Object-Based Automatic Mapping of Winter Wheat Based on Temporal Phenology Patterns Derived from Multitemporal Sentinel-1 and Sentinel-2 Imagery

• Published in: ISPRS international journal of geo-information Vol. 11; no. 8; p. 424
• Main Authors: Wang, Limei, Jin, Guowang, Xiong, Xin, Zhang, Hongmin, Wu, Ke
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Accuracy; Algorithms; Classification; Clustering; Crops; Decision trees; Distance; Euclidean geometry; Field study; Food security; Food supply; Ground truth; Image classification; Image processing; Image segmentation; Imagery; Information processing; Kalman filters; Mapping; multitemporal remote sensing images; Noise; Noise reduction; object-based approach; OTSU; Phenology; Radar imaging; Remote sensing; SAR (radar); Similarity; similarity measure; Similarity measures; Synthetic aperture radar; Triticum aestivum; Vegetation; Wavelet transforms; Wheat; Winter; Winter wheat; winter wheat mapping; China; Henan China
• DOI: 10.3390/ijgi11080424
• ISSN: 2220-9964

Although winter wheat has been mapped by remote sensing in several studies, such mapping efforts did not sufficiently utilize contextual information to reduce the noise and still depended heavily on optical imagery and exhausting classification approaches. Furthermore, the influence of similarity measures on winter wheat identification remains unclear. To overcome these limitations, this study developed an object-based automatic approach to map winter wheat using multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. First, after S1 and S2 images were preprocessed, the Simple Non-Iterative Clustering (SNIC) algorithm was used to conduct image segmentation to obtain homogeneous spatial objects with a fusion of S1 and S2 bands. Second, the temporal phenology patterns (TPP) of winter wheat and other typical land covers were derived from object-level S1 and S2 imagery based on the collected ground truth samples, and two improved distance measures (i.e., a composite of Euclidean distance and Spectral Angle Distance, (ESD) and the difference–similarity factor distance (DSF)) were built to evaluate the similarity between two TPPs. Third, winter wheat objects were automatically identified from the segmented spatial objects by the maximum between-class variance method (OTSU) with distance measures based on the unique TPP of winter wheat. According to ground truth data, the DSF measure was superior to other distance measures in winter wheat mapping, since it achieved the best overall accuracy (OA), best kappa coefficient (Kappa) and more spatial details for each feasible band (i.e., NDVI, VV, and VH/VV), or it obtained results comparable to those for the best one (e.g., NDVI + VV). The resultant winter wheat maps derived from the NDVI band with the DSF measure achieved the best accuracy and more details, and had an average OA and Kappa of 92% and 84%, respectively. The VV polarization with the DSF measure produced the second best winter wheat maps with an average OA and Kappa of 91% and 80%, respectively. The results indicate the great potential of the proposed object-based approach for automatic winter wheat mapping for both optical and Synthetic Aperture Radar (SAR) imagery.