Integration of remote sensing and machine learning for identifying irrigated farmland in Shandong Province of China using optimized training samples

Understanding the distribution of irrigated farmland is of great significance for rational utilization of water resources, timely adjustment of agricultural production policies and protection of food security. Existing studies based on machine learning to identify irrigated farmland mostly use binarized sample labeling (that is, only labeling irrigated and non-irrigated), which may lead to the omission of irrigation samples, resulting in low identification accuracy of irrigated farmland. To avoid this problem, a scheme of assigning irrigation fractions to training samples was proposed in this paper. Firstly, three preliminary irrigation maps were generated using normalized vegetation index, enhanced vegetation index and greenness vegetation index combined with statistical irrigation area data. Then, combined with three different preliminary irrigation maps, the scheme proposed in this study assigned different irrigation scores to each pixel and obtained training samples. Then, two machine learning methods, random forest (RF) and convolutional neural network (CNN), were used to predict the pixel-by-pixel irrigation fraction of the study area, respectively. Identification of 250 m resolution irrigated farmland in Shandong Province from 2018 to 2022 was conducted. Finally, the spatial distribution of irrigated farmland in Shandong Province from 2018 to 2022 was obtained based on verification samples and statistical data, and the spatial and temporal distribution characteristics of irrigated farmland were analyzed. The results showed as follows: 1) Compared with the two binarization methods for obtaining training samples, the county R² of RF and CNN for identifying irrigated farmland was as high as 0.95 and 0.93 when the proposed scheme was used to obtain training samples and identify irrigated farmland; The accuracy of RF identification of irrigated farmland in Shandong Province was better than that of CNN, and the performance was relatively stable in different years. 2) From 2018 to 2022, the spatial distribution of irrigated farmland in Shandong Province was relatively consistent, mainly distributed in the northwest and south of Shandong Province, while the distribution was relatively sparse in Jiaodong Peninsula and central Shandong Province. The statistical irrigated area in Shandong Province showed a slight growth trend in the past five years, and the identified irrigated farmland area in 2022 also showed a slight growth compared with 2018. The scheme of assigning irrigation scores to training samples proposed in this study can effectively improve the accuracy of irrigated farmland identification in Shandong Province, and is a reliable and effective method to identify irrigated farmland at regional scale.