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Remote Sensing
Volume 17
Issue 11
10.3390/rs17111865
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Spatial Prediction of Soil Texture in Low-Relief Agricultural Areas Using Rice and Wheat Growth Information with Spatiotemporal Stability

by
Fei Wang
1,
Peiyu Zhang
2,
Shaomei Chen
1,
Tianyun Shao
3,
Wenhao Lu
1,
Zihan Fang
1,
Changda Zhu
1,
Feng Liu
4,5 and
Jianjun Pan
1,*
1
Soil Resources and Information Technology Laboratory, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
2
State Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
3
Jiangsu Key Laboratory of Coastal Saline Soil Resources Utilization and Ecological Conservation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
4
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
5
University of Chinese Academy of Sciences, Beijing 101408, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(11), 1865; https://doi.org/10.3390/rs17111865
Submission received: 15 March 2025 / Revised: 22 May 2025 / Accepted: 26 May 2025 / Published: 27 May 2025
Versions Notes

Abstract

In low-relief agricultural areas, crop cover makes it challenging to obtain remotely sensed bare soil spectral data for predicting soil texture. Therefore, this study proposed a method for predicting soil texture using crop growth information with spatiotemporal stability. Spatiotemporal Stable Peak (SSP) maps were generated using the Ratio Vegetation Index (RVI) time-series data of rice and wheat, and they were used to represent crop growth information with spatiotemporal stability. Eighty-three soil sampling sites were arranged on the SSP maps with a regular grid. Ridge Regression, Ordinary Kriging, and Co-Kriging were adopted to map soil texture. The results showed that the SSP was closely related to clay and sand contents, with Pearson’s |r| ranging from 0.57 to 0.67. SSP-based Ridge Regression yielded better prediction accuracy (MAE = 3.95 and RMSE = 4.57) than Ordinary Kriging (MAE = 4.45 and RMSE = 5.19) in predicting clay content. The comparison between Ordinary Kriging and SSP-based Co-Kriging further demonstrated the effectiveness of SSP in improving clay content prediction accuracy, with an increase in R2 of 70% and a reduction in RMSE of 3.85%. Similar results were obtained for sand content prediction. These results suggest that SSP can serve as an effective environmental variable for predicting soil texture spatial variation in low-relief agricultural areas.
Keywords: intensively cultivated alluvial plains; digital soil mapping; environment variable intensively cultivated alluvial plains; digital soil mapping; environment variable

Share and Cite

MDPI and ACS Style

Wang, F.; Zhang, P.; Chen, S.; Shao, T.; Lu, W.; Fang, Z.; Zhu, C.; Liu, F.; Pan, J. Spatial Prediction of Soil Texture in Low-Relief Agricultural Areas Using Rice and Wheat Growth Information with Spatiotemporal Stability. Remote Sens. 2025, 17, 1865. https://doi.org/10.3390/rs17111865

AMA Style

Wang F, Zhang P, Chen S, Shao T, Lu W, Fang Z, Zhu C, Liu F, Pan J. Spatial Prediction of Soil Texture in Low-Relief Agricultural Areas Using Rice and Wheat Growth Information with Spatiotemporal Stability. Remote Sensing. 2025; 17(11):1865. https://doi.org/10.3390/rs17111865

Chicago/Turabian Style

Wang, Fei, Peiyu Zhang, Shaomei Chen, Tianyun Shao, Wenhao Lu, Zihan Fang, Changda Zhu, Feng Liu, and Jianjun Pan. 2025. "Spatial Prediction of Soil Texture in Low-Relief Agricultural Areas Using Rice and Wheat Growth Information with Spatiotemporal Stability" Remote Sensing 17, no. 11: 1865. https://doi.org/10.3390/rs17111865

APA Style

Wang, F., Zhang, P., Chen, S., Shao, T., Lu, W., Fang, Z., Zhu, C., Liu, F., & Pan, J. (2025). Spatial Prediction of Soil Texture in Low-Relief Agricultural Areas Using Rice and Wheat Growth Information with Spatiotemporal Stability. Remote Sensing, 17(11), 1865. https://doi.org/10.3390/rs17111865

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