Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies

Basnyat, Brian McConkey Prakash; Lafond, G. P.; Moulin, A.; Pelcat, Yann S.

doi:10.4141/p03-070

Cited by 41 publications

(13 citation statements)

References 30 publications

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“…Consequently, the vegetation indices reached their maximum when the crop shifted from vegetative growth to reproductive phase. These results confirm previous studies that identified the optimal time for remote sensing acquisition about 40 d before harvest (Basnyat et al, 2004;Ren et al, 2008;Becker-Reshef et al, 2010).…”

Section: Modis Vegetation Indices In Forecasting Purposessupporting

confidence: 92%

“…These results confirm previous studies that identified the optimal time for remote sensing acquisition about 40 d before harvest (Basnyat et al, 2004;Ren et al, 2008;Becker-Reshef et al, 2010). Consequently, the vegetation indices reached their maximum when the crop shifted from vegetative growth to reproductive phase.…”

Section: Modis Vegetation Indices In Forecasting Purposessupporting

confidence: 91%

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Integration of Remote Sensing and Crop Modeling for the Early Assessment of Durum Wheat Harvest at the Field Scale

Orlando

Marta

Mancini³

et al. 2015

Crop Science

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In the Mediterranean, durum wheat is one of the major crops, but a high variability of grain yield and protein concentration (GPC) prevents an adequate agronomic planning at the farm or consortium level. Although there are many studies on monitoring of crop production and early prediction of yields, little has been done at the local scale. The aim of this study was to assess simplified integration algorithms (SIAs) for integrating remote sensing information with a crop model, to forecast the GPC and grain yield at the field scale. To this end, the CERES‐Wheat model was run to simulate the seasonal average of grain yield (AVE) and GPC in Val d’ Orcia (Tuscany Region, Italy) during the 2009–2010 and 2010–2011 growing seasons. The performances of different vegetation indices from MODIS imagery in harvest forecasting were assessed and compared. The SIA formulation was based on the simulated AVE and GPC, and on their spatialization in relation to the intraannual variability between the fields described by vegetation indices. The simulated AVE traced the observed trend. The fraction of absorbed photosynthetically active radiation (fPAR) was the best index in describing grain yield, and the related SIA showed at validation good performance at the field scale (r2 = 0.74). Conversely, the SIA was unable to predict GPC due to the low performance of CERES‐Wheat in capturing the interannual variability and to the failure of the fPAR in describing the GPC interfields variability at intermediate canopy reflectance values.

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Section: Modis Vegetation Indices In Forecasting Purposessupporting

confidence: 92%

Section: Modis Vegetation Indices In Forecasting Purposessupporting

confidence: 91%

Integration of Remote Sensing and Crop Modeling for the Early Assessment of Durum Wheat Harvest at the Field Scale

Orlando

Marta

Mancini³

et al. 2015

Crop Science

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“…Prior remote sensing research on canola has demonstrated the importance of timing for image acquisition if estimating relationships with grain yield. It has already been established that flowers in a canola canopy are problematic for relating NDVI to yield (Basnyat et al, 2004;Piekarczyk, 2011). An important temporal finding is that the relationship between NDVI and yield declines as flowering increases (Piekarczyk, 2011); however, no spectral solution to this problem has been offered to-date.…”

Section: Canola Yield Modeling Using Remote Sensingmentioning

confidence: 99%

Spectral considerations for modeling yield of canola

Sulik

Long

2016

Remote Sensing of Environment

128

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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.

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“…In this study, we validated a computational workflow for image acquisition, processing, and analysis to predict the biomass yield based on vegetative indices and plant height measurements of 48,000 ryegrass plants. Previous studies indicated the use of NDVI for ranking cultivars of ryegrass (Wang et al, 2019), field pea, canola, and spring wheat grain yield (Brian McConkey et al, 2004) and lint yield in cotton (Hugie et al, 2018). Considering the plant height and NDVI as a surrogate to predict DMY of individual and plot-level plants, there is a great potential to apply our workflow to be used for ranking of genotypes and cultivars across growing seasons and years.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial

et al. 2020

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Increasing dry matter yield (DMY) is the most important objective in perennial ryegrass breeding programs. Current yield assessment methods like cutting are time-consuming and destructive, non-destructive measures such as scoring yield on single plants by visual inspection may be subjective. These assessments involve multiple measurements and selection procedures across seasons and years to evaluate biomass yield repeatedly. This contributes to the slow process of new cultivar development and commercialisation. This study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass and investigated sensor-based DMY yield estimation of individual plants through normalized difference vegetative index (NDVI) and ultrasonic plant height data extraction. The DMY of 48,000 individual plants representing 50 advanced breeding lines and commercial cultivars was accurately estimated at multiple harvests across the growing season. NDVI, plant height and predicted DMY obtained from aerial and ground-based sensors illustrated the variation within and between cultivars across different seasons. Combining NDVI and plant height of individual plants was a robust method to enable highthroughput phenotyping of biomass yield in ryegrass breeding. Similarly, the plot-level model indicated good to high-coefficients of determination (R 2) between the predicted and measured DMY across three seasons with R 2 between 0.19 and 0.81 and root mean square errors (RMSE) values ranging from 0.09 to 0.21 kg/plot. The model was further validated using a combined regression of the three seasons harvests. This study further sets a foundation for the application of sensor technologies combined with genomic studies that lead to greater rates of genetic gain in perennial ryegrass biomass yield.

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Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies

Cited by 41 publications

References 30 publications

Integration of Remote Sensing and Crop Modeling for the Early Assessment of Durum Wheat Harvest at the Field Scale

Integration of Remote Sensing and Crop Modeling for the Early Assessment of Durum Wheat Harvest at the Field Scale

Spectral considerations for modeling yield of canola

Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial

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