Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields

Xiao, Xiangming; He, Lei; Salas, William; Li, Changsheng; Moore, Berrien; Zhao, Rongkun; Frolking, Steve; Boles, Stephen

doi:10.1080/01431160110115799

Cited by 80 publications

(59 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Measuring the leaf area index (LAI, leaf area per unit ground area) of paddy rice fields provides information on crop growth dynamics and has the potential to be a good indicator of the status of paddy rice throughout the growing season (Xiao et al, 2002). Besides, it is highly correlated with rice biomass and productivity (Dobermann and Pampolino, 1995).…”

Section: Amanullahmentioning

confidence: 99%

Phosphorus and Zinc Interaction Influence Leaf Area Index in Fine vs. Coarse Rice (Oryza sativa L) Genotypes in Northwest Pakistan

Amanullah¹,

Inamullah²,

Shah³

et al. 2016

JPSP

View full text Add to dashboard Cite

Leaf area index (LAI) is a measure of leafiness per unit ground area and denotes the extent of photosynthetic machinery is an important growth and yield-determining factor because it is a major determinant of light interception and transpiration. Phosphorus (P) and zinc (Zn) are the most important factors affecting LAI of rice (Oryza sativa L.). A field experiment was conducted to assess the impact of phosphorus (0, 40, 80, 120 kg P/ ha) and zinc levels (0, 5, 10, 15 kg Zn/ha) on LAI of rice (O. sativa L.) genotypes (fine and coarse [Fakhr-e-Malakand and Pukhraj]). The experiment was conducted on farmer field at Batkhela, Malakadnd in Northwest Pakistan during summer 2011 and 2012. When combined over the 2 years, the data revealed that the highest LAI at three different growth stages (tillering, heading, and physiological maturity [PM]) was obtained with application of the highest P level (120 kg/ha) being at par with 80 kg P/ha, while the lowest LAI was recorded when P was not applied. Similarly, the highest LAI was obtained with application of the two higher Zn levels (10 and 15 kg Zn/ha), while the lowest LAI was recorded when Zn was not applied. In the case of rice genotypes, the highest LAI was obtained from Pukhraj than other two genotypes at all growth stages. The other two rice genotypes (Fakher-e-Malakand and Basmati-385) produced statistically similar LAI at different growth stages. The higher LAI of Pukhraj was attributed to its long and wider leaves that resulted in higher mean single leaf area, leaf area per tiller, per hill, and per square meter. The LAI was the highest at heading stage than at early (tillering) and later (PM) growth stages. The increase in LAI was attributed to the increase in tillers number and leaf area/hill. The increase in LAI showed a positive impact on crop growth rate, dry matter, and yield. Application of 120 kg P+10 kg Zn/ha to rice genotype Pukhraj was more beneficial in terms of higher LAI and productivity in the study area.

show abstract

Section: Amanullahmentioning

confidence: 99%

Phosphorus and Zinc Interaction Influence Leaf Area Index in Fine vs. Coarse Rice (Oryza sativa L) Genotypes in Northwest Pakistan

Amanullah¹,

Inamullah²,

Shah³

et al. 2016

JPSP

View full text Add to dashboard Cite

show abstract

“…Measuring the leaf area index (LAI, leaf area per unit ground area) of paddy rice fields provides information on crop growth dynamics, and has the potential to be a good indicator of the status of paddy rice throughout the growing season (Xiao et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Optimal waveband identification for estimation of leaf area index of paddy rice

Wang

Huang

Zhou

et al. 2008

J. Zhejiang Univ. Sci. B

View full text Add to dashboard Cite

Abstract:The objectives of the study were to select suitable wavebands for rice leaf area index (LAI) estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance (350~2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation: correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350~2 500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1 633 nm. They were followed by 444, 524, 576, 594, 804, 849, 974, 1 074, 1 219, 1 510, and 2 194 nm. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.

show abstract

“…At the early stages of growth, rice fields are a mixture of green rice plants and open water [35]. Water depth generally varies from 2 to 15 cm.…”

Section: Measurement Of Crop Parametersmentioning

confidence: 99%

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

et al. 2016

View full text Add to dashboard Cite

Abstract:The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination (R 2 CV ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models.

show abstract

Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields

Cited by 80 publications

References 10 publications

Phosphorus and Zinc Interaction Influence Leaf Area Index in Fine vs. Coarse Rice (Oryza sativa L) Genotypes in Northwest Pakistan

Phosphorus and Zinc Interaction Influence Leaf Area Index in Fine vs. Coarse Rice (Oryza sativa L) Genotypes in Northwest Pakistan

Optimal waveband identification for estimation of leaf area index of paddy rice

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

Contact Info

Product

Resources

About