Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices

Das, Deb Kumar; Mishra, Kabita; Kalra, Naveen

doi:10.1080/01431169308904421

Cited by 34 publications

(18 citation statements)

References 13 publications

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“…The growing condition of ratoon rice was affected by main rice (Lin et al, 2007;Nakano and Morita, 2007;Yi et al, 2009b;Harrell et al, 2009), and the NDVI could be extensively related to LAI, biomass and predicted yield (Das et al, 1993;Raun et al, 2001;Ma et al, 2001;Inman et al, 2007). So, regression models were established by the relationship between canopy multispectral data of main rice and LAI, biomass and N accumulation of ratoon rice from Experiment 1.…”

Section: Prediction With Regression Modelsmentioning

confidence: 99%

Predicting ratoon rice growth rhythm based on NDVI at key growth stages of main rice

Liu

2015

Chilean J. Agric. Res.

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The growth of ratoon rice (Oryza sativa L.) is affected by growth of main rice, so it could be evaluated by determining growing condition of main rice. Objective of the study was to find regression model for predicting ratoon rice growth rhythm through establishing the quantitative relationship between growth rhythm in ratoon rice and normalized difference vegetation index (NDVI) after heading of main rice. NDVI at key growth stages of main rice was measured using active spectrometry for 12 varieties in 2008 and for 23 varieties in 2009, and compared the physiological indexes of main and ratoon rice. There was an exponential correlation (P < 0.05) between the NDVI after heading of the main rice and biomass, N concentration of green leaves and stems or leaf area index (LAI) after heading of the ratoon rice for all the 12 varieties in 2008. The regression models from the rice variety experiment in 2008 were used to calculate predicted values with NDVI after heading of main rice in 2009. The results showed that the predicted values of biomass, N uptake, and LAI in ratoon rice were significantly different to measured values. However, there were exponential and significantly positive correlation (p < 0.05) between biomass and N uptake and LAI in ratoon rice and NDVI after heading of main rice in 2009. Therefore, it suggested that regression models were not perfect, and need to be improved adding more parameters about spectral characteristic of main and ratoon rice.

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Section: Prediction With Regression Modelsmentioning

confidence: 99%

Predicting ratoon rice growth rhythm based on NDVI at key growth stages of main rice

Liu

2015

Chilean J. Agric. Res.

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“…The normalized difference vegetation index (NDVI) is the function of reflectance of red and near infrared (NIR) bands. It is one of the most extensively applied vegetation indices related to leaf area index, biomass and yield prediction [10][11][12]. Harrell et al found NDVI measurements taken after panicle differentiation (PD) and panicle initiation (PI) have the potential to improve mid-season N crop management decisions in rice, and regression analysis produced two viable yield potential prediction equations at PI (R 2 = 0.36) and PD (R 2 = 0.42) [13].…”

Section: Introductionmentioning

confidence: 99%

Estimating the effect of urease inhibitor on rice yield based on NDVI at key growth stages

Liu¹,

Li²,

Hu³

2014

Front. Agr. Sci. Eng.

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The effect of the urease inhibitor, N-(n-butyl) thiophosphoric triamide (NBPT) at a range of application rates on rice production was examined in a field experiment at Jinxian County, Jiangxi Province, China. The normalized difference vegetation index (NDVI) was measured at key growth stages in both early and late rice. The results showed that the grain yield increased significantly when urea was applied with NBPT, with the highest yield observed at 1.00% NBPT (wt/wt). NDVI differed with the growth stage of rice; it remained steady from the heading to the filling stage. Rice yield could be predicted from the NDVI taken at key rice growing stages, with R 2 ranging from 0.34 to 0.69 in early rice and 0.49 to 0.70 in late rice. The validation test showed that RMSE (t$hm -2 ) values were 0.77 and 0.87 in early and late rice, respectively. Therefore, it was feasible to estimate rice yield for different amounts of urease inhibitor using NDVI.

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“…Spectral reflectance measurements are also being used increasingly as a tool to detect the canopy nitrogen status and allow locally adjusted nitrogen fertilizer applications during the growing season (Mistele & Schmidhalter, 2010). Since grain yield is closely associated with crop growth and the vegetation indices are sensitive to canopy variables such as LAI and biomass that largely determine this growth, spectral data have also been proposed as suitable estimators in yield-predicting models (Aparicio et al, 2000;Das et al, 1993;Ma et al, 2001;Royo et al, 2003). Another way to formulate the relationship between biomass and VI is to use the light use efficiency ( ) model (Kumar & Monteith, 1981) based on the fact that the growth rate of a crop canopy is almost proportional to the rate of interception of radiant energy.…”

Section: Traditional and New Spectral Reflectance Indices For Biomassmentioning

confidence: 99%

Field Measurements of Canopy Spectra for Biomass Assessment of Small-Grain Cereals

Royo¹,

Villegas²

2011

Biomass - Detection, Production and Usage

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Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices

Cited by 34 publications

References 13 publications

Predicting ratoon rice growth rhythm based on NDVI at key growth stages of main rice

Predicting ratoon rice growth rhythm based on NDVI at key growth stages of main rice

Estimating the effect of urease inhibitor on rice yield based on NDVI at key growth stages

Field Measurements of Canopy Spectra for Biomass Assessment of Small-Grain Cereals

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