Crop classification based on GF-1/WFV NDVI time series

Abstract: NDVI (Normalized Difference Vegetation Index) time series has been widely used in collecting vegetation information, while most of the present researches about NDVI time series are limited to moderate or low resolution remote sensing images, which affect the accuracy of vegetation information extraction. With the successful launch of the first satellite GF-1 of China High-resolution Earth Observation System, more opportunities have emerged for the construction of NDVI time series with high temporal and high spatial resolution. In this paper, we attempted to build 16 m resolution NDVI time series using images with wide field of view of GF-1 satellite. Different crops have different NDVI time sequence curves during the whole growth period. However, it should be noted that the same crop has a relatively stable growth process and pattern in the same area, which is the basis for the crop classification by using the time series data. While crop phenological characteristics vary largely during a growing cycle, they vary relatively smaller in the different growing cycles. Adopting data containing a complete crop growth cycle can contribute to the extraction of crop phenological information in the construction of NDVI time series. Furthermore, it can avoid the shortage of using data in a calendar year (January to December) to build NDVI time series. In order to carry out studies on crop classification based on GF-1/WFV NDVI time series data, Tangshan, which is located in Hebei Province, China, was chosen as the study area. Through the analysis of NDVI time series curves of samples, we can draw that NDVI time series was able to clearly distinguish crop phenological differences, capture the growth of crop specific features, and identify crop planting patterns in the study area. Irrigation period had the salient features different from that of upland crops before planting paddy rice, which formed the obvious differences compared with other crops. As far as winter wheat was concerned, its NDVI peak was the unique features different from others in overwintering stage. In addition, by analyzing NDVI time series curves in the study area, crop planting patterns can be summarized as follows: winter wheat and summer corn belonged to the planting patterns of two seasons a year, while the rice or peanuts was in a year planting patterns. Based on GF-1/WFV NDVI time series, maximum likelihood method, Mahalanobis distance, minimum distance, neural network, SVM classification methods were used to classify crop in the study area. The results demonstrated that SVM had the best classification accuracies compared to other classification methods, and its overall classification accuracy reached 96.33%. This research showed that GF-1/WFV NDVI with high resolution can be used for crop classification, and can be applied to large area crop classification of remote sensing due to the characteristics of wide coverage. Furthermore, the Harmonic Analysis of Time Series (HANTS) method was used for NDVI time series smoothing, and the results indicated that the processed NDVI time series can better represent different crop phenological characteristics. Then SVM method was used for classifying crop based on smoothed NDVI time series, and the overall classification accuracy was up to 97.57%, which was superior to the one based on the unsmoothed data. The study opens a new era for the domestic high resolution data on agricultural monitoring, and provides insightful reference for the study on the time series of remote sensing classification research.