Spectral indices for yellow canola flowers

Sulik, John; Long, Dan S.

doi:10.1080/01431161.2015.1047994

Cited by 94 publications

(90 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These minimum coverage percentages decrease with increasing flower coverage, which is not surprising due to the larger decrease in chlorophyll content with increasing flower coverage for higher leaf coverage. This correlates with the research by Sulik and Long (2015) who found the same result in canola, with NDVI decreasing as the yellow flowers became a dominant influence on the spectra.…”

Section: Minimum Flower Coverage For Detectionsupporting

confidence: 91%

Honey Crop Estimation From Space: Detection of Large Flowering Events in Western Australian Forests

Campbell

Fearns

2018

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

View full text Add to dashboard Cite

Abstract. Recent studies have shown that in the spectral space there is often a better spectral separation between leaves and flowers and even between flowers of different species than between leaves of different species. In this study we assess the ability of satellite remotely sensed data to detect the flowering of Red Gum trees (Corymbia calophylla) in Western Australia, the state’s largest annual honey crop. Spectroradiometer measurements of flowers, leaves and groundcover from Red Gum forests were subjected to ANOVA analysis, which showed that flowers are spectrally different to their environment for 92% of the wavelengths between 350nm and 1800nm. A more detailed assessment, using the JM Distance calculation, showed that the spectra can be reliably separated using 10% of the wavelengths, with peak separation between 518nm and 557nm. To assess the ability of satellite-borne sensors to detect the presence of flowers, the spectroradiometer data were convolved with satellite instruments’ response curves to create synthetic remotely sensed datasets on which JM Distance analysis was performed. MODIS blue bands achieved a median JM Distance of greater than 1.9 and therefore should be able to detect the presence of flowers from the environment. Further assessment showed that the shortest wavelength bands for MODIS, VIIRS and Sentinel 3 all occur where the flower spectra have lower reflectance than their natural background. A sensitivity analysis of percentage flower cover for a pixel showed that the highest sensitivity was obtained by dividing the band closest to 520nm by the shortest wavelength band for data from these three sources. The MODIS band 10/band 8 metric was tested for its ability to detect flowers in real-world data using 15 years of qualitative honey harvest data from one apiary site as a proxy for flower density. This test was successful as, while there was some overlap between good, moderate and poor years, the poor years could be separated from the other years with nearly 80% accuracy.

show abstract

Section: Minimum Flower Coverage For Detectionsupporting

confidence: 91%

Honey Crop Estimation From Space: Detection of Large Flowering Events in Western Australian Forests

Campbell

Fearns

2018

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

View full text Add to dashboard Cite

show abstract

“…During the flowering stage there is reduced interception of photosynthetically active radiation (Yates & Steven, 1987). Yellow flowers contribute red light (yellow = green+red) to a canopy-level signal and this added radiation reduces NDVI values (Behrens et al, 2006;Piekarczyk, 2011;Shen et al, 2009), which adversely affects the performance of NDVI to map variability in biomass (Shen et al, 2010;Sulik & Long, 2015) or yield potential. For instance, if there is a field of flowering radish, turnip, mustard, or other Brassica crop-any flower where there is a substantial red light component (i.e., white, red, yellow), then the red channel's value will be a combination of foliar (what NDVI is supposed to index) and floral reflectance.…”

Section: Introductionmentioning

confidence: 99%

“…The floral contribution to the reflectance is manifest most strongly in the green waveband (Sulik & Long, 2015). Yellow canola petal coloration is due to carotenoid absorption (Zhang et al, 2015) at ~450 nm.…”

Section: Introductionmentioning

confidence: 99%

“…It turns out that the reflection of radiation from flowers can provide us with clues about reproductive potential. The authors found that a ratio of green to blue wavebands positively correlates with the number of flowers per unit area (Sulik & Long, 2015). By directly capturing the spectral variation in yellow flower density, we are able to acquire information about the relative yield potential further into the growing season than an index that is more suitable for plant development stages that occur before reproductive growth commences.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spectral considerations for modeling yield of canola

