Mapping irrigated and rainfed wheat areas using high spatial–temporal resolution data generated by Moderate Resolution Imaging Spectroradiometer and Landsat

Lingling Zhang; Hao Feng; Qin’ge Dong; Ning Jin; Tinglong Zhang

doi:10.1117/1.JRS.12.046023

27 November 2018 Mapping irrigated and rainfed wheat areas using high spatial–temporal resolution data generated by Moderate Resolution Imaging Spectroradiometer and Landsat

Lingling Zhang, Hao Feng, Qin’ge Dong, Ning Jin, Tinglong Zhang

Author Affiliations +

Funded by: National High Technology Research and Development Program of China, Ministry of Education and the State Administration of Foreign Experts Affairs, National Natural Science Foundation of China (NSFC)

Journal of Applied Remote Sensing, Vol. 12, Issue 4, 046023 (November 2018). https://doi.org/10.1117/1.JRS.12.046023

Abstract

The detailed area and spatial distribution of irrigated and rainfed wheat can help forecast wheat yield and study water use efficiency. However, the similar spectral characteristics of irrigated and rainfed wheat make it difficult to separate them with low-spatial resolution or several high-spatial resolution images on the high heterogeneity of the southern Loess Plateau. To solve this challenge, this study used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM) to generate time series of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) at a 30-m resolution by fusing Moderate Resolution Imaging Spectroradiometer and Landsat data. Then, the phenological feature extracted from the predicted NDVI is combined with an auxiliary dataset to classify irrigated and rainfed wheat using the support vector machine classifier. An overall classification accuracy of 93.7% and a Kappa coefficient of 0.91 are achieved. Compared with corresponding high-resolution Google Earth images, the spatial distribution of the classification was consistent with actual land cover. This study demonstrates that the classification approach could classify irrigated and rainfed wheat in high heterogeneity regions and crops with smaller spectral characteristic differences. Moreover, it could be implemented across larger geographic regions.

Citation Download Citation

Lingling Zhang, Hao Feng, Qin’ge Dong, Ning Jin, and Tinglong Zhang "Mapping irrigated and rainfed wheat areas using high spatial–temporal resolution data generated by Moderate Resolution Imaging Spectroradiometer and Landsat," Journal of Applied Remote Sensing 12(4), 046023 (27 November 2018). https://doi.org/10.1117/1.JRS.12.046023

Received: 7 July 2018; Accepted: 23 October 2018; Published: 27 November 2018

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