Version 1
: Received: 18 January 2022 / Approved: 18 January 2022 / Online: 18 January 2022 (13:51:27 CET)
How to cite:
Inglada, J. A Bayesian Model for Annual Crop Phenological Parameter Estimation Using Optical High Resolution Image Time Series. Preprints2022, 2022010260. https://doi.org/10.20944/preprints202201.0260.v1
Inglada, J. A Bayesian Model for Annual Crop Phenological Parameter Estimation Using Optical High Resolution Image Time Series. Preprints 2022, 2022010260. https://doi.org/10.20944/preprints202201.0260.v1
Inglada, J. A Bayesian Model for Annual Crop Phenological Parameter Estimation Using Optical High Resolution Image Time Series. Preprints2022, 2022010260. https://doi.org/10.20944/preprints202201.0260.v1
APA Style
Inglada, J. (2022). A Bayesian Model for Annual Crop Phenological Parameter Estimation Using Optical High Resolution Image Time Series. Preprints. https://doi.org/10.20944/preprints202201.0260.v1
Chicago/Turabian Style
Inglada, J. 2022 "A Bayesian Model for Annual Crop Phenological Parameter Estimation Using Optical High Resolution Image Time Series" Preprints. https://doi.org/10.20944/preprints202201.0260.v1
Abstract
Vegetation status assessment is crucial for agricultural monitoring and management. Vegetation indices derived from high resolution image time series can be used to derive key phenological parameters for annual crops. In this work, we propose a procedure for the estimation of these parameters and their associated uncertainties. The approach uses Bayesian inference through Markov Chain Monte Carlo in order to obtain the full joint posterior distribution of the phenological parameters given the satellite observations. The proposed algorithm is quantitatively validated on synthetic data. Its use on real data is presented together with an application to real-time within season estimation allowing for phenology forecasting.
Keywords
phenology; satellite image time series; vegetation index; Bayesian inference
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
