Estimating potato leaf chlorophyll content using ratio vegetation indices

Kooistra, Lammert; Clevers, J.G.P.W.

doi:10.1080/2150704x.2016.1171925

Cited by 55 publications

(44 citation statements)

References 19 publications

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“…Recent work by Kooistra and Clevers [9] has shown that other vegetation indices may be used to estimate leaf chlorophyll content (LCC). They found that the TCARI/OSAVI ratio and the CVI are the most promising as LCC estimators (Table 2).…”

Section: Field Radiometrymentioning

confidence: 99%

“…Calibration curves between the WDVI and LAI were determined by performing both Cropscan measurements and LAI2000 Plant Canopy Analyser (LI-COR, Lincoln, NE, USA) measurements in the field at the same farm for potato experiments with similar set-ups in the years 2010-2014 [9]. Per plot, six measurements per potato row, for four rows (24 values per plot), were taken.…”

Section: Field Radiometrymentioning

confidence: 99%

“…Estimating LCC requires a VI that is sensitive to this LCC but, at the same time, is insensitive to LAI and background effects. Kooistra and Clevers [9] showed that two ratio indices have great potential to be applied using Sentinel-2 data. The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) is one index following the idea of the above ratio approach for estimating LCC [10].…”

Section: Introductionmentioning

confidence: 99%

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Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

Clevers

Kooistra

Brande³

2017

Remote Sensing

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268

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Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010-2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R 2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R 2 of 0.696; RMSEP of 0.062 g·m −2 ). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R 2 of 0.656; RMSEP of 0.066 g·m −2 ) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CI green ) was an accurate and linear estimator of CCC at 10 m (R 2 of 0.818; RMSEP of 0.29 g·m −2 ). Results for CI green were better than for the red-edge chlorophyll index (CI red-edge , R 2 of 0.576, RMSE of 0.43 g·m −2 ). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture.

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Section: Field Radiometrymentioning

confidence: 99%

Section: Field Radiometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

Clevers

Kooistra

Brande³

2017

Remote Sensing

Self Cite

268

150

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“…This finding is in agreement with expectations based on univariate correlations of traits over wavelengths (not shown). The red-edge slope is of particular relevance for leaf chlorophyll content (LCC) because of its enhanced sensitivity to varied and higher chlorophyll levels while circumventing saturation problems as observed in the blue and red due to vast chlorophyll-induced absorption (Gitelson, 2012;Kooistra and Clevers, 2016). Reliance on wavelengths at the onset of the near-infrared follows from the gradual stabilization of reflectance beyond the red-edge, which settles at higher values for increased chlorophyll levels (Lamb et al, 2002).…”

Section: Relevant Wavelengths For Plant Trait Predictionsmentioning

confidence: 99%

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

et al. 2017

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Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant-soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are shortterm and small-scale due to practical constraints for fieldscale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R 2 = 0.79), chlorophyll content (RMSE = 0.11 g m −2 , R 2 = 0.80), Ncontent (RMSE = 1.94 g m −2 , R 2 = 0.68), and fresh biomass (RMSE = 0.72 kg m −2 , R 2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m −2 , respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m −2 , respectively), while the lowest values (76 cm, 0.41 g m −2 , respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAVbased optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

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“…The ground-based apparent LAI (hereafter referred to LAI) was estimated based on weekly measurements with a Cropscan Multispectral Radiometer that used the weighted difference vegetation index (WDVI) [44] for estimating LAI. Calibration curves between the WDVI and LAI were determined by performing both Cropscan measurements and LAI2000 Plant Canopy Analyser measurements in the field at the same farm for potatoes grown in the years 2010-2014 [45]. In 2016, limited measurements with the LAI2000 were available, but validation with these measurements showed estimating LAI with Cropscan data was accurate.…”

Section: Study Areamentioning

confidence: 99%

Mapping Reflectance Anisotropy of a Potato Canopy Using Aerial Images Acquired with an Unmanned Aerial Vehicle

et al. 2017

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Viewing and illumination geometry has a strong influence on optical measurements of natural surfaces due to their anisotropic reflectance properties. Typically, cameras on-board unmanned aerial vehicles (UAVs) are affected by this because of their relatively large field of view (FOV) and thus large range of viewing angles. In this study, we investigated the magnitude of reflectance anisotropy effects in the 500-900 nm range, captured by a frame camera mounted on a UAV during a standard mapping flight. After orthorectification and georeferencing of the images collected by the camera, we calculated the viewing geometry of all observations of each georeferenced ground pixel, forming a dataset with multi-angular observations. We performed UAV flights on two days during the summer of 2016 over an experimental potato field where different zones in the field received different nitrogen fertilization treatments. These fertilization levels caused variation in potato plant growth and thereby differences in structural properties such as leaf area index (LAI) and canopy cover. We fitted the Rahman-Pinty-Verstraete (RPV) model through the multi-angular observations of each ground pixel to quantify, interpret, and visualize the anisotropy patterns in our study area. The Θ parameter of the RPV model, which controls the proportion of forward and backward scattering, showed strong correlation with canopy cover, where in general an increase in canopy cover resulted in a reduction of backward scattering intensity, indicating that reflectance anisotropy contains information on canopy structure. In this paper, we demonstrated that anisotropy data can be extracted from measurements using a frame camera, collected during a typical UAV mapping flight. Future research will focus on how to use the anisotropy signal as a source of information for estimation of physical vegetation properties.

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Estimating potato leaf chlorophyll content using ratio vegetation indices

Cited by 55 publications

References 19 publications

Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

Mapping Reflectance Anisotropy of a Potato Canopy Using Aerial Images Acquired with an Unmanned Aerial Vehicle

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