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Related Experiment Video

Updated: Jun 13, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Cloud top remote sensing by airborne lidar.

J D Spinhirne¹, M Z Hansen, L O Caudill

¹NASA Goddard Greenbelt, Maryland 2077, USA.

|April 15, 2010

Summary

A high-altitude lidar system measured cloud properties like height and backscattering from a 19-km altitude. This automated sensor provided initial data on diverse cloud types, aiding atmospheric research.

Area of Science:

Atmospheric Science
Remote Sensing
Cloud Physics

Background:

Cloud properties are crucial for understanding Earth's radiative balance and climate.
Remote sensing techniques, particularly lidar, offer valuable tools for cloud observation.

Purpose of the Study:

To report initial findings from a novel lidar system deployed on a high-altitude aircraft.
To characterize cloud top height, backscattering, and signal depolarization.

Main Methods:

Utilized a lidar system with a doubled Nd:YAG laser transmitter (5 Hz) onboard a high-altitude research aircraft.
Operated the fully automated, microprocessor-controlled sensor from a nominal 19-km altitude.
Acquired simultaneous passive visible and infrared measurements for comprehensive cloud analysis.

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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

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Published on: September 12, 2017

Main Results:

Successfully obtained measurements of cloud top height, backscattering, and signal depolarization.
Collected data across a wide variety of cloud cover conditions.
Initial observations provide a basis for further cloud property analysis.

Conclusions:

The high-altitude lidar system is capable of acquiring detailed cloud measurements.
The collected data contributes to a better understanding of cloud characteristics and their atmospheric impact.
Further analysis of these initial observations will refine cloud models and remote sensing techniques.