The reflectance at the 950–970 nm region as an indicator of plant water status

Peñuelas, Josep; Filella, Iolanda; Biel, C.; Serrano, Lydia; Savè, Robert

doi:10.1080/01431169308954010

Cited by 944 publications

(641 citation statements)

References 33 publications

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“…This coincides with Peñuelas et al [46], who found that declines in reflectance in the zone between 930 and 970 nm are correlated with canopy water content. In addition, differences were found in the regression coefficient values for the SWIR zone, which has been associated with water content and absorbance.…”

Section: Regression Coefficients For Different Spectral Zonessupporting

confidence: 74%

“…In the NIR zone (800 to 1300 nm), high-yield genotypes showed higher reflectance in all environments, which could be associated with a greater leaf area index and green biomass [44,45]. Between 930 and 970 nm, the lowest reflectance value found for MWS was probably related to the canopy's water content [46]. In MWS and SWS environments, there was an increase in reflectance in this zone due to lower water content.…”

Section: Canopy Reflectance In Genotypes Of Contrasting Grain Yieldmentioning

confidence: 94%

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Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Hernandez

Lobos

Matus³

et al. 2015

Remote Sensing

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Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg·ha −1 , respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY OPEN ACCESSRemote Sens. 2015, 7 2110 variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R 2 ) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

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Section: Regression Coefficients For Different Spectral Zonessupporting

confidence: 74%

Section: Canopy Reflectance In Genotypes Of Contrasting Grain Yieldmentioning

confidence: 94%

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Hernandez

Lobos

Matus³

et al. 2015

Remote Sensing

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“…Hyperspectral remote-sensing data provide ample information in the physiology ecology characteristics of vegetation (Thomas 1977). More researches have focused on the deeper reflectance response of vegetation water to invert vegetation water use, and much progress has been made (Ceccato et al 2001;Penuelas et al 1993). However, ET estimated by hyperspectral remote-sensing data has not been reported.…”

Section: Introductionmentioning

confidence: 99%

“…At the canopy scale, the optimum index model was established by the iteration method instead of the typical measurement (Wullschleger et al 2001;Cleverly et al 2002;Xu et al 2008) and estimation (Penuelas et al 1993;Bodner et al 2007). The hyperspectral response characteristics of the sap flow rate were first explored from the semi-empirical perspective.…”

Section: Et Inversion By Hyperspectral Remote-sensing Datamentioning

confidence: 99%

Multiscale remote-sensing retrieval in the evapotranspiration of Haloxylon ammodendron in the Gurbantunggut desert, China

et al. 2012

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Transpiration, an essential component of surface evapotranspiration, is particularly important in the research of surface evapotranspiration in arid areas. The paper explores the spectral information of the arid vegetal evapotranspiration from a semi-empirical perspective by the measured data and the up-scaling method. The paper inverted the transpiration of Haloxylon ammodendron at the canopy, pixel and regional scales in the southern edge of the Gurbantunggut desert in Xinjiang, China. The results are as follows: at the canopy scale, the optimal exponential model of the sap flow rate based on the hyperspectrum is y = 0.0015e 3.8922x , R 2 = 0.806. At the pixel scale, there was a good linear relationship between the sap flow and the SR index, with a relationship of y = -1197.38x 3 ? 1048.43x 2 -305.47x ? 455.15, R 2 = 0.845. At the regional scale, based on the optimal exponential model and the EO-1 Hyperion remote-sensing data, the transpiration of the study area was inverted. Comparing the results of the SEBAL and SEBS models, the errors of the simulation results were 7.78 and 8.80 %. The paper made full use of the knowledge flow at different scales, bridging the scale difference in canopy and remote-sensing images to avoid the information bottleneck in the up-scaling. However, there are many constraints in the data acquirement, the efficiency of the models, the endmembers determination, the temporal-spatial up-scaling, and the accuracy assessment, which would be improved in future studies.

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“…Hyperspectral data also can provide significant improvements in spectral information content when compared with broadbands for detecting plant stress [9][10] , measuring chlorophyll content of plants [11] , identifying small differences in percent green vegetation cover [12] , extracting biochemical variables such as nitrogen and lignin [13] , discriminating land cover (LC) types [14] , crop moisture variations [15][16] , leaf pigment concentrations [11] , modeling quantitative biophysical and yield characteristics of agricultural crops [17] , improving detection change in sparse vegetation [18][19] and assessing absolute water content in plant leaves [20] .…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing of Vegetation Using Red Edge Position Techniques

Shafri¹,

Salleh²,

Ghiyamat³

2006

American J. of Applied Sciences

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Two red edge position (REP) techniques, Linear and Lagrangian, were applied on hyperspectral data acquired from the HyMap sensor for a forested area in Thetford Forest, UK. Red edge positions of different vegetation covers were extracted with the two approaches from the hyperspectral data. Based on the estimated REPs, the Linear and Lagrangian interpolation methods were compared with ground reference image to analyse different vegetation types and ages. Experimental results of both interpolation techniques indicate that the wavelength and reflectance of REP for younger plants (higher chlorophyll content) shift towards longer wavelength and of higher reflectance in comparison with older plants (lower chlorophyll content).

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The reflectance at the 950–970 nm region as an indicator of plant water status

Cited by 944 publications

References 33 publications

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Multiscale remote-sensing retrieval in the evapotranspiration of Haloxylon ammodendron in the Gurbantunggut desert, China

Hyperspectral Remote Sensing of Vegetation Using Red Edge Position Techniques

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