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

Wang, Fumin; Huang, Jingfeng; Zhou, Qifa; Wang, Xiuzhen

doi:10.1631/jzus.b0820211

Cited by 17 publications

(22 citation statements)

References 33 publications

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“…FD3 was the first derivative at a wavelength of 750 nm and had both the strongest correlation with LAI (R 2 = 0.800) and the highest VIP value (VIP = 1.144) of all features. This confirmed previous findings by Wang et al [12] with 723 nm and Thenkabail et al [10] with 735 nm. CI Red-edge led to an R 2 of 0.766 and a VIP of 1.121, which was more sensitive to LAI variability than the NDVI.…”

Section: Discussionsupporting

confidence: 92%

“…The reflectance in this spectral region is mainly influenced by the arrangement of cells within the mesophyll layer of leaves, as well as by canopy structure, especially the number of vertical leaf layers. NIR water absorption regions are also sensitive to leaf moisture content [12].…”

Section: Discussionmentioning

confidence: 99%

“…Becker et al [11] used second-derivative analysis to identify eight optimal spectral bands in the visible-NIR wavelength region that appeared to contain the majority of the coastal wetland information content of the full spectral resolution. Wang et al [12] employed three methods, including correlation coefficient-based, vegetation index-based and the stepwise regression method to select 15 suitable wavebands for paddy rice LAI estimation. (ii) Spectral position features: Reflectance and absorption features that characterize hyperspectral data are also related to specific physical and chemical crop characteristics [13].…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

et al. 2014

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Abstract:The use of spectral features to estimate leaf area index (LAI) is generally considered a challenging task for hyperspectral data. In this study, the hyperspectral reflectance of winter wheat was selected to optimize the selection of spectral features and to evaluate their performance in modeling LAI at various growth stages during 2008 and 2009. We extracted hyperspectral features using different techniques, including reflectance spectra and first derivative spectra, absorption and reflectance position and vegetation indices. In order to find the best subset of features with the best predictive accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to estimated LAI values. The results indicated that the red edge-NIR spectral region (680 nm-1300 nm) was the most sensitive to LAI. Most features in this region exhibited a high correlation with LAI and had higher VIP values, especially the first derivative waveband at 750 nm (r = 0.900, VIP = 1.144). Adding a large number of features would not

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

et al. 2014

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“…Hence, it is necessary to study the spatial characterization of the relationship between seasonal variation of NPP and climate. The integration of remote sensing data and geographic information system (GIS) technology provides a potential approach to this study (Abdallah et al, 2005;López-Blanco and Villers-Ruiz, 1995;Wang et al, 2008).…”

mentioning

confidence: 99%

Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data

Peng

Huang

Huete

et al. 2010

J. Zhejiang Univ. Sci. B

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Abstract:We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature, precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area, a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01), and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China, NPP showed 16-d, 48-d, and 96-d lagged correlation with air temperature, precipitation, and sunshine percentage, respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d, while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region, the spatial patterns of vegetation-climate relationship became complicated and diversiform, especially for precipitation influences on NPP. In the northern part of the study area, all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.

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“…14,20 A number of researchers 7,37,38 have applied first-order derivative on hyperspectral data for LAI estimation. The spectral regions of 350-399 nm, 1355-1420 nm, 1810-1940 nm and 2470-2500 nm (Figure 2) are known to be noisy and were discarded from the spectra.…”

Section: Discussionmentioning

confidence: 99%

Potential of interval partial least square regression in estimating leaf area index

Kiala¹,

Odindi²,

Mutanga³

2017

S. Afr. J. Sci.

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ARTICLE INCLUDES:× Supplementary material × Data set FUNDING:None Leaf area index (LAI) is a critical parameter in determining vegetation status and health. In tropical grasslands, reliable determination of LAI, useful in determining above ground biomass, provides a basis for rangeland management, conservation and restoration. In this study, interval partial least square regression (iPLSR) in forward mode was compared to partial least square regression (PLSR) to estimate LAI from in-situ canopy hyperspectral data on a heterogeneous grassland at different periods (onset, mid and end) during summer. The performance of the two techniques was determined using the least relative root mean square error to the mean (nRMSEP) and the highest coefficients of determination (R 2 p ) between the predicted and the measured LAI. Results show that iPLSR models could explain LAI variation with R 2 p values ranging from 0.81 to 0.93 and low nRMSEP from 9.39% to 24.71%. The highest accuracies for estimates of LAI using iPLSR were at mid-and end of summer (R 2 p = 0.93 and nRMSEP = 9.39%; R 2 p = 0.89 and nRMSEP = 10.50%, respectively). Pooling data sets from the three assessed periods yielded the highest prediction error (nRMSEP=24.71%). Results show that iPLSR performed better than PLSR, which yielded R 2 pand RMSEP values ranging from 0.36 to 0.65 and from 28.44% to 33.47%, respectively. Overall, this study demonstrates the value of iPLSR in predicting LAI and therefore provides a basis for more accurate mapping and monitoring of canopy characteristics of tropical grasslands. Significance:• The relationship between LAI and canopy reflectance can be used in iPLSR modelling to provide more accurate mapping and monitoring of canopy characteristics for land management and conservation.

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

Cited by 17 publications

References 33 publications

Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data

Potential of interval partial least square regression in estimating leaf area index

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