Extracting Typical Samples Based on Image Environmental Factors to Obtain an Accurate and High-Resolution Soil Type Map

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-04, Vol.16 (7), p.1128
• Main Authors: Zhu, Changda, Zhu, Fubin, Li, Cheng, Yan, Yunxin, Lu, Wenhao, Fang, Zihan, Li, Zhaofu, Pan, Jianjun
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Climate change; conventional soil map; Environmental conditions; Environmental factors; Environmental science; Environmental sciences; Forests; High resolution; Land use; Machine learning; Mapping; Precipitation; Predictions; Remote sensing; Sampling designs; soil classification; Soil maps; soil spatial prediction; Soil surveys; Soil types; Soils; soil–environment relationship; Topography; typical sample; Variance analysis; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16071128

Soil surveying and mapping provide important support for environmental science research on soil and other resources. Due to the rapid change in land use and the long update cycle of soil maps, historical conventional soil maps (CSMs) may be outdated and have low accuracy. Therefore, there is an urgent need for accurate and up-to-date soil maps. Soil has a high correlation with its corresponding environmental factors in space, and typical samples contain an appropriate soil–environment relationship of soil types. Understanding how to extract typical samples according to environmental factors and determine the implied soil–environment relationship is the key to updating soil maps. In this study, a hierarchical typical sample extraction method based on land use type and environmental factors was designed. According to the corresponding relationship between the soil type and the land use type (ST-LU), the outdate soil map patches caused by changes in land use were excluded, follow by typical samples being extracted according to the peak intervals of the soil–environmental factor histograms. Additionally, feature selection was performed through variance analysis and mutual information, and four machine learning models were used to predict soil types. In addition, the influence of environmental factors on soil prediction was discussed, in terms of variable importance analysis. Using an overall common validation set, the results show that the prediction accuracy using typical samples for learning in the modeling set is above 0.8, while the prediction accuracy when using random samples is only about 0.4. Compared with the original soil map, the accuracy and resolution of the predicted soil maps based on typical samples are greatly improved. In general, typical samples can effectively explore the actual soil–environment knowledge implied in the soil type map. By extracting typical samples from historical soil type map and combining them with high-resolution remote sensing data, we can generate new soil type maps with high accuracy and short update cycle. This can provide some references for typical sampling design and soil type prediction.