Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level
Climate change and increasing weather and seasonal dynamics challenge agricultural landscapes. To cope with this challenge information on crop performance is key. This study presents a novel framework for bridging landscape-scale vegetation dynamics with field-level crop phenology using Sentinel-1 r...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Remote Sensing |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2025.1610005/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849225174752165888 |
|---|---|
| author | Johannes Löw Steven Hill Insa Otte Christoph Friedrich Michael Thiel Tobias Ullmann Christopher Conrad |
| author_facet | Johannes Löw Steven Hill Insa Otte Christoph Friedrich Michael Thiel Tobias Ullmann Christopher Conrad |
| author_sort | Johannes Löw |
| collection | DOAJ |
| description | Climate change and increasing weather and seasonal dynamics challenge agricultural landscapes. To cope with this challenge information on crop performance is key. This study presents a novel framework for bridging landscape-scale vegetation dynamics with field-level 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. |
| format | Article |
| id | doaj-art-4b2dfd55cabb4cb3829e46e78749c08b |
| institution | Kabale University |
| issn | 2673-6187 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj-art-4b2dfd55cabb4cb3829e46e78749c08b2025-08-25T05:26:27ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872025-08-01610.3389/frsen.2025.16100051610005Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field levelJohannes Löw0Steven Hill1Insa Otte2Christoph Friedrich3Michael Thiel4Tobias Ullmann5Christopher Conrad6Department of Geoecology, Martin-Luther-University Halle-Wittenberg, Halle, GermanyEarth Observation Research Cluster, University of Würzburg, Würzburg, GermanyEarth Observation Research Cluster, University of Würzburg, Würzburg, GermanyEarth Observation Research Cluster, University of Würzburg, Würzburg, GermanyEarth Observation Research Cluster, University of Würzburg, Würzburg, GermanyEarth Observation Research Cluster, University of Würzburg, Würzburg, GermanyDepartment of Geoecology, Martin-Luther-University Halle-Wittenberg, Halle, GermanyClimate change and increasing weather and seasonal dynamics challenge agricultural landscapes. To cope with this challenge information on crop performance is key. This study presents a novel framework for bridging landscape-scale vegetation dynamics with field-level 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.https://www.frontiersin.org/articles/10.3389/frsen.2025.1610005/fullSentinel-1phenologyInSAR coherencegrowing degree daysDEMMIN |
| spellingShingle | Johannes Löw Steven Hill Insa Otte Christoph Friedrich Michael Thiel Tobias Ullmann Christopher Conrad Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level Frontiers in Remote Sensing Sentinel-1 phenology InSAR coherence growing degree days DEMMIN |
| title | Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level |
| title_full | Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level |
| title_fullStr | Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level |
| title_full_unstemmed | Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level |
| title_short | Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level |
| title_sort | integrating the landscape scale supports sar based detection and assessment of the phenological development at the field level |
| topic | Sentinel-1 phenology InSAR coherence growing degree days DEMMIN |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2025.1610005/full |
| work_keys_str_mv | AT johanneslow integratingthelandscapescalesupportssarbaseddetectionandassessmentofthephenologicaldevelopmentatthefieldlevel AT stevenhill integratingthelandscapescalesupportssarbaseddetectionandassessmentofthephenologicaldevelopmentatthefieldlevel AT insaotte integratingthelandscapescalesupportssarbaseddetectionandassessmentofthephenologicaldevelopmentatthefieldlevel AT christophfriedrich integratingthelandscapescalesupportssarbaseddetectionandassessmentofthephenologicaldevelopmentatthefieldlevel AT michaelthiel integratingthelandscapescalesupportssarbaseddetectionandassessmentofthephenologicaldevelopmentatthefieldlevel AT tobiasullmann integratingthelandscapescalesupportssarbaseddetectionandassessmentofthephenologicaldevelopmentatthefieldlevel AT christopherconrad integratingthelandscapescalesupportssarbaseddetectionandassessmentofthephenologicaldevelopmentatthefieldlevel |