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Open AccessArticle

Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin

1
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
College of Advenved Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
3
Faculty of Agricultural Sciences, Catholic University of Mozambique, Cuamba, Niassa 3305, Mozambique
4
Division of Agriculture Applications, Soils, and Marine (AASMD), National Authority for Remote Sensing & Space Sciences (NARSS), Cairo 11843, Egypt
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Agriculture and Natural Resources, University of California, Davis, CA 95618, USA
6
Meteorological Services Department, Harare BE150, Zimbabwe
7
Department of Soil Sciences, School of Agricultural Sciences, University of Zambia, Lusaka 10101, Zambia
8
Physics Department, University of Zimbabwe, Harare MP167, Mt Pleasant, Zimbabwe
9
Department of Agriculture-Ministry of Agriculture, Mulungushi House, Lusaka 10101, Zambia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2096; https://doi.org/10.3390/rs12132096
Received: 11 June 2020 / Accepted: 24 June 2020 / Published: 30 June 2020
Having updated knowledge of cropland extent is essential for crop monitoring and food security early warning. Previous research has proposed different methods and adopted various datasets for mapping cropland areas at regional to global scales. However, most approaches did not consider the characteristics of farming systems and apply the same classification method in different agroecological zones (AEZs). Furthermore, the acquisition of in situ samples for classification training remains challenging. To address these knowledge gaps and challenges, this study applied a zone-specific classification by comparing four classifiers (random forest, the support vector machine (SVM), the classification and regression tree (CART) and minimum distance) for cropland mapping over four different AEZs in the Zambezi River basin (ZRB). Landsat-8 and Sentinel-2 data and derived indices were used and synthesized to generate thirty-five layers for classification on the Google Earth Engine platform. Training samples were derived from three existing landcover datasets to minimize the cost of sample acquisitions over the large area. The final cropland map was generated at a 10 m resolution. The performance of the four classifiers and the viability of training samples were analysed. All classifiers presented higher accuracy in cool AEZs than in warm AEZs, which may be attributed to field size and lower confusion between cropland and grassland classes. This indicates that agricultural landscape may impact classification results regardless of the classifiers. Random forest was found to be the most stable and accurate classifier across different agricultural systems, with an overall accuracy of 84% and a kappa coefficient of 0.67. Samples extracted over the full agreement areas among existing datasets reduced uncertainty and provided reliable calibration sets as a replacement of costly in situ measurements. The methodology proposed by this study can be used to generate periodical high-resolution cropland maps in ZRB, which is helpful for the analysis of cropland extension and abandonment as well as intensity changes in response to the escalating population and food insecurity. View Full-Text
Keywords: cropland mapping; agricultural diversity; Zambezi River basin; agroecological zones; random forest cropland mapping; agricultural diversity; Zambezi River basin; agroecological zones; random forest
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MDPI and ACS Style

Bofana, J.; Zhang, M.; Nabil, M.; Wu, B.; Tian, F.; Liu, W.; Zeng, H.; Zhang, N.; Nangombe, S.S.; Cipriano, S.A.; Phiri, E.; Mushore, T.D.; Kaluba, P.; Mashonjowa, E.; Moyo, C. Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin. Remote Sens. 2020, 12, 2096.

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