Innovative pheno-network model in estimating crop phenological stages with satellite time series

Abstract

Large-scale remote monitoring of crop phenological development is vital for scheduling farm management activities and estimating crop yields. Tracking crop phenological progress is also crucial to understand agricultural responses to environmental stress and climate change. During the past decade, time series of remotely sensed imagery has been increasingly employed to monitor the seasonal growing dynamics of crops. A variety of curve-fitting based phenological methods have been developed to estimate critical phenological transition dates. However, those phenological methods are typically parametric by making mathematical assumptions of crop phenological processes and usually require year-long satellite observations for parameter training. The assumption and constraint make those methods inadequate for phenological monitoring in heavy cloud-contaminated regions or in complex agricultural systems. The objective of this study is to estimate crop phenological stages with satellite time series using a complex network-based phenological model (i.e., “pheno-network”). The innovative pheno-network model is non-parametric without mathematically defined phenological assumptions and can be constructed with partial-year remote sensing data. Rooted in network theory, the pheno-network model characterizes the complex phenological process with spectrally defined nodes and edges. It provides an innovative network representation to model the temporal dynamics of spectral reflectance of crops throughout the growing season. With corn and soybean in Illinois as a case study, the pheno-network model was devised to estimate their phenological transition dates along the leaf senescence trajectory from 2002 to 2017. Results indicated that the estimated transition dates of corn had strong correlation with its ground-observed mature stage. As for soybean, the estimated transition dates were closely associated with its dropping leaves stage. The pheno-network model shows marked potential to advance phenological monitoring in complex agricultural diversified and intensified systems.