Digital Soil Mapping Based on Fine Temporal Resolution Landsat Data Produced by Spatiotemporal Fusion

Multitemporal Landsat-8 satellite images with fine spatial resolution (i.e., 30 m) are crucial for modern digital soil mapping (DSM). Generally, cloud-free images covering bare topsoil are common choices for DSM. However, the number of effective Landsat-8 data is greatly limited due to cloud contamination coupled with the coarse temporal resolution, and interference of material covering topsoil in most of the months, hindering the development of accurate DSM. To address this issue, temporally dense Landsat images were predicted using a spatiotemporal fusion method to improve DSM. Specifically, the recently developed virtual image pair-based spatiotemporal fusion method was adopted to produce simulated Landsat-8 time-series, by fusing with 500-m moderate resolution imaging spectroradiometer time-series with frequent observations. Subsequently, the simulated Landsat-8 data were used for distinguishing different soil classes via a random forest model. Training and validation samples of soil classes were collected from legacy soil data. Our results indicate that the simulated data were beneficial for improving DSM owing to the increase in class separability. More precisely, after combining the observed and simulated data, the overall accuracy and kappa coefficient were increased by 3.099% and 0.047, respectively. This article explored the potential of the spatiotemporal fusion method for DSM, providing a new solution for remote-sensing-based DSM.

Automated summary

This study used a spatiotemporal fusion method to generate high temporal resolution simulated Landsat-8 data, aiming to improve the accuracy of digital soil mapping (DSM). The results showed that the fused data had increased class separability, leading to a 3.099% increase in overall accuracy and a 0.047 increase in kappa coefficient. This article explores the potential of the spatiotemporal fusion method for DSM, providing a new solution for remote-sensing-based DSM.