Extracting county-level agricultural greenhouses using Sentinel-1/2 data fusion

As a special feature type, agricultural greenhouses were relatively easy to recognize on high spatial resolution remote sensing images with high accuracy. However, most high spatial resolution images had to be purchased commercially, and their availability was limited. In order to improve the economy and convenience of precise extraction of county-level agricultural greenhouses, this study used free and easily available non high spatial resolution Sentinel-1 (radar) and Sentinel-2 (optical) remote sensing images for data fusion, combined with spectral index, texture extraction, principal component analysis and other methods, to construct a multidimensional feature set space, and adopted multiple classification and recognition methods (cases) to identify and extract county-level agricultural greenhouses. The results indicated that: 1) The county’s agricultural greenhouses could be extracted with high precision by utilizing just Sentinel-1/2 (10 m resolution) remote sensing images, bolstered by suitable classification techniques (case). 2) The fusion of Sentinel-1 (radar) and Sentinel-2 (optical) remote sensing data could be helpful for improving the recognition accuracy of agricultural greenhouses. Compared to using only Sentinel-2 (optical) remote sensing data, the overall accuracy of Sentinel-1/2 data fusion had improved by an average of 1.70 percentage points, with a maximum improvement of 3.29 percentage points; 3) Of all the classification and recognition techniques (case) applied in the paper, the object-oriented method performed well in areas with high greenhouse density; But in areas with low greenhouse density, the accuracy was mediocre and exhibited a strong dependence on region (or scene). After the fusion of optical and radar information, the pixel based recursive feature elimination random forest (RF-RFE) method can achieve an average accuracy of 96.45%, with high and stable accuracy and strong regional adaptability. It was suitable for accurate and efficient extraction of agricultural greenhouses from non high-resolution image in county-level. The technical solution proposed in this paper based on Sentinel-1/2 images, could be provided technical support for the economic, rapid, and efficient extraction of agricultural greenhouses in most counties.