Abstract
We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing.
Highlights
Mulching farmland with plastic films can effectively reduce soil moisture evaporation and improve the efficiency of water use
Our results show that the accuracy of the overall classification of plastic-mulched farmland (PMF) and other land cover types was higher than 90% for all three synthetic aperture radar (SAR) data types, the full-pol data had the highest classification accuracy
The development of polarization decomposition technology has allowed polarization decomposition to be more widely used for remote sensing image classification
Summary
Mulching farmland with plastic films can effectively reduce soil moisture evaporation and improve the efficiency of water use. Monitoring changes to the PMF distribution pattern and area is relevant to both current and future agricultural management in order to meet the growing demand for high-quality, sustainable agriculture [2]. Mulching farmland with plastic films has a positive effect on agricultural productivity because it can raise soil temperature and keep soil moisture. Residues of plastic film in fields are beginning to have a negative effect on agricultural environments. Such large areas of PMF will inevitably have an impact on the surface energy balance and will put further pressure on agricultural environments [3]. Accurate monitoring of spatial and temporal changes to PMF distributions is very relevant to the study of environmental change
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