Abstract
A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.
Highlights
One of the requirements for ensuring food security is a timely inventory of agricultural areas and the regional proportion of different crop types [1]
We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest
The classification result using Sentinel-1 in March was significantly better than using Sentinel-2 in March (OA of 47% vs. 39%, kappa of 0.31 vs. 0.22), while the overall accuracy of optical-only classification across the whole season performed slightly better than synthetic aperture radar (SAR)-only classification across the season (OA of 78% vs. 76%, kappa of 0.70 vs. 0.68)
Summary
One of the requirements for ensuring food security is a timely inventory of agricultural areas and the regional proportion of different crop types [1]. To the public sector, the private sector, including the agro- and insurance industries, benefit as well from early season crop inventories as an important component of crop production estimation and agricultural statistics [2,3]. In addition to regional estimates, early crop type information at the parcel level is an essential prerequisite for any crop monitoring activity that aims at early anomaly detection. The most conventional way of performing satellite-based crop classifications is the use of optical imagery. This has started with the Landsat missions [8,9,10]. The spatial, temporal, and spectral resolutions have increased, constantly improving the classification results [11,12,13,14]
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