Early-season crop type mapping using 30-m reference time series

• Published in: Journal of Integrative Agriculture Vol. 19; no. 7; pp. 1897 - 1911
• Main Authors: HAO, Peng-yu, TANG, Hua-jun, CHEN, Zhong-xin, MENG, Qing-yan, KANG, Yu-peng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2020; Elsevier
• Subjects: crop classification; early season; Hengshui; Landsat; reference time series; Sentinel-2; early season; crop classification; reference time series; Landsat; Sentinel-2; Hengshui
• ISSN: 2095-3119; 2352-3425
• DOI: 10.1016/S2095-3119(19)62812-1

Early-season crop type mapping could provide important information for crop growth monitoring and yield prediction, but the lack of ground-surveyed training samples is the main challenge for crop type identification. Although reference time series based method (RBM) has been proposed to identify crop types without the use of ground-surveyed training samples, the methods are not suitable for study regions with small field size because the reference time series are mainly generated using data set with low spatial resolution. As the combination of Landsat data and Sentinel-2 data could increase the temporal resolution of 30-m image time series, we improved the RBM by generating reference normalized difference vegetation index (NDVI)/enhanced vegetation index (EVI) time series at 30-m resolution (30-m RBM) using both Landsat and Sentinel-2 data, then tried to estimate the potential of the reference NDVI/EVI time series for crop identification at early season. As a test case, we tried to use the 30-m RBM to identify major crop types in Hengshui, China at early season of 2018, the results showed that when the time series of the entire growing season were used for classification, overall classification accuracies of the 30-m RBM were higher than 95%, which were similar to the accuracies acquired using the ground-surveyed training samples. In addition, cotton, spring maize and summer maize distribution could be accurately generated 8, 6 and 8 weeks before their harvest using the 30-m RBM; but winter wheat can only be accurately identified around the harvest time phase. Finally, NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time phases. Comparing with the previous RBM, advantage of 30-m RBM is that the method could use the samples of the small fields to generate reference time series and process image time series with missing value for early-season crop classification; while, samples collected from multiple years should be further used so that the reference time series could contain more crop growth conditions.