Peer-reviewedCC BYSensors (Basel, Switzerland)
An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China
- 1Langfang Integrated Natural Resources Survey Center, , Tianjin 300301, China; (M.Y.); (J.M.); (Y.J.)
- 2Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, , Shijiazhuang 050021, China
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BibTeX
@article{ye2026interpretable,
title = {{An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China}},
author = {Mingyuan Ye and Peng Chen and Junchi Ma and Bingxia Liu and Jiahao Qi and Yuan Jing},
journal = {Sensors (Basel, Switzerland)},
year = {2026},
month = oct,
url = {https://europepmc.org/article/PMC/PMC13708883},
note = {CC BY},
}
RIS
TY - JOUR
TI - An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China
AU - Mingyuan Ye
AU - Peng Chen
AU - Junchi Ma
AU - Bingxia Liu
AU - Jiahao Qi
AU - Yuan Jing
PY - 2026
DA - 2026/10/01
JO - Sensors (Basel, Switzerland)
UR - https://europepmc.org/article/PMC/PMC13708883
SN - 1424-8220
ER -