The potential of Sentinel-1 time series for large-scale assessment of maize and wheat phenology across Germany

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F25%3A00637752" target="_blank" >RIV/86652079:_____/25:00637752 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/15481603.2025.2531593" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/15481603.2025.2531593</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/15481603.2025.2531593" target="_blank" >10.1080/15481603.2025.2531593</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The potential of Sentinel-1 time series for large-scale assessment of maize and wheat phenology across Germany

Popis výsledku v původním jazyce

Monitoring crop phenometrics is crucial for understanding crop conditions and dynamics. Dense time series are needed for accurate information. Recent work has shown that remote sensing data can be used effectively for many crops but commonly used optical data often suffer from cloud cover and atmospheric effects. Synthetic Aperture Radar (SAR) sensors provide cloud-free dense time series. This study evaluates the potential of SAR data by analyzing Sentinel-1 VH and VV signals and cross-ratio (CR, VH/VV) time series from 2017 to 2020 for wheat and maize in South and East Germany. Phenometrics were derived using two techniques over smoothed time series: inflection point detection and curvature change rate analysis for specific crops and regions during the growing season. Results were compared with field-level crop growth observations, the Copernicus High-Resolution Vegetation Phenology and Productivity (HR-VPP) product, and phenological data from the German Weather Service (DWD). Spatially explicit maps for Brandenburg, Saxony, and Bavaria were derived, showing the Start, Shooting/Tassel, Maximum, Ripeness, and End of the Season based on thresholds from field-level analysis. SAR data effectively captured growth stages with a novel slope-based approach for detecting curve fluctuations. Wheat phenometrics showed a 4-6 day difference from reference data for most stages, while maize exhibited a 2-10 day difference for emergence, growth, and harvest. Regional results indicated homogeneous spatial distributions for both crops. In conclusion, this study highlights the potential of SAR data for spatially explicit retrieval of crop phenometrics, offering improved results compared to optical data. This approach can also support precision agriculture practices, optimize resource use, and improve yield predictions, contributing to more sustainable agricultural systems.

Název v anglickém jazyce

The potential of Sentinel-1 time series for large-scale assessment of maize and wheat phenology across Germany

Popis výsledku anglicky

Monitoring crop phenometrics is crucial for understanding crop conditions and dynamics. Dense time series are needed for accurate information. Recent work has shown that remote sensing data can be used effectively for many crops but commonly used optical data often suffer from cloud cover and atmospheric effects. Synthetic Aperture Radar (SAR) sensors provide cloud-free dense time series. This study evaluates the potential of SAR data by analyzing Sentinel-1 VH and VV signals and cross-ratio (CR, VH/VV) time series from 2017 to 2020 for wheat and maize in South and East Germany. Phenometrics were derived using two techniques over smoothed time series: inflection point detection and curvature change rate analysis for specific crops and regions during the growing season. Results were compared with field-level crop growth observations, the Copernicus High-Resolution Vegetation Phenology and Productivity (HR-VPP) product, and phenological data from the German Weather Service (DWD). Spatially explicit maps for Brandenburg, Saxony, and Bavaria were derived, showing the Start, Shooting/Tassel, Maximum, Ripeness, and End of the Season based on thresholds from field-level analysis. SAR data effectively captured growth stages with a novel slope-based approach for detecting curve fluctuations. Wheat phenometrics showed a 4-6 day difference from reference data for most stages, while maize exhibited a 2-10 day difference for emergence, growth, and harvest. Regional results indicated homogeneous spatial distributions for both crops. In conclusion, this study highlights the potential of SAR data for spatially explicit retrieval of crop phenometrics, offering improved results compared to optical data. This approach can also support precision agriculture practices, optimize resource use, and improve yield predictions, contributing to more sustainable agricultural systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

GIScience & Remote Sensing

ISSN

1548-1603

e-ISSN

1943-7226

Svazek periodika

62

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

29

Strana od-do

2531593

Kód UT WoS článku

001531027300001

EID výsledku v databázi Scopus

2-s2.0-105010928896