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Related Concept Videos

Precipitation Gravimetry01:03

Precipitation Gravimetry

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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Precipitation and Co-precipitation

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Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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Related Experiment Video

Updated: Jun 30, 2026

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
09:04

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Global Snow-free Leaf Area Index Dataset 1985-2020 for Earth System Modeling.

Wanyi Lin1,2, Hua Yuan3, Wenzong Dong1

  • 1School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China.

Scientific Data
|June 28, 2026
PubMed
Summary
This summary is machine-generated.

A new global snow-free Leaf Area Index (LAI) dataset corrects underestimations caused by snow. This improved LAI data enhances Earth system model accuracy for vegetation and snow interactions.

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Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy
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Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

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Last Updated: Jun 30, 2026

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
09:04

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
09:55

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy
06:28

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

Area of Science:

  • Earth System Science
  • Ecology
  • Remote Sensing

Background:

  • Leaf Area Index (LAI) is crucial for understanding vegetation's role in energy and carbon exchange.
  • Existing satellite LAI products often underestimate values in snow-covered regions.
  • Accurate LAI is vital for Earth system models (ESMs) to simulate climate and ecological processes.

Purpose of the Study:

  • To develop a global, high-resolution (500m) snow-free Leaf Area Index (LAI) dataset from 1985-2020.
  • To correct the underestimation of LAI in snow-affected areas using plant functional type (PFT)-specific leaf lifespan data.
  • To improve the representation of vegetation-snow interactions in ESMs.

Main Methods:

  • Compiled over 2700 leaf lifespan records from the TRY plant trait database.
  • Aggregated leaf lifespan data into plant functional type (PFT)-specific values.
  • Identified snow-affected regions using MODIS data and applied a leaf-lifespan-based correction to LAI.

Main Results:

  • Developed a global snow-free LAI dataset (1985-2020) at 500m resolution.
  • The corrected LAI effectively addresses underestimation in snow-covered regions.
  • Simulations showed improved albedo, net radiation, and gross primary productivity, with reduced snow depth.

Conclusions:

  • The new snow-free LAI dataset provides a reliable input for ESMs.
  • It enhances the modeling of vegetation-snow interactions, surface energy, water, and carbon dynamics.
  • This dataset supports more accurate climate and ecological simulations.