Cropland data fusion and correction using spatial analysis techniques and the Google Earth Engine

Abstract

Accurate regional identification of cropland quantities and spatial distributions is important for cropland monitoring, food security, and sustainable regional development. Various countries and organizations have produced series of land-cover products. However, variability among remote sensors, land-cover classification schemes, and classification methods has resulted in discrepancies. In this study, we develop a novel method to improve cropland data accuracy for the Belt and Road (B&R) region, by fusing and correcting four cropland products: CCI-LC, GFSAD30, MCD12Q1, and FROM-GLC. Spatial analysis techniques are implemented, including climate stratification, consistency assessment, and statistical filtering, to develop training samples for model correction. The Google Earth Engine (GEE) platform and random forest (RF) algorithm are executed with these training samples to correct fused multi-data product and generate a corrected 2015 cropland product. The corrected product indicates that cropland accounts for 14.94% of the B&R region, which is closer to the results found via FAO statistics than the results from any of the four individual land-cover products. On the national scale, the root mean square error between the corrected cropland product quantities and FAO statistics is 11.39% and the correlation coefficient value is 0.77. This indicates that the method exhibits better fitting characteristics. The accuracies of the areas of inconsistency among the four cropland products and our corrected product are assessed using 3112 visually interpreted samples and Google Earth. The overall accuracy of the corrected cropland product is 77.54% in inconsistent areas. The highest accuracy produced by the corrected cropland product indicates the effectiveness of our method, which can rapidly improve cropland data accuracy in heterogeneous regions. Combining the training samples produced by fusing existing cropland products and updating techniques with multi-source remote sensing data from the GEE platform, we foresee potential applications to update global cropland product.