Abstract
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.
Highlights
Global food security has been attracting great concern around the world as the world population is projected to continually grow and the water and thermal condition of farmland is dramatically influenced by this global change
The China Space Administration has announced at the GEO Ministerial Summit in December 2019 in Australia that the global data of GF-1 data [1] at 16 m spatial resolution will be made freely available to the world
A case study of the evaluation of individual capability for crop type mapping from difference satellite data sources, such as GF-1, Sentinel 2 (S2) and Landsat 8 (L8), was carried out in the yellow river irritation area in Ningxia Hui autonomous region of Northwest China. It revealed that the crop type mapping with any of these three kinds of satellite data may achieve the acceptable accuracy since the lowest overall accuracy (OA) may reach 94%
Summary
Global food security has been attracting great concern around the world as the world population is projected to continually grow and the water and thermal condition of farmland is dramatically influenced by this global change. And efficiently monitoring agricultural production is critical to tackle food insecurity issues and support farming management. Rapid development of Earth observation satellites provides a great opportunity to fulfill this requirement globally. Free and uncharged access to satellite images is accelerating methodology development for agricultural monitoring. GF, the acronym of GaoFen in Chinese, which means high resolution in English, is one of the key Earth observation programs in China. The China Space Administration has announced at the GEO Ministerial Summit in December 2019 in Australia that the global data of GF-1 data [1] at 16 m spatial resolution will be made freely available to the world
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