Autor: |
Emily Myers, John Kerekes, Craig Daughtry, Andrew Russ |
Jazyk: |
angličtina |
Rok vydání: |
2021 |
Předmět: |
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Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 12007-12021 (2021) |
Druh dokumentu: |
article |
ISSN: |
2151-1535 |
DOI: |
10.1109/JSTARS.2021.3129148 |
Popis: |
Predictions of crop yield made during the growing season aid in crop management and economic planning. Many yield prediction models are made by performing regression between satellite-derived vegetation indices (VI) and yield. This article studied the effects of time-series end date and satellite imaging frequency on the accuracy of VI-yield correlation. Daily, 3-m resolution, multispectral images were obtained over a maize field near Beltsville, MD, USA, in 2018 and 2019. Plot-average green normalized difference vegetation index (GNDVI) was extracted from these images. GNDVI time-series data were resampled to different revisit intervals, gap-filled and smoothed, temporally realigned, and correlated with plot-average yield at every day of the growing season. These experiments were then repeated with data removed from the end of the time-series. All methods tested performed well on time-series ending 72 d or more after green-up in 2019 (R-squared = 0.95) or time-series ending 65 d or more after green-up in 2018 (Flexfit R-squared = 0.92; shape model fitting R-squared = 0.89). All methods had poor correlation for time-series ending prior to the day of peak GNDVI. Mean R-squared values for GNDVI-yield correlations decreased with increasing revisit intervals. These trends were stronger in the 2019 data, with mean R-squared decreasing by more than 0.05 when sampled from 1 to 30-d revisit intervals (Flexfit) or to 22-d revisit intervals (shape model fitting). These findings, along with cloud-contamination statistics, were used to recommend an optimal methodology for yield correlation and an optimal overpass frequency of 1–4 d for future yield-monitoring satellite systems. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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