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

Li, Xinchuan; Zhang, Youjing; Bao, Yuhai; Luo, Jiancheng; Jin, Xiuliang; Xu, Xingang; Song, Xiaoyu; Yang, Guijun

doi:10.3390/rs6076221

Cited by 113 publications

(82 citation statements)

References 63 publications

(87 reference statements)

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“…As shown in Figure 2, the wavelengths between 730 and 760 nm showed the most important spectral signatures for LAI assessment. The red-edge-NIR wavelengths are recognized as the most important spectral signatures for the assessment of LAI and canopy chlorophyll content [52][53][54][55]. In general, because of the multiple scattering of light in canopies, the reflectance values of the NIR wavelengths increase as LAI increases [56,57].…”

Section: Discussionmentioning

confidence: 99%

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

et al. 2015

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Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on the basis of field hyperspectral measurements. A simple index based on the empirical relationships between LAIs and SIs of all available two-waveband combinations from hyperspectral data is developed by considering the difference between reflectance values at 760 and 739 nm (DSIR760-R739 = R760 -R739). Among published and newly developed SIs, DSIR760-R739 exhibited a significant and strong linear relationship with LAI OPEN ACCESSRemote Sens. 2015, 7 5330 and showed outstanding performance in LAI assessments. The permissible bandwidths for broad-band DSIR760-R739 investigated using simulated reflectance were 5 nm for both 760 and 739 nm center wavelengths. The results indicate that the linear regression model based on the narrow-band and broad-band DSIR760-R739 is a simple but accurate method for timely and nondestructive monitoring of LAI.

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Section: Discussionmentioning

confidence: 99%

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

et al. 2015

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“…The larger the VIP score, the greater the contribution of the variable. The VIP values can be used to identify individual waveband importance and the most effective spectral regions [61,62].…”

Section: Vegetation Index Formula Satellite Sensors Referencementioning

confidence: 99%

Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages

et al. 2017

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For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red (R), and near-infrared (NIR) wavebands have been used to estimate rice N status due to its high spatial resolution, daily revisit capability, and relatively lower cost. This study aimed to evaluate the potential improvements of RapidEye and WorldView-2 data over FORMOSAT-2 for rice N status monitoring, as the former two sensors provide additional wavelengths besides the traditional four wavebands. Ten site-year N rate experiments were conducted in Jiansanjiang, Heilongjiang Province of Northeast China from 2008 to 2011. Plant samples and field hyperspectral data were collected at three growth stages: panicle initiation (PI), stem elongation (SE), and heading (HE). The canopy-scale hyperspectral data were upscaled to simulate the satellite bands. Vegetation index (VI) analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to derive plant N status indicators. The results indicated that the best-performed VIs calculated from the simulated RapidEye and WorldView-2 bands, especially those based on the red edge (RE) bands, explained significantly more variability for above ground biomass (AGB), plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations than their FORMOSAT-2-based counterparts did, especially at the PI and SE stages. The SMLR and PLSR models based on the WorldView-2 bands generally had the best performance, followed by the ones based on the RapidEye bands. The SMLR results revealed that both the NIR and RE bands were important for N status estimation. In particular, the NIR1 band (760-900 nm from RapidEye or 770-895 nm from WorldView-2) was most important for estimating all the N status indicators. The RE band (690-730 nm or 705-745 nm) improved AGB, PNU, and NNI estimations at all three stages, especially at the PI and SE stages. AGB and PNU were best estimated using data across the stages while plant N concentration (PNC) and NNI were best estimated at the HE stage. The PLSR analysis confirmed the significance of the NIR1 band for AGB, PNU, and NNI estimations at all stages except for the HE stage. It also showed the importance of including extra bands (coastal, yellow, and NIR2) from the WorldView-2 sensor for N status estimation. Overall, both the RapidEye and WorldView-2 data with RE bands improved the results relative to FORMOSAT-2 data. However, the WorldView-2 data with three extra bands in the visible and NIR regions showed the highest potential in estimating rice N status.

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“…The traditional models (linear, exponential, power, logarithm, and quadratic polynomial regression) are commonly used in crop LAI and dry AGB estimation in previous studies [25,51]. Partial least squares regression and stepwise multiple regression models have been tested as feasible tools in information extraction [52], but at the same time introduce a lot of features into the model that result in difficulties in application. There is increasing interest in data mining technology in remote sensing, which is considered to be a useful tool to unravel the non-linear relationship between canopy spectral reflectance and crop growth conditions [11,42].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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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.

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

Cited by 113 publications

References 63 publications

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages

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

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