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Related Concept Videos

Precipitation Gravimetry01:03

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
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The moisture content of aggregates is a crucial factor in construction, particularly in concrete mixing, as it influences the total water required in the mix. Moisture content represents the water coated on the exterior surface of the aggregate existing in a saturated and surface-dry condition. The total water content of a moist aggregate is the sum of its moisture content and water absorption.
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Related Experiment Video

Updated: Aug 6, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
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A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset.

Olya Skulovich1, Pierre Gentine2

  • 1Columbia University, Earth and Environmental Engineering Department, New York, NY, 10027, USA. os2328@columbia.edu.

Scientific Data
|March 23, 2023
PubMed
Summary
This summary is machine-generated.

The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset provides long-term, global soil moisture data using machine learning. This remote sensing dataset enables better understanding of hydrological and climate processes.

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Area of Science:

  • Earth Science
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Soil moisture is a critical variable for land surface processes.
  • Long-term, consistent global soil moisture datasets are needed for climate studies.
  • Existing satellite soil moisture data have limited temporal coverage.

Purpose of the Study:

  • To create a consistent, long-term, global soil moisture dataset using AI.
  • To extend the NASA Soil Moisture Active Passive (SMAP) mission data backward in time.
  • To provide a dataset suitable for studying hydrological, carbon cycle, and energy processes.

Main Methods:

  • Machine learning (AI) was used to create the dataset.
  • Data were based on NASA SMAP satellite mission data.
  • Seasonal cycles were removed for neural network training to focus on extremes.

Main Results:

  • The Consistent AI-based Soil Moisture (CASM) dataset covers 2002-2020 globally at 25km resolution.
  • CASM showed a median correlation of 0.66 with 367 in-situ monitoring sites.
  • Both aleatoric and epistemic uncertainties were estimated and included.

Conclusions:

  • The CASM dataset offers a valuable resource for long-term environmental change studies.
  • Consistent soil moisture data are essential for assessing changes in water availability and stress.
  • The inclusion of uncertainty estimates enhances the dataset's utility for scientific research.