Sulik

Long

2016

Remote Sensing of Environment

Self Cite

128

View full text Add to dashboard Cite

Prominent yellow flowers that are present in a Brassica oilseed crop such as canola require careful consideration when selecting a spectral index for yield estimation. This study evaluated spectral indices for multispectral sensors that correlate with the seed yield of Brassica oilseed crops. A small-plot experiment was conducted near Pendleton, Oregon in which spring canola was grown under varying water regimes and nitrogen treatments to create a wide range in oilseed yield. Plot measurements consisted of canopy reflectance at flowering using a hand-held spectroradiometer and seed yield at physiological maturity. Spectroradiometric measurements were converted to MODIS band equivalent reflectance. Selected indices were computed from spectra obtained with the radiometer and correlated with seed yield. A normalized difference yellowness index (NDYI), computed from the green and blue wavebands, overcame limitations of the normalized difference vegetation index (NDVI) during flowering and best modeled variability in relative yield potential. NDYI was more linear and correlated with county-wide oilseed yield data and MODIS satellite data from North Dakota (r 2 ≤ 0.72) than NDVI (r 2 ≤ 0.66). NDYI only requires wavebands in the visible region of the spectrum and can be applied to any satellite or aerial sensor that has blue and green channels. These findings highlight the benefit of using a spectral index that is sensitive to reproductive growth of vegetation instead of vegetative growth for crops with spectrally prominent reproductive canopy elements. Our results indicate that NDYI is a better indicator of yield potential than NDVI during mid-season development stages, especially peak flowering.

show abstract

“…However, the shortages of small spatial coverage, as well as expensive cost of hyperspectral images, limit its application on regional OR mapping.The second category mainly applies supervised classification methods on multispectral images during the flowering period to identify and extract OR at the local scale, since the flowering period is the best phenology stage of identifying OR from other crops [11]. As a member of the Brassicaceae family, OR appears as bright-yellow flowers lasting 30 days (approximately a quarter of its entire growing season) [3,12], which leads to a large difference on the reflectance at green, red, and near-infrared bands when compared with other crop species during the same period because of the radiation reflected by the flower petals [13][14][15]. She et al [11] introduced the effectiveness of identifying OR from other crops during its flowering phase and the difficulty in other growing stages.…”

mentioning

confidence: 99%

A Regional Mapping Method for Oilseed Rape Based on HSV Transformation and Spectral Features

Wang

Fang

Yang

et al. 2018

IJGI

View full text Add to dashboard Cite

This study proposed a colorimetric transformation and spectral features-based oilseed rape extraction algorithm (CSRA) to map oilseed rape at the provincial scale as a first step towards country-scale coverage. Using a stepwise analysis strategy, our method gradually separates vegetation from non-vegetation, crop from non-crop, and oilseed rape from winter wheat. The wide-field view (WFV) images from Chinese Gaofen satellite no. 1 (GF-1) at six continuous flowering stages in Wuxue City, Hubei Province, China are used to extract the unique characteristics of oilseed rape during the flowering period and predict the parameter of the CSRA method. The oilseed rape maps of Hubei Province from 2014 to 2017 are obtained automatically based on the CSRA method using GF-1 WFV images. As a result, the CSRA-derived provincial oilseed rape maps achieved at least 85% overall accuracy of spatial consistency when comparing with local reference oilseed rape maps and lower than 20% absolute error of provincial planting areas when comparing with agricultural census data. The robustness of the CSRA method is also tested on other satellite images including one panchromatic and multispectral image from GF-2 and two RapidEye images. Moreover, the comparison between the CSRA and other previous methods is discussed using the six GF-1 WFV images of Wuxue City, showing the proposed method has better mapping accuracy than other tested methods. These results highlight the potential of our method for accurate extraction and regional mapping capacity for oilseed rape. [6,7]. Thus, using remote sensing data to map the planting areas of OR has become a feasible and efficient approach in recent years.Previous studies about extracting OR by means of remote sensing can be divided into two categories. The first category separates OR from other land cover types using hyperspectral remote sensing data based on the detailed spectral difference. For example, Wilson et al. [8] assessed the spectral separability between OR and four other crop types with a stepwise discriminant analysis, and found that 23 bands could be used to effectively distinguish those five crop types in a suitable situation. Pan et al. [9] noticed that the multi-range spectral feature fitting method could yield a better performance in OR planting area extraction using Hyperion imagery [9]. She et al. [10] found the special movement of the red edge of OR from its original position towards the blue band direction during the flowering period to the pods period and applied to extract OR based on Hyperion imagery. However, the shortages of small spatial coverage, as well as expensive cost of hyperspectral images, limit its application on regional OR mapping.The second category mainly applies supervised classification methods on multispectral images during the flowering period to identify and extract OR at the local scale, since the flowering period is the best phenology stage of identifying OR from other crops [11]. As a member of the Brassicaceae family, OR appears as bright-yellow flower...

show abstract

Spectral indices for yellow canola flowers

Cited by 94 publications

References 17 publications

Honey Crop Estimation From Space: Detection of Large Flowering Events in Western Australian Forests

Honey Crop Estimation From Space: Detection of Large Flowering Events in Western Australian Forests

Spectral considerations for modeling yield of canola

A Regional Mapping Method for Oilseed Rape Based on HSV Transformation and Spectral Features

Contact Info

Product

Resources

About