Crop type classification with satellite imageries is widely applied in support of crop production management and food security strategy. The abundant supply of these satellite data is accelerating and blooming the application of crop classification as satellite data at 10 m to 30 m spatial resolution have been made accessible easily, widely and free of charge, including optical sensors, the wide field of viewer (WFV) onboard the GaoFen (GF, high resolution in English) series from China, the MultiSpectral Instrument (MSI) onboard Sentinel 2 (S2) from Europe and the Operational Land Imager (OLI) onboard Landsat 8 (L8) from USA, thanks to the implementation of the open data policy. There are more options in using the satellite data as these three data sources are available. This paper explored the different capability of these three data sources for the crop type mapping in the same area and within the same growing season. The study was executed in a flat and irrigated area in Northwest China. Nine types of crop were classified using these three kinds of time series of data sources in 2017 and 2018, respectively. The same suites of the training samples and validation samples were applied for each of the data sources. Random Forest (RF) was used as the classifier for the crop type classification. The confusion error matrix with the OA, Kappa and F1-score was used to evaluate the accuracy of the classifications. The result shows that GF-1 relatively has the lowest accuracy as a consequence of the limited spectral bands, but the accuracy is at 93–­94%, which is still excellent and acceptable for crop type classification. S2 achieved the highest accuracy of 96–98%, with 10 available bands for the crop type classification at either 10 m or 20 m. The accuracy of 97–98% for L8 is in the middle but the difference is small in comparison with S2. Any of these satellite data may be used for the crop type classification within the growing season, with a very good accuracy if the training datasets were well tuned.

Agricultural landscapes are characterized by diversity and complexity, which makes crop mapping at a regional scale a top priority for different purposes such as administrative decisions and farming management. Project 32194 of the Dragon 4 Program was implemented to meet the requirements of crop mapping, with the specific objective to develop suitable approaches for precise crop mapping with combined uses of European and Chinese high- and medium-resolution satellite images. Two sub-projects were involved in the project. The first was to focus on the use of time series high-resolution satellite data, including Sentinel-2 (S2, European satellite data) and Gaofen-1 (GF-1, Chinese satellite data), due to their similar spectral bands for Earth observation, while the second was to focus on medium-resolution data sources, i.e., the European Project for On-Board Autonomy–Vegetation (PROBA-V) and Chinese Fengyun-3 Medium Resolution Spectral Imager (FY-3 MERSI) satellite data, also due to their similar spectral channels. The approach of the European Space Agency (ESA) Sent2Agri project for crop mapping was adapted in the first sub-project and applied to the Yellow River irrigated district (YERID) of Ningxia in northwest China in order to assess its ability to accurately identify crop types in China. The goal of the second sub-project was to explore the potential of both European and Chinese medium-resolution satellite data for crop assessment in a large area. Methods to handle the data and retrieve the required information for the precise crop mapping were developed in the study, including the adaptation of the ESA approach to GF-1 data and the application of algorithms for classification. A scheme for the validation of the crop mapping was developed in the study. The results of implementing the scheme to the YERID in Ningxia indicated that the overall accuracies of crop mapping with S2 and GF-1 can be high, up to 94–97%, and the mapping had an accuracy of 88% with the PROBA-V and FY3B-MERSI data. The very high accuracy suggests the possibility of precise crop mapping with the combined use of time series high- and medium-resolution satellite data when suitable approaches are chosen to handle the data for the classification of crop types.