Spectral considerations for modeling yield of canola

Sulik, John; Long, Dan S.

doi:10.1016/j.rse.2016.06.016

Cited by 102 publications

(47 citation statements)

References 39 publications

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“…These derived products included calculated NDVI and EVI products, as well as other combinations of MODIS visible spectral bands to determine the effectiveness of a 'visual band intensity'metric. This is in a similar vein to the work completed by Sulik and Long (2016), who developed a Normalised Difference Yellowness Index (NDYI) to better predict canola yields based on the coverage of yellow canola flowers as it was found that NDVI decreased as flower cover decreased.…”

Section: Minimum Flower Coverage For Detectionsupporting

confidence: 59%

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.

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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.

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Section: Minimum Flower Coverage For Detectionsupporting

confidence: 59%

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.

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“…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.…”

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confidence: 99%

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

Wang

Fang

Yang

et al. 2018

IJGI

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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...

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“…Kanola yüzeylerinin spektral özelliklerinin biyokütle ile ilişkisinin ortaya konduğu bir çalışma ise, Brezilya'da yürütülmüştür [29]. Kanola NDVI verisinin analizinin [30]…”

Section: Tartışma Ve Sonuçunclassified

Bitki Yüzeyinin Enerji Dengesi Bileşenlerinin Belirli Fenolojik Aşamalarda Analizi

Çaldag¹

2019

deufmd

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Öz İklim değişiminin sıcaklık artışları, yağışların azalması, artan buharlaşma gibi etkileri; geniş alanda yetiştirilen bitki türlerinin su ihtiyacına ve üretim değerlerine yönelik çalışmalar için önemlidir. Bitki su tüketimi (evapotranspirasyon) verisi, bir bölgede yapılması planlanan tarımsal sulama projelerinin temelini oluşturmaktadır. Yüzeyler ile atmosfer arasındaki su döngüsünü idare eden yüzey enerji dengesi bileşenlerinin saptanması da bu noktada bir ihtiyaç halini almaktadır. Yüzey enerji dengesi bileşenlerinin saptanması amacıyla yaygın olarak kullanılan Bowen Oranı Enerji Dengesi (Bowen Ratio Energy Balance, BREB) Yöntemi, bu çalışmada kanola yüzeyi üzerine uygulanmıştır. Bu kapsamda Kırklareli Atatürk Toprak Su ve Tarımsal Meteoroloji Araştırma Enstitüsü Müdürlüğü deneme alanında 2016 gelişme döneminin çiçeklenme aşamasında buharlaşma gizli ısı akısı, toprak ısı akısı, net radyasyon, hissedilir ısı akısı ile bitki yüzeyinin normalleştirilmiş fark vejetasyon indeksi (Normalized Difference Vegetation Index, NDVI) belirlenerek, ölçülen bileşenler ile hesaplanan bileşenler arasındaki ilişkiler araştırılmıştır. Sonuçta ölçüm dönemi içinde net radyasyon değerinin en yüksek olduğu tarih; 27 Nisan 2016 olarak belirlenmiştir. Bu tarih, seçilen günler için hesaplanan evapotranspirasyon değerlerinin de en yüksek olduğu zaman aralığı içindedir. Aynı dönemin NDVI değerlerinin de benzer biçimde 22 Mart-14 Nisan 2016 arasında maksimum değerlere ulaştığı belirlenmiştir.

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Spectral considerations for modeling yield of canola

Cited by 102 publications

References 39 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

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

Bitki Yüzeyinin Enerji Dengesi Bileşenlerinin Belirli Fenolojik Aşamalarda Analizi

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