Abstract
Cropland maps at regional or global scales typically have large uncertainty and are also inconsistent with each other. The substantial uncertainty in these cropland maps limits their use in research and management efforts. Many synergy approaches have been developed to generate hybrid cropland maps with higher accuracy from existing cropland maps. However, few studies have compared the advantages, disadvantages, and regional suitability of these approaches. To close this knowledge gap, this study aims to compare two representative synergy methods of cropland mapping: Geographically weighted regression (GWR) and modified fuzzy agreement scoring (MFAS). We assessed how the sample size, quality of input satellite-based maps, and various landscapes influence the accuracy of the synergy maps based on these two methods. The GWR model is a regression analysis predominantly dependent on the cropland percentage of the training samples, while the MFAS method is largely influenced by the consistency of input datasets, and the training samples only play an auxiliary role. Therefore, the GWR method was relatively more sensitive to the number of training samples than the MFAS method. The quality of input maps had a significant impact on both methods, particularly on MFAS. In regions with heterogeneous landscapes and high elevations, the croplands are generally more fragmented, and the consistency of the input satellite-based maps was lower; the application of cropland percentage samples could compensate for the low dataset consistency. Therefore, GWR is more suitable for regions with heterogeneous landscapes, while MFAS is more appropriate for regions with homogeneous landscapes. The MFAS method uses cropland area from the agricultural statistics to calibrate the initial synergy maps, while the GWR model only considers the spatial distribution of cropland and does not make use of the distribution information of cropland area. The MFAS method showed a higher correlation with the statistical data, while GWR model exhibited a stronger relationship with cropland percentage. Our study reveals the advantages, disadvantages, and regional suitability of the two main types of synergy methods (regression analysis methods and data consistency scoring methods) and can inform future synergy cropland mapping efforts.
Highlights
Cropland is a fundamental resource for human existence and societal development [1,2], as it provides most of the products that humans rely on for survival [3]
The cropland percentage predicted by modified fuzzy agreement scoring (MFAS) was higher than that by Geographically weighted regression (GWR) in some regions such as Sichuan Basin, Hunan Province, and North China Plain
Identifying the advantages and limitations of different synergy methods is critical for generating accurate spatial distribution information for synergy cropland mapping
Summary
Cropland is a fundamental resource for human existence and societal development [1,2], as it provides most of the products (e.g., food commodities, feed, fiber, and biofuels) that humans rely on for survival [3]. Accurate information on cropland distribution is of great significance for agricultural monitoring, yield estimation, and food security assessment, and can inform both climate policymaking and efforts to meet zero hunger of the sustainable development goals (SDGs) of the United Nations for 2030 [4,5,6]. Over the past several decades, remote sensing has become the predominant method for acquiring large-scale cropland extent information. Some regional and global cropland maps with spatial resolution varying from 30 m to 1 km have been derived from remote sensing and made freely available to the public. Cropland mapping using remote sensing at regional or global scales is generally a massive task that is labor-intensive and time-consuming. The substantial uncertainty in these land cover/cropland maps limits their application in research and management [14,15,16]
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