First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe
TLDR
The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational Sentinel-2 (S2) data for mapping crop types and tree species and confirmed its expected capabilities to produce reliable land cover maps.Abstract:
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.read more
Citations
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Journal ArticleDOI
Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
TL;DR: Object-based time-weighted dynamic time warping (TWDTW) method achieved comparable classification results to RF in Romania and Italy, but RF achieved better results in the USA, where the classified crops present high intra-class spectral variability.
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Water bodies' mapping from Sentinel-2 imagery with Modified Normalized Difference Water Index at 10-m spatial resolution produced by sharpening the swir band
TL;DR: A novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening, which shows that MND WI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI.
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Copernicus Sentinel-2A Calibration and Products Validation Status
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Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
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Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine
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TL;DR: Wang et al. as mentioned in this paper used the Normalized Difference Vegetation Index (NDVI) trajectory to detect major land cover dynamics in Beijing and classified the land cover types in 2015 with the Google Earth Engine (GEE) cloud calculation.
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