Integrating Landsat, Sentinel-2 and Sentinel-1 time series for mapping nintermediate crops

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F25%3A00636861" target="_blank" >RIV/86652079:_____/25:00636861 - isvavai.cz</a>

Result on the web

<a href="https://www.tandfonline.com/doi/pdf/10.1080/22797254.2025.2507738" target="_blank" >https://www.tandfonline.com/doi/pdf/10.1080/22797254.2025.2507738</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/22797254.2025.2507738" target="_blank" >10.1080/22797254.2025.2507738</a>

Result language

angličtina

Original language name

Integrating Landsat, Sentinel-2 and Sentinel-1 time series for mapping nintermediate crops

Original language description

Intermediate crops are grown between main crops to protect soils and nutrients when fields nwould otherwise be bare. Despite being an essential constituent of cropping systems, spatial ninformation on intermediate crops is scarce. Here, we propose a classification algorithm that ncombines field data, satellite imagery from multiple optical sensors and synthetic-aperture nradar (SAR) data to map intermediate crops across Brandenburg, Germany. We trained random nforest models using different sets of input features, including spectral-temporal metrics from noptical data, metrics derived from SAR data and information on the scheduled main crop. The nbest classification was based on a combination of all input features and achieved an overall naccuracy of 92.9%. Intermediate crops were overestimated, which can be partly attributed to nmisclassification of volunteers and weeds as intermediate crops. The overestimation was nmitigated by aggregating results to the field level. Our results highlight the need for good noptical data coverage during autumn and winter to accurately map intermediate crops while ndemonstrating the ability of SAR data to enhance classification accuracy. Overall, our study nshows the potential of remote sensing methods to capture the characteristics of intermediate ncrops and derive spatially explicit data for monitoring sustainable agricultural practices.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20705 - Remote sensing

Project

<a href="/en/project/EH22_008%2F0004635" target="_blank" >EH22_008/0004635: AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

European Journal of Remote Sensing

ISSN

2279-7254

e-ISSN

2279-7254

Volume of the periodical

58

Issue of the periodical within the volume

1

Country of publishing house

IT - ITALY

Number of pages

18

Pages from-to

2507738

UT code for WoS article

001505351200001

EID of the result in the Scopus database

2-s2.0-105007787641