Semantic Scholar Open Access 2022 385 sitasi

Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany

Lukas Blickensdörfer M. Schwieder Dirk Pflugmacher C. Nendel S. Erasmi +1 lainnya

Lihat Sumber DOI

Abstrak

Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and

Penulis (6)

Lukas Blickensdörfer

M. Schwieder

Dirk Pflugmacher

C. Nendel

S. Erasmi

P. Hostert

Format Sitasi

APA MLA BibTeX

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. https://doi.org/10.1016/j.rse.2021.112831

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.rse.2021.112831

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 385×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.rse.2021.112831
Akses: Open Access ✓