Advancing Digital Image-Based Recognition of Soil Water Content: A Case Study in Bailu Highland, Shaanxi Province, China
文献类型：期刊论文
| 作者 | Zhang, Yaozhong4; Zhang, Han4; Lan, Hengxing2,3; Li, Yunchuang1; Liu, Honggang1; Sun, Dexin4; Wang, Erhao4; Dong, Zhonghong4 |
| 刊名 | WATER
 |
| 出版日期 | 2024-04-01 |
| 卷号 | 16期号:8页码:23 |
| 关键词 | soil water content (SWC) image processing deep learning attention mechanism encoder-decoder architecture |
| DOI | 10.3390/w16081133 |
| 英文摘要 | Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as a nascent approach. To address this, we design the LG-SWC-R3 model based on an attention mechanism to leverage its powerful learning capabilities. To enhance efficiency, we propose a simple yet effective encoder-decoder architecture (PVP-Transformer-ED) designed on the principle of eliminating redundant spatial information from images. This architecture involves masking a high proportion of soil images and predicting the original image from the unmasked area to aid the PVP-Transformer-ED in understanding the spatial information correlation of the soil image. Subsequently, we fine-tune the SWC recognition model on the pre-trained encoder of the PVP-Transformer-ED. Extensive experimental results demonstrate the excellent performance of our designed model (R2 = 0.950, RMSE = 1.351%, MAPE = 0.081, MAE = 1.369%), surpassing traditional models. Although this method involves processing only a small fraction of original image pixels (approximately 25%), which may impact model performance, it significantly reduces training time while maintaining model error within an acceptable range. Our study provides valuable references and insights for the popularization and application of image-based SWC recognition methods. |
| WOS关键词 | SURFACE MOISTURE ; PREDICTION ; GROWTH |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Environmental Sciences & Ecology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001210002200001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/204975]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Dong, Zhonghong |
| 作者单位 | 1.China Construct First Grp Corp Ltd, Xian 710075, Peoples R China 2.Changan Univ, Sch Geol Engn & Geomat, Xian 710064, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Changan Univ, Key Lab Highway Construct Technol & Equipment, Minist Educ, Xian 710064, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Yaozhong,Zhang, Han,Lan, Hengxing,et al. Advancing Digital Image-Based Recognition of Soil Water Content: A Case Study in Bailu Highland, Shaanxi Province, China[J]. WATER,2024,16(8):23. |
| APA | Zhang, Yaozhong.,Zhang, Han.,Lan, Hengxing.,Li, Yunchuang.,Liu, Honggang.,...&Dong, Zhonghong.(2024).Advancing Digital Image-Based Recognition of Soil Water Content: A Case Study in Bailu Highland, Shaanxi Province, China.WATER,16(8),23. |
| MLA | Zhang, Yaozhong,et al."Advancing Digital Image-Based Recognition of Soil Water Content: A Case Study in Bailu Highland, Shaanxi Province, China".WATER 16.8(2024):23. |
入库方式： OAI收割
来源：地理科学与资源研究所
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