Reducing background effects in orchards through spectral vegetation index correction

Beek, Jonathan Van; Tits, Laurent; Somers, Ben; Deckers, T.; Janssens, Pieter; Coppin, Pol

doi:10.1016/j.jag.2014.08.009

Cited by 17 publications

(22 citation statements)

References 29 publications

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“…Synthetic imagery was used in this study to improve our understanding of the effects of a changing viewing geometry on the remotely sensed data and the information derived from it [28,30,31]. The synthetic data provided exact cover fractions, spectral signatures and biophysical variables of the target crop for a range of viewing compositions.…”

Section: Synthetic Imagerymentioning

confidence: 99%

“…In compliance with [28,31], the virtual orchard, developed by [32], consisted of virtual citrus trees (Citrus sinensis L.). The trees were arranged in a 3.5 by 2 m grid (row orientation of 7˝; i.e., north-south direction).…”

Section: Synthetic Imagerymentioning

confidence: 99%

“…However, high spatial resolution satellite imagery over orchard cropping systems will always contain mixtures of canopies and background (i.e., soil, grass and shadow) [27,28]. This mixture effects can be further aggravated by the variable viewing conditions.…”

Section: Introductionmentioning

confidence: 99%

“…This mixture effects can be further aggravated by the variable viewing conditions. Recent studies have shown the necessity and usefulness of the removal or reduction of background effects through either unmixing algorithms [29] or vegetation index corrections [28]. However, both methods were developed for nadir viewing imagery and assumed the presence of the full range of canopy fractions.…”

Section: Introductionmentioning

confidence: 99%

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Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards

et al. 2016

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Stress-related biophysical variables of capital intensive orchard crops can be estimated with proxies via spectral vegetation indices from off-nadir viewing satellite imagery. However, variable viewing compositions affect the relationship between spectral vegetation indices and stress-related variables (i.e., chlorophyll content, water content and Leaf Area Index (LAI)) and could obstruct change detection. A sensitivity analysis was performed on the estimation of biophysical variables via vegetation indices for a wide range of viewing geometries. Subsequently, off-nadir viewing satellite imagery of an experimental orchard was analyzed, while all influences of background admixture were minimized through vegetation index normalization. Results indicated significant differences between nadir and off-nadir viewing scenes (∆R 2 > 0.4). The Photochemical Reflectance Index (PRI), Normalized Difference Infrared Index (NDII) and Simple Ratio Pigment Index (SRPI) showed increased R 2 values for off-nadir scenes taken perpendicular compared to parallel to row orientation. Other indices, such as Normalized Difference Vegetation Index (NDVI), Gitelson and Merzlyak (GM) and Structure Insensitive Pigment Index (SIPI), showed a significant decrease in R 2 values from nadir to off-nadir viewing scenes. These results show the necessity of vegetation index selection for variable viewing applications to obtain an optimal derivation of biophysical variables in all circumstances.

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Section: Synthetic Imagerymentioning

confidence: 99%

Section: Synthetic Imagerymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards

et al. 2016

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“…This results in limited samples and repetitions, which are insufficient to account for the high spatial and temporal variability within and between orchards [4,5]. It is yet well acknowledged that remote sensing can provide non-destructive, time efficient and cost beneficial alternatives for horticulture [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards

et al. 2015

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Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements (i.e., hyperspectral canopy reflectance measurements) as well as yield determination (i.e., total yield and number of fruits per tree) and quality assessment (i.e., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of OPEN ACCESSRemote Sens. 2015, 7 9887 harvest (|r| > 0.6; p < 0.001), while in the rainfed orchard, index values acquired during vegetative growth periods presented stronger correlations with fruit parameters (|r| > 0.6; p < 0.001). The improved planning of remote sensing missions during (rainfed orchards) and after (irrigated orchards) vegetative growth periods could enable growers to more accurately predict production outcomes and improve the production process.

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Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance

et al. 2020

Precision Agric

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Reducing background effects in orchards through spectral vegetation index correction

Cited by 17 publications

References 29 publications

Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards

Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards

Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards

Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance

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