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.

Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI

“…The close similarity in the temporal trends observed for NDVI, GCC, FIPAR, and LAI suggests that potato phenology can be remotely monitored using NDVI. As the season progresses, the greenness of the crop increases, providing distinct and detectable spectral signatures which change with growth stages [38]. Remotely sensed phenology information therefore offers a more dynamic approach to the development of Kc than the FAO-56 approach.…”

Long-Run Equilibrium in the Market of Mobile Services in the USA

“…Note that linear combinations of the sigmoids together with constants are trivially such a Stone algebra.) In the following years, the theory of approximating real data with the sigmoids has seen rapid development, e.g., see [30][31][32][33][34].…”

“…Importantly, there is a trade off between the accuracy of the fit and parsimony. To pick a representation, it is common to gauge models' performance by testing a hypothesis of matching real data with the approximations, e.g., see [30][31][32][33][34].…”

A Data-Driven Farm Typology as a Basis for Agricultural Land Use Decisions

“…We evaluated the results of the questionnaire survey and multivariate statistics with parcel information from the Land Parcel Identification System (LPIS) databases, which is part of the Integrated Administration and Control System (IACS), the main administrative tool for managing farmers' applications for income support. The LPIS provides spatial information on the area under the main crops each year [42,43]. LPIS data were available and merged for the period of 2000-2020.…”