A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Hunt, E. Raymond; Doraiswamy, P. C.; McMurtrey, J. E.; Daughtry, Craig S. T.; Perry, Eileen M.; Akhmedov, B.

doi:10.1016/j.jag.2012.07.020

Cited by 424 publications

(282 citation statements)

References 65 publications

(57 reference statements)

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“…This might be a first proof that the RGBVI could serve as a strong predictor for qualitative differences in crop growth for any captured growth stage. This fits to the findings of Hunt et al (2005Hunt et al ( , 2013Hunt et al ( , 2014 who investigated the potential of such vegetation indices derived from the visible domain for precision agriculture. However, the slopes of the trend lines are different for every date and are decreasing to the later growth stages.…”

Section: Discussionsupporting

confidence: 89%

Comparison of Uncalibrated Rgbvi With Spectrometer-Based Ndvi Derived From Uav Sensing Systems on Field Scale

Bareth

Bolten

Gnyp

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers' field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

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Section: Discussionsupporting

confidence: 89%

Comparison of Uncalibrated Rgbvi With Spectrometer-Based Ndvi Derived From Uav Sensing Systems on Field Scale

Bareth

Bolten

Gnyp

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…As a consequence, a number of spectral indices were developed for foliar Chls estimation. Table 1 lists some of the chlorophyll indicators that have been examined by Vogelmann et al (1993), Elvidge and Chen (1995), Blackburn and Ferwerda (2008), Ustin et al (2009), Féret et al (2011), and Hunt et al (2013. Those indices integrate a couple of specific signatures of visible and near-infrared bands, for example, the reflectance at 445,550,680,700,705,710,720,750,780,800, 860 nm for foliar Chls estimation.…”

Section: Indexmentioning

confidence: 99%

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

et al. 2015

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Abstract. Water deficits can cause chlorophyll degradation which decreases the total concentration of chlorophyll a and b (Chls). Few studies have investigated the effectiveness of spectral indices under water-stressed conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works by sensing leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there were a water deficit in the leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water-stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum.Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter readings. These two data sets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R 2 ). Finally, an experiment was conducted to collect the third data set for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer.The spectral indices in the form of reflectance ratio/difference/slope derived from the Chl b absorption bands (ρ 645 and ρ 455 ) provided Chls estimates with RMSE around 0.40-0.55 mg g −1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (ρ REP ) in regression models. An effective chlorophyll indicator with the form of (ρ 645 -ρ 455 ) / ρ REP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R 2 of 0.90 (P < 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g −1 for the validation samples of fresh and water-stressed leaves, respectively. The average prediction error was within 14 % of the mean absolute error.

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“…Canopy reflectance models have been used widely for investigating the response of vegetation indices to the variation of a number of factors and for understanding the mechanisms of interaction among these factors (Daughtry et al, 2000;Vincini et al, 2014;. Different types of sensors measuring the amount of reflected solar radiation, from low-cost multispectral to high-cost imaging spectrometers, from low spatial to high spatial resolution, and from ground-based to satellite are available (Hunt et al, 2013). Traditional satellite systems such as Landsat have been widely used for agricultural purposes over large areas.…”

Section: Introductionmentioning

confidence: 99%

Yield variability prediction by remote sensing sensors with different spatial resolution

Kumhálová¹,

Matějková²

2017

International Agrophysics

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Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

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A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Cited by 424 publications

References 65 publications

Comparison of Uncalibrated Rgbvi With Spectrometer-Based Ndvi Derived From Uav Sensing Systems on Field Scale

Comparison of Uncalibrated Rgbvi With Spectrometer-Based Ndvi Derived From Uav Sensing Systems on Field Scale

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

Yield variability prediction by remote sensing sensors with different spatial resolution

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