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ORIGINAL RESEARCH article

Front. Remote Sens.

Sec. Microwave Remote Sensing

Volume 6 - 2025 | doi: 10.3389/frsen.2025.1610005

Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level

Provisionally accepted
  • 1Martin Luther University of Halle-Wittenberg, Halle, Germany
  • 2Julius Maximilian University of Würzburg, Würzburg, Bavaria, Germany

The final, formatted version of the article will be published soon.

This study presents a novel framework for bridging landscape-scale vegetation dynamics with fieldlevel crop phenology using Sentinel-1 radar time series. Unlike previous approaches that focus on local algorithm optimisation or SAR feature selection, this work integrates two scales: (1) landscape patterns derived from annual distributions of time series metrics (TSMs) and (2) field-level phenology, both linked to growing degree days (GDD). TSMs were generated through breakpoint analyses over different smoothing intensities for Sentinel-1 polarisation (PolSAR) and interferometric coherence (InSAR) features, capturing crop, orbit and sensor-specific responses. The framework quantifies uncertainties inherent in both remote sensing and ground observations, and evaluates trackable progress (phenological stage detectability) and tracking range (GDD variance around stages) to assess accuracy under variable acquisition geometries, weather and smoothing parameters. Applied to the DEMMIN site (Germany), the analysis revealed consistent TSM-GDD relationships for wheat, rape, and sugar beet, with descriptors such as soil fertility and water availability explaining spatial patterns (R² ≈ 0.8). Key novelties include the identification of low tracking ranges in drought years, the demonstration of the impact of orbit-specific incidence angles on monitoring fidelity, and the highlighting of Sentinel-1's ability to resolve phenological variance across fragmented landscapes. By harmonising multi-scale SAR time series with agro-meteorological data, this approach advances transferable methods for operational crop monitoring, supporting precision agriculture and regional yield assessment beyond localised models.

Keywords: Sentinel-1, Phenology, InSAR coherence, Growing degree days, DEMMIN TWI:0.15, OCS: 0.17 Elevation: 0.17 R squared: 0.85 RMSE: 1.5 MAE: 1.1 Canola SOC, aspect, TWI

Received: 11 Apr 2025; Accepted: 05 Aug 2025.

Copyright: © 2025 Löw, Hill, Otte, Friedrich, Thiel, Ullmann and Conrad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Johannes Löw, Martin Luther University of Halle-Wittenberg, Halle, Germany

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