Exploring the effects of training samples on the accuracy of crop mapping with machine learning algorithm

Fu, Yangyang; Shen, Ruoque; Song, Chaoqing; Dong, Jie; Han, Wei; Ye, Tao; Yuan, Wenping

doi:10.1016/j.srs.2023.100081

Cited by 6 publications

(1 citation statement)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We conducted field surveys in Hebei, Henan, Shandong, Anhui, and Jiangsu provinces in China in 2019 and 2020, and marked 3,054 wintertriticeae crops samples and 4,088 non-winter-triticeae crops samples (pink circles in Fig. 1) using GPS (G120, UniStrong, Beijing, China) (Fu et al, 2023b). For other provinces in China and other countries, we relied on high-resolution images from Google Earth from 2019 to 2020 for visual interpretation and obtained 7,029 winter-triticeae crops samples and 8,897 non-winter-triticeae crops samples (orange triangles in Fig.…”

Section: Validation Samplesmentioning

confidence: 99%

High-resolution mapping of global winter-triticeae crops using a sample-free identification method

Fu,

Chen,

Song

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Winter-triticeae crops, such as winter wheat, winter barley, winter rye, and triticale, are important in human diets and planted worldwide, and thus accurate spatial distribution information of winter-triticeae crops is crucial for monitoring crop production and food security. However, there is still a lack of global high-resolution maps of winter-triticeae crops because of the reliance of existing crop mapping methods on training samples, which limits their application at the global scale. In this study, we propose a new method based on the Winter-Triticeae Crops Index (WTCI) for global winter-triticeae crops mapping. This is a new sample-free method for identifying winter-triticeae crops based on differences in their normalized difference vegetation index (NDVI) characteristics from the heading to the harvesting stages and those of other types of vegetation. Based on this new method, we produced the first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022. Validation in 65 countries worldwide indicated that the method exhibited satisfying performance and stable spatiotemporal transferability, with producer’s accuracy, user’s accuracy and overall accuracy of 81.12 %, 87.85 % and 87.7 %, respectively. The identified area of winter-triticeae crops was consistent with the agricultural statistical area in almost all investigated counties or regions, and the correlation coefficient (R2) between the identified area and the statistical area was over 0.6, while the relative mean absolute error (RMAE) was less than 30 % in all six years. Overall, this study provides a reliable and automatic identification method for winter-triticeae crops without any training samples. The high-resolution distribution maps of global winter-triticeae crops are expected to support multiple agricultural applications. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.12361 (Fu et al., 2023a).

show abstract

Section: Validation Samplesmentioning

confidence: 99%