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Upscaling proximal ...
Upscaling proximal sensor N-uptake predictions in winter wheat (Triticum aestivum L.) with Sentinel-2 satellite data for use in a decision support system
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- Wolters, S. (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för mark och miljö,Department of Soil and Environment
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- Söderström, Mats (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för mark och miljö,Department of Soil and Environment
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- Piikki, Kristin (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för mark och miljö,Department of Soil and Environment
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- Reese, Heather, 1964 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences
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Stenberg, M. (författare)
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(creator_code:org_t)
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- 2021-01-21
- 2021
- Engelska.
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Ingår i: Precision Agriculture. - : Springer Science and Business Media LLC. - 1385-2256 .- 1573-1618. ; 22, s. 1263-1283
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld Yara N-Sensor) during 2017 and 2018. Sentinel-2 satellite data from two processing levels (top-of-atmosphere reflectance, L1C, and bottom-of-atmosphere reflectance, L2A) were extracted and related to the proximal sensor data (n = 251). The utility of five vegetation indices for estimation of N-uptake was compared. A linear model based on the red-edge chlorophyll index (CI) provided the best N-uptake prediction (L1C data: r2 = 0.74, mean absolute error; MAE = 14kgha−1) when models were applied on independent sites and dates. Use of L2A data, rather than L1C, did not improve the prediction models. The CI-based prediction model was applied on all fields in an area with intensive winter wheat production. Statistics on N-uptake at the end of the stem elongation growth stage were calculated for 4169 winter wheat fields > 5ha. Within-field variation in predicted N-uptake was > 30kgNha−1 in 62% of these fields. Predicted N-uptake was compared against N-uptake maps derived from tractor-borne Yara N-Sensor measurements in 13 fields (1.7–30ha in size). The model based on satellite data generated similar information as the tractor-borne sensing data (r2 = 0.81; MAE = 7kgha−1), and can therefore be valuable in a DSS for variable-rate N application. © 2021, The Author(s).
Ämnesord
- LANTBRUKSVETENSKAPER -- Annan lantbruksvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Other Agricultural Sciences (hsv//eng)
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Markvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Soil Science (hsv//eng)
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Jordbruksvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Agricultural Science (hsv//eng)
Nyckelord
- Decision support system
- L2A
- Nitrogen fertilisation
- Precision agriculture
- Sentinel-2
- Variable rate application
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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