@article{9dfb64a0a0aa412b80364dc0e23a8a6f,
title = "Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization",
abstract = "Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ, data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R2 0.36 and RMSE 2.5 t ha−1. This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields.",
keywords = "Hyperspectral reflectance, Potato (Solanum tuberosum L.), Retrieval, SCOPE model, Time series, Yield, UT-Gold-D, ITC-ISI-JOURNAL-ARTICLE, ITC-GOLD",
author = "E. Prikaziuk and G. Ntakos and {ten Den}, Tamara and Pytrik Reidsma and {van der Wal}, Tamme and {van der Tol}, C.",
note = "Funding Information: EP, GN, TvdW and CvdT has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995 . Funding for experiments and TtD and PR came from the project “Yield gap analysis for sustainable potato production (Potato Gap NL)”, project number 16891, funded by NWO-TTW and Holland Innovative Potato. Funding Information: The authors thank ITC GeoScience Lab personnel, Dr. Caroline Lievens, Kathrin Zweers-Peter and Camilla Marcatelli, WUR researcher Inge van de Wiel for the organization of the field work, Open Teelten Lelystad and Proefboerderij Vredepeel for lending the experimental fields. This research was supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu). EP, GN, TvdW and CvdT has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995. Funding for experiments and TtD and PR came from the project “Yield gap analysis for sustainable potato production (Potato Gap NL)”, project number 16891, funded by NWO-TTW and Holland Innovative Potato. Funding Information: The authors thank ITC GeoScience Lab personnel, Dr. Caroline Lievens, Kathrin Zweers-Peter and Camilla Marcatelli, WUR researcher Inge van de Wiel for the organization of the field work, Open Teelten Lelystad and Proefboerderij Vredepeel for lending the experimental fields. This research was supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu) . Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2022",
month = nov,
doi = "10.1016/j.jag.2022.102997",
language = "English",
volume = "114",
journal = "International Journal of Applied Earth Observation and Geoinformation",
issn = "1569-8432",
publisher = "Elsevier B.V.",
}