Monitoring multiyear cropping patterns in the arid regions of northwestern China from 2005 to 2022 using Landsat time-series data

Multiyear cropping patterns refer to the long-term sequence and spatial arrangement of crops within a specific area over an extended period and can be influenced by several factors. Understanding cropping patterns can help optimize crop yield while preserving ecosystems and enhancing ecological benefits. Existing algorithms are typically limited to extracting single or simple planting patterns, making it challenging to accurately capture complex and diverse multiyear planting patterns that span extended periods. Here, we propose a new method (CCDC-LT), which couples two change detection algorithms, i.e. CCDC and LandTrendr, for time-series Landsat images and finally mapped the results to the parcel and plot scales to generate multiyear planting patterns. By establishing identification rules, the method can identify and summarize all the multiyear cropping patterns. We tested our method in a part of the Tarim River Basin in Xinjiang. Our method successfully mapped all cropping patterns from 2005 to 2022, achieving an overall accuracy of 83%. The average margin of error in area estimation for cropping patterns was 16.48%. Our approach could identify prominent cropping patterns, such as continuous cotton monoculture and cotton reclamation, with the producer’s accuracy exceeding 90%, and could capture less prevalent cropping patterns, such as cotton – orchard – cotton, with a user’s accuracy exceeding 80%. Our results showed that the proposed method was effective and robust in identifying multiyear cropping patterns and could be applied in other regions. This multiyear cropping pattern mapping not only provides accurate information on crop types for farmland management but also supports the analysis of drivers of cropping pattern change and their environmental impacts.