Abstract

For farmers, policymakers, and government agencies, it is critical to accurately define agricultural crop phenology and its spatial-temporal variability. At the moment, two approaches are utilized to report crop phenology. On one hand, land surface phenology provides information about the overall trend, whereas weekly reports from USDA-NASS provide information about the development of particular crops at the regional level. High-cadence earth observations might help to improve the accuracy of these estimations and bring more precise crop phenology classifications closer to what farmers demand. The second component of the proposed solution requires the use of robust classifiers (e.g., random forest, RF) capable of successfully managing large data sets. To evaluate this solution, this study compared the output of a RF classifier model using weather, two different satellite sources (Planet Fusion; PF and Sentinel-2; S-2), and ground truth data to improve maize (Zea mays L.) crop phenology classification using two regions of Kansas (Southwest and Central) as a testbed during the 2017 growing season. Our findings suggests that high temporal resolution (PF) data can significantly improve crop classification metrics (f1-score = 0.94) relative to S-2 (f1-score = 0.86). Additionally, a decline in the f1-score between 0.74 and 0.60 was obtained when we assessed the ability of S-2 to extend the temporal forecast for crop phenology. This research highlights the critical nature of very high temporal resolution (daily) earth observation data for crop monitoring and decision making in agriculture.

Highlights

  • IntroductionIn the United States, official phenology crop progress estimates are based on weekly survey data obtained from a network of regional extension agricultural agents [9]

  • When all the variables were combined, we found that the best f1-scores were 0.94 for the SW region and 0.93 for the Central KS (CK) region

  • The study highlights the critical importance of having access to high-cadence earth observation, as well as high-quality ground truth data

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Summary

Introduction

In the United States, official phenology crop progress estimates are based on weekly survey data obtained from a network of regional extension agricultural agents [9]. These reports summarize crop development by agricultural district in percentage terms, combining some growth stages into broader categories. While this is a valuable source of knowledge, the data gathering procedure is time-consuming and labor-intensive, and may not adequately represent a county or district, or the reality of a single farm, with a high degree of fidelity [10].

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