Next Article in Journal
Radiometric Inter-Calibration between Himawari-8 AHI and S-NPP VIIRS for the Solar Reflective Bands
Next Article in Special Issue
Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas
Previous Article in Journal
Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China
Article

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Remote Sens. 2016, 8(3), 166; https://doi.org/10.3390/rs8030166
Received: 14 January 2016 / Revised: 10 February 2016 / Accepted: 15 February 2016 / Published: 23 February 2016
The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational (August 2015) Sentinel-2 (S2) data for mapping crop types and tree species. In the first case study, an S2 image was used to map six summer crop species in Lower Austria as well as winter crops/bare soil. Crop type maps are needed to account for crop-specific water use and for agricultural statistics. Crop type information is also useful to parametrize crop growth models for yield estimation, as well as for the retrieval of vegetation biophysical variables using radiative transfer models. The second case study aimed to map seven different deciduous and coniferous tree species in Germany. Detailed information about tree species distribution is important for forest management and to assess potential impacts of climate change. In our S2 data assessment, crop and tree species maps were produced at 10 m spatial resolution by combining the ten S2 spectral channels with 10 and 20 m pixel size. A supervised Random Forest classifier (RF) was deployed and trained with appropriate ground truth. In both case studies, S2 data confirmed its expected capabilities to produce reliable land cover maps. Cross-validated overall accuracies ranged between 65% (tree species) and 76% (crop types). The study confirmed the high value of the red-edge and shortwave infrared (SWIR) bands for vegetation mapping. Also, the blue band was important in both study sites. The S2-bands in the near infrared were amongst the least important channels. The object based image analysis (OBIA) and the classical pixel-based classification achieved comparable results, mainly for the cropland. As only single date acquisitions were available for this study, the full potential of S2 data could not be assessed. In the future, the two twin S2 satellites will offer global coverage every five days and therefore permit to concurrently exploit unprecedented spectral and temporal information with high spatial resolution. View Full-Text
Keywords: Sentinel-2; forest; cropland; classification; Random Forest Sentinel-2; forest; cropland; classification; Random Forest
Show Figures

Graphical abstract

MDPI and ACS Style

Immitzer, M.; Vuolo, F.; Atzberger, C. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens. 2016, 8, 166. https://doi.org/10.3390/rs8030166

AMA Style

Immitzer M, Vuolo F, Atzberger C. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sensing. 2016; 8(3):166. https://doi.org/10.3390/rs8030166

Chicago/Turabian Style

Immitzer, Markus; Vuolo, Francesco; Atzberger, Clement. 2016. "First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe" Remote Sens. 8, no. 3: 166. https://doi.org/10.3390/rs8030166

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop