al, 2008). Recently, however, more attention has been brought to the usage of biophysical parameters such as leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (fAPAR) (Camacho et al, 2013;Shelestov et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Kolotii

Kussul

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

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Section: Introductionmentioning

confidence: 99%

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Kolotii

Kussul

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The selected spectral vegetation indices related to the amount of green materials (NDVI, GVI or VI) in the maize plants (Rouse et al, 1974;Tucker, 1979). Spectral vegetation indices that are responsive to the green pigments are excellent indicators for vegetation quantity and status or vigour (Salazar et al, 2008), and confirms the selection of the GVI in the top three indices as ranked by the OOB error estimates in this study as relevant to maize grain yield prediction.…”

Section: Random Forest Regression Algorithm For Maize Yields Using Sementioning

confidence: 67%

“…The frequently researched field crops have included wheat (Singh et al, 2002;Thenkabail, 2003;Bullock, 2004;Kastens et al, 2005;Ren et al, 2008), potatoes (Al-Gaadi et al, 2016) rice (Casanova et al, 1998;Noureldin et al, 2013), soybeans (Kastens et al, 2005;Li et al, 2007, You et al, 2017 and maize (Lewis et al, 1998;Shanahan et al, 2001;Baez-Gonzalez et al, 2002;Ferencz et al, 2004;Baez-Gonzalez et al, 2005;Kastens et al, 2005;Kogan et al, 2005;Mkhabela et al, 2005;Li et al, 2007;Inman et al, 2007;Salazar et al, 2008;Panda et al, 2010;Bognár et al, 2011). Most of these studies made used of the normalised difference vegetation index (NDVI) generated from the coarse resolution sensors such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to model yields.…”

Section: Background To Studymentioning

confidence: 99%

Estimation of Maize grain yield using multispectral satellite data sets (SPOT 5) and the random forest algorithm

Ngie¹,

Ahmed²

2018

SA J of Geomatics

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“…stand density) was reported with RSQ values of between 0.57 and 0.79 (Senay et al 2000;Freeman et al 2007;Thorp et al 2008;Bausch et al 2008;Salazar et al 2008) generally with VIs from top reflectance measurements. Dos Santos Simões et al (2005) observed a wide range of RSQ values (0.28-0.98) for DM yield in sugar cane using different VIs and correlated wavelength bands.…”

Section: Comparison Of Nadir and Off-nadir Measurementsmentioning

confidence: 86%

Off-nadir hyperspectral measurements in maize to predict dry matter yield, protein content and metabolisable energy in total biomass

2010

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Sensor-based methods of analysis to assess dry matter yield and quality constituents of crops are time-and labour-saving, and can facilitate site-specific management. Nevertheless, standard nadir measurements of maize (Zea mays cv. Ambrosius), based on top-of-canopy reflectance, are difficult due to plant heights of more than three metres. This study was conducted to explore the potential of off-nadir field spectral measurements for the non-destructive prediction of dry matter yield (DM), metabolisable energy (ME) and crude protein (CP) in total biomass in a maize canopy. Plants were measured at five different heights (0-50, 50-100, 100-50, 150-200 and 200-250 cm above the soil) at three zenith view angles (60°, 75°and 90°, respectively). Modified partial least squares regression was used for analysis of the hyperspectral data (355-2300 nm and 620-1000 nm). Optimum combinations of angle and height as well as an optimum one-sensorstrategy were determined for DM yield, CP and ME in total biomass. Coefficients of determination for off-nadir measurements were compared to nadir measurements; the results showed improved prediction accuracies for DM yield and ME using off-nadir measurements, but not for CP for which nadir measurements were better.

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Using vegetation health indices and partial least squares method for estimation of corn yield

Cited by 43 publications

References 50 publications

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Estimation of Maize grain yield using multispectral satellite data sets (SPOT 5) and the random forest algorithm

Off-nadir hyperspectral measurements in maize to predict dry matter yield, protein content and metabolisable energy in total biomass

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