Comparison of winter wheat NDVI data derived from Landsat 8 and active optical sensor at field scale

Gozdowski, Dariusz; Stępień, Michał; Panek, Ewa; Varghese, James; Bodecka, Elżbieta; Rozbicki, J.; Samborski, Stanisław

doi:10.1016/j.rsase.2020.100409

Cited by 28 publications

(24 citation statements)

References 22 publications

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“…The NDVI is a measure of living vegetation, whose value is determined by the reflection of terrestrial chlorophyll ( Gozdowski et al, 2020 , Moreno et al, 2020 ). According to Ahmed and Singh, 2020 , Costa et al, 2020 , Moreno et al, 2020 , NDVI is captured by satellites, through the spectral features of vegetation that emit wavelengths, represented by photosynthetic pigments, derived from the internal structure and the amount of water present in the vegetation.…”

Section: Introductionmentioning

confidence: 99%

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Toscan

Neckel

Maculan

et al. 2022

Geoscience Frontiers

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Section: Introductionmentioning

confidence: 99%

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Toscan

Neckel

Maculan

et al. 2022

Geoscience Frontiers

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“…According to the slope value of linear fit, passive sensor in the case of both RNDVI as well as RENDVI, registered higher values of the VI's than the active sensor (Appendix Figures A1 and A2). Previous studies (Erdle et al., 2011; Fitzgerald, 2010; Gozdowski et al., 2020) also found a close relationship between RNDVI collected by active and passive sensors. Schut et al.…”

Section: Resultsmentioning

confidence: 58%

“…Gozdowski et al. (2020) reported that a linear model might be adequate to describe the relationship between ground and satellite‐based RNDVI; but it was not easy to obtain a perfect 1:1 relationship due to differences in wavelengths of Red and NIR reflectances and dates of data acquisition.…”

Section: Resultsmentioning

confidence: 99%

“…Researchers also found no difference in the ability to detect N rate effects on a maize crop canopy between aerial and ground-based active sensors (Krienke et al, 2017). Gozdowski et al (2020) reported that a linear model might be adequate to describe the relation- ship between ground and satellite-based RNDVI; but it was not easy to obtain a perfect 1:1 relationship due to differences in wavelengths of Red and NIR reflectances and dates of data acquisition. Linear regression coefficient (r 2 ) and p value of F statistics, for the relationships between grain yield and protein concentration with vegetation indices from active and passive sensors' reflectance data, are presented in Table 2.…”

Section: Relationship Between Active and Passive Sensorsmentioning

confidence: 99%

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Comparisons of sensors to predict spring wheat grain yield and protein content

Veverka

Chatterjee

Carlson³

2021

Agronomy Journal

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Canopy vegetation indices can predict the spatial variability in grain yield before harvest across large spatial scale. Spring wheat (Triticum aestivum L.) canopy reflectance data using a hand-held active sensor (RapidScan CS-45) and a small unmanned aerial vehicle (sUAV)-based passive light optical sensor (Micasense Red-Edge) were collected three times during flag leaf to heading stages (in the month of June and July) from 16 site-years across the Red River Valley of Minnesota and North Dakota. Potential of vegetation indices (VIs), red normalized vegetation (RNDVI) and red edge normalized vegetation (RENDVI), were compared to predict grain yield and protein content. Linear regression between two sensors showed a significant (p < .05) relationship based on RNDVI (R 2 = .69) and RENDVI (R 2 = .55) values (n = 48). Only for the 2nd year, RENDVI from both sensors could predict the grain yield; but only the RENDVI from sUAV-based passive sensor could predict the protein content at Haun stages between five and eight (R 2 = .60, RMSE = 14.8, p = .02). Use of sUAV based passive sensor has potential to predict protein content but selecting optimal growth stage and validation of developed regression models in neighboring fields are critical for the accurate prediction.Abbreviations: RENDVI, rededge normalized vegetation index; RNDVI, red normalized vegetation index; sUAV, small unmanned aerial vehicle.

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“…Canopy spectral reflectance indices like the normalized difference vegetation index (NDVI) are useful for estimating crop yield at multiple scales in space [2][3][4] because the absorption and reflectance of red and near infrared wavelengths is a good proxy for leaf area, which in turn is a good proxy for growth [5] and yield [6]. Following this notion, the yields of many different crops have been estimated using NDVI and related vegetation indices using aerial and satellite-based platforms [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

The spatial dynamics of wheat yield and protein content at the field scale

Stoy¹,

Khan²,

Wipf³

et al. 2021

Preprint

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Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to predict their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA and tested the ability of normalized difference vegetation index (NDVI) measurements from an unpiloted aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to wheat measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was ‘averaged out’ at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact yield and GPC.

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Comparison of winter wheat NDVI data derived from Landsat 8 and active optical sensor at field scale

Cited by 28 publications

References 22 publications

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Comparisons of sensors to predict spring wheat grain yield and protein content

The spatial dynamics of wheat yield and protein content at the field scale

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