Estimating Foliar Pigment Concentration of Rice Crop Using Integrated Hyperspectral Index

Liu, Zhanyu; Huang, Wenjiang; Mao, Guofu; Li, Cunjun; Xu, Xingang; Ding, Xiaodong; Shi, Jingjing; Zhou, Bin

doi:10.1007/978-3-642-27278-3_28

Cited by 3 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our findings revealed that LAI increased up to heading, and thereafter decreased (Table 3). The similar changes in LAI at different growth phases of rice has been reported earlier [12,53,54].…”

Section: Discussionsupporting

confidence: 87%

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

Prabhakar,

Gopinath,

Ravi Kumar

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages, and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground-based hyperspectral canopy spectral reflectance measurements were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Forsyth County, GA, USA; spectral range: 350–2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, 8 were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, the elongation stage was the most accurately estimated using vegetation indices that exhibited a significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue, and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.

show abstract

Section: Discussionsupporting

confidence: 87%

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

Prabhakar,

Gopinath,

Ravi Kumar

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Our findings revealed that LAI increased upto heading and thereafter decreased (Table 3). The similar changes in LAI at different growth phases of rice has been reported earlier [54,55].…”

Section: Discussionsupporting

confidence: 86%

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

Prabhakar,

Gopinath,

Nakka

et al. 2023

Preprint

View full text Add to dashboard Cite

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground based hyperspectral canopy spectral reflectance measurements was recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, eight were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, elongation stage was the most accurately estimated using vegetation indices that exhibited significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.

show abstract

“…Moreover, the visible regions of the spectral spaces are useful for estimating the vegetation fraction in multi-crop and background datasets ( Gitelson et al, 2002 ). The LAI was maximum at the booting stage after the subsequent loss in the LAI with each growth step ( Liu et al, 2012 ) due to the loss in photosynthetic activity under the same environmental conditions ( Wu et al, 2016 ). The rice yield was positively correlated with the LAI near the heading, maintaining the larger leaf area duration before heading until harvesting ( Song et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

et al. 2017

View full text Add to dashboard Cite

Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices.

show abstract

Estimating Foliar Pigment Concentration of Rice Crop Using Integrated Hyperspectral Index

Cited by 3 publications

References 23 publications

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

Contact Info

Product

Resources

About