A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning

TL;DR

This study introduces a machine learning framework that fuses Sentinel-1 and Sentinel-2 satellite data with climate information to accurately estimate crop phenology stages at high spatial resolution, aiding agricultural decision-making.

Contribution

It presents a novel fusion approach using ML to combine satellite and climate data for detailed crop phenology prediction, improving accuracy and transferability across Germany.

Findings

Achieved R2 > 0.43 in phenology prediction

Mean Absolute Error of 6 days across stages

Effective transferability across spatial and temporal scales

Abstract

Crop phenology describes the physiological development stages of crops from planting to harvest which is valuable information for decision makers to plan and adapt agricultural management strategies. In the era of big Earth observation data ubiquity, attempts have been made to accurately detect crop phenology using Remote Sensing (RS) and high resolution weather data. However, most studies have focused on large scale predictions of phenology or developed methods which are not adequate to help crop modeler communities on leveraging Sentinel-1 and Sentinal-2 data and fusing them with high resolution climate data, using a novel framework. For this, we trained a Machine Learning (ML) LightGBM model to predict 13 phenological stages for eight major crops across Germany at 20 m scale. Observed phonologies were taken from German national phenology network (German Meteorological Service; DWD) between 2017 and 2021. We proposed a thorough feature selection analysis to find the best combination of RS and climate data to detect phenological stages. At national scale, predicted phenology resulted in a reasonable precision of R2 > 0.43 and a low Mean Absolute Error of 6 days, averaged over all phenological stages and crops. The spatio-temporal analysis of the model predictions demonstrates its transferability across different spatial and temporal context of Germany. The results indicated that combining radar sensors with climate data yields a very promising performance for a multitude of practical applications. Moreover, these improvements are expected to be useful to generate highly valuable input for crop model calibrations and evaluations, facilitate informed agricultural decisions, and contribute to sustainable food production to address the increasing global food demand.