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Diario global de 9 km de detección remota de la humedad del suelo (2015-2025) con aprendizaje guiado por

Sijia Feng¹, Aoyang Li^2,3,4,5, Rui Zhou^2,3,4,5

¹Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, 8000, Denmark.

Scientific data

|February 12, 2026

Resumen

La estimación precisa de la humedad del suelo es crucial para comprender el clima de la Tierra. Un nuevo modelo de aprendizaje automático guiado por procesos (PGML) mejora la precisión de los datos satelitales de humedad del suelo (SM), especialmente en entornos difíciles.

Área de la Ciencia:

Ciencias de la Tierra y Ciencias Ambientales.
La detección remota con sensores.
Hidrología Hidrología Hidrología.

Sus antecedentes:

La humedad exacta del suelo superficial (SM) es vital para la dinámica del hidroclima.
Las misiones SM satelitales como SMAP se enfrentan a limitaciones de precisión en terrenos complejos y con vegetación debido a modelos simplificados.
Los métodos existentes luchan con la parametrización empírica y los procesos de transferencia radiativa simplificados.

Objetivo del estudio:

Desarrollar y validar un marco de aprendizaje automático guiado por procesos (PGML, por sus siglas en inglés) para una mejor estimación de la superficie diaria global de SM.
Integrar las teorías del modelo de transferencia radiativa (RTM) con el aprendizaje profundo para mejorar la predicción de SM.
Para superar las limitaciones de los productos actuales de SM por satélite en entornos difíciles.

Principales métodos:

Desarrolló un marco PGML que integra RTM y teorías hidrológicas con aprendizaje profundo.
Diseñó una función de costo basada en la eficiencia de Kling-Gupta para el entrenamiento de modelos.
Entrenó el modelo con simulaciones de RTM y lo afinó utilizando mediciones in situ.

Principales resultados:

El modelo PGML demostró una fuerte concordancia con las mediciones in situ (R = 0,868, RMSE imparcial = 0,054 m3/m3).
Logró predicciones precisas de superficie diaria global de 9 km de SM desde abril de 2015 hasta junio de 2025.
El marco de PGML mejoró con éxito la precisión de SM en regiones difíciles.

Conclusiones:

El marco PGML mejora significativamente la precisión de la humedad superficial del suelo derivada de satélites.
Este enfoque tiene un gran potencial para avanzar en la gestión de los recursos hídricos y los ecosistemas.
La integración de teorías físicas con el aprendizaje automático ofrece un poderoso camino para el monitoreo ambiental.

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