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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

<a href="/cs/project/EH22_008%2F0004635" target="_blank" >EH22_008/0004635: AdAgriF - Pokročilé metody redukce emisí a sekvestrace skleníkových plynů v zemědělské a lesní krajině pro mitigaci změny klimatu</a><br>

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

European Journal of Remote Sensing

ISSN

2279-7254

e-ISSN

2279-7254

Svazek periodika

58

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

18

Strana od-do

2507738

Kód UT WoS článku

001505351200001

EID výsledku v databázi Scopus

2-s2.0-105007787641