Publications

Myers, E; Kerekes, J; Daughtry, C; Russ, A (2021). Effects of Satellite Revisit Rate and Time-Series Smoothing Method on Throughout-Season Maize Yield Correlation Accuracy. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 14, 12007-12021.

Abstract
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.

DOI:
10.1109/JSTARS.2021.3129148

ISSN:
1939-1404