Fractional crop-planting area projection by integrating geographic grid data and agricultural statistics based on random forest regression

Abstract

Accurate fractional crop-planting area (FCPA) mapping is a challenging task as it requires leveraging the advantages of geographic data in detailed spatial expression and agricultural statistics in the description of crop types and quantitative characteristics. We present a robust method to disaggregate the agricultural statistics within each county unit to 1-km scale grid by combining particle swarm optimization (PSO)-based feature selection with Random Forest (RF) regression, and an iterative area-gapallocation(IAGA) method is implemented to reconcile the discrepancies between the disaggregating results and statistics. The agriculture in Gansu Province, China, is characterized by complex heterogeneous smallholder farming landscapes. We tested this methodology in Gansu and explored the synergistic estimation of FCPA for six types of crops (i.e. wheat, maize, oil-bearing, vegetable,orchards, and other crops) in 2010. The results showed that the derived FCPA maps matched well with the statistics in terms of quantity, while also providing spatial details. The quantitative evaluation results indicated that the derived FCPA had good accuracy,with a higher R2 above 0.97, a lower RMSE below 1%, and an absolute error between 1.53-5.24%. The proposed methodology provides valuable insights for practical large-scale FCPA mapping at a high spatial resolution in a cost-effective manner.

Keywords

• fractional crop-planting area
• geographic grid data
• agricultural statistics
• rf
• pso