Abstract:

Satellite remote sensing is an important technique for cropland resources survey, while time series of remote sensing images, particularly, are of great practical significance for cropland extraction. Optical remote sensing imagery is largely affected by illumination and atmospheric conditions, which limits available satellite images within a year, especially where cloudy or rainy weather frequently occurs. Synthetic Aperture Radar (SAR), on the other hand, is able to acquire data throughout the day under any weather condition. However, owing to the influence of speckle noise, very little work has been done to use SAR image time series for feature extraction. This study examines the applicability of SAR image time series for cropland extraction, and Xuzhou City in Jiangsu Province was chosen as the study area. A total of 11 ENVISAT ASAR images covering the study area and dated from December 2009 to December 2010 were selected to establish a SAR time series as experimental data. Thirty cropland sampling regions with the size of 5 pixels × 5 pixels were visually chosen to calculate the consistency of cropland backscatter signatures in the temporal domain, at both neighboring location (inside each sampling region) and remote location (beyond the sampling regions). Euclidean distance method, correlation method, and dynamic time warp (DTW) method were then adopted to extract cropland pixels in the study area. The experiment results show high backscattering consistency for neighboring cropland pixels, with a coefficient of variation of 9.96%. A lower but still satisfactory backscattering consistency was derived by remote cropland pixels in the study area, with a coefficient of variation of 15.27%. Despite the inherent speckle noises of SAR data, the general characteristics of time series for cropland backscatter coefficient correspond well with crop calendar. For the three selected methods, correlation method performed best, which produced a correctness of 86.25% and completeness of 80.70%. Euclidean method took the second place, with a correctness of 76.40% and a completeness of 71.93%. DTW achieved the lowest accuracy, with a correctness of 62.15% and completeness of 77.78%. This research shows that as a new data organizing form, time series of SAR images can be used for cropland extraction effectively.

Key words: time series, SAR, cropland, consistency, coefficient of variation, correlation coefficient, DTW