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Scaling field data to calibrate and validate moderate spatial resolution remote sensing models

Photogrammetric Engineering and Remote Sensing

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Abstract

Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor's field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure. 

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Scaling field data to calibrate and validate moderate spatial resolution remote sensing models
Series title:
Photogrammetric Engineering and Remote Sensing
Volume
73
Issue:
8
Year Published:
2007
Language:
English
Contributing office(s):
Earth Resources Observation and Science (EROS) Center (Geography)
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Photogrammetric Engineering and Remote Sensing
First page:
945
Last page:
954
Online Only (Y/N):
N
Additional Online Files(Y/N):
N