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Related Experiment Video

Updated: Oct 2, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
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Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization.

Qixin Liu1,2, Xingfa Gu1,2,3, Xinran Chen1,2

  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

Optimizing sample quantity and quality using sparse sample exploitation (SSE) and new parameters significantly improves soil moisture content (SMC) retrieval accuracy from remote sensing data.

Keywords:
artificial neural networkoptical remote sensing imagesample optimizationsoil moisture contentsynthetic aperture radar

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Area of Science:

  • Geoscience
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Soil moisture content (SMC) is crucial in geoscience.
  • Artificial neural networks (ANNs) are used for SMC retrieval from remote sensing data.
  • Sample quantity and quality critically impact ANN-based SMC retrieval accuracy.

Purpose of the Study:

  • To optimize sample quantity and quality for improved SMC retrieval.
  • To address sample scarcity caused by cloud cover and instrument malfunction.
  • To enhance feature description with novel input parameters.

Main Methods:

  • Developed a sparse sample exploitation (SSE) method to utilize typically excluded data.
  • Optimized new input parameters beyond basic ones for better feature description.
  • Employed Sentinel-1 SAR and Landsat-8 images for SMC retrieval in eastern Austria.

Main Results:

  • The SSE method increased available samples from 264 to 635.
  • SMC retrieval accuracy was markedly improved by the SSE method.
  • Optimized parameters, especially elevation, enhanced the inversion effect.

Conclusions:

  • The SSE method effectively increases sample size and improves SMC retrieval accuracy.
  • Incorporating optimized parameters further refines SMC retrieval results.
  • Elevation emerged as the most influential parameter for SMC inversion.