The red-edge bands of Sentinel-2 allow for a greater diversity of spectral Vegetation Indices (VIs) to be calculated and used for vegetation characterization. We evaluated the utility of a selection of 40 VIs to derive Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and fraction of vegetation Cover (fCover) of winter wheat crop using regression method. We calibrated models for specific winter wheat development stages and compared the predictions with all-season models. The most useful VIs could be grouped into several types: (1) indices which use green and NIR band, (2) indices based on red edge bands, (3) indices which use red and NIR band and (4) the MCARI/OSAVIre index. It was found that fAPAR and fCover could be predicted with good accuracy using all-season models (rRMSE of 14% and 23% respectively), while LAI showed lower accuracy (rRMSE = 45%). The LAI model calibrated over the tillering stage was recommended for usage in the early stages of crop development. Compared with the existing methods for biophysical variables retrieval from Sentinel-2 data (i.e. the Level2B processor in SNAP) the regression approach based on VIs showed to be a viable alternative.
Abstract: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 km 2 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
OPEN ACCESSRemote Sens. 2015, 7 13844 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".
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