Abstract
AbstractChina is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international cereals trade. However, it still lacks a long-term maize distribution dataset with fine spatial resolution, because the existing high spatial resolution satellite datasets suffer from data gaps caused by cloud cover, especially in humid and cloudy regions. This study aimed to produce a long-term, high-resolution maize distribution map for China (China Crop Dataset–Maize, CCD-Maize) identifying maize in 22 provinces and municipalities from 2001 to 2020. The map was produced using a high spatiotemporal resolution fused dataset and a phenology-based method called Time-Weighted Dynamic Time Warping. A validation based on 54,281 field survey samples with a 30-m resolution showed that the average user’s accuracy and producer’s accuracy of CCD-Maize were 77.32% and 80.98%, respectively, and the overall accuracy was 80.06% over all 22 provinces.
Funder
Open Research Program of the International Research Center of Big Data for Sustainable Development Goals
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Cited by
12 articles.
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