Predicting root-zone soil moisture using a small sample deep learning approach

Yalin Xie

doi:10.1016/j.jhydrol.2026.136348

Predicting root-zone soil moisture using a small sample deep learning approach

2026-09-02

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AI

张永强 (Yongqiang Zhang)

DOI：10.1016/j.jhydrol.2026.136348

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Abstract

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• A small-sample deep learning approach, TabPFN, is introduced for root-zone soil moisture prediction. • TabPFN outperforms conventional baseline models, remaining stable across temporal scales and varying grid densities. • TabPFN supports data-sparse RZSM regionalization for drought monitoring and water assessment.

Keywords:

Root-zone soil moisture

TabPFN

Regionalization

Data scarcity

Predicting root-zone soil moisture using a small sample deep learning approach

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