Derivation of the red edge index using the MERIS standard band setting

Clevers, J.G.P.W.; Jong, S.M. de; Epema, G.F.; Meer, Freek D. van der; Bakker, W.H.; Skidmore, Andrew K.; Schölte, K.

doi:10.1080/01431160110104647

Cited by 162 publications

(88 citation statements)

References 17 publications

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“…The NDRE and RECI indices significantly improved the estimation results compared to NDVI and RVI in our research (Tables 5 and 6). This is because the RE reflectance is highly correlated with chlorophyll content [69,70], and is responsive to variation in LAI or biomass [30,71]. In addition, it is insensitive to background effects [72].…”

Section: Importance Of the Red Edge And Other Bands For N Status Estimentioning

confidence: 97%

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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Section: Importance Of the Red Edge And Other Bands For N Status Estimentioning

confidence: 97%

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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“…The results therefore indicate that the continuous addition of variables may not always improve LAI estimation accuracy. Indeed, the inclusion of an increasing number of less important spectral features in PLSR models can negatively influence prediction accuracy [22,26].…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, VIs, such as the soil adjusted vegetation index (SAVI; [17]), second modified SAVI (MSAVI2; [18]), renormalized difference vegetation index (RDVI; [19]) and triangular vegetation index (TVI; [20]), have now been devised to improve LAI estimation. It also has been demonstrated that VIs with red-edge bands are good predictors of widely variable green LAI, such as CI Red-edge [21,22].…”

Section: Introductionmentioning

confidence: 99%

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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“…The red edge represents the region of abrupt change in leaf reflectance between 680 nm and 780 nm caused by the combined effects of strong Chl absorption in the red and leaf internal scattering in the NIR spectral ranges [19]. Increasing in the amount of chlorophyll, results in a change of the major Chl absorption feature centered around 680 nm causing a shift in the slope and red edge position towards the longer wavelengths [20].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of the Influence of Biotic Stress on Plant Biophysical Variables

Krezhova¹,

Maneva²,

Petrov³

et al. 2017

RadJ

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Abstract. Hyperspectral remote sensing provides for significant advancement in the evaluation of the subtle changes in biophysical and biochemical attributes of the crop plants. Accurate estimates of leaf pigments, nitrogen, dry matter, water content, and leaf area index (LAI) from remotely sensed data can assist in determining the vegetation physiological state. In this paper, hyperspectral remote sensing measurements of the leaf reflectance were applied for assessing the effect of biotic stress (viral infection) on the spectral behaviour and biophysical variables of young potato plants, cultivar Agata, infected with Potato Virus Y (PVY). Spectral reflectance data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (350-1100 nm) with a spectral resolution of 1.5 nm. For the assessment of differences between the reflectance data of healthy and infected plant data processing techniques, such as Student's t-test, first derivative analyses, and estimation of vegetation indices, were applied. The analyses were performed in green, red, red edge and near infrared spectral ranges (450-850 nm) where the differences were the most significant and give information about changes in the chlorophyll and pigment content, moisture content, cells structures, and plant stress. Several vegetation indices (NDVI -Normalized Difference Vegetation Index, fD -Disease Index, SR -Simple Ratio, TCARI -Transformed Chlorophyll Absorption Reflectance Index, etc.) were computed and the best results for assessing the changes in the physiological state of the plants gave TCARI. A strong relationship was found between the results of the spectral analyses and the serological test DAS-ELISA was applied to assess the presence and the degree of the PVY infection.

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Derivation of the red edge index using the MERIS standard band setting

Cited by 162 publications

References 17 publications

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

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

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

Remote Sensing of the Influence of Biotic Stress on Plant Biophysical Variables

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