Regional-scale prediction and assessment of soil organic matter content in the Yuanmou Dry-Hot Valley, Southwest China, using satellite-derived phenological time series and deep learning.

Authors

Dengdeng Ding, Jida Yang, Sihe Deng, Hongye Zhu, Zhengbo Ma, Chengxiu Fu, Junlang Deng, Dingyun Zhao, Zhen Long, Xiaoyan Wang, Peiwen Yang, Qibin Chen, Qing Zhang

Published in

Environmental monitoring and assessment. Volume 198. Issue 8. Jul 12, 2026. Epub Jul 12, 2026.

Abstract

The dry-hot valley region is characterized by pronounced topographic relief and a complex land-use mosaic, resulting in strong nonlinearity and fine-scale spatial heterogeneity in soil organic matter (SOM). These characteristics increase the difficulty and uncertainty of remote sensing-based digital soil mapping. Although Yuanmou County has been widely investigated, limited attention has been paid to regional-scale SOM prediction in dry-hot valley ecosystems using long-term satellite-derived phenological dynamics and deep learning approaches. To address this gap, this study integrated 475 topsoil samples, topographic factors, environmental covariates, and multi-year satellite-derived phenology time-series variables to develop a regional-scale SOM prediction and assessment framework in Yuanmou County, Yunnan Province, China. The predictive performance of four models was compared, including a convolutional neural network (CNN), a CNN-random forest hybrid model (CNN-RF), a CNN-long short-term memory model (CNN-LSTM), and an attention-augmented CNN-LSTM model (CNN-LSTM-Att). The results showed that model architecture had a substantial influence on SOM prediction accuracy, with overall performance ranked as CNN-LSTM-Att > CNN-LSTM > CNN-RF > CNN. Among the four models, CNN-LSTM-Att achieved the best performance on the independent test set, with R² = 0.61, RMSE = 2.49 g kg⁻¹, and MAE = 1.39 g kg⁻¹. The incorporation of temporal modeling and the attention mechanism improved the extraction of dynamic phenological signals associated with SOM formation, resulting in a more refined spatial representation. The CNN-LSTM-Att prediction map clearly identified low-SOM areas along the northern valley corridor and high-SOM zones in the forest-dominated southern and eastern regions, while showing stronger sensitivity to local patches and transitional ecotones. Overall, coupling long-term phenological dynamics with an attention mechanism improved both the predictive accuracy and spatial expressiveness of SOM mapping in complex terrain, providing a useful methodological reference for SOM assessment and precision land management in dry-hot valley regions.

PMID:
42437861
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.