Extraction and dynamics of planting structure in Hetao Irrigation District of Inner Mongolia from 2000 to 2021 using deep learning

Hetao Irrigation District is one of the most important grain and oil production bases in China. The spatial and temporal evolution of the cropping structure over the years can provide the basic data for the yield of farmland and the amount of water for irrigation in the future adjustment of agricultural structures. In this study, the errors from the different sensors were calibrated to calculate the normalized difference vegetation index (NDVI) of the irrigation area for the whole year using Landsat-5 and Landsat-8 remote sensing images. The MODIS NDVI data was also used to interpolate the missing areas. The MODIS NDVI data was interpolated and S-G filtered for the missing areas of the image. The spectral and texture features were also calculated after that. The samples were then augmented to obtain 13004 samples with a uniform distribution. The deep learning of the multilayer perceptron (MLP) neural network was constructed to adjust the MLP model for the actual planting and climatic conditions. The model was then migrated each year to obtain the planting structure from 2000 to 2021. Finally, a systematic analysis was made of the multi-year spatial and temporal changes. The results showed that the overall classification accuracy reached 89% in 2021, and the Kappa coefficient was 0.87. Overall, the classification accuracy was ranked in descending order as follows: wheat, maize, alfalfa, vegetables, sunflower (melons), and rice. Specifically, the accuracies of the rice and melons were lower at 0.86 and 0.88, respectively, in terms of the user’s accuracy indexes. All accuracies were higher than 90% in the main crops of maize, wheat, and alfalfa. The relative error of the area in each year was less than 8%, compared with the statistical planting area. The smaller relative error was found in the sunflower, corn and wheat, whereas, the larger one was vegetables and melons (5% to 8%). A comparison of the MLP classification revealed that there was a relatively high consistency of the spatial distribution. Meanwhile, the contiguous planting area was distributed in the form of plots. The planting area of sunflower and maize showed an upward trend between 2000-2021, whereas, the planting area of wheat shrunk considerably. The sunflower was distributed mainly in the eastern region in 2015-2021, maize growing areas are mainly distributed in the central and western regions, but in 2010 scattered distribution, no contiguous planting area. The wheat planted area accounted for a relatively large proportion of the total in 2000-2010, mainly in the northwestern region and eastern part of the irrigation area, and then shrunk considerably in 2010. The evolution of sunflower acreage occurred more during the study period, followed by maize, all of which was mainly transferred from the wheat. The high classification accuracy and migration of the improved model can provide a strong reference for agricultural management and rational resource utilization in the Hetao Irrigation District of Inner Mongolia.