Crop Phenology Modelling Using Proximal and Satellite Sensor Data

Abstract

Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.

Plant phenotyping relevance

衛星・近接センサーデータから作物の生育段階を推定し、センサー間比較とモデル適合性を評価しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's field phenology observations, DHP imagery, and satellite-derived fAPAR/fCover time series are paper-specific phenotyping assets, but the authors state they are available only upon request from the data owners; no public repository, code deposit, or authors' public URL is provided.

No evidence-backed public reproduction asset is currently recorded.