Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria

Remote Sensing

Eugenia Roumenina³  Clement Atzberger¹  Vassil Vassilev³  Petar Dimitrov³  Ilina Kamenova³  Martin Banov²  Lachezar Filchev³  Georgi Jelev³  Anton Vrieling⁴  Yoshio Inoue⁴ 

[1] Institute of Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences, Vienna (BOKU), 1180 Wien, Austria; E-Mail:;Institute of Soil Science, Agrotechnologies and Plant Protection “Nikola Poushkarov”, Agricultural Academy, 1080 Sofia, Bulgaria; E-Mail:;Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS), 1113 Sofia, Bulgaria; E-Mails:Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS), 1113 Sofia, Bulgaria;

关键词: PROBA-V;    single- and multi-date crop identification;    NDVI time series;    cluster analysis;    GSD;   

DOI  :  10.3390/rs71013843

来源: mdpi

【 摘 要 】

The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to coarser resolution data (e.g., PROBA-V at 300 m) for a 2250 km² test site in Bulgaria. The focus was on winter and summer crop mapping with three to five classes. For classification, single- and multi-date spectral data were used as well as NDVI time series. Our results demonstrate that crop identification using 100 m PROBA-V data performed significantly better in all experiments compared to the PROBA-V 300 m data. PROBA-V multispectral imagery, acquired in spring (March) was the most appropriate for winter crop identification, while satellite data acquired in summer (July) was superior for summer crop identification. The classification accuracy from PROBA-V 100 m compared to PROBA-V 300 m was improved by 5.8% to 14.8% depending on crop type. Stacked multi-date satellite images with three to four images gave overall classification accuracies of 74%–77% (PROBA-V 100 m data) and 66%–70% (PROBA-V 300 m data) with four classes (wheat, rapeseed, maize, and sunflower). This demonstrates that three to four image acquisitions, well distributed over the growing season, capture most of the spectral and temporal variability in our test site. Regarding the PROBA-V NDVI time series, useful results were only obtained if crops were grouped into two broader crop type classes (summer and winter crops). Mapping accuracies decreased significantly when mapping more classes. Again, a positive impact of the increased spatial resolution was noted. Together, the findings demonstrate the positive effect of the 100 m resolution PROBA-V data compared to the 300 m for crop mapping. This has important implications for future data provision and strengthens the arguments for a second generation of this mission originally designed solely as a “gap-filler mission”.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.