Abstract
Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. Here, we proposed a new framework for mapping cotton fields based on Sentinel-1/2 data for different phenological periods, random forest classifiers, and the multi-scale image segmentation method. A cotton field map for 2019 at a spatial resolution of 10 m was generated for northern Xinjiang, a dominant cotton planting region in China. The overall accuracy and kappa coefficient of the map were 0.932 and 0.813, respectively. The results showed that the boll opening stage was the best phenological phase for mapping cotton fields and the cotton fields was identified most accurately at the early boll opening stage, about 40 days before harvest. Additionally, Sentinel-1 and the red edge bands in Sentinel-2 are important for cotton field mapping, and there is great potential for the fusion of optical images and microwave images in crop mapping. This study provides an effective approach for high-resolution and high-accuracy cotton field mapping, which is vital for sustainable monitoring and management of cotton planting.
Highlights
The random forest models under different decision trees were built in the study, and the overall accuracy and kappa coefficient were used as the evaluation criteria to explore the optimal number of decision trees
The random forest classifiers under different decision trees were constructed in this study, and the overall accuracy (OA) and kappa coefficient were used as the evaluation criteria to explore the optimal number of decision trees
The results showed that the OA and kappa coefficient of the model increased significantly when the number of the decision tree increased from 0 to 21, and remained basically unchanged after the decision tree number reached 21 (Figure 4)
Summary
It is widely acknowledged that crop monitoring has become a significant field in remote sensing-based earth observation [1]. Remote sensing has been the key approach in crop monitoring, especially at the global or national scale. It has a large observation area, the monitoring period is short, and it provides fast, accurate, and objective crop information [2,3] when compared to time-consuming and laborious field surveys, which are hampered by the scattered patterns and various sizes of the farmland in China. And accurate cotton field mapping is vital to sustainable monitoring and management of cotton economics
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