Extraction of soybean planting areas based on multi-temporal Sentinel-1/2 data

Han Ding; Linsheng Huang

doi:10.1117/12.2684169

1 August 2023 Extraction of soybean planting areas based on multi-temporal Sentinel-1/2 data

Han Ding, Linsheng Huang

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541S (2023) https://doi.org/10.1117/12.2684169
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Accurate knowledge of soybean distribution is essential for regulating the soybean market and promoting sustainable development. This research presents a method for identifying soybean planting areas using multispectral data and feature selection models. The study area was the county of Guoyang, Anhui Province. Sentinel-1/2 data from three different time periods were used to calculate vegetation indices, textures, and other features. Initially, non-cropland elements in the study area were gradually masked by constructing decision trees. Then ReliefF algorithm was combined with Random Forest, Support Vector Machine, and BP Neural Network to develop three classification models: ReliefF-RF, ReliefFSVM, and ReliefF-BPNN, and the best feature subsets were selected as input for each of the three models, an overall distribution map of soybeans in the study area was obtained. Finally, the performance of three models was evaluated by utilizing the distribution results of soybeans obtained from the classification of planet images and seven uniformly distributed sample plots (500m × 500m) within the study area. The results showed that the Kappa coefficient was between 0.73 to 0.85, and the overall accuracy ranged from 87.42% to 93.22%. The ReliefF-RF model had the best performance in the experiment.

Citation Download Citation

Han Ding and Linsheng Huang "Extraction of soybean planting areas based on multi-temporal Sentinel-1/2 data", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541S (1 August 2023); https://doi.org/10.1117/12.2684169

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KEYWORDS

Data modeling

Vegetation

Statistical modeling

Decision trees

Short wave infrared radiation

Image classification

Satellites

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