Impact of High-Cadence Earth Observation in Maize Crop Phenology Classification

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.

Suitability of Satellite Imagery for Surveillance of Maize Ear Damage by Cotton Bollworm (Helicoverpa armigera) Larvae

“…Based on satellite imagery, sweet, seed, and commercial grain maize crops [53], and varieties and hybrids [54], can be differentiated. Changes in maize phenology [55,56] can be detected. Further, the yield estimation of maize relies on optical remote sensing, primarily based on the time series of satellite imagery [55,57].…”

“…According to our review, there is no rapid, cost-and labor-effective CBW damage surveillance method for maize, despite the high economic impact of the CBW. Satellitederived surface reflectance provides insights into the visual characteristics and changes in maize [47,55,56,70] that play an important role in determining the oviposition preferences of CBW adults under maize field conditions [13,30,[33][34][35][36][37][38][39]75]. Our hypothesis is that models, based on satellite imagery, can be developed, that will improve CBW surveillance (including both monitoring and prediction) for maize crops, aligned with an optimal time period and maize phenology.…”