Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning

Nikaein, T.; Kummer, Vineet; Steele-Dunne, S.C.; López-Dekker, Paco

doi:10.1109/IGARSS46834.2022.9883154

Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning

Conference paper (2022)

Authors

T. Nikaein Mathematical Geodesy and Positioning -

Vineet Kummer

S.C. Steele-Dunne Mathematical Geodesy and Positioning -

Paco López-Dekker Mathematical Geodesy and Positioning -

Research Group

Mathematical Geodesy and Positioning () (TU Delft)

DOI: https://doi.org/10.1109/IGARSS46834.2022.9883154

SAR Simulation Sentinel-1 DSSAT Crop Forward-model

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Published Date

2022

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Remote Sensing

Research Group

Mathematical Geodesy and Positioning

Abstract

This paper presents an approach to implement a forward model for Sentinel-1 copol and crosspol backscatter and coherence using crop bio-geophysical parameters namely leaf area index, biomass, canopy height, soil moisture and root zone moisture as inputs for the maize. These required input parameters are generated using Decision Support System for Agrotechnology Transfer (DSSAT), one of the state-of-the-art crop growth models. The predicted SAR signal is generated using Support Vector Regression (SVR) over all the maize fields in an agricultural region, Flevoland, Netherlands. The correlation between simulated signal and observed signal is evaluated.

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